The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021-2026 Passenger Cars and Light Trucks, 24174-25278 [2020-06967]

Download as PDF 24174 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations ENVIRONMENTAL PROTECTION AGENCY 40 CFR Parts 86 and 600 DEPARTMENT OF TRANSPORTATION National Highway Traffic Safety Administration 49 CFR Parts 523, 531, 533, 536, and 537 [NHTSA–2018–0067; EPA–HQ–OAR–2018– 0283; FRL 10000–45–OAR] RIN 2127–AL76; RIN 2060–AU09 The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021–2026 Passenger Cars and Light Trucks Environmental Protection Agency and National Highway Traffic Safety Administration. ACTION: Final rule. AGENCY: khammond on DSKJM1Z7X2PROD with RULES2 SUMMARY: EPA and NHTSA, on behalf of the Department of Transportation, are issuing final rules to amend and establish carbon dioxide and fuel economy standards. Specifically, EPA is amending carbon dioxide standards for model years 2021 and later, and NHTSA is amending fuel economy standards for model year 2021 and setting new fuel economy standards for model years 2022–2026. The standards set by this action apply to passenger cars and light trucks, and will continue our nation’s progress toward energy independence and carbon dioxide reduction, while recognizing the realities of the marketplace and consumers’ interest in purchasing vehicles that meet all of their diverse needs. These final rules represent the second part of the Administration’s action related to the August 24, 2018 proposed Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule. These final rules follow the agencies’ actions, taken September 19, 2019, to ensure One National Program for automobile fuel economy VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 and carbon dioxide emissions standards, by finalizing regulatory text related to preemption under the Energy Policy and Conservation Act and withdrawing a waiver previously provided to California under the Clean Air Act. DATES: This final rule is effective on June 29, 2020. Judicial Review: NHTSA and EPA undertake this joint action under their respective authorities pursuant to the Energy Policy and Conservation Act and the Clean Air Act. Pursuant to CAA section 307(b), 42 U.S.C. 7607(b), any petitions for judicial review of this action must be filed in the United States Court of Appeals for the D.C. Circuit. Given the inherent relationship between the agencies’ action, any challenges to NHTSA’s regulation under 49 U.S.C. 32909 should also be filed in the United States Court of Appeals for the D.C. Circuit. ADDRESSES: EPA and NHTSA have established dockets for this action under Docket ID Nos. EPA–HQ–OAR–2018– 0283 and NHTSA–2018–0067, respectively. All documents in the docket are listed in the http:// www.regulations.gov index. Although listed in the index, some information is not publicly available, e.g., confidential business information (CBI) or other information whose disclosure is restricted by statute. Certain other material, such as copyrighted material, will be publicly available in hard copy in EPA’s docket, and electronically in NHTSA’s online docket. Publicly available docket materials can be found either electronically in www.regulations.gov by searching for the dockets using the Docket ID numbers above, or in hard copy at the following locations: EPA: EPA Docket Center, EPA/DC, EPA West, Room 3334, 1301 Constitution Ave. NW, Washington, DC. The Public Reading Room is open from 8:30 a.m. to 4:30 p.m., Monday through Friday, excluding legal holidays. The PO 00000 Frm 00002 Fmt 4701 Sfmt 4700 telephone number for the Public Reading Room is (202) 566–1744. NHTSA: Docket Management Facility, M–30, U.S. Department of Transportation (DOT), West Building, Ground Floor, Rm. W12–140, 1200 New Jersey Ave. SE, Washington, DC 20590. The DOT Docket Management Facility is open between 9 a.m. and 5 p.m. Eastern Time, Monday through Friday, except Federal holidays. FOR FURTHER INFORMATION CONTACT: EPA: Christopher Lieske, Office of Transportation and Air Quality, Assessment and Standards Division, Environmental Protection Agency, 2000 Traverwood Drive, Ann Arbor, MI 48105; telephone number: (734) 214– 4584; fax number: (734) 214–4816; email address: lieske.christopher@ epa.gov, or contact the Assessment and Standards Division, email address: otaq@epa.gov. NHTSA: James Tamm, Office of Rulemaking, Fuel Economy Division, National Highway Traffic Safety Administration, 1200 New Jersey Avenue SE, Washington, DC 20590; telephone number: (202) 493–0515. SUPPLEMENTARY INFORMATION: Does this action apply to me? This action affects companies that manufacture or sell new light-duty vehicles, light-duty trucks, and medium-duty passenger vehicles, as defined under EPA’s CAA regulations,1 and passenger automobiles (passenger cars) and non-passenger automobiles (light trucks) as defined under NHTSA’s CAFE regulations.2 Regulated categories and entities include: 1 ‘‘Light-duty vehicle,’’ ‘‘light-duty truck,’’ and ‘‘medium-duty passenger vehicle’’ are defined in 40 CFR 86.1803–01. Generally speaking, a ‘‘light-duty vehicle’’ is a passenger car, a ‘‘light-duty truck’’ is a pick-up truck, sport-utility vehicle, or minivan up to 8,500 lbs. gross vehicle weight rating, and a ‘‘medium-duty passenger vehicle’’ is a sport-utility vehicle or passenger van from 8,500 to 10,000 lbs. gross vehicle weight rating. 2 ‘‘Passenger car’’ and ‘‘light truck’’ are defined in 49 CFR part 523. E:\FR\FM\30APR2.SGM 30APR2 This list is not intended to be exhaustive, but rather provides a guide regarding entities likely to be regulated by this action. To determine whether particular activities may be regulated by this action, you should carefully examine the regulations. You may direct questions regarding the applicability of this action to the person listed in FOR FURTHER INFORMATION CONTACT. khammond on DSKJM1Z7X2PROD with RULES2 I. Executive Summary II. Overview of Final Rule III. Purpose of the Rule IV. Purpose of Analytical Approach Considered as Part of Decision-Making V. Regulatory Alternatives Considered VI. Analytical Approach as Applied to Regulatory Alternatives VII. What does the analysis show, and what does it mean? VIII. How do the final standards fulfill the agencies’ statutory obligations? IX. Compliance and Enforcement X. Regulatory Notices and Analyses I. Executive Summary NHTSA (on behalf of the Department of Transportation) and EPA are issuing final rules to adopt and modify standards regulating corporate average fuel economy and tailpipe carbon dioxide (CO2) emissions and use/ leakage of other air conditioning refrigerants for passenger cars and light trucks for MYs 2021–2026.3 These final 3 Throughout this document and the accompanying FRIA, the agencies will often use the VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 rules follow the proposal issued in August 2018 and respond to each agency’s legal obligation to set standards based on the factors Congress directed them to consider, as well as the direction of the United States Supreme Court in Massachusetts v. EPA, which stated that ‘‘there is no reason to think the two agencies cannot both administer their obligations and yet avoid inconsistency.’’ 4 These standards are the product of significant and ongoing work by both agencies to craft regulatory requirements for the same group of vehicles and vehicle manufacturers. This work aims to facilitate, to the extent possible within the statutory directives issued to each agency, the ability of automobile manufacturers to meet all requirements under both programs with a single national fleet under one national program of fuel economy and tailpipe CO2 emission regulation. The CAFE and CO2 emissions standards established by these final rules will increase in stringency at 1.5 percent per year from MY 2020 levels over MYs 2021–2026. The ‘‘1.5 percent’’ regulatory alternative is new for the final rule and was not expressly analyzed in the NPRM, but it is a logical outgrowth of the NPRM analysis, being term ‘‘CO2’’ or ‘‘tailpipe CO2’’ to refer broadly to EPA’s suite of light duty vehicle GHG standards. 4 549 U.S. 497, 532 (2007). PO 00000 Frm 00003 Fmt 4701 Sfmt 4700 24175 well within the range of alternatives then considered and consistent with discussions by both the agencies and commenters that there are benefits to having standards that increase at the same rate for all fleets. These standards apply to light-duty vehicles, which NHTSA divides for purposes of regulation into passenger cars and light trucks, and EPA divides into passenger cars, light-duty trucks, and mediumduty passenger vehicles (i.e., sport utility vehicles, cross-over utility vehicles, and light trucks). Both the CAFE and CO2 standards are vehiclefootprint-based, as are the standards currently in effect. These standards will become more stringent for each model year from 2021 to 2026, relative to the MY 2020 standards. Generally, the larger the vehicle footprint, the less numerically stringent the corresponding vehicle CO2 and miles-per-gallon (mpg) targets. As a result of the footprint-based standards, the burden of compliance is distributed across all vehicle footprints and across all manufacturers. Each manufacturer is subject to individualized standards for passenger cars and light trucks, in each model year, based on the vehicles it produces. When standards are carefully crafted, both in terms of the footprint curves and the rate of increase in stringency of those curves, manufacturers are not E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.000</GPH> Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 24176 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations compelled to build vehicles of any particular size or type. Knowing that many readers are accustomed to considering CAFE and CO2 emissions standards in terms of the mpg and grams-per-mile (g/mi) values that the standards are projected to eventually require, the agencies include those projections here. EPA’s standards are projected to require, on an average industry fleet-wide basis, 201 grams per mile (g/mi) of CO2 in model year 2030, while NHTSA’s standards are projected to require, on an average industry fleetwide basis, 40.5 miles per gallon (mpg) in model year 2030. The agencies note that real-world CO2 is typically 25 percent higher and real-world fuel economy is typically 20 percent lower than the CO2 and CAFE compliance values discussed here, and also note that a portion of EPA’s expected ‘‘CO2’’ improvements will in fact be made through improvements in minimizing air conditioning leakage and through use of alternative refrigerants, which will not contribute to fuel economy but will contribute toward reductions of climate-related emissions. In these final rules, NHTSA and EPA are reaching similar conclusions on similar grounds: even though each agency has its own distinct statutory authority and factors, the relevant considerations overlap in many ways. Both agencies recognize that they are balancing the relevant considerations in somewhat different ways from how they may have been balanced previously, as in the 2012 final rule and in EPA’s Initial Determination, but the current balancing is called for in light of the facts before the agencies. The balancing in these final rules is also somewhat different from how the agencies balanced their respective considerations in the proposal, in part because of updates to analytical inputs and methodologies, previewed in the NPRM and made in response to public comments, that collectively resulted in changes to the analytical outputs. For example, between the notice and final rule, the agencies updated fuel price projections to somewhat greater values, updated the analysis fleet to MY 2017, updated estimates of the efficacy and cost of fuel-saving technologies, revised procedures for calculating impacts on vehicle sales and scrappage, updated models for estimating highway safety impacts, updated estimates of highway congestion costs, and updated estimates of annual mileage accumulation, holding VMT (before applying the rebound effect) constant between regulatory alternative. Moreover, the cost-benefit analysis conducted for these final rules has even been overtaken by VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 events in many ways over recent weeks. Based upon current events, and for additional reasons discussed in Section VI.D.1 the benefits of saving additional fuel through more stringent standards are potentially even smaller than estimated in this rulemaking analysis. The standards finalized today fit the pattern of gradual, tough, but feasible stringency increases that take into account real world performance, shifts in fuel prices, and changes in consumer behavior toward crossovers and SUVs and away from more efficient sedans. This approach ensures that manufacturers are provided with sufficient lead time to achieve standards, considering the cost of compliance. The costs to both industry and automotive consumers would have been too high under the standards set forth in 2012, and by lowering the auto industry’s costs to comply with the program, with a commensurate reduction in per-vehicle costs to consumers, the standards enhance the ability of the fleet to turn over to newer, cleaner and safer vehicles. More stringent standards also have the potential for overly aggressive penetration rates for advanced technologies relative to the penetration rates seen in the final standards, especially in the face of an unknown degree of consumer acceptance of both the increased costs and of the technologies themselves—particularly given current projections of relatively low fuel prices during that timeframe. As a kind of insurance policy against future fuel price volatility, standards that increase at 1.5 percent per year for cars and trucks will help to keep fleet fuel economy higher than they would be otherwise when fuel prices are low, which is not improbable over the next several years.5 At the same time, the standards help to address these issues by maintaining incentives to promote broader deployment of advanced technologies, and so provides a means of encouraging their further penetration while leaving manufacturers alternative technology choices. Steady, gradual increases in stringency ensure that the benefits of reduced GHG emissions and fuel consumption are achieved without 5 For example, EIA currently expects U.S. retail gasoline prices to average $2.14/gallon in 2020, compared to $2.69/gallon in 2019 (see https:// www.eia.gov/outlooks/steo/archives/mar20.pdf), and $3.68/gallon in 2012 (see https://www.eia.gov/ dnav/pet/hist/LeafHandler.ashx?n=PET&s=EMM_ EPM0_PTE_NUS_DPG&f=A). While gasoline prices may foreseeably rise over the rulemaking time frame, it is also very foreseeable that they will not rise to the $4–5/gallon that many Americans saw over the 2008–2009 time frame, that caused the largest shift seen toward smaller and higher-fueleconomy vehicles. See, e.g., Figure VIII–2 below. PO 00000 Frm 00004 Fmt 4701 Sfmt 4700 the potential for disruption to automakers or consumers. Standards that increase at 1.5 percent per year represent a reasonable balance of additional technology and required per-vehicle costs, consumer demand for fuel economy, fuel savings and emissions avoided given the foreseeable state of the global oil market and the minimal effect on climate between finalizing 1.5 percent standards versus more stringent standards. The final standards will also result in year-overyear improvements in fleetwide fuel economy, resulting in energy conservation that helps address environmental concerns, including criteria pollutant, air toxic pollutant, and carbon emissions. The agencies project that under these final standards, required technology costs would be reduced by $86 to $126 billion over the lifetimes of vehicles through MY 2029. Equally important, purchase prices costs to U.S. consumers for new vehicles would be $977 to $1,083 lower, on average, than they would have been if the agencies had retained the standards set forth in the 2012 final rule and originally upheld by EPA in January 2017. While these final standards are estimated to result in 1.9 to 2.0 additional billion barrels of fuel consumed and from 867 to 923 additional million metric tons of CO2 as compared to current estimates of what the standards set forth in 2012 would require, the agencies explain at length below why the overall benefits of the final standards outweigh these additional costs.6 For the CAFE program, overall (fleetwide) net benefits vary from $16.1 billion at a 7 percent discount rate to ¥$13.1 billion at a 3 percent discount rate. For the CO2 program, overall (fleetwide) societal net benefits vary from $6.4 billion at a 7 percent discount rate to ¥$22.0 billion at a 3 percent discount rate. The net benefits straddle zero, and are very small relative to the scale of reduced required technology costs, which range from $86.3 billion to $126.0 billion for the CAFE and CO2 programs across 7 percent and 3 percent discount rates. Likewise, net benefits are very small relative to the scale of reduced retail fuel savings over the full life of all vehicles manufactured during the 2021 through 2029 model years, which range from $108.6 billion to $185.1 billion for the CAFE and CO2 programs across 7 percent and 3 percent discount rates. Similarly, all of the alternatives have small net benefits, ranging from $18.4 billion to ¥$31.1 6 1.9 to 2.0 barrels of fuel is approximately 78 to 84 gallons of fuel. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 billion for the CAFE and CO2 programs across 7 percent and 3 percent discount rates.7 NHTSA and EPA believe their analysis of the final rule represents the best available science, evidence, and methodologies for assessing the impacts of changes in CAFE and CO2 emission standards. In fact, the agencies note that today’s analysis represents a marked improvement over prior rulemakings. Previously, the agencies were unable to model the impact of the standards on new vehicle sales or the retirement of older vehicles in the fleet, and, instead, were forced to assume, contrary to economic theory and empirical evidence, that the number of new vehicles sold and older vehicles scrapped remained static across regulatory alternatives. Today’s analysis—as commenters to previous rulemakings and EPA’s Science Advisory Board have argued is necessary 8—quantifies the sales and scrappage impacts of the standards, including the associated safety benefits, and represents a significant step forward in agencies’ ability to comprehensively analyze the impacts of CAFE and CO2 emission standards. However, the agencies also believe it is important to be transparent about analytical limitations. For example, EPA’s Science Advisory Board stressed that the agencies account for ‘‘evolving consumer preferences for performance and other vehicle attributes,’’ 9 yet due to limitations on the agencies’ current ability to model buyers’ choices among combinations of various attributes and their costs, the primary analysis does not account for the consumer benefits of other vehicle features that may be sacrificed for costly technologies that improve fuel economy. The agencies’ analysis assumes that under these final standards, attributes of new cars and light trucks other than fuel economy would remain identical to those under the baseline standards, so that changes in sales prices and fuel economy would be the only sources of benefits or costs to new car and light truck buyers. In other words, the agencies’ primary analysis does not consider that producers will likely respond to buyers’ demands by reallocating some their savings in production costs due to lower technology costs to add or improve 7 See Table II–12 to Table II–15 for costs, benefits and net benefits. 8 Science Advisory Board, U.S. EPA. Review of EPA’s Proposed SAFE rule at 4 (Feb. 27, 2020), available at https://yosemite.epa.gov/sab/ sabproduct.nsf/LookupWebProjectsCurrentBOARD/ 1FACEE5C03725F268525851F006319BB/$File/EPASAB-20-003+.pdf [hereinafter ‘‘SAB Report’’]. 9 SAB at 10. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 other attributes that consumers value more highly than the increases in fuel economy the augural standards would have required. The agencies have long debated whether and how best to model the consumer benefits of other vehicle attributes, and note that they have made considerable progress.10 However, despite these potential analytical shortcomings, the agencies reaffirm that today’s analysis represents the most complete and rigorous examination of CAFE and CO2 emission standards to date, and provide decision-makers a powerful analytical tool—especially since the limitations are known, do not bias the central analysis’ results, and are afforded due consideration. In terms of the agencies’ respective statutory authorities, EPA is setting national tailpipe CO2 emissions standards for passenger cars and light trucks under section 202(a) of the Clean Air Act (CAA),11 and taking other 10 In their evaluations of previous CAFE and CO 2 rules, the agencies attempted to account for this possibility by conducting sensitivity analyses that reduced the fuel savings and other benefits to vehicle buyers by a significant fraction. For example, NHTSA’s analysis supporting the Final Rule establishing CAFE standards for model year 2012–16 cars and light trucks tested the sensitivity of their central estimates of social costs and benefits to the assumptions that 25 percent and 50 percent of benefits to buyers were offset by opportunity costs of foregone improvements in attributes other than fuel economy; see NHTSA, Final Regulatory Impact Analysis: Corporate Average Fuel Economy for Model year 2012–16 Passenger Cars and Light Trucks, March 2010, at 563–565 and Table X–9, at 566–56; see also, NHTSA, Final Regulatory Impact Analysis: Corporate Average Fuel Economy for Model year 2017–25 Passenger Cars and Light Trucks, August 2012, at 1087 and Tables X–18a, X– 18b, and X–18c, at 1099–1104. The agencies acknowledged that this was not a completely satisfactory way to represent the sacrifices in vehicles’ other attributes that car and light truck manufacturers might find it necessary to make in order to comply with the increasingly stringent standards those previous rules established. At the time, however, the agencies were unable to identify specific attributes that manufacturers were most likely to sacrifice, measure the tradeoffs between increased fuel economy and improvements in those attributes, or assess the potential losses in utility to car and light truck buyers. In an effort to improve on their previous treatment of this issue, the agencies’ evaluation of this final rule includes a sensitivity case that assumes manufacturers redirect their technology cost savings from complying with less stringent standards to instead improve a combination of cars’ and light trucks’ other attributes that offers benefits to their buyers significantly exceeding those costs. The magnitude of these (net) benefits is interpreted as the opportunity cost of the improvements in vehicles’ other attributes that would have been sacrificed if the augural standards had been enacted. The method the agencies use to approximate these benefits, together with its effect on the rule’s overall benefits and costs, is discussed in detail in Section VI.D.1.b)(8). Briefly, the results of this sensitivity analysis suggest the Final Rule would generate net benefits for the CAFE and CO2 programs ranging from $34.9 to $55.4 billion at 3% and 7% discount rates. 11 42 U.S.C. 7521(a). PO 00000 Frm 00005 Fmt 4701 Sfmt 4700 24177 actions under its authority to establish metrics and measure passenger car and light truck fleet fuel economy pursuant to the Energy Policy and Conservation Act (EPCA),12 while NHTSA is setting national corporate average fuel economy (CAFE) standards under EPCA, as amended by the Energy Independence and Security Act (EISA) of 2007.13 As summarized above and as discussed in much greater detail below, the agencies believe that these represent appropriate levels of CO2 emissions standards and maximum feasible CAFE standards for MYs 2021–2026, pursuant to their respective statutory authorities. Sections III and VIII below contain detailed discussions of both agencies’ statutory obligations and authorities. Section 202(a) of the CAA requires EPA to establish standards for emissions of pollutants from new motor vehicles that cause or contribute to air pollution that may reasonably be anticipated to endanger public health or welfare. Standards under section 202(a) thus take effect only ‘‘after providing such period as the Administrator finds necessary to permit the development and application of the requisite technology, giving appropriate consideration to the cost of compliance within such period.’’ 14 In establishing such standards, EPA must consider issues of technical feasibility, cost, and available lead time, among other things. EPCA, as amended by EISA, contains a number of provisions governing how NHTSA must set CAFE standards. EPCA requires that the Department of Transportation establish separate passenger car and light truck standards 15 at ‘‘the maximum feasible average fuel economy level that the Secretary decides the manufacturers can achieve in that model year,’’ 16 based on the agency’s consideration of four statutory factors: technological feasibility, economic practicability, the effect of other standards of the Government on fuel economy, and the need of the United States to conserve energy.17 EPCA does not define these terms or specify what weight to give each concern in balancing them—such considerations are left within the discretion of the Secretary of Transportation (delegated to NHTSA) based upon current information. Accordingly, NHTSA interprets these factors and determines the appropriate weighting that leads to the maximum 12 49 U.S.C. 32904(c). U.S.C. 32902. 14 CAA Sec. 202(a); 42 U.S.C. 7512(a)(2). 15 49 U.S.C. 32902(b)(1). 16 49 U.S.C. 32902(a). 17 49 U.S.C. 32902(f). 13 49 E:\FR\FM\30APR2.SGM 30APR2 24178 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations and net benefits represent the impacts of the standards over the full lifetimes of the vehicles sold or projected to be sold during model years 1978–2029. For this analysis, negative signs are used for changes in costs or benefits that decrease from those that would have resulted from the existing/augural standards. Any changes that would increase either costs or benefits are shown as positive changes. Thus, an alternative that decreases both costs and benefits, will show declines (i.e., a negative sign) in both categories. From Table I–1 and Table I–2, the preferred alternative (Alternative 3) is estimated to decrease costs relative to the baseline by $182 to $280 billion over the lifetime of MYs 1978–2029 passenger vehicles (range determined by discount rate across both CAFE and CO2 programs). It will also decrease benefits from $175 to $294 billion over the life of these MY fleets. The net impact will be a decrease from $22 billion to an increase of $16 billion in total net benefits to society over this roughly 52-year timeframe. Annualized, this amounts to roughly ¥$0.8 to 1.2 billion in net benefits per year. BILLING CODE 4910–59–P ER30AP20.002</GPH> N2O standards. EPA is also extending the ‘‘0 g/mi upstream’’ incentive for electric vehicles beyond its current sunset of MY 2021, through MY 2026. EPA is also establishing a credit multiplier for natural gas vehicles through the 2026 model year. Otherwise, compliance flexibilities in the two programs do not change significantly for the final rule. These changes should help to streamline manufacturer use of those flexibilities in certain respects. While manufacturers and suppliers sought a number of other additional compliance flexibilities, the agencies have concluded that the aforementioned existing flexibilities are reasonable and appropriate, and that additional flexibilities are not justified. Table I–1 and Table I–2 present the total costs, benefits, and net benefits for the 2021–2026 preferred alternative CAFE and CO2 levels, relative to the MY 2022–2025 existing/augural standards (with the MY 2025 standards repeated for MY 2026) and current MY 2021 standard. The preferred alternative exhibits a stringency rate increase of 1.5 percent per year for both passenger cars and light trucks. The values in Table I– 1 and Table I–2 display (in total and annualized forms) costs for all MYs 1978–2029 vehicles, and the benefits 18 49 U.S.C. 32902(b)(2)(A) and (C). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 19 49 PO 00000 U.S.C. 32902(b)(2)(B). Frm 00006 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.001</GPH> khammond on DSKJM1Z7X2PROD with RULES2 feasible standards given the circumstances present at the time of promulgating each CAFE standard rulemaking. While EISA, for MYs 2011– 2020, additionally required that standards increase ‘‘ratably’’ and be set at levels to ensure that the CAFE of the industry-wide combined fleet of new passenger cars and light trucks reach at least 35 mpg by MY 2020,18 EISA requires that standards for MYs 2021– 2030 simply be set at the maximum feasible level as determined by the Secretary (and by delegation, NHTSA).19 In the NPRM, the agencies sought comment on a variety of possible changes to existing compliance flexibilities that have been created over the past several years. The vast majority of the existing compliance flexibilities are not being changed, but a small number of flexibilities related to realworld fuel efficiency improvements are being finalized. In addition, EPA will continue to allow manufacturers to make improvements relating to air conditioning refrigerants and leakage and will credit those improvements toward CO2 compliance, and EPA is making no changes in the amounts of credits available. EPA is also not making any changes to the existing CH4 and Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24179 Table I–3 and Table I–4 lists costs, benefits, and net benefits for all seven alternatives that were examined. as well as from the perspective of society and the consumer. ER30AP20.004</GPH> standards. Impacts are presented in monetized and non-monetized values, VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00007 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.003</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Table I–5 and Table I–6 show a summary of various impacts of the preferred alternative for CAFE and CO2 VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00008 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.005</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24180 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations The agencies note that the NPRM drew more public comments (and, particularly, more pages of substantive comments) than any rulemaking in the history of the CAFE or CO2 tailpipe emissions programs—exceeding 750,000 comments. The agencies recognized in the NPRM that the proposal was significantly different from the final rules set forth in 2012, and explained at length the reasons for those differences—namely, that new information and considerations, along with an expanded and updated analysis, VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 had led to different tentative conclusions. Today’s final rules represent a further evolution of the work that supported the proposal, based on improved quantitative methodology and in careful consideration of the hundreds of thousands of public comments and deep reflection on the serious issues before the agencies. Simply put, the agencies have heard the comments, and today’s analysis and decision reflect the agencies’ grappling with the issues commenters raised, as well as all of the other information before the agencies. PO 00000 Frm 00009 Fmt 4701 Sfmt 4700 These programs and issues are weighty, and the agencies believe that a reasonable balance has been struck in these final rules between the many competing national needs that these regulatory programs collectively address. II. Overview of Final Rule A. Summary of Proposal In the NPRM, the National Highway Traffic Safety Administration (NHTSA) and the Environmental Protection Agency (EPA) (collectively, ‘‘the E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.006</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C 24181 24182 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 agencies’’) proposed the ‘‘Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021– 2026 Passenger Cars and Light Trucks’’ (SAFE Vehicles Rule). The proposed SAFE Vehicles Rule would set Corporate Average Fuel Economy (CAFE) and carbon dioxide (CO2) emissions standards, respectively, for passenger cars and light trucks manufactured for sale in the United States in model years (MYs) 2021 through 2026.20 The agencies explained that they must act to propose and finalize these standards and do not have discretion to decline to regulate. Congress requires NHTSA to set CAFE standards for each model year.21 Congress also requires EPA to set emissions standards for lightduty vehicles if EPA has made an ‘‘endangerment finding’’ that the pollutant in question—in this case, CO2—‘‘cause[s] or contribute[s] to air pollution which may reasonably be anticipated to endanger public health or welfare.’’ 22 NHTSA and EPA proposed the standards concurrently because tailpipe CO2 emissions standards are directly and inherently related to fuel economy standards,23 and, if finalized, the rules would apply concurrently to the same fleet of vehicles. By working together to develop the proposals, the 20 NHTSA sets CAFE standards under the Energy Policy and Conservation Act of 1975 (EPCA), as amended by the Energy Independence and Security Act of 2007 (EISA). EPA sets CO2 standards under the Clean Air Act (CAA). 21 49 U.S.C. 32902. 22 42 U.S.C. 7521; see also 74 FR 66495 (Dec. 15, 2009) (‘‘Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act’’). 23 See, e.g., 75 FR 25324, at 25327 (May 7, 2010) (‘‘The National Program is both needed and possible because the relationship between improving fuel economy and reducing tailpipe CO2 emissions is a very direct and close one. The amount of those CO2 emissions is essentially constant per gallon combusted of a given type of fuel. Thus, the more fuel efficient a vehicle is, the less fuel it burns to travel a given distance. The less fuel it burns, the less CO2 it emits in traveling that distance. [citation omitted] While there are emission control technologies that reduce the pollutants (e.g., carbon monoxide) produced by imperfect combustion of fuel by capturing or converting them to other compounds, there is no such technology for CO2. Further, while some of those pollutants can also be reduced by achieving a more complete combustion of fuel, doing so only increases the tailpipe emissions of CO2. Thus, there is a single pool of technologies for addressing these twin problems, i.e., those that reduce fuel consumption and thereby reduce CO2 emissions as well.’’). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 agencies aimed to reduce regulatory burden on industry and improve administrative efficiency. The agencies discussed some of the history leading to the proposal, including the 2012 final rule, the expectations regarding a mid-term evaluation as required by EPA regulation, and the rapid process over 2016 and early 2017 by which EPA issued its first Final Determination that the CO2 standards set in 2012 for MYs 2022–2025 remained appropriate based on the information then before the EPA Administrator.24 The agencies also discussed President Trump’s direction in March 2017 to restore the original mid-term evaluation timeline, and EPA’s subsequent information-gathering process and announcement that it would reconsider the January 2017 Determination.25 EPA ultimately concluded that the standards set in 2012 for MYs 2022–2025 were no longer appropriate.26 For NHTSA, in turn, the ‘‘augural’’ CAFE standards for MYs 2022–2025 were never final, and as explained in the 2012 final rule, NHTSA was obligated from the beginning to undertake a new rulemaking to set CAFE standards for MYs 2022–2025. The NPRM thus began the rulemaking process for both agencies to establish new standards for MYs 2022–2025 passenger cars and light trucks. Standards were concurrently proposed for MY 2026 in order to provide regulatory stability for as many years as is legally permissible for both agencies together. The NPRM also included revised standards for MY 2021 passenger cars and light trucks, because the agencies tentatively concluded, based on the information and analysis then before them, that the CAFE standards previously set for MY 2021 were no longer maximum feasible, and the CO2 standards previously set for MY 2021 were no longer appropriate. Agencies always have authority under the Administrative Procedure Act to revisit previous decisions in light of new facts, as long as they provide notice and an opportunity for comment, and the agencies stated that it is plainly the best practice to do so when changed circumstances so warrant.27 24 See 83 FR at 42987 (Aug.24, 2018). 25 Id. 26 83 FR 16077 (Apr. 2, 2018). FCC v. Fox Television, 556 U.S. 502 (2009). 27 See PO 00000 Frm 00010 Fmt 4701 Sfmt 4700 The NPRM proposed to maintain the CAFE and CO2 standards applicable in MY 2020 for MYs 2021–2026, and took comment on a wide range of alternatives, including different stringencies and retaining existing CO2 standards and the augural CAFE standards.28 Table II–1, Table II–2, and Table II–3 show the estimates, under the NPRM analysis, of what the MY 2020 CAFE and CO2 curves would translate to, in terms of miles per gallon (mpg) and grams per mile (g/mi), in MYs 2021–2026, as well as the regulatory alternatives considered in the NPRM. In addition to retaining the MY 2020 CO2 standards through MY 2026, EPA proposed and sought comment on excluding air conditioning refrigerants and leakage, and nitrous oxide and methane emissions for compliance with CO2 standards after model year 2020, in order to improve harmonization with the CAFE program. EPA also sought comment on whether to change existing methane and nitrous oxide standards that were finalized in the 2012 rule. The proposal was accompanied by a 1,600 page Preliminary Regulatory Impact Analysis (PRIA) and, for NHTSA, a 500 page Draft Environmental Impact Statement (DEIS), with more than 800 pages of appendices and the entire CAFE model, including the software source code and documentation, all of which were also subject to comment in their entirety and all of which received significant comments. BILLING CODE 4910–59–P 28 The agencies noted that this did not mean that the miles per gallon and grams per mile levels that were estimated for the MY 2020 fleet in 2012 would be the ‘‘standards’’ going forward into MYs 2021– 2026. Both NHTSA and EPA set CAFE and CO2 standards, respectively, as mathematical functions based on vehicle footprint. These mathematical functions that are the actual standards are defined as ‘‘curves’’ that are separate for passenger cars and light trucks, under which each vehicle manufacturer’s compliance obligation varies depending on the footprints of the cars and trucks that it ultimately produces for sale in a given model year. It was the MY 2020 CAFE and CO2 curves that the agencies proposed would continue to apply to the passenger car and light truck fleets for MYs 2021–2026. The mpg and g/mi values which those curves would eventually require of the fleets in those model years would be known for certain only at the ends of each of those model years. While it is convenient to discuss CAFE and CO2 standards as a set ‘‘mpg,’’ ‘‘g/mi,’’ or ‘‘mpg-e’’ number, attempting to define those values based on the information then before the agency would necessarily end up being inaccurate. E:\FR\FM\30APR2.SGM 30APR2 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00011 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24183 ER30AP20.007</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 29 The carbon dioxide equivalents of air conditioning refrigerant leakage, nitrous oxide emissions, and methane emissions were included for compliance with the EPA standards for all MYs under the baseline/no action alternative in the VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 NPRM. Carbon dioxide equivalent is calculated using the Global Warming Potential (GWP) of each of the emissions. 30 Beginning in MY 2021, the proposal provided that the GWP equivalents of air conditioning PO 00000 Frm 00012 Fmt 4701 Sfmt 4700 refrigerant leakage, nitrous oxide emissions, and methane emissions would no longer be able to be included with the tailpipe CO2 for compliance with tailpipe CO2 standards. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.008</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24184 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations The agencies explained in the NPRM that new information had been gathered and new analysis performed since publication of the 2012 final rule establishing CAFE and CO2 standards for MYs 2017 and beyond and since issuance of the 2016 Draft TAR and EPA’s 2016 and early 2017 ‘‘mid-term evaluation’’ process. This new information and analysis helped lead the agencies to the tentative conclusion that holding standards constant at MY 2020 levels through MY 2026 was maximum feasible, for CAFE purposes, and appropriate, for CO2 purposes. The agencies further explained that technologies had played out differently in the fleet from what the agencies previously assumed: That while there remain a wide variety of technologies available to improve fuel economy and reduce CO2 emissions, it had become clear that there were reasons to temper previous optimism about the costs, effectiveness, and consumer acceptance of a number of technologies. In addition, over the years between the previous analyses and the NPRM, automakers VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 had added considerable amounts of technologies to their new vehicle fleets, meaning that the agencies were no longer free to make certain assumptions about how some of those technologies could be used going forward. For example, some technologies that could be used to improve fuel economy and reduce emissions had not been used entirely for that purpose, and some of the benefit of these technologies had gone instead toward improving other vehicle attributes. Other technologies had been tried, and had been met with significant customer acceptance issues. The agencies underscored the importance of reflecting the fleet as it stands today, with the technology it has and as that technology has been used, and considering what technology remains on the table at this point, whether and when it can realistically be available for widespread use in production, and how much it would cost to implement. The agencies also acknowledged the math of diminishing returns: As CAFE and CO2 emissions standards increase in stringency, the benefit of continuing to PO 00000 Frm 00013 Fmt 4701 Sfmt 4700 increase in stringency decreases. In mpg terms, a vehicle owner who drives a light vehicle 15,000 miles per year (a typical assumption for analytical purposes) 31 and trades in a vehicle with fuel economy of 15 mpg for one with fuel economy of 20 mpg, will reduce their annual fuel consumption from 1,000 gallons to 750 gallons—saving 250 gallons annually. If, however, that owner were to trade in a vehicle with fuel economy of 30 mpg for one with fuel economy of 40 mpg, the owner’s annual gasoline consumption would drop from 500 gallons/year to 375 gallons/year—only 125 gallons even though the mpg improvement is twice as large. Going from 40 to 50 mpg would save only 75 gallons/year. Yet each additional fuel economy improvement becomes much more expensive as the easiest to achieve low-cost technological improvement options are chosen. In CO2 terms, if a vehicle emits 300 g/mi CO2, 31 A different vehicle-miles-traveled (VMT) assumption would change the absolute numbers in the example, but would not change the mathematical principles. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.009</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C 24185 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations a 20 percent improvement is 60 g/mi, so the vehicle would emit 240 g/mi; but if the vehicle emits 180 g/mi, a 20 percent improvement is only 36 g/mi, so the vehicle would get 144 g/mi. In order to continue achieving similarly large (on an absolute basis) emissions reductions, the percentage reduction must also continue to increase. Related, average real-world fuel economy is lower than average fuel economy required under CAFE and CO2 standards. The 2012 Federal Register notice announcing augural CAFE and CO2 standards extending through MY 2025 indicated that, if met entirely through the application of fuel-saving technology, the MY 2025 CO2 standards would result in an average requirement equivalent to 54.5 mpg. However, because the CO2 standards provide credit for reducing leakage of AC refrigerants and/or switching to lowerGWP refrigerants, and these actions do not affect fuel economy, the notice explained that the corresponding fuel economy requirement (under the CAFE program) would be 49.7 mpg. These estimates were based on a market forecast grounded in the MY 2008 fleet. The notice also presented analysis using a market forecast grounded in the MY 2010 fleet, showing a 48.7 mpg average CAFE requirement. In the real world, fuel economy is, on average, about 20% lower than as measured under regulatory test procedures. In the real world, then, these new standards were estimated to require 39.0–39.8 mpg. Today’s analysis indicates that the requirements under the baseline/augural CAFE standards would average 46.6 mpg in MY 2029. The lower value results from changes in the fleet forecast which reflects consumer preference for larger vehicles than was forecast for the 2012 rulemaking. In the real world, the requirements average about 37.1 mpg. Under the final standards issued today, the regulatory test procedure requirements average 40.5 mpg, corresponding to 33.2 mpg in the real world. Buyers of new vehicles experience real-world fuel economy, with levels varying among drivers (due to a wide range of factors). Vehicle fuel economy labels provide average realworld fuel economy information to buyers. Vehicle owners also face fuel prices at the pump. The agencies noted in the NPRM that when fuel prices are high, the value of fuel saved may be enough to offset the cost of further fuel economy/emissions reduction improvements, but the agencies recognized that then-current projections of fuel prices by the Energy Information Administration did not indicate particularly high fuel prices in the foreseeable future. The agencies explained that fundamental structural shifts had occurred in global oil markets since the 2012 final rule, largely due to the rise of U.S. production and export of shale oil. The consequence over time of diminishing returns from more stringent fuel economy/emissions reduction standards, especially when combined with relatively low fuel prices, is greater difficulty for automakers to find a market of consumers willing to buy vehicles that meet the increasingly stringent standards. American consumers have long demonstrated that in times of relatively low fuel prices, fuel economy is not a top priority for the majority of them, even when highly fuel efficient vehicle models are available. The NPRM analysis sought to improve how the agencies captured the effects of higher new vehicle prices on fleet composition as a whole by including an improved model for vehicle scrappage rates. As new vehicle prices increase, consumers tend to continue using older vehicles for longer, slowing fleet turnover and thus slowing improvements in fleet-wide fuel economy, reductions in CO2 emissions, reductions in criteria pollutant emissions, and advances in safety. That aspect of the analysis was also driven by the agencies’ updated estimates of average per-vehicle cost increases due to VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00014 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.010</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24186 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations higher standards, which were several hundred dollars higher than previously estimated. The agencies cited growing concerns about affordability and negative equity for many consumers under these circumstances, as loan amounts grow and loan terms extend. For all of the above reasons, the agencies proposed to maintain the MY 2020 fuel economy and CO2 emissions standards for MYs 2021–2026. The agencies explained that they estimated, relative to the standards for MYs 2021– 2026 put forth in 2012, that an additional 0.5 million barrels of oil would be consumed per day (about 2 to 3 percent of projected U.S. consumption) if that proposal were finalized, but that they also expected the additional fuel costs to be outweighed by the cost savings from new vehicle purchases; that more than 12,700 onroad fatalities and significantly more injuries would be prevented over the lifetimes of vehicles through MY 2029 as compared to the standards set forth in the 2012 final rule over the lifetimes of vehicles as more new and safer vehicles are purchased than the current (and augural) standards; and that environmental impacts, on net, would be relatively minor, with criteria and toxic air pollutants not changing noticeably, and with estimated atmospheric CO2 concentrations increasing by 0.65 ppm (a 0.08 percent increase), which the agencies estimated would translate to 0.003 degrees Celsius of additional temperature increase relative to the standards finalized in 2012. Under the NPRM analysis, the agencies tentatively concluded that maintaining the MY 2020 curves for MYs 2021–2026 would save American auto consumers, the auto industry, and the public a considerable amount of money as compared to EPA retaining the previously-set CO2 standards and NHTSA finalizing the augural standards. The agencies explained that this had been identified as the preferred alternative, in part, because it appeared to maximize net benefits compared to the other alternatives analyzed, and recognizing the statutory considerations for both agencies. Relative to the standards issued in 2012, under CAFE standards, the NPRM analysis estimated that costs would decrease by $502 billion overall at a three-percent discount rate ($335 billion at a sevenpercent discount rate) and benefits were estimated to decrease by $326 billion at a three-percent discount rate ($204 billion at a seven-percent discount rate). Thus, net benefits were estimated to increase by $176 billion at a threepercent discount rate and $132 billion at VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 a seven-percent discount rate. The estimated impacts under CO2 standards were estimated to be similar, with net benefits estimated to increase by $201 billion at a three-percent discount rate and $141 billion at a seven-percent discount rate. The NPRM also sought comment on a variety of potential changes to NHTSA’s and EPA’s compliance programs for CAFE and CO2 as well as related programs, including questions about automaker requests for additional flexibilities and agency interest in reducing market-distorting incentives and improving transparency; and on a proposal to withdraw California’s CAA preemption waiver for its ‘‘Advanced Clean Car’’ regulations, with an accompanying discussion of preemption of State standards under EPCA.32 The agencies sought comment broadly on all aspects of the proposal. B. Public Participation Opportunities and Summary of Comments The NPRM was published on NHTSA’s and EPA’s websites on August 2, 2018, and published in the Federal Register on August 24, 2018, beginning a 60-day comment period. The agencies subsequently extended the official comment period for an additional three days, and left the dockets open for more than a year after the start of the comment period, considering late comments to the extent practicable. A separate Federal Register notice also published on August 24, 2018, which announced the locations, dates, and times of three public hearings to be held on the proposal: One in Fresno, California, on September 24, 2018; one in Dearborn, Michigan, on September 25, 2018; and one in Pittsburgh, Pennsylvania, on September 26, 2018. Each hearing started at 10 a.m. local time; the Fresno hearing ended at 5:10 p.m. and resulted in a 235 page transcript; the Dearborn hearing ran until 5:26 p.m. and resulted in a 330 page transcript; and the Pittsburgh hearing ran until 5:06 p.m. and also resulted in a 330 page transcript. Each hearing also collected several hundred pages of comments from participants, in addition to the hearing transcripts. Besides the comments submitted as part of the public hearings, NHTSA’s docket received a total of 173,359 public comments in response to the proposal as of September 18, 2019, and EPA’s docket a total of 618,647 public comments, for an overall total of 32 Agency actions relating to California’s CAA waiver and EPCA preemption have since been finalized, see 84 FR 51310 (Sept. 27, 2019), and will not be discussed in great detail as part of this final rule. PO 00000 Frm 00015 Fmt 4701 Sfmt 4700 24187 792,006. NHTSA also received several hundred comments on its DEIS to the separate DEIS docket. While the majority of individual comments were form letters, the agencies received over 6,000 pages of substantive comments on the proposal. Many commenters generally supported the proposal and many commenters opposed it. Commenters supporting the proposal tended to cite concerns about the cost of new vehicles, while commenters opposing the proposal tended to cite concerns about additional fuel expenditures and the impact on climate change. Many comments addressed the modeling used for the analysis, and specifically the inclusion, operation, and results of the sales and scrappage modules that were part of the NPRM’s analysis, while many addressed the NPRM’s safety findings and the role that those findings played in the proposal’s justification. Many other comments addressed California’s standards and role in Federal decision-making; as discussed above, those comments are further summarized and responded to in the separate Federal Register notice published in September 2019. Nearly every aspect of the NPRM’s analysis and discussion received some level of comment by at least one commenter. The comments received, as a whole, were both broad and deep, and the agencies appreciate the level of engagement of commenters in the public comment process and the information and opinions provided. C. Changes in Light of Public Comments and New Information The agencies made a number of changes to the analysis between the NPRM and the final rule in response to public comments and new information that was received in those comments or otherwise became available to the agencies. While these changes, their rationales, and their effects are discussed in detail in the sections below, the following represents a highlevel list of some of the most significant changes: • Some regulatory alternatives were dropped from consideration, and one was added; • updated analysis fleet, and changes to technologies on ‘‘baseline’’ vehicles within the fleet to reflect better their current properties and improve modeling precision; • no civil penalties assumed to be paid after MY 2020 under CAFE program; • updates and expansions in accounting for certain over-compliance E:\FR\FM\30APR2.SGM 30APR2 24188 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations • updated fuel costs based on the AEO 2019 version of NEMS; • a variety of technology updates in response to comments and new information; • updated accounting of rebound VMT between regulatory alternatives; • updated estimates of the macroeconomic cost of petroleum dependence; • updated response of total new vehicle sales to increases in fuel efficiency and price; and • updated response of vehicle retirement rates to changes in new vehicle fuel efficiency and transaction price. Sections IV and VI below discuss these updates in significant detail. D. Final Standards—Stringency As explained above, the agencies have chosen to set CAFE and CO2 standards that increase in stringency by 1.5 percent year over year for MYs 2021– 2026. Separately, EPA has decided to retain the A/C refrigerant and leakage and CH4 and N2O standards set forth in 2012 for MYs 2021 and beyond, and the where: TARGETFE is the fuel economy target (in mpg) applicable to a specific vehicle model type with a unique footprint combination, a is a minimum fuel economy target (in mpg), b is a maximum fuel economy target (in mpg), c is the slope (in gallons per mile per square foot, or gpm, per square foot) of a line relating fuel consumption (the inverse of fuel economy) to footprint, and d is an intercept (in gpm) of the same line. where: TARGETFE is the fuel economy target (in mpg) applicable to a specific vehicle model type with a unique footprint combination, a, b, c, and d are as for passenger cars, but taking values specific to light trucks, e is a second minimum fuel economy target (in mpg), f is a second maximum fuel economy target (in mpg), g is the slope (in gpm per square foot) of a second line relating fuel consumption (the inverse of fuel economy) to footprint, and values, respectively, of the set of included values. For example, MIN[40,35] = 35 and MAX(40, 25) = 40, such that MIN[MAX(40, 25), 35] = 35. For light trucks, also consistent with prior rulemakings, NHTSA is defining fuel economy targets as follows: h is an intercept (in gpm) of the same second line. The final CAFE standards (described in terms of their footprint-based curves) are as follows, with the values for the coefficients changing over time: ER30AP20.012</GPH> Here, MIN and MAX are functions that take the minimum and maximum stringency of the CO2 standards in this final rule reflect the ‘‘offset’’ also established in 2012 based on assumptions made at that time about anticipated HFC emissions reductions. When the agencies state that stringency will increase at 1.5 percent per year, that means that the footprint curves which actually define the standards for CAFE and CO2 emissions will become more stringent at 1.5 percent per year. Consistent with Congress’s direction in EISA to set CAFE standards based on a mathematical formula, which EPA harmonized with for the CO2 emissions standards, the standard curves are equations, which are slightly different for CAFE and CO2, and within each program, slightly different for passenger cars and light trucks. Each program has a basic equation for a fleet standard, and then values that change to cause the stringency changes are the coefficients within the equations. For passenger cars, consistent with prior rulemakings, NHTSA is defining fuel economy targets as follows: VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00016 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.011</GPH> khammond on DSKJM1Z7X2PROD with RULES2 credits, including early credits earned in EPA’s program; • updates and expansions to CAFE Model’s technology paths; • updates to inputs defining the range of manufacturer-, technology-, and product-specific constraints; • updates to allow the model to adopt a more advanced technology if it is more cost-effective than an earlier technology on the path; • precision improvements to the modeling of A/C efficiency and off-cycle credits; • updates to model’s ‘‘effective cost’’ metric; • extended explicit simulation of technology application through MY 2050; • expanded presentation of the results to include ‘‘calendar year’’ analysis; • quantifying different types of health impacts from changes in air pollution, rather than only accounting for such impacts in aggregate estimates of the social costs of air pollution; • updated costs to 2018 dollars; Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 y-axis represents fuel economy, showing that in the CAFE context, targets are higher (fuel economy) for smaller PO 00000 Frm 00017 Fmt 4701 Sfmt 4700 footprint vehicles and lower for larger footprint vehicles: BILLING CODE 4910–59–C E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.013</GPH> khammond on DSKJM1Z7X2PROD with RULES2 These equations are presented graphically below, where the x-axis represents vehicle footprint and the 24189 24190 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00018 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.014</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–P Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations domestic passenger automobile fleets manufactured for sale in the U.S. by all manufacturers in the model year, which projection shall be published in the Federal Register when the standard for that model year is promulgated in accordance with 49 U.S.C. 32902(b).33 Any time NHTSA establishes or changes a passenger car standard for a model year, the MDPCS for that model year must also be evaluated or re-evaluated and established accordingly. Thus, this final rule establishes the applicable MDPCS for MYs 2021–2026. Table II–8 lists the minimum domestic passenger car standards. EPA CO2 standards are as follows. Rather than expressing these standards as linear functions with accompanying minima and maxima, similar to the approach NHTSA has followed since 2005 in specifying attribute-based standards, the following tables specify flat standards that apply below and above specified footprints, and a linear function that applies between those footprints. The two approaches are mathematically identical. For passenger cars with a footprint of less than or equal to 41 square feet, the gram/mile CO2 target value is selected for the appropriate model year from Table II–9: ER30AP20.016</GPH> EPCA, as amended by EISA, requires that any manufacturer’s domesticallymanufactured passenger car fleet must meet the greater of either 27.5 mpg on average, or 92 percent of the average fuel economy projected by the Secretary for the combined domestic and non- 33 49 U.S.C. 32902(b)(4). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00019 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.015</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C 24191 For passenger cars with a footprint of greater than 56 square feet, the gram/ mile CO2 target value is selected for the appropriate model year from Table II– 10: For passenger cars with a footprint that is greater than 41 square feet and less than or equal to 56 square feet, the gram/mile CO2 target value is calculated VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00020 Fmt 4701 Sfmt 4700 using the following equation and rounded to the nearest 0.1 grams/mile. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.018</GPH> Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations ER30AP20.017</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24192 Target CO2 = [a × f] + b Where f is the vehicle footprint and a and b are selected from Table II–11 for the appropriate model year: For light trucks with a footprint of less than or equal to 41 square feet, the gram/mile CO2 target value is selected for the appropriate model year from Table II–12: VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00021 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 24193 ER30AP20.019</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24194 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations value is selected for the appropriate model year from Table II–13: ER30AP20.021</GPH> specified in the table below for each model year, the gram/mile CO2 target VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00022 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.020</GPH> khammond on DSKJM1Z7X2PROD with RULES2 For light trucks with a footprint greater than the minimum value 24195 Target CO2 = (a × f) + b For light trucks with a footprint that is greater than 41 square feet and less than or equal to the maximum footprint value specified in Table II–14 below for each model year, the gram/mile CO2 target value is calculated using the following equation and rounded to the nearest 0.1 grams/mile. Where f is the footprint and a and b are selected from Table II–14 below for the appropriate model year: These equations are presented graphically below, where the x-axis represents vehicle footprint and the y- axis represents the CO2 target. The targets are lower for smaller footprint vehicles and higher for larger footprint vehicles: VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 BILLING CODE 4910–59–P Frm 00023 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.022</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00024 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.023</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24196 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations BILLING CODE 4910–59–C Except that EPA elected to apply a slightly different slope when defining passenger car targets, CO2 targets may be expressed as direct conversion of fuel economy targets, as follows: emissions, but EPCA provides no basis to count reduced HFC emissions toward CAFE levels. BILLING CODE 4910–59–P ER30AP20.025</GPH> For the reader’s benefit, Table II–15, Table II–16, and Table II–17 show the estimates, under the final rule analysis, of what the MYs 2021–2026 CAFE and CO2 curves would translate to, in terms of miles per gallon (mpg) and grams per mile (g/mi). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00025 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.024</GPH> khammond on DSKJM1Z7X2PROD with RULES2 where 8887 g/gal relates grams of CO2 emitted to gallons of fuel consumed, and OFFSET reflects the fact that that HFC emissions from lower-GWP A/C refrigerants and less leak-prone A/C systems are counted toward average CO2 24197 24198 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00026 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.026</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations the final standards than under the proposed standards: ER30AP20.028</GPH> requirements are more stringent (i.e., for CAFE, higher, and for CO2, lower) under VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00027 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.027</GPH> khammond on DSKJM1Z7X2PROD with RULES2 As the following tables demonstrate, averages of manufacturers’ estimated 24199 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations E. Final Standards—Impacts khammond on DSKJM1Z7X2PROD with RULES2 This section summarizes the estimated costs and benefits of the MYs 2021–2026 CAFE and CO2 emissions standards for passenger cars and light trucks, as compared to the regulatory alternatives considered. These estimates helped inform the agencies’ choices among the regulatory alternatives considered and provide further confirmation that the final standards are maximum feasible, for NHTSA, and appropriate, for EPA. The costs and benefits estimated to result from the CAFE standards are presented first, followed by those estimated to result from the CO2 standards. For several reasons, the estimates for costs and benefits presented for the different programs, while consistent, are not 34 See identical. NHTSA’s and EPA’s standards are projected to result in slightly different fuel efficiency improvements. EPA’s CO2 standard is nominally more stringent in part due to its assumptions about manufacturers’ use of air conditioning leakage/refrigerant replacement credits, which are expected to result in reduced emissions of HFCs. NHTSA’s final standards are based solely on assumptions about fuel economy improvements, and do not account for emissions reductions that do not relate to fuel economy. In addition, the CAFE and CO2 programs offer somewhat different program flexibilities and provisions, primarily because NHTSA is statutorily prohibited from considering some flexibilities when establishing CAFE standards, while EPA is not.34 The analysis underlying this final rule reflects many of those additional EPA flexibilities, which contributes to differences in how the agencies estimate manufacturers could comply with the respective sets of standards, which in turn contributes to differences in estimated impacts of the standards. These differences in compliance flexibilities are discussed in more detail in Section IX below. Table II–20 to Table II–23 present all subcategories of costs and benefits of this final rule for all seven alternatives proposed. Costs include application of fuel economy technology to new vehicles, consumer surplus, crash costs due to changes in VMT, as well as, noise and congestion. Benefits include fuel savings, consumer surplus, refueling time, and clean air. BILLING CODE 4910–59–P 49 U.S.C. 32902(h); CAA Sec. 202(a). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00028 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.029</GPH> 24200 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00029 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24201 ER30AP20.030</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00030 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.031</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24202 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00031 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24203 ER30AP20.032</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00032 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.033</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24204 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00033 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24205 ER30AP20.034</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00034 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.035</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24206 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00035 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24207 ER30AP20.036</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00036 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.037</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24208 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations F. Other Programmatic Elements khammond on DSKJM1Z7X2PROD with RULES2 1. Compliance and Flexibilities Automakers seeking to comply with the CAFE and CO2 standards are generally expected to add fuel economyimproving technologies to their new vehicles to boost their overall fleet fuel economy levels. Readers will remember that improving fuel economy directly reduces CO2 emissions, because CO2 is a natural and inevitable byproduct of fossil fuel combustion to power vehicles. The CAFE and CO2 programs contain a variety of compliance provisions and flexibilities to accommodate better automakers’ production cycles, to reward real-world fuel economy improvements that cannot be reflected in the 1975-developed test VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 procedures, and to incentivize the production of certain types of vehicles. While the agencies sought comment on a broad variety of changes and potential expansions of the programs’ compliance flexibilities in the NPRM, the agencies determined, after considering the comments, to make a few changes to the flexibilities proposed in the NPRM in this final rule. The most noteworthy change is the retention, in the CO2 program, of the flexibilities that allow automakers to continue to use HFC reductions toward their CO2 compliance, and that extend the ‘‘0 grams/mile’’ assumption for electric vehicles through MY 2026 (i.e., recognizing only the tailpipe emissions of full battery-electric vehicles and not recognizing the upstream emissions caused by the electricity usage of those PO 00000 Frm 00037 Fmt 4701 Sfmt 4725 vehicles). In the NPRM, EPA had proposed to remove and sought comment on removing those flexibilities from the CO2 program, but determined not to remove them in this final rule. EPA and NHTSA are also removing from the programs, starting in MY 2022, the credit/FCIV for full-size pickup trucks that are either hybrids or overperforming by a certain amount relative to their targets, and allowing technology suppliers to begin the petition process for off-cycle credits/adjustments. Table II–24, Table II–25, Table II–26, and Table II–27 provide a summary of the various compliance provisions in the two programs; their authorities; and any changes included as part of this final rule: BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.038</GPH> BILLING CODE 4910–59–C 24209 24210 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00038 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.039</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–P 35 The CAFE program uses an energy efficiency metric and standards that are expressed in miles per gallon. For PHEVs and BEVs, to determine gasoline the equivalent fuel economy for operation on electricity, a Petroleum Equivalency Factor (PEF) is applied to the measured electrical consumption. The PEF for electricity was established by the Department of Energy, as required by statute, and includes an accounting for upstream energy associated with the production and distribution for electricity relative to gasoline. Therefore, the CAFE program includes upstream accounting based on the metric that is consistent with the fuel economy VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 Providing a technology neutral basis by which manufacturers meet fuel economy and CO2 emissions standards encourages an efficient and level playing field. The agencies continue to have a desire to minimize incentives that disproportionately favor one technology over another. Some of this may involve regulations established by metric. The PEF for electricity also includes an incentive that effectively counts only 15 percent of the electrical energy consumed. PO 00000 Frm 00039 Fmt 4701 Sfmt 4700 24211 other Federal agencies. In the near future, NHTSA and EPA intend to work with other relevant Federal agencies to pursue regulatory means by which we can further ensure technology neutrality in this field. 2. Preemption/Waiver As discussed above, the issues of Clean Air Act waivers of preemption under Section 209 and EPCA/EISA preemption under 49 U.S.C. 32919 are not addressed in today’s final rule, as E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.040</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24212 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations they were the subject of a separate final rulemaking action by the agencies in September 2019. While many comments were received in response to the NPRM discussion of those issues, those comments have been addressed and responded to as part of that separate rulemaking action. khammond on DSKJM1Z7X2PROD with RULES2 III. Purpose of the Rule The Administrative Procedure Act (APA) requires agencies to incorporate in their final rules a ‘‘concise general statement of their basis and purpose.’’ 36 While the entire preamble document represents the agencies’ overall explanation of the basis and purpose for this regulatory action, this section within the preamble is intended as a direct response to that APA (and related CAA) requirements. Executive Order 12866 further states that ‘‘Federal agencies should promulgate only such regulations as are required by law, are necessary to interpret the law, or are made necessary by compelling public need, such as material failures of private markets to protect or improve the health and safety of the public, the environment, or the well-being of the American people.’’ 37 Section III.C of the FRIA accompanying this rulemaking discusses at greater length the question of whether a market failure exists that these final rules may address. NHTSA and EPA are legally obligated to set CAFE and GHG standards, respectively, and do not have the authority to decline to regulate.38 The agencies are issuing these final rules to fulfill their respective statutory obligations to provide maximum feasible fuel economy standards and limit emissions of pollutants from new motor vehicles which have been found to endanger public health and welfare (in this case, specifically carbon dioxide (CO2); EPA has already set standards for methane (CH4), nitrous oxide (N2O), and hydrofluorocarbons (HFCs) and is not revising them in this rule). Continued progress in meeting these statutory obligations is both legally necessary and good for America—greater energy security and reduced emissions protect the American public. The final standards continue that progress, albeit at a slower rate than the standards finalized in 2012. National annual gasoline consumption and CO2 emissions currently total about 140 billion gallons and 5,300 million metric tons, 36 5 U.S.C. 553(c); see also Clean Air Act section 307(d)(6)(A), 42 U.S.C. 7607(d)(6)(A). 37 E.O. 12866, Section 1(a). 38 For CAFE, see 49 U.S.C. 32902; for CO , see 42 2 U.S.C. 7521(a). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 respectively. The majority of this gasoline (about 130 billion gallons) is used to fuel passenger cars and light trucks, such as will be covered by the CAFE and CO2 standards issued today. Accounting for both tailpipe emissions and emissions from ‘‘upstream’’ processes (e.g., domestic refining) involved in producing and delivering fuel, passenger cars and light trucks account for about 1,500 million metric tons (mmt) of current annual CO2 emissions. The agencies estimate that under the standards issued in 2012, passenger car and light truck annual gasoline consumption would steadily decline, reaching about 80 billion gallons by 2050. The agencies further estimate that, because of this decrease in gasoline consumption under the standards issued in 2012, passenger car and light truck annual CO2 emissions would also steadily decline, reaching about 1,000 mmt by 2050. Under the standards issued today, the agencies estimate that, instead of declining from about 140 billion gallons annually today to about 80 billion gallons annually in 2050, passenger car and light truck gasoline consumption would decline to about 95 billion gallons. The agencies correspondingly estimate that instead of declining from about 1,500 mmt annually today to about 1,000 mmt annually in 2050, passenger car and light truck CO2 emissions would decline to about 1,100 mmt. In short, the agencies estimate that under the standards issued today, annual passenger car and light truck gasoline consumption and CO2 emissions will continue to steadily decline over the next three decades, even if not quite as rapidly as under the previously-issued standards. The agencies also estimate that these impacts on passenger car and light truck gasoline consumption and CO2 emissions will be accompanied by a range of other energy- and climaterelated impacts, such as reduced electricity consumption (because today’s standards reduce the estimated rate at which the market might shift toward electric vehicles) and increased CH4 and N2O emissions. These estimated impacts, discussed below and in the FEIS accompanying today’s notice, are dwarfed by estimated impacts on gasoline consumption and CO2 emissions. As explained above, these final rules set or amend fuel economy and carbon dioxide standards for model years 2021– 2026. Many commenters argued that it was not appropriate to amend previously-established CO2 and CAFE standards, generally because those commenters believed that the PO 00000 Frm 00040 Fmt 4701 Sfmt 4700 administrative record established for the 2012 final rule and EPA’s January 2017 Final Determination was superior to the record that informed the NPRM, and that that prior record led necessarily to the policy conclusion that the previously-established standards should remain in place.39 Some commenters similarly argued that EPA’s Revised Final Determination—which, for EPA, preceded this regulatory action—was invalid because, they allege, it did not follow the procedures established for the mid-term evaluation that EPA codified into regulation,40 and also because the Revised Final Determination was not based on the prior record.41 The agencies considered a range of alternatives in the proposal, including the baseline/no action alternative of retaining the existing EPA carbon dioxide standards. As the agencies explained in the proposal, the proposal was entirely de novo, based on an entirely new analysis reflecting the best and most up-to-date information available to the agencies.42 This rulemaking action is separate and distinct from EPA’s Revised Final Determination, which itself was neither a proposed nor a final decision that the standards ‘‘must’’ be revised. EPA retained full discretion in this rulemaking to revise the standards or not revise them. In any event, the case law is clear that agencies are free to reconsider their prior decisions.43 With that legal principle in mind, the agencies agree with commenters that the amended (and new) CO2 and CAFE standards must be consistent with the 39 Comments arguing that the prior record was superior to the current record, and thus a better basis for decision-making, will be addressed throughout the balance of this preamble. 40 40 CFR 86.1818–12(h). 41 See, e.g., comments from the States and Cities, Attachment 1, Docket No. NHTSA–2018–0067– 11735, at 40–42; CARB, Detailed Comments, Docket No. NHTSA–2018–0067–11873, at 71–72; CBD et. al, Appendix A, Docket No. NHTSA–2018–0067– 12000, at 214–228. 42 83 FR 42968, 42987 (Aug. 24, 2018). 43 See, e.g., Encino Motorcars, LLC v. Navarro, 136 S. Ct. 2117, 2125 (2016) (‘‘Agencies are free to change their existing policies as long as they provide a reasoned explanation for the change.’’); FCC v. Fox Television Stations, Inc., 556 U.S. 502, 515 (2009) (When an agency changes its existing position, it ‘‘need not always provide a more detailed justification than what would suffice for a new policy created on a blank slate. Sometimes it must—when, for example, its new policy rests on factual findings that contradict those which underlay its prior policy; or when its prior policy has engendered serious reliance interests that must be taken into account . . . . In such cases it is not that further justification is demanded by the mere fact of policy change, but that a reasoned explanation is needed for disregarding facts and circumstances that underlay or were engendered by the prior policy.’’) E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations CAA and EPCA/EISA, respectively, and this preamble and the accompanying FRIA explain in detail why the agencies believe they are consistent. The section below discusses briefly the authority given to the agencies by their respective governing statutes, and the factors that Congress directed the agencies to consider as they exercise that authority in pursuit of fulfilling their statutory obligations. A. EPA’s Statutory Requirements EPA is setting national CO2 standards for passenger cars and light trucks under Section 202(a) of the Clean Air Act (CAA).44 Section 202(a) of the CAA requires EPA to establish standards for emissions of pollutants from new motor vehicles which cause or contribute to air pollution which may reasonably be anticipated to endanger public health or welfare.45 In establishing such standards, EPA considers issues of technical feasibility, cost, available lead time, and other factors. Standards under section 202(a) thus take effect only ‘‘after providing such period as the Administrator finds necessary to permit the development and application of the requisite technology, giving appropriate consideration to the cost of compliance within such period.’’ 46 EPA’s statutory requirements are further discussed in Section VIII.A. B. NHTSA’s Statutory Requirements NHTSA is setting national Corporate Average Fuel Economy (CAFE) standards for passenger cars and light trucks for each model year as required under EPCA, as amended by EISA.47 EPCA mandates a motor vehicle fuel economy regulatory program that balances statutory factors in setting minimum fuel economy standards to facilitate energy conservation. EPCA allocates the responsibility for implementing the program between NHTSA and EPA as follows: NHTSA sets CAFE standards for passenger cars 44 42 U.S.C. 7521(a). Coalition for Responsible Regulation v. EPA, 684 F.3d 102, 114–115 (D.C. Cir. 2012) (‘‘ ‘If EPA makes a finding of endangerment, the Clean Air Act requires the [a]gency to regulate emissions of the deleterious pollutant from new motor vehicles . . . . Given the non-discretionary duty in Section 202(a)(1) and the limited flexibility available under Section 202(a)(2), which this court has held related only to the motor vehicle industry, . . . EPA had no statutory basis on which it could ground [any] reasons for further inaction’ ’’) (quoting Massachusetts v. EPA, 549 U.S. 497, 533– 35 (2007). 46 42 U.S.C. 7521(a)(2). 47 EPCA and EISA direct the Secretary of Transportation to develop, implement, and enforce fuel economy standards (see 49 U.S.C. 32901 et. seq.), which authority the Secretary has delegated to NHTSA at 49 CFR 1.94(c). khammond on DSKJM1Z7X2PROD with RULES2 45 See VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 and light trucks; EPA establishes the procedures for testing, tests vehicles, collects and analyzes manufacturers’ data, and calculates the individual and average fuel economy of each manufacturer’s passenger cars and light trucks; and NHTSA enforces the standards based on EPA’s calculations. The following sections enumerate specific statutory requirements for NHTSA in setting CAFE standards and NHTSA’s interpretations of them, where applicable. Many comments were received on these requirements and interpretations. Because this is intended as an overview section, those comments will be addressed below in Section VIII rather than here, and the agencies refer readers to that part of the document for more information. For each future model year, EPCA (as amended by EISA) requires that DOT (by delegation, NHTSA) establish separate passenger car and light truck standards at ‘‘the maximum feasible average fuel economy level that the Secretary decides the manufacturers can achieve in that model year,’’ 48 based on the agency’s consideration of four statutory factors: ‘‘technological feasibility, economic practicability, the effect of other motor vehicle standards of the Government on fuel economy, and the need of the United States to conserve energy.’’ 49 The law also allows NHTSA to amend standards that are already in place, as long as doing so meets these requirements.50 EPCA does not define these terms or specify what weight to give each concern in balancing them; thus, NHTSA defines them and determines the appropriate weighting that leads to the maximum feasible standards given the circumstances in each CAFE standard rulemaking.51 EISA added several other requirements to the setting of separate passenger car and light truck standards. Standards must be ‘‘based on 1 or more vehicle attributes related to fuel economy and express[ed] . . . in the form of a mathematical function.’’ 52 New standards must also be set at least 18 months before the model year in question, as would amendments to increase standards previously set.53 48 49 U.S.C. 32902(a) and (b). U.S.C. 32902(f). 50 49 U.S.C. 32902(g). 51 See Center for Biological Diversity v. NHTSA, 538 F.3d 1172, 1195 (9th Cir. 2008) (hereafter ‘‘CBD v. NHTSA’’) (‘‘The EPCA clearly requires the agency to consider these four factors, but it gives NHTSA discretion to decide how to balance the statutory factors—as long as NHTSA’s balancing does not undermine the fundamental purpose of the EPCA: Energy conservation.’’) 52 49 U.S.C. 32902(b)(3)(A). 53 49 U.S.C. 32902(a), (g)(2). 49 49 PO 00000 Frm 00041 Fmt 4701 Sfmt 4700 24213 NHTSA must regulations prescribing average fuel economy standards for at least 1, but not more than 5, model years at a time.54 A number of comments addressed these requirements; for the reader’s reference, those comments will be summarized and responded to in Section VIII. EISA also added the requirement that NHTSA set a minimum standard for domesticallymanufactured passenger cars,55 which will also be discussed further in Section VIII below. For MYs 2011–2020, EISA further required that the separate standards for passenger cars and for light trucks be set at levels high enough to ensure that the achieved average fuel economy for the entire industry-wide combined fleet of new passenger cars and light trucks reach at least 35 mpg not later than MY 2020, and standards for those years were also required to ‘‘increase ratably.’’ 56 For model years after 2020, standards must be set at the maximum feasible level.57 1. Factors That Must Be Considered in Deciding What Levels of CAFE Standards are ‘‘Maximum Feasible’’ (a) Technological Feasibility ‘‘Technological feasibility’’ refers to whether a particular method of improving fuel economy can be available for commercial application in the model year for which a standard is being established. Thus, in determining the level of new standards, the agency is not limited to technology that is already being commercially applied at the time of the rulemaking. For this rulemaking, NHTSA has evaluated and considered all types of technologies that improve real-world fuel economy, although not every possible technology was expressly included in the analysis, as discussed in Section VI and also in Section VIII. (b) Economic Practicability ‘‘Economic practicability’’ refers to whether a standard is one ‘‘within the 54 49 U.S.C. 39202(b)(3)(B). U.S.C. 32902(b)(4). 56 49 U.S.C. 32902(b)(2)(A) and (C). NHTSA has CAFE standards in place that are projected to result in industry-achieved fuel economy levels over 35 mpg in MY 2020. EPA typically provides verified final CAFE data from manufacturers to NHTSA several months or longer after the close of the MY in question, so the actual MY 2020 fuel economy will not be known until well after MY 2020 has ended. The standards for all MYs up to and including 2020 are known and not at issue in this regulatory action, so these provisions are noted for completeness rather than immediate relevance to this final rule. Because neither of these requirements apply after MY 2020, they are not relevant to this rulemaking and will not be discussed further. 57 49 U.S.C. 32902(b)(2)(B). 55 49 E:\FR\FM\30APR2.SGM 30APR2 24214 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations financial capability of the industry, but not so stringent as to’’ lead to ‘‘adverse economic consequences, such as a significant loss of jobs or the unreasonable elimination of consumer choice.’’ 58 The agency has explained in the past that this factor can be especially important during rulemakings in which the automobile industry is facing significantly adverse economic conditions (with corresponding risks to jobs). Economic practicability is a broad factor that includes considerations of the uncertainty surrounding future market conditions and consumer demand for fuel economy in addition to other vehicle attributes.59 In an attempt to evaluate the economic practicability of different future levels of CAFE standards (i.e., the regulatory alternatives considered in this rulemaking), NHTSA considers a variety of factors, including the annual rate at which manufacturers can increase the percentage of their fleet(s) that employ a particular type of fuel-saving technology, the specific fleet mixes of different manufacturers, assumptions about the cost of the standards to consumers, and consumers’ valuation of fuel economy, among other things, including, in part, safety. It is important to note, however, that the law does not preclude a CAFE standard that poses considerable challenges to any individual manufacturer. The Conference Report for EPCA, as enacted in 1975, makes clear, and the case law affirms, ‘‘a determination of maximum feasible average fuel economy should not be keyed to the single manufacturer which might have the most difficulty achieving a given level of average fuel economy.’’ 60 Instead, NHTSA is compelled ‘‘to weigh the benefits to the nation of a higher fuel economy standard against the difficulties of individual automobile manufacturers.’’ 61 Accordingly, while the law permits NHTSA to set CAFE standards that exceed the projected capability of a particular manufacturer as long as the standard is economically practicable for the industry as a whole, the agency cannot simply disregard that 58 67 FR 77015, 77021 (Dec. 16, 2002). e.g., Center for Auto Safety v. NHTSA (‘‘CAS’’), 793 F.2d 1322 (D.C. Cir. 1986) (Administrator’s consideration of market demand as component of economic practicability found to be reasonable); Public Citizen v. NHTSA, 848 F.2d 256 (D.C. Cir. 1988) (Congress established broad guidelines in the fuel economy statute; agency’s decision to set lower standard was a reasonable accommodation of conflicting policies). 60 Center for Auto Safety v. NHTSA (‘‘CAS’’), 793 F.2d 1322, 1352 (D.C. Cir. 1986). 61 Id. khammond on DSKJM1Z7X2PROD with RULES2 59 See, VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 impact on individual manufacturers.62 That said, in setting fuel economy standards, NHTSA does not seek to maintain competitive positions among the industry players, and notes that while a particular CAFE standard may pose difficulties for one manufacturer as being too high or too low, it may also present opportunities for another. NHTSA has long held that the CAFE program is not necessarily intended to maintain the competitive positioning of each particular company. Rather, it is intended to enhance the fuel economy of the vehicle fleet on American roads, while protecting motor vehicle safety and paying close attention to the economic risks. (c) The Effect of Other Motor Vehicle Standards of the Government on Fuel Economy ‘‘The effect of other motor vehicle standards of the Government on fuel economy’’ involves an analysis of the effects of compliance with emission, safety, noise, or damageability standards on fuel economy capability and thus on average fuel economy. In many past CAFE rulemakings, NHTSA has said that it considers the adverse effects of other motor vehicle standards on fuel economy. It said so because, from the CAFE program’s earliest years,63 the effects of such compliance on fuel economy capability over the history of the program have been negative ones. For example, safety standards that have the effect of increasing vehicle weight lower vehicle fuel economy capability and thus decrease the level of average fuel economy that the agency can determine to be feasible. NHTSA has considered the additional weight that it estimates would be added in response to new safety standards during the rulemaking timeframe. NHTSA has also accounted for EPA’s ‘‘Tier 3’’ standards for criteria pollutants in its estimates of technology effectiveness.64 The NPRM also discussed how EPA’s CO2 standards for light-duty vehicles and California’s Advanced Clean Cars program fit into NHTSA’s consideration of ‘‘the effect of other motor vehicle standards of the Government on fuel economy.’’ The agencies note that on September 19, 2019, to ensure One National Program for automobile fuel economy and carbon dioxide emissions standards, the agencies finalized regulatory text related to preemption of 62 Id. (‘‘. . . the Secretary must weigh the benefits to the nation of a higher average fuel economy standard against the difficulties of individual automobile manufacturers.’’) 63 42 FR 63184, 63188 (Dec. 15, 1977). See also 42 FR 33534, 33537 (Jun. 30, 1977). 64 See Section VI, below. PO 00000 Frm 00042 Fmt 4701 Sfmt 4700 State tailpipe CO2 standards and Zero Emission Vehicle (ZEV) mandates under EPCA and partial withdrawal of a waiver previously provided to California under the Clean Air Act.65 This final rule’s impact on State programs—including California’s—will therefore be somewhat different from the NPRM’s consideration. In the interest of brevity, this preamble will hold further discussion of that point, along with responses to comments received, until Section VIII. (d) The Need of the United States To Conserve Energy ‘‘The need of the United States to conserve energy’’ means ‘‘the consumer cost, national balance of payments, environmental, and foreign policy implications of our need for large quantities of petroleum, especially imported petroleum.’’ 66 Environmental implications principally include changes in emissions of carbon dioxide and criteria pollutants and air toxics. Prime examples of foreign policy implications are energy independence and security concerns. (1) Consumer Costs and Fuel Prices Fuel for vehicles costs money for vehicle owners and operators. All else equal (and this is an important qualification), consumers benefit from vehicles that need less fuel to perform the same amount of work. Future fuel prices are a critical input into the economic analysis of potential CAFE standards because they determine the value of fuel savings both to new vehicle buyers and to society, the amount of fuel economy that the new vehicle market is likely to demand in the absence of new standards, and they inform NHTSA about the consumer cost of the nation’s need for large quantities of petroleum. In this final rule, NHTSA’s analysis relies on fuel price projections estimated using the version of NEMS used for the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook for 2019.67 Federal government agencies generally use EIA’s price projections in their assessment of future energy-related policies. (2) National Balance of Payments Historically, the need of the United States to conserve energy has included consideration of the ‘‘national balance of payments’’ because of concerns that importing large amounts of oil created a 65 84 FR 51310 (Sept. 27, 2019). FR 63184, 63188 (1977). 67 The analysis for the proposal relied on fuel price projections from AEO 2017; the difference in the projections is discussed in Section VI. 66 42 E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 significant wealth transfer to oilexporting countries and left the U.S. economically vulnerable.68 As recently as 2009, nearly half of the U.S. trade deficit was driven by petroleum,69 yet this concern has largely lain fallow in more recent CAFE actions, in part because other factors besides petroleum consumption have since played a bigger role in the U.S. trade deficit.70 Given significant recent increases in U.S. oil production and corresponding decreases in oil imports, this concern seems likely to remain fallow for the foreseeable future.71 Increasingly, changes in the price of fuel have come to represent transfers between domestic consumers of fuel and domestic producers of petroleum rather than gains or losses to foreign entities. As flagged in the NPRM, some commenters raised concerns about 68 See, e.g., 42 FR 63184, 63192 (Dec. 15, 1977) (‘‘A major reason for this need [to reduce petroleum consumption] is that the importation of large quantities of petroleum creates serious balance of payments and foreign policy problems. The United States currently spends approximately $45 billion annually for imported petroleum. But for this large expenditure, the current large U.S. trade deficit would be a surplus.’’) 69 See ‘‘Today in Energy: Recent improvements in petroleum trade balance mitigate U.S. trade deficit,’’ U.S. Energy Information Administration (Jul. 21, 2014), available at https://www.eia.gov/ todayinenergy/detail.php?id=17191. 70 See, e.g., Nida C ¸ akir Melek and Jun Nie, ‘‘What Could Resurging U.S. Energy Production Mean for the U.S. Trade Deficit,’’ Mar. 7, 2018, Federal Reserve Bank of Kansas City. Available at https:// www.kansascityfed.org/publications/research/mb/ articles/2018/what-could-resurging-energyproduction-mean. The authors state that ‘‘The decline in U.S. net energy imports has prevented the total U.S. trade deficit from widening further. . . . In 2006, petroleum accounted for about 16 percent of U.S. goods imports and about 3 percent of U.S. goods exports. By the end of 2017, the share of petroleum in total goods imports declined to 8 percent, while the share in total goods exports almost tripled, shrinking the U.S. petroleum trade deficit. Had the petroleum trade deficit not improved, all else unchanged, the total U.S. trade deficit would likely have been more than 35 percent wider by the end of 2017.’’ 71 For an illustration of recent increases in U.S. production, see, e.g., ‘U.S. crude oil and liquid fuels production,’’ Short-Term Energy Outlook, U.S. Energy Information Administration (Aug. 2019), available at http://www.eia.gov/outlooks/steo/ images/Fig16.png. EIA noted in April 2019 that ‘‘Annual U.S. crude oil production reached a record level of 10.96 million barrels per day (b/d) in 2018, 1.6 million b/d (17%) higher than 2017 levels. In December 2018, monthly U.S. crude oil production reached 11.96 million b/d, the highest monthly level of crude oil production in U.S. history. U.S crude oil production has increased significantly over the past 10 years, driven mainly by production from tight rock formations using horizontal drilling and hydraulic fracturing. EIA projects that U.S. crude oil production will continue to grow in 2019 and 2020, averaging 12.3 million b/d and 13.0 million b/d, respectively.’’ ‘‘Today in Energy: U.S. crude oil production grew 17% in 2018, surpassing the previous record in 1970,’’ EIA, Apr. 9, 2019. Available at http://www.eia.gov/todayinenergy/ detail.php?id=38992. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 potential economic consequences for automaker and supplier operations in the U.S. due to disparities between CAFE standards at home and their counterpart fuel economy/efficiency and CO2 standards abroad. NHTSA finds these concerns more relevant to technological feasibility and economic practicability considerations than to the national balance of payments. The discussion in Section VIII below addresses this topic in more detail. (3) Environmental Implications Higher fleet fuel economy can reduce U.S. emissions of various pollutants by reducing the amount of oil that is produced and refined for the U.S. vehicle fleet, but can also increase emissions by reducing the cost of driving, which can result in more vehicle miles traveled (i.e., the rebound effect). Thus, the net effect of more stringent CAFE standards on emissions of each pollutant depends on the relative magnitude of both its reduced emissions in fuel refining and distribution and increases in its emissions from vehicle use. Fuel savings from CAFE standards also necessarily results in lower emissions of CO2, the main greenhouse gas emitted as a result of refining, distributing, and using transportation fuels. Reducing fuel consumption directly reduces CO2 emissions because the primary source of transportation-related CO2 emissions is fuel combustion in internal combustion engines. NHTSA has considered environmental issues, both within the context of EPCA and the context of the National Environmental Policy Act (NEPA), in making decisions about the setting of standards since the earliest days of the CAFE program. As courts of appeal have noted in three decisions stretching over the last 20 years,72 NHTSA defined ‘‘the need of the United States to conserve energy’’ in the late 1970s as including, among other things, environmental implications. In 1988, NHTSA included climate change concepts in its CAFE notices and prepared its first environmental assessment addressing that subject.73 It cited concerns about climate change as one of its reasons for limiting the extent of its reduction of the CAFE standard for MY 1989 passenger cars.74 Since then, NHTSA has considered the effects of 72 CAS, 793 F.2d 1322, 1325 n. 12 (D.C. Cir. 1986); Public Citizen, 848 F.2d 256, 262–63 n. 27 (D.C. Cir 1988) (noting that ‘‘NHTSA itself has interpreted the factors it must consider in setting CAFE standards as including environmental effects’’); CBD, 538 F.3d 1172 (9th Cir. 2007). 73 53 FR 33080, 33096 (Aug. 29, 1988). 74 53 FR 39275, 39302 (Oct. 6, 1988). PO 00000 Frm 00043 Fmt 4701 Sfmt 4700 24215 reducing tailpipe emissions of CO2 in its fuel economy rulemakings pursuant to the need of the United States to conserve energy by reducing petroleum consumption. (4) Foreign Policy Implications U.S. consumption and imports of petroleum products can impose additional costs (i.e., externalities) on the domestic economy that are not reflected in the market price for crude petroleum or in the prices paid by consumers for petroleum products such as gasoline. NHTSA has said previously that these costs can include (1) higher prices for petroleum products resulting from the effect of U.S. oil demand on world oil prices, (2) the risk of disruptions to the U.S. economy caused by sudden increases in the global price of oil and its resulting impact on fuel prices faced by U.S. consumers, and (3) expenses for maintaining the strategic petroleum reserve (SPR) to provide a response option should a disruption in commercial oil supplies threaten the U.S. economy, to allow the U.S. to meet part of its International Energy Agency obligation to maintain emergency oil stocks, and to provide a national defense fuel reserve.75 Higher U.S. consumption of crude oil or refined petroleum products increases the magnitude of these external economic costs, thus increasing the true economic cost of supplying transportation fuels above the resource costs of producing them. Conversely, reducing U.S. consumption of crude oil or refined petroleum products (by reducing motor fuel use) can reduce these external costs. While these costs are considerations, the United States has significantly increased oil production capabilities in recent years, to the extent that the U.S. is currently producing enough oil to satisfy nearly all of its energy needs and is projected to continue to do so (or even become a net energy exporter in the near future).76 This has added stable new supply to the global oil market, which ameliorates the U.S.’ need to 75 While the U.S. maintains a military presence in certain parts of the world to help secure global access to petroleum supplies, that is neither the primary nor the sole mission of U.S. forces overseas. Additionally, the scale of oil consumption reductions associated with CAFE standards would be insufficient to alter any existing military missions focused on ensuring the safe and expedient production and transportation of oil around the globe. See the FRIA’s discussion on energy security for more information on this topic. 76 See AEO 2019, at 14 (‘‘In the Reference case, the United States becomes a net exporter of petroleum liquids after 2020 as U.S. crude oil production increases and domestic consumption of petroleum products decreases.’’). Available at https://www.eia.gov/outlooks/aeo/pdf/aeo2019.pdf. E:\FR\FM\30APR2.SGM 30APR2 24216 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations conserve energy from a security perspective even given that oil is a global commodity. The agencies discuss this issue in more detail in Section VIII below. khammond on DSKJM1Z7X2PROD with RULES2 (2) Factors That NHTSA Is Prohibited From Considering EPCA states that in determining the level at which it should set CAFE standards for a particular model year, NHTSA may not consider the ability of manufacturers to take advantage of several EPCA provisions that facilitate compliance with CAFE standards and thereby can reduce their costs of compliance.77 As discussed further below, NHTSA cannot consider compliance credits that manufacturers earn by exceeding the CAFE standards and then use to achieve compliance in years in which their measured average fuel economy falls below the standards. NHTSA also cannot consider the use of alternative fuels by dual-fueled vehicles (such as plug-in hybrid electric vehicles) nor the availability of dedicated alternative fuel vehicles (such as battery electric or hydrogen fuel cell vehicles) in any model year. EPCA encourages the production of alternative fuel vehicles by specifying that their fuel economy is to be determined using a special calculation procedure that results in those vehicles being assigned a higher fuel economy level than they actually achieve. For non-statutory incentives that NHTSA developed by regulation, NHTSA does not consider these incentives subject to the EPCA prohibition on considering flexibilities. These topics will be addressed further in Section VIII below. (3) Other Considerations in Determining Maximum Feasible CAFE Standards NHTSA historically has interpreted EPCA’s statutory factors as including consideration for potential adverse safety consequences in setting CAFE standards. Courts have consistently recognized that this interpretation is reasonable. As courts have recognized, ‘‘NHTSA has always examined the safety consequences of the CAFE standards in its overall consideration of relevant factors since its earliest rulemaking under the CAFE program.’’ 78 The courts have consistently upheld NHTSA’s implementation of EPCA in this manner.79 Thus, in evaluating what 77 49 U.S.C. 32902(h). Enterprise Institute v. NHTSA, 901 F.2d 107, 120 n. 11 (D.C. Cir. 1990) (‘‘CEI–I’’) (citing 42 FR 33534, 33551 (Jun. 30, 1977). 79 See, e.g., Competitive Enterprise Institute v. NHTSA, 956 F.2d 321, 322 (D.C. Cir. 1992) (‘‘CEI– II’’) (in determining the maximum feasible fuel 78 Competitive VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 levels of stringency would result in maximum feasible standards, NHTSA assesses the potential safety impacts and considers them in balancing the statutory considerations and to determine the maximum feasible level of the standards.80 Many commenters addressed the NPRM’s analysis of safety impacts; those comments will be summarized and responded to in Section VI.D.2 and also in each agency’s discussion in Section VIII. The above sections explain what Congress thought was important enough to codify when it directed each agency to regulate, and begin to explain how the agencies have interpreted those directions over time and in this final rule. The next section looks more closely at the interplay between Congress’s direction to the agencies and the aspects of the market that these regulations affect, as follows. IV. Purpose of Analytical Approach Considered as Part of Decision-Making A. Relationship of Analytical Approach to Governing Law Like the NPRM, today’s final rule is supported by extensive analysis of potential impacts of the regulatory alternatives under consideration. Below, Section VI reviews the analytical approach, Section VII summarizes the results of the analysis, and Section VIII explains how the final standards— informed by this analysis—fulfill the agencies’ statutory obligations. Accompanying today’s notice, a final Regulatory Impact Analysis (FRIA) and, economy standard, ‘‘NHTSA has always taken passenger safety into account,’’ citing CEI–I, 901 F.2d at 120 n. 11); Competitive Enterprise Institute v. NHTSA, 49 F.3d 481, 483–83 (D.C. Cir. 1995) (same); Center for Biological Diversity v. NHTSA, 538 F.3d 1172, 1203–04 (9th Cir. 2008) (upholding NHTSA’s analysis of vehicle safety issues with weight in connection with the MYs 2008–2011 light truck CAFE rulemaking). 80 NHTSA stated in the NPRM that ‘‘While we discuss safety as a separate consideration, NHTSA also considers safety as closely related to, and in some circumstances a subcomponent of, economic practicability. On a broad level, manufacturers have finite resources to invest in research and development. Investment into the development and implementation of fuel saving technology necessarily comes at the expense of investing in other areas such as safety technology. On a more direct level, when making decisions on how to equip vehicles, manufacturers must balance cost considerations to avoid pricing further consumers out of the market. As manufacturers add technology to increase fuel efficiency, they may decide against installing new safety equipment to reduce cost increases. And as the price of vehicles increase beyond the reach of more consumers, such consumers continue to drive or purchase older, less safe vehicles. In assessing practicability, NHTSA also considers the harm to the nation’s economy caused by highway fatalities and injuries.’’ 83 FR at 43209 (Aug. 24, 2018). Many comments were received on this issue, which will be discussed further in Section VIII below. PO 00000 Frm 00044 Fmt 4701 Sfmt 4700 for NHTSA’s consideration, a final Environmental Impact Analysis (FEIS), together provide a more extensive and detailed enumeration of related methods, estimates, assumptions, and results. The agencies’ analysis has been constructed specifically to reflect various aspects of governing law applicable to CAFE and CO2 standards, and has been expanded and improved in response to comments received to the NPRM and based on additional work by the agencies. The analysis aided the agencies in implementing their statutory obligations, including the weighing of competing considerations, by reasonably informing the agencies about the estimated effects of choosing different regulatory alternatives. The agencies’ analysis makes use of a range of data (i.e., observations of things that have occurred), estimates (i.e., things that may occur in the future), and models (i.e., methods for making estimates). Two examples of data include (1) records of actual odometer readings used to estimate annual mileage accumulation at different vehicle ages and (2) CAFE compliance data used as the foundation for the ‘‘analysis fleet’’ containing, among other things, production volumes and fuel economy levels of specific configurations of specific vehicle models produced for sale in the U.S. Two examples of estimates include (1) forecasts of future GDP growth used, with other estimates, to forecast future vehicle sales volumes and (2) the ‘‘retail price equivalent’’ (RPE) factor used to estimate the ultimate cost to consumers of a given fuel-saving technology, given accompanying estimates of the technology’s ‘‘direct cost,’’ as adjusted to account for estimated ‘‘cost learning effects’’ (i.e., the tendency that it will cost a manufacturer less to apply a technology as the manufacturer gains more experience doing so). The agencies’ analysis makes use of several models, some of which are actually integrated systems of multiple models. As discussed in the NPRM, the agencies’ analysis of CAFE and CO2 standards involves two basic elements: First, estimating ways each manufacturer could potentially respond to a given set of standards in a manner that considers potential consumer response; and second, estimating various impacts of those responses. Estimating manufacturers’ potential responses involves simulating manufacturers’ decision-making processes regarding the year-by-year application of fuel-saving technologies to specific vehicles. Estimating impacts involves calculating resultant changes in new vehicle costs, estimating a E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 variety of costs (e.g., for fuel) and effects (e.g., CO2 emissions from fuel combustion) occurring as vehicles are driven over their lifetimes before eventually being scrapped, and estimating the monetary value of these effects. Estimating impacts also involves consideration of the response of consumers—e.g., whether consumers will purchase the vehicles and in what quantities. Both of these basic analytical elements involve the application of many analytical inputs. The agencies’ analysis uses the CAFE Model to estimate manufacturers’ potential responses to new CAFE and CO2 standards and to estimate various impacts of those responses. The model may be characterized as an integrated system of models. For example, one model estimates manufacturers’ responses, another estimates resultant changes in total vehicle sales, and still another estimates resultant changes in fleet turnover (i.e., scrappage). The CAFE model makes use of many inputs, values of which are developed outside of the model and not by the model. For example, the model applies fuel prices; it does not estimate fuel prices. The model does not determine the form or stringency of the standards; instead, the model applies inputs specifying the form and stringency of standards to be analyzed and produces outputs showing effects of manufacturers working to meet those standards, which become the basis for comparing between different potential stringencies. The agencies also use EPA’s MOVES model to estimate ‘‘tailpipe’’ (a.k.a. ‘‘vehicle’’ or ‘‘downstream’’) emission factors for criteria pollutants,81 and use four DOE and DOE-sponsored models to develop inputs to the CAFE model, including three developed and maintained by DOE’s Argonne National Laboratory. The agencies use the DOE Energy Information Administration’s (EIA’s) National Energy Modeling System (NEMS) to estimate fuel prices,82 and use Argonne’s Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model to estimate emissions rates from fuel production and distribution processes.83 DOT also sponsored DOE/Argonne to 81 See https://www.epa.gov/moves. Today’s final rule used version MOVES2014b, available at https://www.epa.gov/moves/latest-version-motorvehicle-emission-simulator-moves. 82 See https://www.eia.gov/outlooks/aeo/info_ nems_archive.php. Today’s final rule uses fuel prices estimated using the Annual Energy Outlook (AEO) 2019 version of NEMS (see https:// www.eia.gov/outlooks/aeo/data/browser/#/?id=3AEO2019&cases=ref2019&sourcekey=0). 83 Information regarding GREET is available at https://greet.es.anl.gov/index.php. Today’s notice uses the 2018 version of GREET. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 use Argonne’s Autonomie full-vehicle modeling and simulation system to estimate the fuel economy impacts for roughly a million combinations of technologies and vehicle types.84 85 Section VI.B.3, below, and the accompanying final RIA document details of the agencies’ use of these models. In addition, as discussed in the final EIS accompanying today’s notice, DOT relied on a range of climate and photochemical models to estimate impacts on climate, air quality, and public health. The EIS discusses and documents the use of these models. As further explained in the NPRM,86 to prepare for analysis supporting the proposal, DOT expanded the CAFE model to address EPA statutory and regulatory requirements through a yearby-year simulation of how manufacturers could comply with EPA’s CO2 standards, including: • Calculation of vehicle models’ CO2 emission rates before and after application of fuel-saving (and, therefore, CO2-reducing) technologies; • Calculation of manufacturers’ fleet average CO2 emission rates; • Calculation of manufacturers’ fleet average CO2 emission rates under attribute-based CO2 standards; • Accounting for adjustments to average CO2 emission rates reflecting reduction of air conditioner refrigerant leakage; • Accounting for the treatment of alternative fuel vehicles for CO2 compliance; • Accounting for production ‘‘multipliers’’ for PHEVs, BEVs, compressed natural gas (CNG) vehicles, and fuel cell vehicles (FCVs); • Accounting for transfer of CO2 credits between regulated fleets; and • Accounting for carried-forward (a.k.a. ‘‘banked’’) CO2 credits, including credits from model years earlier than modeled explicitly. 84 As part of the Argonne simulation effort, individual technology combinations simulated in Autonomie were paired with Argonne’s BatPAC model to estimate the battery cost associated with each technology combination based on characteristics of the simulated vehicle and its level of electrification. Information regarding Argonne’s BatPAC model is available at http:// www.cse.anl.gov/batpac/. 85 In addition, the impact of engine technologies on fuel consumption, torque, and other metrics was characterized using GT POWER simulation modeling in combination with other engine modeling that was conducted by IAV Automotive Engineering, Inc. (IAV). The engine characterization ‘‘maps’’ resulting from this analysis were used as inputs for the Autonomie full-vehicle simulation modeling. Information regarding GT Power is available at https://www.gtisoft.com/gt-suiteapplications/propulsion-systems/gt-power-enginesimulation-software. 86 83 FR 42986, 43003 (Aug. 24, 2018). PO 00000 Frm 00045 Fmt 4701 Sfmt 4700 24217 As further discussed in the NPRM, although EPA had previously developed a vehicle simulation tool (‘‘ALPHA’’) and a fleet compliance model (‘‘OMEGA’’), and had applied these in prior actions, having considered the facts before the Agency in 2018, EPA determined that, ‘‘it is reasonable and appropriate to use DOE/Argonne’s model for full-vehicle simulation, and to use DOT’s CAFE model for analysis of regulatory alternatives.’’ 87 As discussed below and in Section VI.B.3, some commenters—some citing deliberative EPA staff communications during NPRM development, and one submitting comments by a former EPA staff member closely involved in the origination of the above-mentioned OMEGA model—took strong exception to EPA’s decision to rely on DOE/ Argonne and DOT-originated models as the basis for analysis informing EPA’s decisions regarding CO2 standards. Some commenters argued that the EPA Administrator must consider exclusively models and analysis originating with EPA staff, and that to do otherwise would be arbitrary and capricious. As explained below (and as explained in the NPRM), it is reasonable for the Administrator to consider analysis and information produced from many sources, including, in this instance, the DOE/Argonne and DOT models. The Administrator has the discretion to determine what information reasonably and appropriately informs decisions regarding emissions standards. Some commenters conflated models with decisions, suggesting that the former mechanically determine the latter. The CAA authorizes the EPA Administrator, not a model, to make decisions about emissions standards, just as EPCA provides similar authority to the Secretary. Models produce analysis, the results of which help to inform decisions. However, in making such decisions, the Administrator may and should consider other relevant information beyond the outputs of any models—including public comment— and, in all cases, must exercise judgment in establishing appropriate standards. Some commenters conflated models with inputs and/or with results of the modeling. All of the models mentioned above rely on inputs, including not only data (i.e., facts), but also estimates (inputs about the future are estimates, not data). Given these inputs, the models produce estimates—ultimately, the agencies’ reported estimates of the potential impacts of standards under 87 83 E:\FR\FM\30APR2.SGM FR 42986, 43000 (Aug. 24, 2018). 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24218 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations consideration. In other words, inputs do not define models; models use inputs. Therefore, disagreements about inputs do not logically extend to disagreements about models. Similarly, while models determine resulting outputs, they do so based on inputs. Therefore, disagreements about results do not necessarily imply disagreements about models; they may merely reflect disagreements about inputs. With respect to the Administrator’s decisions regarding models underlying today’s analysis, comments regarding inputs, therefore, are more appropriately addressed separately, which is done so below in Section VI. The EPA Administrator’s decision to continue relying on the DOE/Argonne Autonomie tool and DOT CAFE model rather than on the corresponding tools developed by EPA staff is informed by consideration of comments on results and on technical aspects of the models themselves. As discussed below, some commenters questioned specific aspects of the CAFE model’s simulation of manufacturer’s potential responses to CO2 standards. Considering these comments, the CAFE model applied in the final rule’s analysis includes some revisions and updates. For example, the ‘‘effective cost’’ metric used to select among available opportunities to apply fuel-saving technologies now uses a ‘‘cost per credit’’ metric rather than the metric used for the NPRM. Also, the model’s representation of sales ‘‘multipliers’’ EPA has included for CNG vehicles, PHEVs, BEVs, and FCVs reflects current EPA regulations or, as an input-selectable option, an alternative approach under consideration. On the other hand, some commenters questioning the CAFE model’s approach to some CO2 program features appear to ignore the fact that prior analysis by EPA (using EPA’s OMEGA) model likewise did not account for the same program features. For example, some stakeholders took issue with the CAFE model’s approach to accounting for banked CO2 credits and, in particular, credits banked prior to the model years accounted for explicitly in the analysis. In the course of updating the basis for analysis fleet from model year 2016 to model year 2017, the agencies have since updated corresponding inputs. However, even though the ability to carry forward credits impacts outcomes, EPA’s OMEGA model used in previous rulemakings never attempted to account for credit banking and, indeed, lacking a year-by-year structure, cannot account for credit banking. Therefore, at least with respect to this important CO2 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 program flexibility, the CAFE model provides a more complete and realistic basis for estimating actual impacts of new CO2 standards. For its part, NHTSA remains confident that the combination of the Autonomie and CAFE models remains the best available for CAFE rulemaking analysis, and notes, as discussed below, that even the environmental group coalition stated that the CAFE model is aligned with EPCA requirements.88 In late 2001, after Congress discontinued an extended series of budget ‘‘riders’’ prohibiting work on CAFE standards, NHTSA and the DOT Volpe Center began development of a modeling system appropriate for CAFE rulemaking analysis, because other available models were not designed with this purpose in mind, and lacked capabilities important for CAFE rulemakings. For example, although NEMS had procedures to account for CAFE standards, those procedures did not provide the ability to account for specific manufacturers, as is especially relevant to the statutory requirement that NHTSA consider the economic practicability of any new CAFE standards. Also, as early as the first rulemaking making use of this early CAFE model, commenters stressed the importance of product redesign schedules, leading developers to introduce procedures to account for product cadence. In the 2003 notice regarding light truck standards for MYs 2005–2007, NHTSA stated that ‘‘we also changed the methodology to recognize that capital costs require employment of technologies for several years, rather than a single year. . . . In our view, this makes the Volpe analysis more consistent with the [manually implemented] Stage analysis and better reflects actual conditions in the automotive industry.’’ 89 Since that time, NHTSA and the Volpe Center have significantly refined the CAFE model with each of rulemaking. For example, for the 2006 rulemaking regarding standards for MYs 2008–2011 light trucks, NHTSA introduced the ability to account for attribute-based standards, account for the social cost of CO2 emissions, estimate stringencies at which net benefits would be maximized, and perform probabilistic uncertainty analysis (i.e., Monte Carlo simulation).90 For the 2009 rulemaking regarding standards for MY 2011 passenger cars and light trucks, we introduced the ability to account for 88 Environmental group coalition, NHTSA–2018– 0067–12000, Appendix A, at 24–25. 89 68 FR at 16885 (Apr. 7, 2003). 90 71 FR at 17566 et seq. (Apr. 6, 2006). PO 00000 Frm 00046 Fmt 4701 Sfmt 4700 attribute-based passenger car standards, and the ability to apply ‘‘synergy factors’’ to estimate how some technology pairings impact fuel consumption,91 For the 2010 rulemaking regarding standards for MYs 2012–2016, we introduced procedures to account for FFV credits, and to account for product planning as a multiyear consideration.92 For the 2012 rulemaking regarding standards for MYs 2017–2025, we introduced several new procedures, such as (1) accounting for electricity used to charge electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), (2) accounting for use of ethanol blends in flexible-fuel vehicles (FFVs), (3) accounting for costs (i.e., ‘‘stranded capital’’) related to early replacement of technologies, (4) accounting for previously-applied technology when determining the extent to which a manufacturer could expand use of the technology, (5) applying technology-specific estimates of changes in consumer value, (6) simulating the extent to which manufacturers might utilize EPCA’s provisions regarding generation and use of CAFE credits, (7) applying estimates of fuel economy adjustments (and accompanying costs) reflecting increases in air conditioner efficiency, (8) reporting privately-valued benefits, (9) simulating the extent to which manufacturers might voluntarily apply technology beyond levels needed for compliance with CAFE standards, and (10) estimating changes in highway fatalities attributable to any applied reductions in vehicle mass.93 Also for the 2012 rulemaking, we began making use of Autonomie to estimate fuel consumption impacts of different combinations of technologies, using these estimates to specify inputs to the CAFE model.94 In 2016, providing analyses for both the draft TAR regarding light-duty CAFE standards and the final rule regarding fuel consumption standards for heavy-duty pickup trucks and vans, we greatly expanded the agency’s use of Autonomie-based full vehicle simulations and introduced the ability to simulate compliance with attributebased standards for heavy-duty pickups and vans.95 And, as discussed at length in the NPRM and below, for this rulemaking, we have, among other things, refined procedures to account for impacts on highway travel and safety, 91 74 FR at 14196 et seq. (Mar. 30, 3009). FR at 25599 et seq. (May 7, 2010). 93 77 FR 63009 et seq. (Oct. 15, 2012). 94 77 FR at 62712 et seq. (Oct. 15, 2012). 95 81 FR at 73743 et seq. (Oct. 25, 2016); Draft TAR, available at Docket No. NHTSA–2016–0068– 0001, Chapter 13. 92 75 E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations added procedures to simulate compliance with CO2 standards, refined procedures to account for compliance credits, and added procedures to account for impacts on sales, scrappage, and employment. We have also significantly revised the model’s graphical user interface (GUI) in order to make the model easier to operate and understand. Like any model, both Autonomie and the CAFE model benefit from ongoing refinement. However, NHTSA is confident that this combination of models produces a more realistic characterization of the potential impacts of new standards than would another combination of available models. Some stakeholders, while commenting on specific aspects of the inputs, models, and/or results, commended the agencies’ exclusive reliance on the DOE/Argonne Autonomie tool and DOT CAFE model. With respect to CO2 standards, these stakeholders noted not only technical reasons to use these models rather than the EPA models, but also other reasons such as efficiency, transparency, and ease with which outside parties can exercise models and replicate the agencies’ analysis. These comments are discussed below and in Section VI. Nevertheless, some comments regarding the model’s handling of CAFE and/or CO2 standards, and some comments regarding the model’s estimation of resultant impacts, led the agencies to make changes to specific aspects of the model. Comments on and changes to the inputs and model are discussed below and in Section VI; results are discussed in Section VII and in the accompanying RIA; and the meaning of results in the context of the applicable statutory requirements is discussed in Section VIII. As explained, the analysis is designed to reflect a number of statutory and regulatory requirements applicable to CAFE and tailpipe CO2 standard setting. EPCA contains a number of requirements governing the scope and nature of CAFE standard setting. Among these, some have been in place since EPCA was first signed into law in 1975, and some were added in 2007, when Congress passed EISA and amended EPCA. The CAA, as discussed elsewhere, provides EPA with very broad authority under Section 202(a), and does not contain EPCA/EISA’s prescriptions. In the interest of harmonization, however, EPA has adopted some of the EPCA/EISA requirements into its tailpipe CO2 regulations, and NHTSA, in turn, has created some additional flexibilities by regulation not expressly envisioned by EPCA/EISA in order to harmonize better VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 with some of EPA’s programmatic decisions. EPCA/EISA requirements regarding the technical characteristics of CAFE standards and the analysis thereof include, but are not limited to, the following, and the analysis reflects these requirements as summarized: Corporate Average Standards: 49 U.S.C. 32902 requires standards that apply to the average fuel economy levels achieved by each corporation’s fleets of vehicles produced for sale in the U.S.96 CAA Section 202(a) does not preclude the EPA Administrator from expressing CO2 standards as de facto fleet average requirements, and EPA has adopted a similar approach in the interest of harmonization. The CAFE Model, used by the agencies to conduct the bulk of today’s analysis, calculates the CAFE and CO2 levels of each manufacturer’s fleets based on estimated production volumes and characteristics, including fuel economy levels, of distinct vehicle models that could be produced for sale in the U.S. Separate Standards for Passenger Cars and Light Trucks: 49 U.S.C. 32902 requires the Secretary of Transportation to set CAFE standards separately for passenger cars and light trucks. CAA Section 202(a) does not preclude the EPA Administrator from specifying CO2 standards separately for passenger cars and light trucks, and EPA has adopted a similar approach. The CAFE Model accounts separately for passenger cars and light trucks, including differentiated standards and compliance. Attribute-Based Standards: 49 U.S.C. 32902 requires the Secretary of Transportation to define CAFE standards as mathematical functions expressed in terms of one or more vehicle attributes related to fuel economy. This means that for a given manufacturer’s fleet of vehicles produced for sale in the U.S. in a given regulatory class and model year, the applicable minimum CAFE requirement (i.e., the numerical value of the requirement) is computed based on the applicable mathematical function, and the mix and attributes of vehicles in the manufacturer’s fleet. In the 2012 final rule that first established CO2 standards, EPA also adopted an attribute-based standard under its broad CAA Section 96 This differs from safety standards and traditional emissions standards, which apply separately to each vehicle. For example, every vehicle produced for sale in the U.S. must, on its own, meet all applicable federal motor vehicle safety standards (FMVSS), but no vehicle produced for sale must, on its own, federal fuel economy standards. Rather, each manufacturer is required to produce a mix of vehicles that, taken together, achieve an average fuel economy level no less than the applicable minimum level. PO 00000 Frm 00047 Fmt 4701 Sfmt 4700 24219 202(a) authority. The CAFE Model accounts for such functions and vehicle attributes explicitly. Separately Defined Standards for Each Model Year: 49 U.S.C. 32902 requires the Secretary to set CAFE standards (separately for passenger cars and light trucks) at the maximum feasible levels in each model year. CAA Section 202(a) allows EPA to establish CO2 standards separately for each model year, and EPA has chosen to do so for this final rule, similar to the approach taken in the previous light-duty vehicle CO2 standard-setting rules. The CAFE Model represents each model year explicitly, and accounts for the production relationships between model years.97 Separate Compliance for Domestic and Imported Passenger Car Fleets: 49 U.S.C. 32904 requires the EPA Administrator to determine CAFE compliance separately for each manufacturers’ fleets of domestic passenger cars and imported passenger cars, which manufacturers must consider as they decide how to improve the fuel economy of their passenger car fleets. CAA 202(a) does not preclude the EPA Administrator from determining compliance with CO2 standards separately for a manufacturer’s domestic and imported car fleets, but EPA did not include such a distinction in either the 2010 or 2012 final rules, and EPA did not propose or ask for comment on taking such an approach in the proposal. The CAFE Model is able to account explicitly for this requirement when simulating manufacturers’ potential responses to CAFE standards, but combines any given manufacturer’s domestic and imported cars into a single fleet when simulating that manufacturer’s potential response to CO2 standards. Minimum CAFE Standards for Domestic Passenger Car Fleets: 49 U.S.C. 32902 requires that domestic passenger car fleets achieve CAFE levels no less than 92 percent of the industrywide average level required under the applicable attribute-based CAFE standard, as projected by the Secretary at the time the standard is promulgated. CAA 202(a) does not preclude the EPA Administrator from correspondingly requiring that domestic passenger car fleets achieve CO2 levels no greater than 108.7 percent (1/0.92 = 1.087) of the projected industry-wide average CO2 97 For example, a new engine first applied to given vehicle model/configuration in model year 2020 will most likely be ‘‘carried forward’’ to model year 2021 of that same vehicle model/configuration, in order to reflect the fact that manufacturers do not apply brand-new engines to a given vehicle model every single year. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24220 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations requirement under the attribute-based standard, but the GHG program that EPA designed in the 2010 and 2012 final rules did not include such a distinction, and EPA did not propose or seek comment on such an approach in the proposal. The CAFE Model is able to account explicitly for this requirement for CAFE standards, and sets this requirement aside for CO2 standards. Civil Penalties for Noncompliance: 49 U.S.C. 32912 prescribes a rate (in dollars per tenth of a mpg) at which the Secretary is to levy civil penalties if a manufacturer fails to comply with a CAFE standard for a given fleet in a given model year, after considering available credits. Some manufacturers have historically demonstrated a willingness to treat CAFE noncompliance as an ‘‘economic’’ choice, electing to pay civil penalties rather than achieving full numerical compliance across all fleets. The CAFE Model calculates civil penalties for CAFE shortfalls and provides means to estimate that a manufacturer might stop adding fuel-saving technologies once continuing to do so would be effectively more ‘‘expensive’’ (after accounting for fuel prices and buyers’ willingness to pay for fuel economy) than paying civil penalties. In contrast, the CAA does not authorize the EPA Administrator to allow manufacturers to sell noncompliant fleets and instead only pay civil penalties; manufacturers who choose to pay civil penalties for CAFE compliance tend to employ EPA’s moreextensive programmatic flexibilities to meet tailpipe CO2 emissions standards. Thus, the CAFE Model does not allow civil penalty payment as an option for CO2 standards. Dual-Fueled and Dedicated Alternative Fuel Vehicles: For purposes of calculating CAFE levels used to determine compliance, 49 U.S.C. 32905 and 32906 specify methods for calculating the fuel economy levels of vehicles operating on alternative fuels to gasoline or diesel through MY 2020. After MY 2020, methods for calculating alternative fuel vehicle (AFV) fuel economy are governed by regulation. The CAFE Model is able to account for these requirements explicitly for each vehicle model. However, 49 U.S.C. 32902 requires that maximum feasible CAFE standards be set in a manner that does not presume manufacturers can respond by producing new dedicated alternative fuel vehicle (AFV) models. The CAFE model can be run in a manner that excludes the additional application of dedicated AFV technologies in model years for which maximum feasible standards are under VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 consideration. As allowed under NEPA for analysis appearing in EISs informing decisions regarding CAFE standards, the CAFE Model can also be run without this analytical constraint. CAA 202(a) does not preclude the EPA Administrator adopting analogous provisions, but EPA has instead opted through regulation to ‘‘count’’ dual- and alternative fuel vehicles on a CO2 basis (and through MY 2026, to set aside emissions from electricity generation). The CAFE model accounts for this treatment of dual- and alternative fuel vehicles when simulating manufacturers’ potential responses to CO2 standards. For natural gas vehicles, both dedicated and dual-fueled, EPA is establishing a multiplier of 2.0 for model years 2022–2026. Creation and Use of Compliance Credits: 49 U.S.C. 32903 provides that manufacturers may earn CAFE ‘‘credits’’ by achieving a CAFE level beyond that required of a given fleet in a given model year, and specifies how these credits may be used to offset the amount by which a different fleet falls short of its corresponding requirement. These provisions allow credits to be ‘‘carried forward’’ and ‘‘carried back’’ between model years, transferred between regulated classes (domestic passenger cars, imported passenger cars, and light trucks), and traded between manufacturers. However, these provisions also impose some specific statutory limits. For example, CAFE compliance credits can be carried forward a maximum of five model years and carried back a maximum of three model years. Also, EPCA/EISA caps the amount of credit that can be transferred between passenger car and light truck fleets, and prohibits manufacturers from applying traded or transferred credits to offset a failure to achieve the applicable minimum standard for domestic passenger cars. The CAFE Model explicitly simulates manufacturers’ potential use of credits carried forward from prior model years or transferred from other fleets.98 49 U.S.C. 32902 98 As explained in Section VI, the CAFE Model does not explicitly simulate the potential that manufacturers would carry CAFE or CO2 credits back (i.e., borrow) from future model years, or acquire and use CAFE compliance credits from other manufacturers. At the same time, because EPA has elected to not limit credit trading, the CAFE Model can be exercised in a manner that simulates unlimited (a.k.a. ‘‘perfect’’) CO2 compliance credit trading throughout the industry (or, potentially, within discrete trading ‘‘blocs’’). The agencies believe there is significant uncertainty in how manufacturers may choose to employ these particular flexibilities in the future: for example, while it is reasonably foreseeable that a manufacturer who over-complies in one year may ‘‘coast’’ through several subsequent years relying on those credits rather than continuing to make PO 00000 Frm 00048 Fmt 4701 Sfmt 4700 prohibits consideration of manufacturers’ potential application of CAFE compliance credits when setting maximum feasible CAFE standards. The CAFE Model can be operated in a manner that excludes the application of CAFE credits after a given model year. CAA 202(a) does not preclude the EPA Administrator adopting analogous provisions. EPA has opted to limit the ‘‘life’’ of compliance credits from most model years to 5 years, and to limit borrowing to 3 years, but has not adopted any limits on transfers (between fleets) or trades (between manufacturers) of compliance credits. The CAFE Model is able to account for the absence of limits on transfers of CO2 standards. Insofar as the CAFE model can be exercised in a manner that simulates trading of CO2 compliance credits, such simulations treat trading as unlimited.99 EPA has considered manufacturers’ ability to use credits as part of its decisions on these final standards, and the CAFE model is now able to account for that. Statutory Basis for Stringency: 49 U.S.C. 32902 requires the Secretary to set CAFE standards at the maximum feasible levels, considering technological feasibility, economic practicability, the need of the Nation to conserve energy, and the impact of other government standards. EPCA/EISA authorizes the Secretary to interpret technology improvements, it is harder to assume with confidence that manufacturers will rely on future technology investments (that may not pan out as expected, as if market demand for ‘‘targetbeater’’ vehicles is lower than expected) to offset prior-year shortfalls, or whether/how manufacturers will trade credits with market competitors rather than making their own technology investments. Historically, carry-back and trading have been much less utilized than carry-forward, for a variety of reasons including higher risk and preference not to ‘‘pay competitors to make fuel economy improvements we should be making’’ (to paraphrase one manufacturer), although the agencies recognize that carry-back and trading are used more frequently when standards require more technology application than manufacturers believe their markets will bear. Given the uncertainty just discussed, and given also the fact that the agencies have yet to resolve some of analytical challenges associated with simulating use of these flexibilities, the agencies consider borrowing and trading to involve sufficient risk that it is prudent to support today’s decisions with analysis that sets aside the potential that manufacturers could come to depend widely on borrowing and trading. While compliance costs in real life may be somewhat different from what is modeled today as a result of this analytical decision, that is broadly true no matter what, and the agencies do not believe that the difference would be so great that it would change the policy outcome. 99 To avoid making judgments (that would invariably turn out to be at least somewhat incorrect) about possible future trading activity, the model simulates trading by combining all manufacturers into a single entity, so that the most cost-effective choices are made for the fleet as a whole. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations these factors, and as the Department’s interpretation has evolved, NHTSA has continued to expand and refine its qualitative and quantitative analysis. For example, as discussed below in Section VI.B.3, the Autonomie simulations reflect the agencies’ judgment that it would not be economically practicable for a manufacturer to ‘‘split’’ an engine shared among many vehicle model/ configurations into a myriad of versions each optimized to a single vehicle model/configuration. Also responding to evolving interpretation of these EPCA/EISA factors, the CAFE Model has been expanded to address additional impacts in an integrated manner. For example, the CAFE Model version used for the NPRM analysis included the ability to estimate impacts on labor utilization internally, rather than as an external ‘‘off model’’ or ‘‘post processing’’ analysis. In addition, NEPA requires the Secretary to issue an EIS that documents the estimated impacts of regulatory alternatives under consideration. The EIS accompanying today’s notice documents changes in emission inventories as estimated using the CAFE model, but also documents corresponding estimates—based on the application of other models documented in the EIS, of impacts on the global climate, on tropospheric air quality, and on human health. Regarding CO2 standards, CAA 202(a) provides general authority for the establishment of motor vehicle emissions standards, and the final rule’s analysis, like that accompanying the agencies’ proposal, addresses impacts relevant to the EPA Administrator’s decision making, such as technological feasibility, air quality impacts, costs to industry and consumers, and lead time necessary for compliance. Other Factors: Beyond these statutory requirements applicable to DOT and/or EPA are a number of specific technical characteristics of CAFE and/or CO2 regulations that are also relevant to the construction of today’s analysis. These are discussed at greater length in Section II.F. For example, EPA has defined procedures for calculating average CO2 levels, and has revised procedures for calculating CAFE levels, to reflect manufacturers’ application of ‘‘off-cycle’’ technologies that increase fuel economy (and reduce CO2 emissions) in ways not reflected by the long-standing test procedures used to measure fuel economy. Although too little information is available to account for these provisions explicitly in the same way that the agencies have accounted for other technologies, the VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 CAFE Model does include and makes use of inputs reflecting the agencies’ expectations regarding the extent to which manufacturers may earn such credits, along with estimates of corresponding costs. Similarly, the CAFE Model includes and makes use of inputs regarding credits EPA has elected to allow manufacturers to earn toward CO2 levels (not CAFE) based on the use of air conditioner refrigerants with lower global warming potential (GWP), or on the application of technologies to reduce refrigerant leakage. In addition, EPA has elected to provide that through model year 2021, manufacturers may apply ‘‘multipliers’’ to plug-in hybrid electric vehicles, dedicated electric vehicles, fuel cell vehicles, and hydrogen vehicles, such that when calculating a fleet’s average CO2 levels (not CAFE), the manufacturer may, for example, ‘‘count’’ each electric vehicle twice. The CAFE Model accounts for these multipliers, based on either current regulatory provisions or on alternative approaches. Although these are examples of regulatory provisions that arise from the exercise of discretion rather than specific statutory mandate, they can materially impact outcomes. Section VI.B explains in greater detail how today’s analysis addresses them. Benefits of Analytical Approach The agencies’ analysis of CAFE and CO2 standards involves two basic elements: First, estimating ways each manufacturer could potentially respond to a given set of standards in a manner that considers potential consumer response; and second, estimating various impacts of those responses. Estimating manufacturers’ potential responses involves simulating manufacturers’ decision-making processes regarding the year-by-year application of fuel-saving technologies to specific vehicles. Estimating impacts involves calculating resultant changes in new vehicle costs, estimating a variety of costs (e.g., for fuel) and effects (e.g., CO2 emissions from fuel combustion) occurring as vehicles are driven over their lifetimes before eventually being scrapped, and estimating the monetary value of these effects. Estimating impacts also involves consideration of the response of consumers—e.g., whether consumers will purchase the vehicles and in what quantities. Both of these basic analytical elements involve the application of many analytical inputs. As mentioned above, the agencies’ analysis uses the CAFE model to estimate manufacturers’ potential responses to new CAFE and CO2 standards and to estimate various PO 00000 Frm 00049 Fmt 4701 Sfmt 4700 24221 impacts of those responses. DOT’s Volpe National Transportation Systems Center (often simply referred to as the ‘‘Volpe Center’’) develops, maintains, and applies the model for NHTSA. NHTSA has used the CAFE model to perform analyses supporting every CAFE rulemaking since 2001, and the 2016 rulemaking regarding heavy-duty pickup and van fuel consumption and CO2 emissions also used the CAFE model for analysis.100 NHTSA recently arranged for a formal peer review of the model. In general, reviewers’ comments strongly supported the model’s conceptual basis and implementation, and commenters provided several specific recommendations. The agency agreed with many of these recommendations and has worked to implement them wherever practicable. Implementing some of the recommendations would require considerable further research, development, and testing, and will be considered going forward. For a handful of other recommendations, the agency disagreed, often finding the recommendations involved considerations (e.g., other policies, such as those involving fuel taxation) beyond the model itself or were based on concerns with inputs rather than how the model itself functioned. A report available in the docket for this rulemaking presents peer reviewers’ detailed comments and recommendations, and provides DOT’s detailed responses.101 As also mentioned above, the agencies use EPA’s MOVES model to estimate tailpipe emission factors, use DOE/EIA’s NEMS to estimate fuel prices,102 and use Argonne’s GREET model to estimate downstream emissions rates.103 DOT also sponsored DOE/Argonne to use the Autonomie full-vehicle modeling and simulation tool to estimate the fuel economy impacts for roughly a million 100 While both agencies used the CAFE Model to simulate manufacturers’ potential responses to standards, some model inputs differed EPA’s and DOT’s analyses, and EPA also used the EPA MOVES model to calculate resultant changes in emissions inventories. See 81 FR 73478, 73743 (Oct. 25, 2016). 101 Docket No. NHTSA–2018–0067–0055. 102 See https://www.eia.gov/outlooks/aeo/info_ nems_archive.php. Today’s notice uses fuel prices estimated using the Annual Energy Outlook (AEO) 2019 version of NEMS (see https://www.eia.gov/ outlooks/archive/aeo19/ and https://www.eia.gov/ outlooks/aeo/data/browser/#/?id=3AEO2019&cases=ref2019&sourcekey=0). 103 Information regarding GREET is available at https://greet.es.anl.gov/index.php. Availability of NEMS is discussed at https://www.eia.gov/ outlooks/aeo/info_nems_archive.php. Today’s notice uses fuel prices estimated using the AEO 2019 version of NEMS. E:\FR\FM\30APR2.SGM 30APR2 24222 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 combinations of technologies and vehicle types.104 105 EPA developed two models after 2009, referred to as the ‘‘ALPHA’’ and ‘‘OMEGA’’ models, which provide some of the same capabilities as the Autonomie and CAFE models. EPA applied the OMEGA model to conduct analysis of tailpipe CO2 emissions standards promulgated in 2010 and 2012, and the ALPHA and OMEGA models to conduct analysis discussed in the above-mentioned 2016 Draft TAR and Proposed and 2017 Initial Final Determinations regarding standards beyond 2021. In an August 2017 notice, the agencies requested comments on, among other things, whether EPA should use alternative methodologies and modeling, including DOE/ Argonne’s Autonomie full-vehicle modeling and simulation tool and DOT’s CAFE model.106 Having reviewed comments on the subject and having considered the matter fully, the agencies have determined it is reasonable and appropriate to use DOE/Argonne’s model for full-vehicle simulation, and to use DOT’s CAFE model for analysis of regulatory alternatives. EPA interprets Section 202(a) of the CAA as giving the agency broad discretion in how it develops and sets CO2 emissions standards for light-duty vehicles. Nothing in Section 202(a) mandates that EPA use any specific model or set of models for analysis of potential CO2 standards for light-duty vehicles. EPA weighs many factors when determining appropriate levels for CO2 standards, including the cost of compliance (see Section 202(a)(2)), lead time necessary for compliance (id.), safety (see NRDC v. EPA, 655 F.2d 318, 336 n. 31 (D.C. Cir. 1981)) and other impacts on consumers,107 and energy impacts 104 As part of the Argonne simulation effort, individual technology combinations simulated in Autonomie were paired with Argonne’s BatPAC model to estimate the battery cost associated with each technology combination based on characteristics of the simulated vehicle and its level of electrification. Information regarding Argonne’s BatPAC model is available at http:// www.cse.anl.gov/batpac/. 105 Furthermore, the impact of engine technologies on fuel consumption, torque, and other metrics was characterized using GT POWER simulation modeling in combination with other engine modeling that was conducted by IAV Automotive Engineering, Inc. (IAV). The engine characterization ‘‘maps’’ resulting from this analysis were used as inputs for the Autonomie full-vehicle simulation modeling. Information regarding GT Power is available at https://www.gtisoft.com/gtsuite-applications/propulsion-systems/gt-powerengine-simulation-software. 106 82 FR 39551, 39553 (Aug. 21, 2017). 107 Since its earliest Title II regulations, EPA has considered the safety of pollution control technologies. See 45 FR 14496, 14503 (1980). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 associated with use of the technology.108 Using the CAFE model allows consideration of a number of factors. The CAFE model explicitly evaluates the cost of compliance for each manufacturer, each fleet, and each model year; it accounts for lead time necessary for compliance by directly incorporating estimated manufacturer production cycles for every vehicle in the fleet, ensuring that the analysis does not assume vehicles can be redesigned to incorporate more technology without regard to lead time considerations; it provides information on safety effects associated with different levels of standards and information about many other impacts on consumers, and it calculates energy impacts (i.e., fuel saved or consumed) as a primary function, besides being capable of providing information about many other factors within EPA’s broad CAA discretion to consider. Because the CAFE model simulates a wide range of actual constraints and practices related to automotive engineering, planning, and production, such as common vehicle platforms, sharing of engines among different vehicle models, and timing of major vehicle redesigns, the analysis produced by the CAFE model provides a transparent and realistic basis to show pathways manufacturers could follow over time in applying new technologies, which helps better assess impacts of potential future standards. Furthermore, because the CAFE model also accounts fully for regulatory compliance provisions (now including CO2 compliance provisions), such as adjustments for reduced refrigerant leakage, production ‘‘multipliers’’ for some specific types of vehicles (e.g., PHEVs), and carried-forward (i.e., banked) credits, the CAFE model provides a transparent and realistic basis to estimate how such technologies might be applied over time in response to CAFE or CO2 standards. There are sound reasons for the agencies to use the CAFE model going forward in this rulemaking. First, the CAFE and CO2 fact analyses are inextricably linked. Furthermore, the analysis produced by the CAFE model and DOE/Argonne’s Autonomie addresses the agencies’ analytical needs. The CAFE model provides an explicit year-by-year simulation of manufacturers’ application of technology to their products in response to a year-by-year progression of CAFE 108 See George E. Warren Corp. v. EPA, 159 F.3d 616, 623–624 (D.C. Cir. 1998) (ordinarily permissible for EPA to consider factors not specifically enumerated in the Act). PO 00000 Frm 00050 Fmt 4701 Sfmt 4700 standards and accounts for sharing of technologies and the implications for timing, scope, and limits on the potential to optimize powertrains for fuel economy. In the real world, standards actually are specified on a year-by-year basis, not simply some single year well into the future, and manufacturers’ year-by-year plans involve some vehicles ‘‘carrying forward’’ technology from prior model years and some other vehicles possibly applying ‘‘extra’’ technology in anticipation of standards in ensuing model years, and manufacturers’ planning also involves applying credits carried forward between model years. Furthermore, manufacturers cannot optimize the powertrain for fuel economy on every vehicle model configuration—for example, a given engine shared among multiple vehicle models cannot practicably be split into different versions for each configuration of each model, each with a slightly different displacement. The CAFE model is designed to account for these real-world factors. Considering the technological heterogeneity of manufacturers’ current product offerings, and the wide range of ways in which the many fuel economyimproving/CO2 emissions-reducing technologies included in the analysis can be combined, the CAFE model has been designed to use inputs that provide an estimate of the fuel economy achieved for many tens of thousands of different potential combinations of fuelsaving technologies. Across the range of technology classes encompassed by the analysis fleet, today’s analysis involves more than a million such estimates. While the CAFE model requires no specific approach to developing these inputs, the National Academy of Sciences (NAS) has recommended, and stakeholders have commented, that fullvehicle simulation provides the best balance between realism and practicality. DOE/Argonne has spent several years developing, applying, and expanding means to use distributed computing to exercise its Autonomie full-vehicle modeling and simulation tool over the scale necessary for realistic analysis of CAFE or average tailpipe CO2 emissions standards. This scalability and related flexibility (in terms of expanding the set of technologies to be simulated) makes Autonomie well-suited for developing inputs to the CAFE model. In addition, DOE/Argonne’s Autonomie also has a long history of development and widespread application by a much wider range of users in government, academia, and industry. Many of these users apply E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 Autonomie to inform funding and design decisions. These real-world exercises have contributed significantly to aspects of Autonomie important to producing realistic estimates of fuel economy levels and CO2 emission rates, such as estimation and consideration of performance, utility, and driveability metrics (e.g., towing capability, shift business, frequency of engine on/off transitions). This steadily increasing realism has, in turn, steadily increased confidence in the appropriateness of using Autonomie to make significant investment decisions. Notably, DOE uses Autonomie for analysis supporting budget priorities and plans for programs managed by its Vehicle Technologies Office (VTO). Considering the advantages of DOE/Argonne’s Autonomie model, it is reasonable and appropriate to use Autonomie to estimate fuel economy levels and CO2 emission rates for different combinations of technologies as applied to different types of vehicles. Commenters have also suggested that the CAFE model’s graphical user interface (GUI) facilitates others’ ability to use the model quickly—and without specialized knowledge or training—and to comment accordingly.109 For the NPRM, NHTSA significantly expanded and refined this GUI, providing the ability to observe the model’s real-time progress much more closely as it simulates year-by-year compliance with either CAFE or CO2 standards.110 Although the model’s ability to produce realistic results is independent of the model’s GUI, the CAFE model’s GUI appears to have facilitated stakeholders’ meaningful review and comment during the comment period. The question of whether EPA’s actions should consider and be informed by analysis using non-EPAstaff-developed modeling tools has generated considerable debate over time. Even prior to the NPRM, certain commenters had argued that EPA could not consider, in setting tailpipe CO2 emissions standards, any information derived from non-EPA-staff-developed modeling. Many of the pre-NPRM 109 From Docket Number EPA–HQ–OAR–2015– 0827, see Comment by Global Automakers, Docket ID EPA–HQ–OAR–2015–0827–9728, at 34. 110 The updated GUI provides a range of graphs updated in real time as the model operates. These graphs can be used to monitor fuel economy or CO2 ratings of vehicles in manufacturers’ fleets and to monitor year-by-year CAFE (or average CO2 ratings), costs, avoided fuel outlays, and avoided CO2-related damages for specific manufacturers and/or specific fleets (e.g., domestic passenger car, light truck). Because these graphs update as the model progresses, they should greatly increase users’ understanding of the model’s approach to considerations such as multiyear planning, payment of civil penalties, and credit use. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 concerns focused on inputs used by the CAFE model for prior rulemaking analyses.111 112 113 Because inputs are exogenous to any model, they do not determine whether it would be reasonable and appropriate for EPA to use NHTSA’s model for analysis. Other concerns focused on certain characteristics of the CAFE model that were developed to align the model better with EPCA and EISA. The model has been revised to accommodate both EPCA/EISA and CAA analysis, as explained further below. Some commenters also argued that use of any models other than ALPHA and OMEGA for CAA analysis would constitute an arbitrary and capricious delegation of EPA’s decision-making authority to NHTSA, if NHTSA models are used for analysis instead.114 As discussed above, the CAFE Model—as with any model— is used to provide analysis, and does not result in decisions. Decisions are made by EPA in a manner that is informed by modeling outputs, sensitivity cases, public comments, any many other pieces of information. Comments responding to the NPRM’s use of the CAFE model and Autonomie rather than also (for CO2 standards) 111 For example, EDF previously stated that ‘‘the data that NHTSA needs to input into its model is sensitive confidential business information that is not transparent and cannot be independently verified, . . .’’ and it claimed ‘‘the OMEGA model’s focus on direct technological inputs and costs—as opposed to industry self-reported data—ensures the model more accurately characterizes the true feasibility and cost effectiveness of deploying greenhouse gas reducing technologies.’’ EDF, EPA– HQ–OAR–2015–0827–9203, at 12. These statements are not correct, as nothing about either the CAFE or OMEGA model either obviates or necessitates the use of CBI to develop inputs. 112 As another example, CARB previously stated that ‘‘another promising technology entering the market was not even included in the NHTSA compliance modeling’’ and that EPA assumes a five-year redesign cycle, whereas NHTSA assumes a six to seven-year cycle.’’ CARB, EPA–HQ–OAR– 2015–0827–9197, at 28. Though presented as criticisms of the models, these comments—at least with respect to the CAFE model—actually concern model inputs. NHTSA did not agree with CARB about the commercialization potential of the engine technology in question (‘‘Atkinson 2’’) and applied model inputs accordingly. Also, rather than applying a one-size-fits-all assumption regarding redesign cadence, NHTSA developed estimates specific to each vehicle model and applied these as model inputs. 113 As another example, NRDC has argued that EPA should not use the CAFE model because it ‘‘allows manufacturers to pay civil penalties in lieu of meeting the standards, an alternative compliance pathway currently allowed under EISA and EPCA.’’ NRDC, EPA–HQ–OAR–2015–0827–9826, at 37. While the CAFE model can simulate civil penalty payment, NRDC’s comment appears to overlook the fact that this result depends on model inputs; the inputs can easily be specified such that the CAFE model will set aside civil penalty payment as an alternative to compliance. 114 See, e.g., CBD et al., NHTSA–2018–0067– 12057, at 9. PO 00000 Frm 00051 Fmt 4701 Sfmt 4700 24223 ALPHA and OMEGA were mixed. For example, the environmental group coalition stated that the CAFE model is aligned with EPCA requirements,115 but also argued (1) that EPA is legally prohibited from ‘‘delegat[ing] technical decision-making to NHTSA;’’ 116 (2) that ‘‘EPA must exercise its technical and scientific expertise’’ to develop CO2 standards and ‘‘Anything less is an unlawful abdication of EPA’s statutory responsibilities;’’ 117 (3) that EPA staff is much more qualified than DOT staff to conduct analysis relating to standards and has done a great deal of work to inform development of standards; 118 (4) that ‘‘The Draft TAR and 2017 Final Determination relied extensively on use of sophisticated EPA analytic tools and methodologies,’’ i.e., the ‘‘peer reviewed simulation model ALPHA,’’ ‘‘the agency’s vehicle teardown studies,’’ and the ‘‘peer-reviewed OMEGA model to make reasonable estimates of how manufacturers could add technologies to vehicles in order to meet a fleet-wide [CO2 emissions] standard;’’ 119 (5) that NHTSA had said in the MYs 2012–2016 rulemaking that the Volpe [CAFE] model was developed to support CAFE rulemaking and incorporates features ‘‘that are not appropriate for use by EPA in setting [tailpipe CO2] standards;’’ 120 (6) allegations that some EPA staff had disagreed with aspects of the NPRM analysis and had requested that ‘‘EPA’s name and logo should be removed from the DOT-NHTSA Preliminary Regulatory Impact Analysis document’’ and stated that ‘‘EPA is relying upon the technical analysis performed by DOT– NHTSA for the [NPRM];’’ 121 (7) that EPA had developed ‘‘a range of relevant new analysis’’ that the proposal ‘‘failed to consider,’’ including ‘‘over a dozen 2017 and 2018 EPA peer reviewed SAE articles;’’ 122 (8) that EPA’s OMEGA modeling undertaken during NPRM development ‘‘found costs half that of NHTSA’s findings,’’ ‘‘Yet NHTSA did not correct the errors in its modeling and analysis, and the published proposal drastically overestimates the cost of complying . . . .;’’ 123 (9) that some EPA staff had requested that the technology ‘‘HCR2’’ be included in the NPRM analysis, ‘‘Yet NHTSA overruled 115 Environmental group coalition, NHTSA– 2018–0067–12000, Appendix A, at 24–25. 116 Id. at 12. 117 Id. at 14. 118 Id. at 15–17. 119 Id. at 17. 120 Id. at 18. 121 Id. at 19. 122 Id. at 20. 123 Id. at 21. E:\FR\FM\30APR2.SGM 30APR2 24224 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 EPA and omitted the technology;’’ 124 (10) that certain EPA staff had initially ‘‘rejected use of the CAFE model for development of the proposed [tailpipe CO2] standards;’’ 125 (11) that there are ‘‘many specific weaknesses of the modeling results derived in this proposal through use of the Volpe and Autonomie models’’ and that the CAFE model is ‘‘not designed in accordance with’’ Section 202(a) of the CAA because (A) EPA ‘‘is not required to demonstrate that standards are set at the maximum feasible level year-by-year,’’ (B) because EPCA ‘‘preclude[s NHTSA] from considering vehicles powered by fuels other than gas or diesel’’ and EPA is not similarly bound, and (C) because the CAFE model assumed that the value of an overcompliance credit equaled $5.50, the value of a CAFE penalty.126 Because of all of these things, the environmental group coalition stated that the proposal was ‘‘unlawful’’ and that ‘‘Before proceeding with this rulemaking, EPA must consider all relevant materials including these excluded insights, perform its own analysis, and issue a reproposal to allow for public comment.’’ 127 Some environmental organizations and States contracted for external technical analyses augmenting general comments such as those summarized above. EDF engaged a consultant, Richard Rykowski, for a detailed review of the agencies’ analysis.128 Among Mr. Rykowski’s comments, a few specifically involve differences between these two models. Mr. Rykowski recommended NHTSA’s CAFE model replace its existing ‘‘effective cost’’ metric (used to compare available options to add specific technologies to specific vehicles) with a ‘‘ranking factor’’ used for the same purpose. As discussed below in Section VI.A, the model for today’s final rule adopts this recommendation. He also states that (1) ‘‘EPA has developed a better way to isolate and reject cost ineffective combinations of technologies . . . [and] includes only these 50 or so technology combinations in their OMEGA model runs;’’ (2) ‘‘NHTSA’s arbitrary and rigid designation of leader-follower vehicles for engine, transmission and platform level technologies unrealistically slows the rollout of technology into the new vehicle fleet;’’ (3) ‘‘the Volpe Model is 124 Id. at 21–22. at 23. 126 Id. at 24–25. 127 Id. at 27. 128 EDF, NHTSA–2018–0067–12108, Appendix B. See also EPA, Peer Review of the Optimization Model for Reducing Emissions of Greenhouse Gases from Automobiles (OMEGA) and EPA’s Response to Comments, EPA–420–R–09–016, September 2009. 125 Id. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 not capable of reasonably simulating manufacturers’ ability to utilize CO2 credits to smooth the introduction of technology throughout their vehicle line-up;’’ and (4) ‘‘the Volpe Model is not designed to reflect the use of these [A/C leakage] technologies and refrigerants.’’ 129 Mr. Rogers’s analysis focuses primarily on the agencies’ published analysis, but mentions that some engine ‘‘maps’’ (estimates—used as inputs to full vehicle simulation—of engine fuel consumption under a wide range of engine operating conditions) applied in Autonomie show greater fuel consumption benefits of turbocharging than those applied previously by EPA to EPA’s ALPHA model, and these benefits could have caused NHTSA’s CAFE model to estimate an unrealistically great tendency toward turbocharged engines (rather than high compression ratio engines).130 Mr. Rogers also presents alternative examples of yearby-year technology application to specific vehicle models, contrasting these with year-by-year results from the agencies’ NPRM analysis, concluding that ‘‘that the use of logical, unrestricted technology pathways, with incremental benefits supported by industry-accepted vehicle simulation and dynamic system optimization and calibration, together with publicly-defensible costs, allows cost-effective solutions to achieve target fuel economy levels which meet MY 2025 existing standards.’’ 131 Mr. Duleep’s analysis also focuses primarily on the agencies’ published analysis, but does mention that (1) ‘‘the Autonomie modeling assumes no engine change when drag and rolling resistance reductions are implemented, as well as no changes to the transmission gear ratios and axle ratios, . . . [but] the EPA ALPHA model adjusts for this effect;’’ (2) ‘‘baseline differences in fuel economy [between two manufacturers’ different products using similar technologies] are carried for all future years and this exaggerates the differences in technology adoption requirements and costs between manufacturers; (3) ‘‘assumptions [that most technology changes are best applied as part of a vehicle redesign or freshening] result in unnecessary distortion in technology paths and may bias results of costs for different manufacturers;’’ and (4) that for the sample results shown for the Chevrolet Equinox ‘‘the publicly available EPA lumped parameter model (which was used to support the 2016 rulemaking) and 2016 TAR cost data . . . results in an estimate of attaining 52.2 mpg for a cost of $2110, which is less than half the cost estimated in the PRIA.’’ 132 Beyond these comments regarding differences between EPA’s models and the Argonne and DOT models applied for the NPRM, these and other technical reviewers had many specific comments about the agencies’ analysis for the NPRM, and these comments are discussed in detail below in Section VI.B. Manufacturers, on the other hand, supported the agencies’ use of Autonomie and the CAFE model rather than, in EPA’s case, the ALPHA and OMEGA models. Expressly identifying the distinction between models and model inputs, Global Automakers stated that: The agencies provided a new, updated analysis based on the most up-to-date data, using a proven and long-developed modeling tool, known as the Volpe model, and offering numerous options to best determine the right regulatory and policy path for ongoing fuel efficiency improvements in our nation. Now, all stakeholders have an opportunity to come to the table as part of the public process to provide input, data, and information to help shape the final rule.133 This NPRM’s use of a single model to evaluate alternative scenarios for both programs provides consistency in the technical analysis, and Global Automakers supports the Volpe model’s use as it has proven to be a transparent and user-friendly option in this current analysis. The use of the Volpe model has allowed for a broad range of stakeholders, with varying degrees of technical expertise, to review the data inputs to provide feedback on this proposed rule. The Volpe model’s accompanying documentation has historically provided a clear explanation of all sources of input and constraints critical to a transparent modeling process. Other inputs have come from modeling that is used widely by other sources, specifically the Autonomie model, allowing for a robust validation, review and reassessment.134 The Alliance commented, similarly, that ‘‘at least at this time, NHTSA’s modeling systems are superior to EPA’s’’ and ‘‘as such, we support the Agencies’ decision to use NHTSA’s modeling tools for this rulemaking and recommend that both Agencies continue on this path. We encourage Agencies to work together to provide input to the single common set of tools.’’ 135 132 H-D 129 EDF, op. cit., at 73–75. 130 Roush Industries, NHTSA–2018–0067–11984, at 17–21. 131 Roush Industries, NHTSA–2018–0067–11984, at 17–30. PO 00000 Frm 00052 Fmt 4701 Sfmt 4700 Systems, op. cit., at 48, et seq. Automakers, NHTSA–2018–0067– 12032, at 2. 134 Global Automakers, NHTSA–2018–0067– 12032, Attachment A, at A–12. 135 Alliance, NHTSA–2018–0067–12073, at 134. 133 Global E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Regarding the agencies’ use of Argonne’s Autonomie model rather than EPA’s ALPHA model, the Alliance commented that (1) ‘‘the benefits of virtually all technologies and their synergistic effects are now determined with full vehicle simulations;’’ (2) ‘‘vehicle categories have been increased to 10 to better recognize the range of 0– 60 performance characteristics within each of the 5 previous categories, in recognition of the fact that many vehicles in the baseline fleet significantly exceeded the previously assumed 0–60 performance metrics. This provides better resolution of the baseline fleet and more accurate estimates of the benefits of technology. . . .;’’ (3) ‘‘new technologies (like advanced cylinder deactivation) are included, while unproven combinations (like Atkinson engines with 14:1 compression, cooled EGR, and cylinder deactivation in combination) have been removed;’’ (4) ‘‘Consistent with the recommendation of the National Academy of Sciences and manufacturers, gradeability has been included as a performance metric used in engine sizing. This helps prevent the inclusion of small displacement engines that are not commercially viable and that would artificially inflate fuel savings;’’ (5) ‘‘the Alliance believes NHTSA’s tools (Autonomie/Volpe) are superior to EPA’s (APLHA[sic]/LPM/OMEGA). This is not surprising since NHTSA’s tools have had a significant head start in development. . . .’’ (6) ‘‘the Autonomie model was developed at Argonne National Lab with funding from the Department of Energy going back to the PNGV (Partnership for Next Generation Vehicles) program in the 1990s. Autonomie was developed from the start to address the complex task of combining 2 power sources in a hybrid powertrain. It is a physics-based, forward looking, vehicle simulator, fully documented with available training,’’ and (7) ‘‘EPA’s ALPHA model is also a physics-based, forward looking, vehicle simulator. However, it has not been validated or used to simulate hybrid powertrains. The model has not been documented with any instructions making it difficult for users outside of EPA to run and interpret the model.’’ 136 Regarding the use of NHTSA’s CAFE model rather than EPA’s OMEGA model, the Alliance stated that (1) at 135. at 134. 138 Id. at 135. NHTSA’s model appropriately differentiate between domestic and imported automobiles; (2) in NHTSA’s model, ‘‘dynamic estimates of vehicle sales and scrappage in response to price changes replace unrealistic static sales and scrappage numbers;’’ (3) NHTSA’s model ‘‘has new capability to perform [CO2 emissions] analysis with [tailpipe CO2] program flexibilities;’’ (4) ‘‘the baseline fleet [used in NHTSA’s model] has been appropriately updated based on both public and manufacturer data to reflect the technologies already applied, particularly tire rolling resistance;’’ and (5) ‘‘some technologies have been appropriately restricted. For example, low rolling resistance tires are no longer allowed on performance vehicles, and aero improvements are limited to maximum levels of 15% for trucks and 10% for minivans.’’ 137 The Alliance continued, noting that ‘‘NHTSA’s Volpe model also predates EPA’s OMEGA model. More importantly, the new Volpe model considers several factors that make its results more realistic.’’ 138 As factors leading the Volpe model to produce results that are more realistic than those produced by OMEGA, the Alliance commented that (1) ‘‘The Volpe model includes estimates of the redesign and refresh schedules of vehicles based on historical trends, whereas the OMEGA model uses a fixed, and too short, time interval during which all vehicles are assumed to be fully redesigned. . . .;’’ (2) ‘‘The Volpe model allows users to phase-in technology based on year of availability, platform technology sharing, phase-in caps, and to follow logical technology paths per vehicle. . . .;’’ (3) ‘‘The Volpe model produces a year-by year analysis from the baseline model year through many years in the future, whereas the OMEGA model only analyzes a fixed time interval. . . .;’’ (4) ‘‘The Volpe model recognizes that vehicles share platforms, engines, and transmissions, and that improvements to any one of them will likely extend to other vehicles that use them’’ whereas ‘‘The OMEGA model treats each vehicle as an independent entity. . . .;’’ (5) ‘‘The Volpe model now includes sales and scrappage effects;’’ and (6) ‘‘The Volpe model is now capable of analyzing for CAFE and [tailpipe CO2] compliance, each with unique program restrictions and flexibilities.’’ 139 The Alliance also incorporated by reference concerns it raised regarding EPA’s OMEGA-based analysis supporting EPA’s proposed and prior final determinations.140 The Alliance further stated that ‘‘For all of the above reasons and to avoid duplicate efforts, the Alliance recommends that the Agencies continue to use DOT’s Volpe and Autonomie modeling system, rather than continuing to develop two separate systems. EPA has demonstrated through supporting Volpe model code revisions and by supplying engine maps for use in the Autonomie model that their expertise can be properly represented in the rulemaking process without having to develop separate or new tools.’’ 141 Some individual manufacturers provided comments supporting and elaborating on the above comments by Global Automakers and the Alliance. For example, FCA commented that ‘‘the modeling performed by the agencies should illuminate the differences between the CAFE and [tailpipe CO2 emissions] programs. This cannot be accomplished when each agency is using different tools and assumptions. Since we believe NHTSA possesses the better set of tools, we support both agencies using Autonomie for vehicle modeling and Volpe (CAFE) for fleet modeling.’’ 142 Honda stated that ‘‘The current version of the CAFE model is reasonably accurate in terms of technology efficiency, cost, and overall compliance considerations, and reflects a notable improvement over previous agency modeling efforts conducted over the past few years. We found the CAFE model’s characterization of Honda’s ‘‘baseline’’ fleet—critical modeling minutiae that provide a technical foundation of the agencies’ analysis—to be highly accurate. We commend NHTSA and Volpe Center staff on these updates, as well as on the overall transparency of the model. The model’s graphical user interface (GUI) makes it easier to run, model functionality is thoroughly documented, and the use of logical, traceable input and output files accommodates easy tracking of results.’’ 143 Similarly, in an earlier presentation to the agencies, Honda included the following slide comparing EPA’s OMEGA model to DOT’s CAFE (Volpe) model, and making recommendations regarding future improvements to the latter: 144 136 Id. 139 Id. 142 FCA, 137 Id. 140 Id. 143 Honda, VerDate Sep<11>2014 at 135–136. at 136. 141 Id. at 136. 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00053 Fmt 4701 24225 NHTSA–2018–0067–11943, at 82. EPA–HQ–OAR–2018–0283, at 21–22. 144 Honda, NHTSA–2018–0067–12019, at 12. Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 Toyota, in addition to arguing that the agencies’ application of model inputs (e.g., an analysis fleet based on MY 2016 compliance data) produced more realistic results than in the draft TAR and in EPA’s former proposed and final determinations, also stressed the importance of the CAFE model’s yearby-year accounting for product redesigns, stating that this produces more realistic results than the OMEGAbased results shown previously by EPA: The modeling now better accounts for factors that limit the rate at which new technologies enter and then diffuse through a manufacturer’s fleet. Bringing new or improved vehicles and technologies to market is a several-year, capital-intensive undertaking. Once new designs are introduced, a period of stability is required so investments can be amortized. Vehicle and technology introductions are staggered over time to manage limited resources. Agency modeling now better recognizes the inherent constraints imposed by realities that dictate product cadence. We agree with the agencies’ understanding that ‘‘the simulation of compliance actions that manufacturers might take is constrained by the pace at which new technologies can be applied in the new vehicle market,’’ and we are encouraged to learn that ‘‘agency modeling can now account for the fact that individual vehicle models undergo significant redesigns relatively infrequently.’’ The preamble correctly notes that manufacturers try to keep costs down by applying most major changes mainly during vehicle redesigns and more modest changes during product refresh, and that redesign cycles for vehicle models can VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 range from six to ten years, and eight to tenyears for powertrains. This appreciation for standard business practice enables the modeling to more accurately capture the way vehicles share engines, transmissions, and platforms. There are now more realistic limits placed on the number of engines and transmissions in a powertrain portfolio which better recognizes manufacturers must manage limited engineering resources and control supplier, production, and service costs. Technology sharing and inheritance between vehicle models tends to limit the rate of improvement in a manufacturer’s fleet.145 These comments urging EPA to use NHTSA’s CAFE model echo comments provided in response to a 2018 peer review of the model. While identifying various opportunities for improvement, peer reviewers expressed strong overall support for the CAFE model’s technical approach and execution. For example, one reviewer, after offering many specific technical recommendations, concluded as follows: The model is impressive in its detail, and in the completeness of the input data that it uses. Although the model is complex, the reader is given a clear account of how variables are variously divided and combined to yield appropriate granularity and efficiency within the model. The model tracks well a simplified version of the realworld and manufacturing/design decisions. The progression of technology choices and cost benefit choices is clear and logical. In a 145 Toyota, NHTSA–2018–0067–12098, Attachment 1, at 3 et seq. PO 00000 Frm 00054 Fmt 4701 Sfmt 4700 few cases, the model simply explains a constraint, or a value assigned to a variable, without defending the choice of the value or commenting on real-world variability, but these are not substantive omissions. The model will lend itself well to future adaptation or addition of variables, technologies and pathways.146 Although the peer review charge focused solely on the CAFE model, another peer reviewer separately recommended that EPA ‘‘consider opportunities for EPA to use the output from the Volpe Model in place of their OMEGA Model output’’ 147 More recently, in response to the NPRM, Dr. Julian Morris, an economist at George Washington University, commented extensively on the superiority of the agencies’ NPRM analysis to previous analyses, offering the following overall assessment: I have assessed the plausibility of the analyses undertaken by NHTSA and EPA in relation to the proposed SAFE rule. I found that the agencies have undertaken a thorough—one might even say exemplary— analysis, improving considerably on earlier analyses undertaken by the agencies of previous rules relating to CAFE standards and [tailpipe CO2] emission standards. Of particular note, the agencies included more realistic estimates of the rebound effect, developed a sophisticated model of the 146 NHTSA, CAFE Model Peer Review, DOT HS 812 590, Available at https://www.nhtsa.gov/ document/cafe-model-peer-review, at 250. 147 Id. at 287–288 and 304. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.041</GPH> 24226 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 scrappage effect, and better accounted for various factors affecting vehicle fatality rates.148 The agencies carefully considered these and other comments regarding which models to apply when estimating potential impacts of each of the contemplated regulatory alternatives. For purposes of estimating the impacts of CAFE standards, even the coalition of environmental advocates observed that the CAFE model reflects EPCA’s requirements. As discussed below in Section VI.A, EPCA imposes specific requirements not only on how CAFE standards are to be structured (e.g., including a minimum standard for domestic passenger cars), but also on how CAFE standards are to be evaluated (e.g., requiring that the potential to produce additional AFVs be set aside for the model years under consideration), and the CAFE model reflects these requirements, and the agencies consider the CAFE model to be the best available tool for CAFE rulemaking analysis. Regarding the use of Autonomie to construct fuel consumption (i.e., efficiency) inputs to the CAFE model, the agencies recognize that other vehicle simulation tools are available, including EPA’s recentlydeveloped ALPHA model. However, as also discussed in Section VI.B.3, Autonomie has a much longer history of development and refinement, and has been much more widely applied and validated. Moreover, Argonne experts have worked carefully for several years to develop methods for running large arrays of simulations expressly structured and calibrated for use in DOT’s CAFE model. Therefore, the agencies consider Autonomie to be the best available tool for constructing such inputs to the CAFE model. While the agencies have also carefully considered potential specific model refinements, as well as the merits of potential changes to model inputs and assumptions, none of these potential refinements and input have led either agency to reconsider using the CAFE model and Autonomie for CAFE rulemaking analysis. With respect to estimating the impacts of CO2 standards, even though Argonne and the agencies have adapted Autonomie and the CAFE model to support the analysis of CO2 standards, environmental groups, California, and other States would prefer that EPA use the models it developed during 2009– 2018 for that purpose.149 Arguments that EPA revert to its ALPHA and 148 Morris, J., OAR–2018–0283–4028, at 6–11. last-finalized versions of EPA’s OMEGA model and ALPHA tools were published in 2016 and 2017, respectively. 149 The VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 OMEGA models fall within three general categories: (1) Arguments that EPA’s models would have selected what commenters consider better (i.e., generally more stringent) standards, (2) arguments that EPA’s models are technically superior, and (3) arguments that the law requires EPA use its own models. The first of these arguments—that EPA’s models would have selected better standards—conflates the analytical tool used to inform decisionmaking with the action of making the decision. As explained elsewhere in this document and as made repeatedly clear over the past several rulemakings, the CAFE model (or, for that matter, any model) neither sets standards nor dictates where and how to set standards; it simply informs as to the potential effects of setting different levels of standards. In this rulemaking, EPA has made its own decisions regarding what CO2 standards would be appropriate under the CAA. The third of these arguments—that EPA is legally required to use only models developed by its own staff—is also without merit. The CAA does not require the agency to create or use a specific model of its own creation in setting tailpipe CO2 standards. The fact that EPA’s decision may be informed by non-EPA-created models does not, in any way, constitute a delegation of its statutory power to set standards or decision-making authority.150 Arguing to the contrary would suggest, for example, that EPA’s decision would be invalid because it relied on EIA’s Annual Energy Outlook for fuel prices for all of its regulatory actions rather than developing its own model for estimating future trends in fuel prices. Yet, all Federal agencies that have occasion to use forecasts of future fuel prices regularly (and appropriately) defer to EIA’s expertise in this area and rely on EIA’s NEMS-based analysis in the AEO, even when those same agencies are using EIA’s forecasts to inform their own decision-making. 150 ‘‘[A] federal agency may turn to an outside entity for advice and policy recommendations, provided the agency makes the final decisions itself.’’ U.S. Telecom. Ass’n v. FCC, 359 F.3d 554, 565–66 (D.C. Cir. 2004). To the extent commenters meant to suggest outside parties have a reliance interest in EPA using ALPHA and OMEGA to set standards, EPA and NHTSA do not agree a reliance interest is properly placed on an analytical methodology, which consistently evolves from rule to rule. Even if it were, all parties that closely examined ALPHA and OMEGA-based analyses in the past either also simultaneously closely examined CAFE and Autonomie-based analyses in the past, or were fully capable of doing so, and thus, should face no additional difficulty now they have only one set of models and inputs/outputs to examine. PO 00000 Frm 00055 Fmt 4701 Sfmt 4700 24227 Similarly, this argument would mean that the agencies could not rely on work done by contractors or other outside consultants, which is contrary to regular agency practice across the entirety of the Federal Government. The specific claim here that use of the CAFE model instead of ALPHA and OMEGA is somehow illegitimate is similarly unpersuasive. The CAFE and CO2 rules have, since Massachusetts v. EPA, all been issued as joint rulemakings, and, thus are the result of a collaboration between the two agencies. This was true when the rulemakings used separate models for the different programs and continues to be true in today’s final rule, where the agencies take the next step in their collaborative approach by now using simply one model to simulate both programs. In 2007, immediately following this Supreme Court decision, the agencies worked together toward standards for model years 2011–2015, and EPA made use of the CAFE model for its work toward possible future CO2 standards. That the agencies would need to continue the unnecessary and inefficient process of using two separate combinations of models as the joint National Program continues to mature, therefore, runs against the idea that the agencies, over time, would best combine resources to create an efficient and robust regulatory program. For the reasons discussed throughout today’s final rule, the agencies have jointly determined that Autonomie and the CAFE model have significant technical advantages, including important additional features, and are therefore the more appropriate models to use to support both analyses. Further, the fact that Autonomie and CAFE models were initially developed by DOE/Argonne and NHTSA does not mean that EPA has no role in either these models or their inputs. That is, the development process for CAFE and CO2 standards inherently requires technical and policy examinations and deliberations between staff experts and decision-makers in both agencies. Such engagements are a healthy and important part of any rulemaking activity—and particularly so with joint rulemakings. The Supreme Court stated in Massachusetts v. EPA that, ‘‘The two obligations [to set CAFE standards under EPCA and to set tailpipe CO2 emissions standards under the CAA] may overlap, but there is no reason to think the two agencies cannot both administer their obligations and yet E:\FR\FM\30APR2.SGM 30APR2 24228 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 avoid inconsistency.’’ 151 When agency experts consider analytical issues and agency decision-makers decide on policy, which is informed (albeit not dictated) by the outcome of that work, they are working together as the Court appears to have intended in 2007, even if legislators’ intentions have varied in the decades since EPCA and the CAA have been in place.152 Regulatory overlap necessarily involves deliberation, which can lead to a more balanced, reasonable, and improved analyses, and better regulatory outcomes. It did here. The existence of deliberation is not per se evidence of unreasonableness, even if some commenters believe a different or preferred policy outcome would or should have resulted.153 Over the 44 years since EPCA established the requirement for CAFE standards, NHTSA, EPA and DOE career staff have discussed, collaborated on, and debated engineering, economic, and other aspects of CAFE regulation, through focused meetings and projects, informal exchanges, publications, conferences and workshops, and rulemakings. Part of this expanded exchange has involved full vehicle simulation. While tools such as PSAT (the DOE-sponsored simulation tool that predated Autonomie) were in use prior to 2007, including for discrete engineering studies supporting inputs to CAFE rulemaking analyses, these tools’ information and computing requirements were such that NHTSA had determined (and DOE and EPA had concurred) that it was impractical to more fully integrate full vehicle simulation into rulemaking analyses. Since that time, computing capabilities have advanced dramatically, and the agencies now agree that such integration 151 Massachusetts v. EPA, 549 U.S. 497, 532 (2007). 152 For example, when wide-ranging amendments to the CAA were being debated, S. 1630 contained provisions that, if enacted, would have authorized automotive CO2 emissions standards and prescribed specific average levels to be achieved by 1996 and 2000. In a letter to Senators, then-Administrator William K. Reilly noted that the Bill ‘‘requires for the first time control of emissions of carbon dioxide; this is essentially a requirement to improve fuel efficiency’’ and outlined four reasons the H.W. Bush Administration opposed the requirement, including that ‘‘it is inappropriate to add this very complex issue to the Clean Air Act which is already full of complicated and controversial issues.’’ Reilly, W., Letter to U.S. Senators (January 26, 1990). The CAA amendments ultimately signed into law did not contain these or any other provisions regarding regulation of CO2 emissions. 153 See, e.g., U.S. House of Representatives, Committee on Oversight and Government Reform, Staff Report, 112th Congress, ‘‘A Dismissal of Safety, Choice, and Cost: The Obama Administration’s New Auto Regulations,’’ August 10, 2012, at 19–21 and 33–34. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 of full vehicle simulation—such as the large-scale exercise of Autonomie to produce inputs to the CAFE Model—can make for more robust CAFE and CO2 rulemaking analysis. This is not to say, though, that experts always agree on all methods and inputs involved with full vehicle simulation. Differences in approach and inputs lead to differences in results. For example, compared to other publicly available tools that can be practicably exercised at the scale relevant to fleetwide analysis needed for CAFE and CO2 rulemaking analysis, DOE/Argonne’s Autonomie model is more advanced, spans a wider range of fuel-saving technologies, and represents them in more specific detail, leaving fewer ‘‘gaps’’ to be filled with other models (risking inconsistencies and accompanying errors). These differences discussed in greater detail below in Section VI.B.3. Perhaps most importantly, the CAFE model considers fuel prices in determining both which technologies are applied and the total amount of technology applied, in the case where market forces demand fuel economy levels in excess of the standards. While OMEGA can apply technology in consideration of fuel prices, OMEGA will apply technology to reach the same level of fuel economy (or CO2 emissions) if fuel prices are 3, 5, or 20 dollars, which violates the SAB’s requirement that the analysis ‘‘account for [. . .] future fuel prices .’’ 154 Furthermore, it produces a counterintuitive result. If fuel prices become exorbitantly high, we would expect consumers to place an emphasis on additional fuel efficiency as the potential for extra fuel savings is tremendous. Moreover, DOE has for many years used Autonomie (and its precursor model, PSAT) to produce analysis supporting fuel economy-related research and development programs involving billions of dollars of public investment, and NHTSA’s CAFE model with inputs from DOE/Argonne’s Autonomie model has produced analysis supporting rulemaking under the CAA. In 2015, EPA proposed new tailpipe CO2 standards for MY 2021– 2027 heavy-duty pickups and vans, finalizing those standards in 2016. Supporting the NPRM and final rule, EPA relied on analysis implemented by NHTSA using NHTSA’s CAFE model, and NHTSA used inputs developed by 154 See SAB Report 10 (‘‘Constructing each of the scenarios is challenging and involve extensive scientific, engineering, and economic uncertainties. Projecting the baseline requires the agencies to account for a wide range of variables including: The number of new vehicles sold, future fuel prices,. . . .’’). PO 00000 Frm 00056 Fmt 4701 Sfmt 4700 DOE/Argonne using DOE/Argonne’s Autonomie model. CBD questioned this history, asserting that, ‘‘EPA conducted a separate analysis using a different iteration of the CAFE model rather than rely on the version which NHTSA used, again resulting and parallel but corroborative modeling results.’’ 155 CBD’s comment mischaracterizes EPA’s actual use of the CAFE Model. As explained in the final rule, EPA’s ‘‘Method B’’ analysis was developed as follows: In Method B, the CAFE model from the NPRM was used to project a pathway the industry could use to comply with each regulatory alternative, along with resultant impacts on per-vehicle costs. However, the MOVES model was used to calculate corresponding changes in total fuel consumption and annual emissions for pickups and vans in Method B. Additional calculations were performed to determine corresponding monetized program costs and benefits.156 In other words, a version of NHTSA’s CAFE Model was used to perform the challenging part of the analysis—that is, the part that involves accounting for manufacturers’ fleets, accounting for available fuel-saving technologies, accounting for standards under consideration, and estimating manufacturers’ potential responses to new standards—EPA’s MOVES model was used to perform ‘‘downstream’’ calculations of fuel consumption and tailpipe emissions, and used spreadsheets to calculate even more straightforward calculations of program costs and benefits. While some stakeholders perceive these differences as evidencing a meaningfully independent approach, in fact, the EPA staff’s analysis was, at its core, wholly dependent on NHTSA’s CAFE Model, and on that model’s use of Autonomie simulations. Given the above, the only remaining argument for EPA to revert to its previously-developed models rather than relying on Autonomie and the CAFE model would be that the former are so technically superior to the latter that even model refinements and input changes cannot lead Autonomie and the CAFE model to produce appropriate and reasonable results for CO2 rulemaking analysis. As discussed below, having considered a wide range of technical differences, the agencies find that the Autonomie and CAFE models currently provide the best analytical combination for CAFE and tailpipe CO2 emissions rulemaking analysis. As discussed 155 CBD, et al., 2018–0067–12000, Appendix A, at 27. 156 81 E:\FR\FM\30APR2.SGM FR 73478, 73506–07 (October 25, 2016). 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations below in Section VI.B.3, Autonomie not only has a longer and wider history of development and application, but also DOE/Argonne’s interaction with automakers, supplier and academies on continuous bases had made individual sub-models and assumptions more robust. Argonne has also been using research from DOE’s Vehicle Technology Office (VTO) at the same time to make continuous improvements in Autonomie.157 Also, while Autonomie uses engine maps as inputs, and EPA developed engine maps that could have been used for today’s analysis, EPA declined to do so, and those engine maps were only used in a limited capacity for reasons discussed below in Section VI.C.1. As also discussed below in Section VI.A.4, the CAFE model accounts for some important CO2 provisions that EPA’s OMEGA model cannot account for. For example, the CAFE model estimates the potential that any given manufacturer might apply CO2 compliance credits it has carried forward from some prior model year. While one commenter, Mr. Rykowski, takes issue with how the CAFE model handles credit banking, he does not acknowledge that EPA’s OMEGA model, lacking a year-by-year representation of compliance, is altogether incapable of accounting for the earning and use of banked compliance credits. Also, although Mr. Rykowski’s comments regarding A/C leakage and refrigerants are partially correct insofar as the CAFE model does not account for leakagereducing technologies explicitly, the comment is as applicable to OMEGA as it is to the CAFE model and, in any event, data regarding which vehicles have which leakage-reducing technologies was not available for the MY 2016 fleet. Nevertheless, as discussed in Section VI.A.4, NHTSA has refined the CAFE model’s accounting for the cost of leakage reduction technologies. The agencies have also considered Mr. Rykowski’s comments suggesting that using OMEGA would be preferable because, rather than selecting from hundreds of thousands of potential combinations of technologies, OMEGA includes only the ‘‘50 or so’’ combinations that EPA has already determined to be cost-effective. The ‘‘better way’’ of making this determination is also effectively a model, but the separation of this model from OMEGA is, as evidenced by 157 U.S. DOE Benefits & Scenario Analysis publications is available at https:// www.autonomie.net/publications/fuel_economy_ report.html. Last accessed November 14, 2019. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 manufacturers’ comments, obfuscatory, especially in terms of revealing how specific vehicle model/configurations initial engineering properties are aligned with specific initial technology combinations. By using a full set of technology combinations, the CAFE model makes very clear how each vehicle model/configuration is assigned to a specific initial combination and, hence, how subsequently fuel consumption and cost changes are accounted for. Moreover, EPA’s separation of ‘‘thinning’’ process from OMEGA’s main compliance simulation makes sensitivity analysis difficult to implement, much less follow. The agencies find, therefore, that the CAFE model’s approach of retaining a full set of vehicle simulation results throughout the compliance simulation to be more realistic (e.g., more capable of reflecting manufacturer- and vehicle-specific factors), more responsive to changes in model inputs (e.g., changes to fuel prices, which could impact the relative attractiveness of different technologies), more transparent, and more amenable to independent corroboration the agencies’ analysis. Regarding comments by Messrs. Duleep, Rogers, and Rykowski suggesting that the CAFE model, by tying most technology application to planned vehicle redesigns and freshening, is too restrictive, the agencies disagree. As illustrated by manufacturers’ comments cited above, as reinforced by both extensive product planning information provided to the agencies, and as further reinforced by extensive publicly available information, manufacturers tend to not make major changes to a specific vehicle model/configuration in one model year, and then make further major changes to the same vehicle model/configuration the next model year. There is ample evidence that manufacturers strive to avoid such discontinuity, complexity, and waste, and in the agencies’ view, while it is impossible to represent every manufacturer’s decision-making process precisely and with certainty, the CAFE model’s approach of using estimated product design schedules provides a realistic basis for estimating what manufacturers could practicably do. Also, the relevant inputs are simply inputs to the CAFE model, and if it is actually more realistic to assume that a manufacturer can change major technology on all of its products every year, the CAFE model can easily be operated with every model year designated as a redesign year for every product, but as discussed throughout this document, the agencies consider PO 00000 Frm 00057 Fmt 4701 Sfmt 4700 24229 this to be extremely unrealistic. While this means the CAFE model can be run without a year-by-year representation that carries forward technologies between model years, doing so would be patently unrealistic (as reflected in some stakeholders’ comments in 2002 on the first version of the CAFE model). Conversely, the OMEGA model cannot be operated in a way that accounts for what the agencies consider to be very real product planning considerations. However, having also considered Mr. Rykowski’s comments about the CAFE model’s ‘‘effective cost’’ metric, and having conducted side-by-side testing documented in the accompanying FRIA, the agencies are satisfied that an alternative ‘‘cost per credit’’ metric is also a reasonable metric to use for estimating how manufacturers might selected among available options to add specific fuel-saving technologies to specific vehicles.158 Therefore, NHTSA has revised the CAFE model accordingly, as discussed below in Section VI.A.4. Section VI.C.1 also addresses Mr. Rogers’s comments on engine maps used as estimates to full vehicle simulation. In any event, because engine maps are inputs to full vehicle modeling and simulation, the relative merits of specific maps provide no basis to prefer one vehicle simulation modeling system over another. Similarly, Section VI.B.3 also addresses Mr. Duleep’s comments preferring EPA’s prior approach, using ALPHA, of effectively assuming that a manufacturer would incur no additional cost by reoptimizing every powertrain to extract the full fuel economy potential of even the smallest incremental changes to aerodynamic drag and tire rolling resistance. Mr. Duleep implies that Autonomie is flawed because the NPRM analysis did not apply Autonomie in a way that makes such assumptions. The agencies discuss powertrain sizing and calibration in Section VI.B.3, and note here that such assumptions are not inherent to 158 As discussed in the FRIA, results vary with model inputs, among manufacturers, and across model years, but compared to the NPRM’s ‘‘effective cost’’ metric, the ‘‘cost per credit’’ metric appears to more frequently produce less expensive solutions than more expensive solutions, at least when simulating compliance with CO2 standards. Differences are more mixed when simulating compliance with CAFE standards, and even when simulating compliance with CO2 standards, results simulating ‘‘perfect’’ trading of CO2 compliance credits are less intuitive when the ‘‘cost per credit metric.’’ Nevertheless, and while less expensive solutions are not necessarily ‘‘optimal’’ solutions (e.g., if gasoline costs $7 per gallon and electricity is free, expensive electrification could be optimal), the agencies consider it reasonable to apply the ‘‘cost per credit’’ metric for the analysis supporting today’s rulemaking. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24230 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Autonomie; like engine maps, these are inputs to full vehicle simulation. Therefore, neither of these comments by Mr. Rogers and Mr. Duleep lead the agencies to find reason not to use Autonomie. None of this is to say that Autonomie and the CAFE model as developed and applied for the NPRM left no room for improvement. In the NPRM and RIA, the agencies discussed plans to continue work in a range of specific technical areas, and invited comment on all aspects of the analysis. As discussed below in Chapter VI, the agencies received extensive comment on the published model, inputs, and analysis, both in response to the NPRM and, for newly-introduced modeling capabilities (estimation of sales, scrappage, and employment effects), in response to additional peer review conducted in 2019. The agencies have carefully considered these comments, refined various specific technical aspects of the CAFE model (like the ‘‘effective cost’’ metric mentioned above), and have also updated inputs to both Autonomie and the CAFE model. Especially given these refinements and updates, as discussed throughout this rule, EPA maintains that for CO2 rulemaking analysis, Autonomie and the CAFE model have advantages that warrant relying on them rather than on EPA’s ALPHA and OMEGA models. Some examples of such advantages include: A longer history of ongong development and application for rulemaking, including by EPA; documentation and model operation stakeholders have found to be comparatively clear and enabling of independent replication of agency analyses; a mechanism to explicitly reflect the fact that manufacturers’ product decisions are likely to be informed by fuel prices; better integration of various model functions, enabling efficient sensitivity analysis; and an annual time step that makes it possible to conduct report results on both a calendar year and model year basis, to estimate accruing impacts on vehicle sales and scrappage, and to account for the fact that not every vehicle can be designed in every model year; and other advantages discussed throughout today’s notice. Therefore, recognizing that models inform but do not make regulatory decisions, EPA has elected to rely solely on the Autonomie 159 As often stated, ‘‘It’s difficult to make predictions, especially about the future.’’ See, e.g., https://quoteinvestigator.com/2013/10/20/nopredict/. 160 See, e.g., 77 FR 62785 (Oct. 15, 2012) (‘‘If EPA initiates a rulemaking [to revise standards for MYs 2022–2025], it will be a joint rulemaking with VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 and CAFE models to produce today’s analysis of regulatory alternatives for CO2 standards. The following sections provide a brief technical overview of the CAFE model, including changes NHTSA made to the model since 2012, and differences between the current analysis, the analysis for the 2016 Draft TAR and for the 2017 Proposed Determination/2018 Final Determination, and the 2018 NPRM, before discussing inputs to the model and then diving more deeply into how the model works. For more information on the latter topic, see the CAFE model documentation, available in the docket for this rulemaking and on NHTSA’s website. perspectives, and modeling techniques that have created substantive differences between the current analysis and the analysis conducted in 2012 to support the final rule. To the greatest extent possible, we have calculated the impacts of these changes on the 2012 analysis. a) The Value of Fuel Savings We describe in detail (below) the changes to critical assumptions, The value of fuel savings associated with the preferred alternative in the 2012 final rule is primarily a consequence of two assumptions: 162 The fuel price forecast and the assumed growth in fuel economy in the baseline alternative against which savings are measured. Therefore, as the value of fuel savings accounted for nearly 80 percent of the total benefits of the 2012 rule, each of these assumptions is consequential. With a lower fuel price projection and an expectation that new vehicle buyers respond to fuel prices, the 2012 rule would have shown much smaller fuel savings attributable to the more stringent standards. Projected fuel prices are considerably lower today than in 2012, the agencies now understand new vehicle buyers to be at least somewhat responsive to fuel prices, and the agencies have therefore updated corresponding model inputs to produce an analysis the agencies consider to be more realistic. The first of these assumptions, fuel prices, was simply an artifact of the timing of the rule. Following recent periodic spikes in the national average gasoline price and continued volatility after the great recession, the fuel price forecast then produced by EIA (as part of AEO 2011) showed a steady march toward historically high, sustained gasoline prices in the United States. However, the actual series of fuel prices has skewed much lower. As it has turned out, the observed fuel price in the years between the 2012 final rule and this rule has frequently been lower than the ‘‘Low Oil Price’’ sensitivity case in the 2011 AEO, even when adjusted for inflation. The following graph compares fuel prices underlying the 2012 final rule to fuel prices applied in the analysis reported in today’s notice, expressing both projections in 2010 dollars. The differences are clear and significant: NHTSA. . . . NHTSA’s development of its proposal in that later rulemaking will include the making of economic and technology analyses and estimates that are appropriate for those model years and based on then-current information.’’). 161 Securing America’s Energy Future, NHTSA– 2018–0067–12172, at 39. 162 The value of fuel savings is also affected by the rebound effect assumption, assumed lifetime VMT accumulation, and the simulated penetration of alternative fuel technologies. However, each of these ancillary factors is small compared to the impact of the two factors discussed in this subsection. 1. What assumptions have changed since the 2012 final rule? Any analysis of regulatory actions that will be implemented several years in the future, and whose benefits and costs accrue over decades, requires a large number of assumptions. Over such time horizons, many, if not most, of the relevant assumptions in such an analysis are inevitably uncertain.159 The 2012 CAFE/CO2 rule considered regulatory alternatives for model years through MY 2025 (17 model years after the 2008 market information that formed the basis of the analysis) that accrued costs and benefits into the 2060s. Not only was the new vehicle market in 2025 unlikely to resemble the market in 2008, but so, too, were fuel prices. It is natural, then, that each successive CAFE/CO2 analysis should update assumptions to reflect better the current state of the world and the best current estimates of future conditions.160 However, beyond the issue of unreliable projections about the future, a number of agency assertions have proven similarly problematic. In fact, Securing America’s Future Energy (SAFE) stated in their comments on the NPRM: Although the agencies argue ‘‘circumstances have changed’’ and ‘‘analytical methods and inputs have been updated,’’ a thorough analysis should provide a side-by-side comparison of the changing circumstances, methods, and inputs used to arrive at this determination . . . They represent a rapid, dramatic departure from the agencies’ previous analyses, without time for careful review and consideration.161 PO 00000 Frm 00058 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 The discrepancy in fuel prices is important to the discussion of differences between the current rule and the 2012 final rule, because that discrepancy leads in turn to differences in analytical outputs and thus to differences in what the agencies consider in assessing what levels of standards are reasonable, appropriate, and/or maximum feasible. As an example, the agencies discuss in Sections VI.D.3 Simulating Environmental Impacts of Regulatory Alternatives and VIII.A.3 EPA’s Conclusion that the Final CO2 Standards are Appropriate and Reasonable that fuel price projections from the 2012 rule were one assumption, among others, that could have led to overestimates of the health benefits that resulted from reducing criteria pollutant emissions. Yet the agencies caution readers not to interpret this discrepancy as a reflection of negligence on the part of the agencies, or on the part of EIA. Long-term predictions are challenging and the fuel price projections in the 2012 rule were within the range of conventional wisdom at the time. However, it does suggest that fuel economy and tailpipe CO2 regulations set almost two decades into the future are vulnerable to VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 surprises, in some ways, and reinforces the value of being able to adjust course when critical assumptions are proven inaccurate. This value was codified in regulation when EPA bound itself to the mid-term evaluation process as part of the 2012 final rule.163 To illustrate this point clearly, substituting the current (and observed) fuel price forecast for the forecast used in the 2012 final rule creates a significant difference in the value of fuel savings. Even under identical discounting methods (see Section 2, below), and otherwise identical inputs in the 2012 version of the CAFE Model, the current (and historical) fuel price forecast reduces the value of fuel savings by $150 billion—from $525 billion to $375 billion (in 2009 dollars). The second assumption employed in the 2012 (as well as the 2010) final rule, that new vehicle fuel economy never improves unless manufacturers are required to increase fuel economy in the new vehicle market by increasingly stringent regulations, is more problematic. Despite the extensive set of recent academic studies showing, as discussed in Section VI.D.1.a)(2), that consumers value at least some portion, 163 See PO 00000 40 CFR 86–1818–12(h). Frm 00059 Fmt 4701 Sfmt 4700 24231 and in some studies nearly all, of the potential fuel savings from higher levels of fuel economy at the time they purchase vehicles, the agencies assumed in past rulemakings that buyers of new vehicles would never purchase, and manufacturers would never supply, vehicles with higher fuel economy than those in the baseline (MY 2016 in the 2012 analysis), regardless of technology cost or prevailing fuel prices in future model years. In calendar year 2025, the 2012 final rule assumed gasoline would cost nearly $4.50/gallon in today’s dollars, and continue to rise in subsequent years. Even recognizing that higher levels of fuel economy would be achieved under the augural/existing standards than without them, the assertion that fuel economy and CO2 emissions would not improve beyond 2016 levels in the presence of nearly $5/ gallon gasoline is not supportable. This is highlighted by the observed increased consumer demand for higher-fueleconomy vehicles during the gas price spike of 2008, when average U.S. prices briefly broke $4/gallon. In the 2012 final rule, this assumption—that fuel economy and emissions would never improve absent regulation—created a persistent gap in fuel economy between E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.042</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24232 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations the baseline and augural standards that grew to 13 mpg (at the industry average, across all vehicles) by MY 2025. In the 2016 Draft TAR, NHTSA’s analysis included the assumption that manufacturers would deploy, and consumers would demand, any technology that recovered its own cost in the first year of ownership through avoided fuel costs. However, in both the Draft TAR and the Proposed and Final Determination documents, EPA’s analysis assumed that the fuel economy levels achieved to reach compliance with MY 2021 standards would persist indefinitely, regardless of fuel prices or technology costs. By substituting the conservative assumption that consumers are willing to purchase fuel economy improvements that pay for themselves with avoided fuel expenditures over the first 2.5 years 164 (identical to the assumption in this final rule’s central analysis) the gap in industry average fuel economy between the baseline and augural scenarios narrows from 13 mpg in 2025 to 6 mpg in 2025. As a corollary, acknowledging that fuel economy would continue to improve in the baseline under the fuel price forecast used in the final rule erodes the value of fuel savings attributable to the preferred alternative. While each gallon is still worth as much as was assumed in 2012, fewer gallons are consumed in the baseline due to higher fuel economy levels in new vehicles. In particular, the khammond on DSKJM1Z7X2PROD with RULES2 164 Greene, D.L. and Welch, J.G., ‘‘Impacts of fuel economy improvements on the distribution of income in the U.S.,’’ Energy Policy, Volume 122, November 2018, pp. 528–41 (‘‘Four nationwide random sample surveys conducted between May 2004 and January 2013 produced payback period estimates of approximately three years, consistent with the manufacturers’ perceptions.’’) (The 2018 article succeeds Greene and Welch’s 2017 publication titled ‘‘The Impact of Increased Fuel Economy for Light-Duty Vehicles on the Distribution of Income in the U.S.: A Retrospective and Prospective Analysis,’’ Howard H. Baker Jr. Center for Public Policy, March 2017, which Consumers Union, CFA, and ACEEE comments include as Attachment 4, Docket NHTSA–2018– 0067–11731). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 number of gallons saved by the preferred alternative selected in 2012 drops from about 180 billion to 50 billion once we acknowledge the existence of even a moderate market for fuel economy.165 The value of fuel savings is similarly eroded, as higher fuel prices lead to correspondingly higher demand for fuel economy even in the baseline—reducing the value of fuel savings from $525 billion to $190 billion. The magnitude of the fuel economy improvement in the baseline is a consequence of both the fuel prices assumed in the 2012 rule (already discussed as being higher than both subsequent observed prices and current projections) and the assumed technology costs. In 2012, a number of technologies were assumed to have negative incremental costs—meaning that applying those technologies to existing vehicles would both improve their fuel economy and reduce the cost to produce them. Asserting that the baseline would experience no improvement in fuel economy without regulation is equivalent to asserting that manufacturers, despite their status as profit maximizing entities, would not apply these cost-saving technologies unless forced to do so by regulation. While this issue is discussed in greater detail in Section VI.B the combination of inexpensive (or free) technology and high fuel prices created a logically inconsistent perspective in the 2012 rule—where consumers never demanded additional fuel economy, despite high fuel costs, and manufacturers never supplied 165 Readers should note that this is not an estimate of the total amount of fuel that will be consumed or not consumed by the fleet as a whole, but simply the amount of fuel that will be consumed or not consumed as a direct result of the regulation. As illustrated in Section VII, light-duty vehicles in the U.S. would continue to consume considerable quantities of fuel and emit considerable quantities of CO2 even under the baseline/augural standards, and agencies’ analysis shows that the standards finalized today will likely increase fuel consumption and CO2 emissions by a small amount. PO 00000 Frm 00060 Fmt 4701 Sfmt 4700 additional fuel economy, despite the availability of inexpensive (or cost saving) technology to do so. Many commenters on earlier rules supported the assumption that fuel economy would not improve at all in the absence of standards. In fact, some commenters still support this position. For example, EDF commented to the NPRM that, ‘‘NHTSA set the Volpe model to project that, with CAFE standards remaining flat at MY 2020 levels through MY 2026, automakers would over-comply with the MY 2020 standards by 9 grams/mile of CO2 for cars and 15 g/mi of CO2 for light trucks during the 2029–2032 timeframe, plus 1%/year improvements beyond MY 2032. This assumption unreasonably obscures the impacts of the rollback and is not reflective of historical compliance performance.’’ 166 EDF is mistaken in two different ways: (1) By acknowledging the existence of a well-documented market for fuel economy, rather than erroneously inflating the benefits associated with increasing standards, this assumption serves to isolate the benefits actually attributable to each regulatory alternative, and (2) it is, indeed, reflective of historical compliance performance. While the agencies rely on the academic literature (and comments from companies that build and sell automobiles) to defend the assertion that a market for fuel economy exists, the industry’s historical CAFE compliance performance is a matter of public record.167 As shown in Figure IV–3, Figure IV–4, and Figure IV–5 for more than a decade, the industry average CAFE has exceeded the standard for each regulatory class—by several mpg during periods of high fuel prices. BILLING CODE 4910–59–P 166 EDF, NHTSA–2018–0067–11996, Comments to DEIS, at 4. 167 Data from CAFE Public Information Center (PIC), https://one.nhtsa.gov/cafe_pic/CAFE_PIC_ Home.htm, last accessed 10/08/2019. E:\FR\FM\30APR2.SGM 30APR2 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00061 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24233 ER30AP20.043</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C While this rulemaking has shown the impact of deviations from the 2012 rule assumptions individually, these two assumptions affect the value of fuel savings jointly. Replacing the fuel price forecast with the observed historical and current projected prices, and including any technology that pays for itself in the first 2.5 years of ownership through avoided fuel expenditures, reduces the value of fuel savings from $525 billion in the 2012 rule to $140 billion, all else VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 equal. Interestingly, this reduction in the value of fuel savings is smaller than the result when assuming only that the desired payback period is nonzero. While it may seem counterintuitive, it is entirely consistent. The number of gallons saved under the preferred alternative is actually higher when modifying both assumptions, compared to only modifying the payback period. Updating both assumptions leads to about 100 PO 00000 Frm 00062 Fmt 4701 Sfmt 4700 billion gallons saved by the preferred alternative in 2012, compared to only 50 billion from changing only the payback period, and 180 billion in the 2012 analysis. This occurs because the fuel economy in the baseline is lower when updating both the fuel price and the payback period—the gap between the augural standards and the baseline grows to 9 mpg, rather than only 6 mpg when updating only the payback period. Despite the existence of inexpensive E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.044</GPH> 24234 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 technology in both cases, with lower fuel prices there are fewer opportunities to apply technology that will pay back quickly. As a consequence, the number of gallons saved by the preferred alternative in 2012 increases—but each gallon saved is worth less because the price of fuel is lower. b) Technology Cost While the methods used to identify cost-effective technologies to improve fuel economy in new vehicles have continuously evolved since 2012 (as discussed further in Section IV.B.1), as have the estimated cost of individual technologies, the inclusion of a market response in all scenarios (including the baseline) has changed the total technology cost associated with a given alternative. As also discussed in Section VI.B, acknowledging the existence of a market for fuel economy leads to continued application of the most costeffective technologies in the baseline— and in other less stringent alternatives— up to the point at which there are no remaining technologies whose cost is fully offset by the value of fuel saved in the first 30 months of ownership. The application of this market-driven technology has implications for fuel economy levels under lower stringencies (as discussed earlier), but also for the incremental technology cost associated with more stringent alternatives. As lower stringency alternatives (including the 2012 baseline) accrue more technology, the incremental cost of more stringent alternatives decreases. By including a modest market for fuel economy, and preserving all other assumptions from the 2012 final rule, the incremental cost of technology attributable to the preferred alternative decreases from about $140 billion to about $72 billion. This significant reduction in technology cost is somewhat diminished by the associated reduction in the value of fuel savings (a decrease of $385 billion) when acknowledging the existence of a market for fuel economy. Another consequence of these changes is that the incremental cost of fuel economy technology is responsive to fuel price, as it should be. Under higher prices (as were assumed in 2012), consumers demand higher fuel economy in the new vehicle market. Under lower prices (as have occurred since the 2012 rule) consumers demand less fuel economy than would have been consistent with the fuel price assumptions in 2012.168 Including a 168 This is why dozens of studies examining the ability of fuel taxes (and carbon taxes, which produce the same result for transportation fuels) to VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 market response in the analysis ensures that, in each case, the cost of fuel economy technology within an alternative is consistent with those assumptions. Using the same fuel price forecast that supports this rule, and the same estimate of market demand for fuel economy, the incremental cost of technology in the preferred alternative would rise back up to about $110 billion. c) The Social Cost of Carbon (SCC) Emissions As discussed extensively in the NPRM, the agencies’ perspective regarding the social cost of carbon has narrowed in focus. While the 2012 final rule considered the net present value of global damages resulting from carbon emitted by vehicles sold in the U.S. between MY 2009 and MY 2025, the NPRM (and this final rule) consider only those damages that occur to the United States and U.S. territories. As a result of this change in perspective, the value of estimated damages per-ton of carbon is correspondingly smaller. Had the 2012 final rule utilized the same perspective on the social cost of carbon, the benefits associated with the preferred alternative would have been about $11 billion, rather than $53 billion. However, the savings associated with carbon damages are a consequence of both the assumed cost per-ton of damages and the number of gallons of fuel saved. As discussed above, the gallons saved in the 2012 final rule were likely inflated as a result of both fuel price forecasts and the assumption that no market exists for fuel economy improvements. Correcting the estimate of gallons saved from the preferred alternative in the 2012 rule and considering only the domestic social cost of carbon further reduces the savings in carbon damages to $6 billion. d) Safety Neutrality In the 2012 final rule, the agencies showed a ‘‘safety neutral’’ compliance solution; that is, a compliance solution that produced no net increase in onroad fatalities for MYs 2017–2025 vehicles as a result of technology changes associated with the preferred alternative. In practice, safety neutrality was achieved by expressly limiting the availability of mass reduction technology to only those vehicles whose usage causes fewer fatalities with decreased mass. This result was discussed as one possible solution, where manufacturers chose technology reduce CO2 emissions have found cost-effective opportunities available for those pricing mechanisms. PO 00000 Frm 00063 Fmt 4701 Sfmt 4700 24235 solutions that limited the amount of mass reduction applied, and concentrated the application on vehicles that improve the safety of other vehicles on the roads (primarily by reducing the mass differential in collisions). However, it implicitly assumed that each and every manufacturer would leave cost-effective technologies unused on entire market segments of vehicles in order to preserve a safety neutral outcome at the fleet level for a given model year (or set of model years) whose useful lives stretched out as far as the 2060s. Removing these restrictions tells a different story. When mass reduction technology, determined in the model to be a costeffective solution (particularly in later model years, when more advanced levels of mass reduction were expected to be possible), is unrestricted in its application, the 2012 version of the CAFE Model chooses to apply it to vehicles in all segments. This has a small effect on technology costs, increasing compliance costs in the earliest years of the program by a couple billion dollars, and reducing compliance costs for MYs 2022—2025 by a couple billion dollars. However, the impact on safety outcomes is more pronounced. Also starting with the model and inputs used for the 2012 final rule (and, as an example, focusing on that rule’s 2008-based market forecast), removing the restrictions on the application of mass reduction technology results in an additional 3,400 fatalities over the full lives of MYs 2009–2025 vehicles in the baseline,169 and another 6,900 fatalities over those same vehicle lives under the preferred alternative. The result, a net increase of 3,500 fatalities under the preferred alternative relative to the baseline, also produces a net social cost of $18 billion. The agencies’ current treatment of both mass reduction technology, which can greatly improve the effectiveness of certain technology packages by reducing road load, and estimated fatalities and now account for both general exposure (omitted in the 2012 final rule modeling) and fatality risk by age of the vehicle, further changes the story around mass reduction technology application for compliance and its relationship to onroad safety. 2. What methods have changed since the 2012 final rule? Simulating how manufacturers might respond to CAFE/CO2 standards 169 Relative to the continuation of vehicle mass from the 2008 model year carried forward into the future. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24236 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations requires information about existing products being offered for sale, as well as information about the costs and effectiveness of technologies that could be applied to those vehicles to bring the fleets in which they reside into compliance with a given set of standards. Following extensive additional work and consideration since the 2012 analysis, both agencies now use the CAFE Model to simulate these compliance decisions. This has several practical implications which are discussed in greater detail in Section VI.A. Briefly, this change represents a shift toward including a number of realworld production constraints—such as component sharing across a manufacturer’s portfolio—and product cadence, where only a subset of vehicles in a given model year are redesigned (and thus eligible to receive fuel economy technology). Furthermore, the year-by-year accounting ensures a continuous evolution of a manufacturer’s product portfolio that begins with the market data of an initial model year (model year 2017, in this analysis) and continues through the last year for which compliance is simulated. Finally, the modeling approach has migrated from one that relied on the simple product of single values to estimate technology effectiveness to a model that relies on full vehicle simulation to determine the effectiveness of any combination of fuel economy technologies. The combination of these changes has greatly improved the realism of simulated vehicle fuel economy for combinations of technologies across vehicle systems and classes. In addition to these changes to the portions of the analysis that represent the supply of fuel economy (by manufacturer, fleet, and model year) in the new vehicle market, this analysis contains changes to the representation of consumer demand for fuel economy. One such measure was discussed above—the notion that consumers will demand some amount of fuel economy improvement over time, consistent with technology costs and fuel prices. However, another deviation from the 2012 final rule analysis reflects overall demand for new vehicles. Across ten alternatives, ranging from the baseline (freezing future standards at 2016 levels) to scenarios that increased stringency by seven percent per year, from 2017 through 2025, the 2012 analysis showed no response in new vehicle sales, down to the individual model level. This implied that, regardless of changes to vehicle cost or attributes driven by stringency increases, no fewer (or VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 possibly more) units of any single model would be sold in any year, in any alternative. Essentially, that analysis asserted that the new vehicle market does not respond, in any way, to average new vehicle prices across the alternatives—regardless of whether the incremental cost is $1,600/vehicle (as it was estimated to be under the preferred alternative) or nearly $4,000/vehicle (as it was in under the 7 percent alternative). Both the NPRM and this final rule, while not employing pricing models or full consumer choice models to address differentiated demand within brands or manufacturer portfolios, have incorporated a modeled sales response that seeks to quantify what was not quantified in previous rulemakings. An important accounting method has also changed since the 2012 final rule was published. At the time of that rule, the agencies used an approach to discounting that combined attributes of a private perspective and a social perspective in their respective benefit cost analyses. This approach was logically inconsistent, and further reinforced some of the exaggerated estimates of fuel savings, social benefits (from reduced externalities), and technology costs described above. The old method discounted the value of all incremental quantities, whether categorized as benefits or costs, to the model year of the vehicle to which they accrued. This approach is largely acceptable for use in a private benefit cost analysis, where the costs and benefits accrue to the buyer of a new vehicle (in the case of this policy) who weighs their discounted present values at the time of purchase. However, the private perspective would not include any costs or benefits that are external to the buyer (e.g., congestion or the social cost of carbon emissions). For an analysis that compares benefits and costs from the social perspective, attempting to estimate the relative value of a policy to all of society rather than just buyers of new vehicles, this approach is more problematic. The discounting approach in the 2012 final rule was particularly distortionary for a few reasons. The fact that benefits and costs occurred over long time periods in the 2012 rule, and the standards isolated the most aggressive stringency increases in the latter years of the program, served to allow benefits that occurred in 2025 (for example) to enter the accounting without being discounted, provided that they accrued to the affected vehicles during their first year of ownership. In a setting where numerous inputs (e.g., fuel price and social cost of carbon) increase over time, benefits were able to grow faster than PO 00000 Frm 00064 Fmt 4701 Sfmt 4700 the discount rate in some cases— essentially making them infinite. The interpretation of discounting for externalities was equally problematic. For example, the discounting approach in the 2012 final rule would have counted a ton of CO2 not emitted in CY 2025 in multiple ways, despite the fact that the social cost of carbon emissions was inherently tied to the calendar year in which the emissions occurred. Were the ton avoided by a MY 2020 vehicle, which would have been five years old in CY 2025, the value of that ton would have been the social cost of carbon times 0.86, but would have been undiscounted if that same ton had been saved by a MY 2025 vehicle in its initial year of usage. This approach was initially updated in the 2016 Draft TAR to be consistent with common economic practice for benefit-cost analysis, and this analysis continues that approach. In the social perspective, all benefits and costs are discounted back to the decision year based on the calendar year in which they occur. Had the agencies utilized such an approach in the 2012 final rule, net benefits would have been reduced by about 20 percent, from $465 billion to $374 billion—not accounting for any of the other adjustments discussed above. 3. How have conditions changed since the 2012 final rule was published? The 2012 final rule relied on market and compliance information from model year 2008 to establish standards for model years 2017–2025. However, in the intervening years, both the market and the industry’s compliance positions have evolved. The industry has undergone a significant degree of change since the MY 2008 fleet on which the 2012FR was based. Entire brands (Pontiac, Oldsmobile, Saturn, Hummer, Mercury, etc.) and companies (Saab, Suzuki, Lotus) have exited the U.S. market, while others (most notably Tesla) have emerged. Several dozen nameplates have been retired and dozens of other created in that time. Overall, the industry has offered a diverse set of vehicle models that have generally higher fuel economy than the prior generation, and an ever-increasing set of alternative fuel powertrains. As Table IV–1 shows, alternative powertrains have steadily increased under CAFE/CO2 regulations. Under the standards between 2011 and 2018, the number of electric vehicle offerings in the market has increased from 1 model to 57 models (inclusive of all plug-in vehicles that are rated for use on the highway), and hybrids (like the Toyota Prius) have increased from 20 models to E:\FR\FM\30APR2.SGM 30APR2 24237 43 models based on data from DOE’s Alternative Fuels Data Center. Fuel efficient diesel vehicles have similarly been on the rise in that period, more than doubling the number of offerings. Flexible fuel vehicles (FFVs), capable of operating on both gasoline and E85 remain readily available in the market, but have been excluded from the table due to both their lower fuel economy and demonstrated consumer reluctance to operate FFVs on E85. They have historically been used to improve a manufacturer’s compliance position, rather than other alternative fuel systems that reduce fuel consumption and save buyers money. Not only have alternative powertrain options proliferated since the 2012 FR, the average fuel economy of new vehicles within each body style has increased. However, the more dramatic effect may lie in the range of fuel economies available within each body style. Figure IV–6 shows the distribution of new vehicle fuel economy (in miles per gallon equivalent) by body style for MYs 2008, 2016, and 2020 (simulated). Each box represents the 25th and 75th percentiles, where 25 and 75 percent of new models offered are less fuel efficient than that level. Not only has the median fuel economy improved (the median shows the point at which 50 percent of new models are less efficient) under the CAFE/CO2 programs, but the range of available fuel economies (determined by the length of the boxes and their whiskers) has increased as well. For example, the 25th percentile of pickup truck fuel economy in 2020 is expected to be significantly more efficient than 75 percent of the pickups offered in 2008. In MY 2008, there were only a few SUVs offered with rated fuel economies above 34MPG. By MY 2020 almost half of the SUVs offered will have higher fuel economy ratings—with almost 20 percent of offerings exceeding 40MPG. The improvement in passenger car styles has been no less dramatic. As with the other styles, the range of available fuel economies has increased under the CAFE/CO2 programs and the distribution of available fuel economies skewed higher—with 40 percent of MY 2020 models exceeding 40MPG. The attribute-based standards are designed to encourage manufacturers to improve vehicle fuel economy across their portfolios, and they have clearly done so. Not only have the higher ends of the distributions increased, the lower ends in all body styles have improved as well, where the least fuel efficient 25 percent of vehicles offered in MY 2016 (and simulated in 2020) are more fuel efficient than the most efficient 25 percent of vehicles offered in MY 2008. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00065 Fmt 4701 Sfmt 4700 BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.045</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C Some commentershave argued that consumers will be harmed by any set of standards lower than the baseline (augural) standards because buyers of new vehicles will be forced to spend more on fuel than they would have under the augural standards. However, as Figure IV–6 shows, the range of fuel economies available in the new market is already sufficient to suit the needs of buyers who desire greater fuel economy 170 Circles represent specific outlying vehicle models. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 rather than interior volume or some other attributes. Full size pickup trucks are now available with smaller turbocharged engines paired with 8 and 10-speed transmissions and some mild electrification. Buyers looking to transport a large family can choose to purchase a plug-in hybrid minivan. There were 57 electric models available in 2018, and hybrid powertrains are no longer limited to compact cars (as they once were). Buyers can choose hybrid SUVs with all-wheel and four-wheel drive. While these kinds of highly PO 00000 Frm 00066 Fmt 4701 Sfmt 4700 efficient options were largely absent from some body styles in MY 2008, this is no longer the case. Given that highMPG vehicles are widely available, consumers must also value other vehicle attributes (e.g., acceleration and loadcarrying capacity) that can can also be improved with the same technologies that can be used to improve fuel economy. Manufacturers have accomplished a portfolio-wide improvement by improving the combustion efficiency of engines (through direct injection and E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.046</GPH> 24238 24239 turbocharging), migrating from four and five speed transmissions to 8 and 10 speed transmissions, and electrifying to varying degrees. All of this has increased both production costs and fuel efficiency during a period of economic expansion and low energy prices. While the vehicles offered for sale have increased significantly in efficiency between MY 2008 and MY 2020, the sales-weighted average fuel economy has achieved less improvement. Despite stringency increases of about five percent (yearover-year) between 2012 and 2016, the sales-weighted average fuel economy increased marginally. Figure IV–7 shows an initial increase in average new vehicle fuel economy (the heavy solid line, shown in mpg as indicated on the left y axis), followed by relative stagnation as fuel prices (the light dashed lines, shown in dollars per gallon as indicated on the right y axis) fell and remained low.171 It is worth noting that average new vehicle fuel economy observed a brief spike during the year that the Tesla Model 3 was introduced (as a consequence of strong initial sales volumes, as pre-orders were satisfied, and fuel economy ratings that are significantly higher than the industry average), and settled around 27.5 MPG in Fall 2019. Average fuel economy receded further over the next several months to 26.6 MPG in February 2020.172 In their NPRM comments, manufacturers expressed concern that CAFE standards had already increased to the point where the price increases necessary to recoup manufacturers’ increased costs for providing further increases in fuel economy outweigh the value of fuel savings.173 174 The agencies do not agree that this point has already been reached by previous stringency increases, but acknowledge the reality of diminishing marginal returns to improvements in fuel economy. A driver with a 40MPG vehicle uses about 300 gallons of fuel per year. Increasing the fuel economy of that vehicle to 50MPG, a 25 percent increase, would likely be over $1000 in additional technology cost. However, that driver would only save 25 percent of their annual fuel consumption, or 75 gallons out of 300 gallons. Even at $3/gallon, higher than the current national average, that represents $225 per year in fuel savings. That means that the buyer’s $1000 investment in additional fuel economy pays back in just under 4.5 years (undiscounted). The agencies’ respective programs have created greater access to high MPG vehicles in all classes and encouraged the proliferation of alternative fuels and powertrains. But if the value of the fuel savings is insufficient to motivate buyers to invest in ever greater levels of fuel economy, manufacturers will face challenges in the market. While Figure IV–3 through Figure IV– 5 illustrate the trends in historical CAFE compliance for the entire industry, the figures contain another relevant fact. After several consecutive years of increasing standards, the achieved and required levels converge. When the standards began increasing again for passenger cars in 2011, the prior year had industry CAFE levels 5.6 mpg and 7.7 mpg in excess of their standards for domestic cars and imported cars, respectively. Yet, by 2016, the consecutive year-over-year increases had eroded the levels of overcompliance. Light trucks similarly exceeded their standard prior to increasing standards, which began in 2005. Yet, by 2011, after several consecutive years of stringency increases, the industry light-truck average CAFE was merely compliant with the rising standard. This is largely due to the fact that stringency requirements have increased at a faster rate than achieved fuel 171 Ward’s Automotive, https:// www.wardsauto.com/industry/fuel-economy-indexshows-slow-improvement-april. Last accessed Dec. 13, 2019. 172 Ward’s Automotive, https:// wardsintelligence.informa.com/WI964622/FuelEconomy-Slightly-Down-in-February. Last accessed Mar. 9, 2020. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00067 Fmt 4701 Sfmt 4700 173 NHTSA–2018–0067–12064–25. 174 NHTSA–2018–0067–12073–2. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.047</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations economy levels for several years. The attribute-based standards took effect in 2011 for all regulatory classes, although light truck CAFE standards had been increasing since 2005. Since 2004, light truck stringency has increased an average of 2.7 percent per year, while light truck’s compliance fuel economy has increased by an average of 1.7 percent over the same period.175 For the passenger classes, a similar story unfolds over a shorter period of time. Year over year stringency increases have averaged 4.7 percent per year for domestic cars (though increases in the first two years were about 8 percent— with lower subsequent increases), but achieved fuel economy increases averaged only 2.2 percent per year over the same period. Imported passenger cars were similar to domestic cars, with average annual stringency increases of 4.4 percent but achieved fuel economy levels increasing an average of only 1.4 percent per year from 2011 through 2017. Given that each successive percent increase in stringency is harder to achieve than the previous one, longterm discrepancies between required and achieved year-over-year increases cannot be offset indefinitely with existing credit banks, as they have been so far. With the fuel price increases fresh in the minds of consumers, and the great recession only recently passed, the CAFE stringency increases that began in 2011 (and subsequent CAFE/CO2 stringency increases after EPA’s program was first enforced in MY 2012) had something of a head start. As Figure IV–3 through Figure IV–5 illustrate, the standards were not binding in MY 2011—even manufacturers that had historically paid civil penalties were earning credits for overcompliance. It took two years of stringency increase to catch up to the CAFE levels already present in MY 2011. However, seven consecutive years of increases for passenger cars and a decade of increases for light trucks has changed the credit situation. Figure IV–8 shows CAFE credit performance for regulated fleets— the solid line represents the number of fleets generating shortfalls and the dashed line represents the number of fleets earning credits in each model year. Fewer than half as many fleets earned surplus credits for over-compliance in MY 2017 compared to MY 2011—and this trend is persistent. The story varies from one manufacturer to another, but it seems sufficient to state the obvious— when the agencies conducted the analysis to establish standards through MY 2025 back in 2012, most (if not all) manufacturers had healthy credit positions. That is no longer the case, and each successive increase requires many fleets to not only achieve the new level from the resulting increase, but to resolve deficits from the prior year as well. The large sums of credits, which last five years under both programs, have allowed most manufacturers to resolve shortfalls. But the light truck fleet, in particular, has a dwindling supply of credits available for purchase or trade. The CO2 program has a provision that allows credits earned during the early years of overcompliance to be applied through MY 2021. This has reduced the compliance burden in the last several years, as intended, but will not mitigate the compliance challenges some OEMs would face if the baseline standards remained in place and energy prices persisted at current levels. 175 Both the standards and these calculations are defined in consumption space—gallons per mile— which also translates directly into CO2 based on the carbon content of the fuel consumed. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00068 Fmt 4701 Sfmt 4700 BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.048</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24240 BILLING CODE 4910–59–C Table IV–2 shows the credits earned by each manufacturer over time.176 As the table shows, when the agencies considered future standards in 2012, most manufacturers were earning credits in at least one fleet. However, the bold values show years with deficits and even some manufacturers who started out in strong positions, such as Ford’s passenger car fleet, have seen growing deficits in recent years. While 176 MY 2017 values represent estimated earned credits based on MY 2017 final compliance data. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00069 Fmt 4701 Sfmt 4700 24241 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.049</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations the initial banks for early-action years eases the burden of CO2 compliance for many OEMs, the year-to-year compliance story is similar to CAFE, see Table IV–3. Credit position and shortfall rates clearly illustrate manufacturers’ fleet performance relative to the standards. Recognizing that manufacturers plan compliance over several model years at any given time, sporadic shortfalls may not be evidence of undue difficulty, but sustained, widespread, growing shortfalls should probably be viewed as evidence that standards previously believed to be manageable might no longer be so. While NHTSA is prohibited by statute from considering availability of credits (and thus, size of credit banks) in determining maximum feasible standards, it does consider shortfalls as part of its determination. EPA has no such prohibition under the CAA and is free to consider both credits and shortfalls. These increasing credit shortfalls are occurring at a time that the industry is deploying more technology than the agencies anticipated when establishing future standards in 2012. The agencies’ projections of transmission technologies were mixed. While the agencies expected the deployment of 8-speed transmissions to about 25 percent of the market by MY 2018, transmissions with eight or more gears account for almost 30 percent of the market. However, the agencies projected no CVT transmissions in future model years, instead projecting high penetration of DCTs. However, CVTs currently make up more than 20 percent of new transmissions. The tradeoff between advanced engines and electrification was also underestimated. While the agencies projected penetration rates of turbocharged engines that are higher than we’ve observed in the market (45 percent compared to 30 percent), the estimated penetration of electric technologies was significantly lower. The agencies projected a couple percent of strong hybrids—which we’ve seen— but virtually no PHEVs or EVs. While the volumes of those vehicles are still only a couple percent of the new vehicle market, they are heavily credited under both programs and can significantly improve compliance positions even at smaller volumes. Even lower-level electrification technologies, like stop- VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00070 Fmt 4701 Sfmt 4700 start systems, are significantly more prevalent than we anticipated (stop-start systems were projected to be in about 2 percent of the market, compared to over 20 percent in the 2018 fleet). Despite technology deployment that is comparable to 2012 projections, and occasionally more aggressive, passenger car and light truck fleets have slightly lower fuel economy than projected. As fleet volumes have shifted along the footprint curve, the standards have decreased as well (relative to the expectation in 2012), but less so. While compliance deficits have been modest, they have been accompanied by record sales for several years. This has not only depleted existing credit banks, but created significant shortfalls that may be more difficult to overcome if sales recede from record levels. V. Regulatory Alternatives Considered Agencies typically consider regulatory alternatives in proposals as a way of evaluating the comparative effects of different potential ways of accomplishing their desired goal. NEPA E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.050</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24242 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations requires agencies (in this case, NHTSA, but not EPA) to compare the potential environmental impacts of their proposed actions to those of a reasonable range of alternatives. Executive Orders 12866 and 13563 and OMB Circular A–4 also encourage agencies to evaluate regulatory alternatives in their rulemaking analyses. Alternatives analysis begins with a ‘‘no-action’’ alternative, typically described as what would occur in the absence of any regulatory action. This final rule, like the proposal, includes a no-action alternative, described below, as well as seven ‘‘action alternatives.’’ The final standards may, in places, be referred to as the ‘‘preferred alternative,’’ which is NEPA parlance, but NHTSA and EPA intend ‘‘final standards’’ and ‘‘preferred alternative’’ to be used interchangeably for purposes of this rulemaking. In the proposal, NHTSA and EPA defined the different regulatory alternatives (other than the no-action alternative) in terms of percentincreases in CAFE and CO2 stringency from year to year. Percent increases in stringency referred to changes in the standards year over year—as in, standards that become 1 percent more stringent each year. Readers should recognize that those year-over-year changes in stringency are not measured in terms of mile per gallon or CO2 gram per mile differences (as in, 1 percent more stringent than 30 miles per gallon in one year equals 30.3 miles per gallon in the following year), but in terms of shifts in the footprint functions that form the basis for the actual CAFE and CO2 standards (as in, on a gallon or gram per mile basis, the CAFE and CO2 standards change by a given percentage from one model year to the next). Under some alternatives, the rate of change was the same for both passenger cars and light trucks; under others, the rate of change differed. Like the no-action alternative, all of the alternatives considered in the proposal were more stringent than the preferred alternative. Alternatives considered in the proposal also varied in other significant ways. Alternatives 3 and 7 in the proposal involved a gradual discontinuation of CAFE and average CO2 adjustments reflecting the use of technologies that improve air conditioner efficiency or otherwise improve fuel economy under conditions not represented by long-standing fuel economy test procedures (off-cycle adjustments, described in further detail in Section IX, although the proposal itself would have retained these flexibilities. Commenters responding to the request for comment on phasing out VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 these flexibilities generally supported maintaining the existing program, as proposed. Some commenters suggested changes to the existing program that were not discussed in the NPRM. Such changes would be beyond the scope of this rulemaking and were not considered. Section IX contains a more thorough summary of these comments and the issues they raise, as well as the agencies’ responses. Consistent with the decision to retain these flexibilities in the final rule, alternatives reflecting their phase-out have not been considered in this final rule. Additionally, in the NPRM for this rule, EPA proposed to exclude the option for manufacturers partially to comply with tailpipe CO2 standards by generating CO2-equivalent emission adjustments associated with air conditioning refrigerants and leakage after MY 2020. This approach was proposed in the interest of improved harmonization between the CAFE and tailpipe CO2 emissions programs because this optional flexibility cannot be available in the CAFE program.177 Alternatives 1 through 8 excluded this option. EPA requested comment ‘‘on whether to proceed with [the] proposal to discontinue accounting for A/C leakage, methane emissions, and nitrous oxide emissions as part of the CO2 emissions standards to provide for 177 For the CAFE program, carbon-based tailpipe emissions (including CO2, HC, and CO) are measured, and fuel economy is calculated using a carbon balance equation. EPA also uses carbonbased emissions (CO2, HC, and CO, the same as for CAFE) to calculate tailpipe CO2 for use in determining compliance with its standards. In addition, under the no-action alternative for the proposal and under all alternatives in the final rule, in determining compliance, EPA includes on a CO2 equivalent basis (using Global Warming Potential (GWP) adjustment) A/C refrigerant leakage credits, at the manufacturer’s option, and nitrous oxide and methane emissions. EPA also has separate emissions standards for methane and nitrous oxides. The CAFE program does not include or account for A/C refrigerant leakage, nitrous oxide and methane emissions because they do not impact fuel economy. Under Alternatives 1–8 in the proposal, the standards were closely aligned for gasoline powered vehicles because compliance with the fleet average standard for such vehicles is based on tailpipe CO2, HC, and CO for both programs and not emissions unrelated to fuel economy, although diesel and alternative fuel vehicles would have continued to be treated differently between the CAFE and CO2 programs. While such an approach would have significantly improved harmonization between the programs, standards would not have been fully aligned because of the small fraction of the fleet that uses diesel and alternative fuels (as described in the proposal, such vehicles made up approximately four percent of the MY 2016 fleet), as well as differences involving EPCA/EISA provisions EPA has not adopted, such as minimum standards for domestic passenger cars and limits on credit transfers between regulated fleets. The proposal to eliminate flexibilities associated with A/C refrigerants and leakage was not adopted for this final rule, and the reasons for and implications of that decision are discussed further below. PO 00000 Frm 00071 Fmt 4701 Sfmt 4700 24243 better harmony with the CAFE program, or whether to continue to consider these factors toward compliance and retain that as a feature that differs between the programs.’’ 178 EPA stated that if EPA were to proceed with excluding A/C refrigerant credits as proposed, ‘‘EPA would consider whether it is appropriate to initiate a new rulemaking to regulate these programs independently . . . .’’ 179 EPA also stated that ‘‘[i]f the agency decides to retain the A/C leakage . . . provisions for CO2 compliance, it would likely reinsert the current A/C leakage offset and increase the stringency levels for CO2 compliance by the offset amounts described above (i.e., 13.8 g/mi equivalent for passenger cars and 17.2 g/ mi equivalent for light trucks). EPA received comments from a wide range of stakeholders, most of whom opposed the elimination of these flexibility provisions. Specifically, the two major trade organizations of auto manufacturers, as well as some individual automakers, supported retaining these provisions. Global Automakers commented that ‘‘[a]ir conditioning refrigerant leakage . . . should be included for compliance with the EPA standards for all MYs, even if it means a divergence from the NHTSA standards.’’ 180 Global provides several detailed reasons for their comments, including that the existing provisions are ‘‘. . . important to maintaining regulatory flexibility through real [CO2] emission reductions and would prevent the potential for additional bifurcated, separate programs at the state level.’’ 181 The Alliance similarly commented that it ‘‘supports continuation of the full air conditioning refrigerant leakage credits under the [CO2] standards.’’ 182 Some individual 178 83 FR at 43193 (Aug. 24, 2018). at 43194. 180 Global, NHTSA–2018–0067–12032, Appendix A at A–5. 181 Id. Global also stated that excluding A/C leakage credits would ‘‘. . . greatly limit the ability [of manufacturers] to select the most cost-effective approach for technology improvements and result in a costlier, separate set of regulations that actually relate to the overall GHG standards.’’ Global also expressed concern that issuing separate regulations for A/C leakage could take too long and create a gap in which States might act to separately regulate or even ban refrigerants, and supported continued inclusion of A/C leakage and refrigerant regulation in EPA’s GHG program to avoid risk of an ensuing patchwork. Global argued that manufacturers had already invested to meet the existing program, and that ‘‘the proposed phase-out also creates another risk that manufacturers will have stranded capital in technologies that are not fully amortized.’’ Global Automakers, EPA–HQ–OAR–2018–0283–5704, Attachment A, at A.43–44. 182 Alliance, NHTSA–2018–0067–12073, Full Comment Set, at 12. Alliance also expressed 179 Id. E:\FR\FM\30APR2.SGM Continued 30APR2 24244 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 manufacturers, including General Motors,183 Fiat Chrysler,184 and BMW,185 also commented in support of maintaining the current A/C refrigerant and leakage credits. Auto manufacturing suppliers who addressed A/C refrigerant and leakage credits also generally supported retaining the existing provisions. MEMA commented that ‘‘It is essential for supplier investment and jobs, and continuous innovation and improvements in the technologies that the credit programs continue and expand to broaden the compliance pathways. MEMA urges the agencies to continue the current credit and incentives programs . . . . ’’ 186 DENSO also supported maintaining the current provisions.187 However, BorgWarner supported the proposed removal of A/C refrigerant credits ‘‘for harmonization reasons,’’ while encouraging EPA to regulate A/C refrigerants and leakage separately from the CO2 standards.188 The two producers of a lower GWP refrigerant, Chemours and Honeywell, concern about stranded capital and risk of patchwork of state regulation if MAC direct credits were not retained in the Federal GHG program. Id. at 80–81. 183 General Motors, NHTSA–2018–0067–11858, Appendix 4, at 1 (‘‘General Motors supports the extensive comments from the Alliance of Automobile Manufacturers regarding flexibility mechanisms, and incorporates them by reference. In particular, the Alliance cites the widening gap between the regulatory standards and actual industry-wide new vehicle average fuel economy that has become evident since 2016, despite the growing use of improvement ‘credits’ from various flexibility mechanisms, such as off-cycle technology credits, mobile air conditioner efficiency credits, mobile air conditioner refrigerant leak reduction credits and credits from electrified vehicles.’’) 184 FCA, NHTSA–2018–0067–11943, at 8. FCA also expressed concern about patchwork in the absence of a federal rule. Id. 185 BMW, EPA–HQ–OAR–2018–4204, at 3. BMW stated that ‘‘Today’s rules allow flexibilities to be used by the motor vehicle manufacturers for fuel saving technologies and efficiency gains which are not covered in the applicable test procedures. To enhance the future use of these technologies and to reward motor vehicle manufacturer’s investments taken for future innovations, the agencies should consider the continuation of current flexibilities for the model years 2021 to 2026.’’ 186 MEMA, available at https://www.mema.org/ sites/default/files/resource/MEMA%20CAFE %20and%20GHG%20Vehicle %20Comments%20FINAL%20with%20Appendices %20Oct%2026%202018.pdf, comment at p. 2. MEMA also expressed concern about stranded capital investments by suppliers and supplier jobs if the direct MAC credits were not available; stated that the credits were an important compliance flexibility and ‘‘one of the highest values of any credit offered in the EPA program;’’ and stated that ‘‘Harmonizing the programs does not require making them identical or equivalent. Rather, harmonization can be achieved by better coordinating the two programs to the extent feasible while allowing each agency to implement its separate and distinct mandate.’’ Id. at 15–16. 187 DENSO, NHTSA–2018–0067–11880, at 8. 188 BorgWarner, NHTSA–2018–0067–11895, at 10. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 commented extensively in support of continuing to allow A/C refrigerant and leakage credits for CO2 compliance, making both economic and legal arguments. Both Chemours and Honeywell stated that A/C refrigerant and leakage credits were a highly costeffective way for OEMs to comply with the CO2 standards,189 with Chemours suggesting that OEM compliance strategies are based on the assumption that these credits will be available for CO2 compliance 190 and that any increase in stringency above the proposal effectively necessitates that the credits remain part of the program.191 Honeywell stated that all OEMs (and a variety of other parties) supported retaining the credits for CO2 compliance,192 and Chemours, Honeywell, and CBD et al. all noted that OEMs are already using the credits for low GWP refrigerants in more than 50 percent of the MY 2018 vehicles produced for sale in the U.S.193 The American Chemistry Council also stated that the ‘‘auto industry widely supports the credits, and U.S. chemical manufacturers are at a loss as to why EPA would propose to eliminate such a successful flexible compliance program.’’ 194 In response to NPRM statements expressing concern that the A/C refrigerant and leakage credits could be market distorting, both Chemours and Honeywell disagreed,195 arguing that the credits were simply a highly cost-effective means of complying with the CO2 standards,196 and that removal of the credits at this point would, itself, distort the market for refrigerants. Honeywell argued that eliminating the A/C credit program from CO2 compliance would put the U.S. at a competitive disadvantage with other countries, and would risk U.S. jobs.197 Regarding the NPRM’s statements that removing the A/C refrigerant and leakage credits from CO2 compliance would promote harmonization with the CAFE program, these commenters argued that harmonization was not a valid basis for that aspect of the proposal. Chemours, Honeywell, and CBD et al. all argued that Section 202(a) creates no obligation to harmonize the [CO2] program with the CAFE program.198 Chemours further argued that to the extent disharmonization between the programs existed, it should be addressed via stringency changes (i.e., reducing CAFE stringency relative to CO2 stringency) rather than ‘‘dropping low-cost compliance options.’’ 199 These commenters also expressed concern that the proposal constituted an EPA decision not to regulate HFC emissions from motor vehicles at all. Commenters argued that the NPRM provided no legal analysis or reasoned explanation for stopping regulation of HFCs,200 and that Massachusetts v. EPA requires any final rule to regulate all greenhouse gases from motor vehicles and not CO2 alone,201 suggesting that there was a high likelihood of a lapse in regulation because EPA had not yet proposed a new way of regulating HFC emissions.202 Because the NPRM provided no specific information about how EPA might regulate non-CO2 emissions separately, commenters argued that the lack of clarity was inherently disruptive to OEMs.203 CBD et al. argued that any lapse in regulation is ‘‘illegal on its face’’ and that even creating a separate standard for HFC emissions would be ‘‘illegal’’ because it ‘‘would increase costs to manufacturers and result in environmental detriment by removing any incentive to use the most aggressive approaches to curtail emissions of these highly potent GHGs.’’ 204 Environmental organizations,205 other NGOs, academic institutions, consumer organizations, and state governments 206 also commented in support of continuing the existing provisions. EPA has considered its proposed approach to A/C refrigerant and leakage 189 Chemours at 1 (‘‘MVAC credits many times offer the ‘least cost’ approach to compliance . . .’’) and 9; Honeywell at 6. 190 Chemours at 6–7; both Chemours and Honeywell expressed concern about OEM reliance on the expectation that HFC credits would continue to be part of the CO2 program (Chemours at 31; Honeywell at 16–20) and that investments in alternative refrigerants would be stranded (Chemours at 1, 3, 4–6; Honeywell at 2, 7–8). 191 Chemours at 7. 192 Honeywell at 8–11. 193 Chemours at 4; Honeywell at 6–7; CBD et al. at 46–47. 194 American Chemistry Council, EPA–HQ–OAR– 2018–0283–1415, at 9–10 (comments similar to Chemours and Honeywell). 195 Chemours at 1; Honeywell at 13. 196 Chemours at 29–30; Honeywell at 13–14. 197 Honeywell at 20–21. 198 Chemours at 23–24; Honeywell at 11–12; CBD et al. at 47. 199 Chemours at 9–11. 200 Chemours at 1–2; Honeywell at 11. 201 Chemours at 18–19; Honeywell at 14–16. 202 Chemours at 6; Honeywell at 16. 203 Chemours at 21; Honeywell at 16; ICCT at I– 39. 204 CBD et al. at 46. 205 ICCT, NHTSA–2018–0067–11741, Full Comments, at 4 (describing ‘‘air conditioning GHGreduction technologies [as] available, cost-effective, and experiencing increased deployment by many companies due to the standards.’’); CBD et al., Appendix A, at 45–47. 206 CARB, NHTSA–2018–0067–11873, Detailed Comments, at 120–121; Washington State Department of Ecology, NHTSA–2018–0067–11926, at 6 (HFCs are an important GHG; compliance flexibility is important). PO 00000 Frm 00072 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations credits in light of these comments. EPA believes that maintaining this element of its program is consistent with EPA’s authority under Section 202(a) to establish standards for reducing emissions from LDVs. Thus, maintaining the optional HFC credit program is appropriate. In addition, EPA recognizes the value of regulatory flexibility and compliance options, and has concluded that the advantages from retaining the existing A/C refrigerant/ leakage credit program and associated offset between the CO2 and CAFE standards—in terms of providing for a more-comprehensive regulation of emissions from light-duty vehicles— outweigh the disadvantages resulting from the lack of harmonization. Regarding the comment from BorgWarner about how having a separate A/C refrigerant and leakage regulation would allow for better harmonization between the programs, the agencies accept this to be an accurate statement, but believe the benefits of continued refrigerant regulation as an option for CO2 compliance outweigh the problems associated with lack of harmonization with the CAFE program. For these reasons, EPA is not finalizing the proposed provisions, and is making no changes in the A/C refrigerant and leakage-related provisions of the current program. In light of this conclusion, EPA does not need to address the legal arguments made by CBD et al. and CARB about regulating refrigerant-related emissions separately, or potential lapses in regulation of refrigerant emissions while such a program could be developed. As with A/C refrigerant and leakage credits, EPA proposed to exclude nitrous oxide and methane from average performance calculations after model year 2020, thereby removing these optional program flexibilities. Alternatives 1 through 8 excluded this option. EPA sought comment on whether to remove those aspects of the program that allow a manufacturer to use nitrous oxide and methane emissions reductions for compliance with its CO2 average fleet standards because such a flexibility is not allowed in the NHTSA CAFE program, or whether to retain the flexibilities as a feature that differs between the programs. Further, EPA sought comment on whether to change the existing methane and nitrous oxide standards. Specifically, EPA requested information from the public on whether the existing standards are appropriate, or whether they should be revised to be less stringent or more stringent based on any updated data. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 The Alliance in its comments may have misunderstood EPA’s proposal to mean that EPA was proposing to eliminate regulation of methane and nitrous oxide emissions altogether. The Alliance commented in support of such a proposal as they understood it, to eliminate the standards to provide better harmony between the two compliance programs.207 The Alliance commented that ‘‘[n]ot only is emission of these two substances from vehicles a relatively minor contribution to GHG emissions, the Alliance has continuing concern regarding measurement and testing technologies for nitrous oxide.’’ 208 The Alliance commented further that if ‘‘EPA decides instead to continue to regulate methane and nitrous oxide, the Alliance recommends that EPA re-assess whether the levels of the standards remain appropriate and to retain the current compliance flexibilities. Furthermore, in this scenario, the Alliance also recommends that methane and nitrous oxide standards be assessed as a fleet average and as the average of FTP and HFET test cycles.’’ 209 Several individual manufacturers submitted similar comments, including Ford,210 FCA,211 Volvo,212 and Mazda.213 Ford also commented that it does not support the proposal to maintain the existing N2O/CH4 standards while removing the program flexibilities.214 The Alliance further commented that ‘‘data from the 2016 EPA report on lightduty vehicle emissions supports the position that CH4 and N2O have minimal impact on total GHG emissions, reporting only 0.045 percent in exceedance of the standard. This new information makes it apparent that CH4 and N2O contribute a de minimis 207 Alliance, NHTSA–2018–0067–12073, Full Comment Set, at 13. 208 Id. 209 Id. 210 Ford, EPA–HQ–OAR–2018–0283–5691, at 4. 211 FCA, NHTSA–2018–0067–11943, at 9. 212 Volvo, NHTSA–2018–0067–12036, at 5. 213 Mazda, NHTSA–2018–0067–11727, at 3 (‘‘In reality, these emissions are at deminimis levels and have very little, if any, impact on global warming. So, the need to regulate these emissions as part of the GHG program, or separately, is unclear. Although most current engines can comply with the existing requirements, there are some existing and upcoming new technologies that may not be able to fully comply. These technologies can provide substantial CO2 reductions.’’). 214 Ford, at 4 (‘‘Finally, without the ability to incorporate exceedances into CREE, each vehicle will need to employ hardware solutions if they do not comply. We do not believe it was EPA’s intent in the original rulemaking to require additional after-treatment, with associated cost increases, explicitly for the control and reduction of an insignificant contributor to GHG emissions. Therefore, we do not support the proposal to maintain the existing N2O/CH4 standards while removing the CREE exceedance pathway.’’). PO 00000 Frm 00073 Fmt 4701 Sfmt 4700 24245 amount to GHG emissions. Additionally, gasoline CH4 and N2O performance is within the current standards. Finally, the main producers of CH4 and N2O emissions are flex fuel (E85) and diesel vehicles, and these vehicles have been declining in sales as compared to gasoline-fueled vehicles.’’ 215 The Alliance also commented that CH4 and N2O have minimal opportunities to be catalytically treated, as N2O is generated in the catalyst and CH4 has a low conversion efficiency compared to other emissions. EPA did not intend that additional hardware should be required to comply with the CH4 or N2O standards on any vehicle.’’ 216 Global Automakers commented in support of continuing inclusion of nitrous oxide and methane emissions standards for all MYs, even if it means a divergence from the NHTSA standards for these program elements in the regulations, ‘‘because they are complementary to EPA’s program, and are better managed through a coordinated federal policy. They are also important to maintaining regulatory flexibility through real [CO2] emission reductions and would prevent the potential for additional bifurcated, separate programs at the state level.’’ 217 Global Automakers recommended that they remain in place per the existing program but continued to support that the N2O testing is not necessary. Global Automakers commented that it ‘‘strongly recommends reducing the need for N2O testing or eliminating these test requirements in their entirety. It should be sufficient to allow manufacturers to attest to compliance with the N2O capped standards based upon good engineering judgment, development testing, and correlation to NOX emissions. EPA could, however, maintain the option to request testing to be performed for new technologies only, which could have unknown impacts on N2O emissions.’’ 218 Hyundai 219 and Kia 220 submitted similar comments. Others commented in support of retaining the existing program. MECA commented that it supports the existing standards for methane and nitrous oxide because catalyst technologies provided by MECA members that reduce these climate forcing gases are readily 215 Alliance, NHTSA–2018–0067–12073, Full Comment Set, at 43. 216 Id. at 44. 217 Global, NHTSA–2018–0067–12032, at 4, 5. 218 Global, Appendix A, NHTSA–2018–0067– 12032, at A–44, fn. 89. 219 Hyundai, EPA–HQ–OAR–2018–0283–4411, at 7. 220 Kia, EPA–HQ–OAR–2018–0283–4195, at 8–9. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24246 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations available and cost-effective.221 MECA also commented that the ability to trade reductions in these pollutants in exchange for CO2 gives vehicle manufacturers the flexibilities they need to comply with the emission limits by the most cost-effective means.222 CBD et al. commented that the alternative compliance mechanisms currently available in the program exist to provide cost-effective options for compliance, and were considered by manufacturers to be a necessary element of the program for certain types of vehicles.223 CBD et al. further argued that ‘‘[e]liminating these flexibilities consequently imposes costs on manufacturers without discernible environmental benefits,’’ and suggested that harmonization with the CAFE program was not a relevant decision factor for EPA.224 Several other parties commented generally in support of retaining the existing program for A/ C leakage credits, discussed above, and N2O and CH4 standards.225 After considering these comments, EPA is retaining the regulatory provisions related to the N2O and CH4 standards with no changes, specifically including the existing flexibilities that accompany those standards. EPA is not adopting its proposal to exclude nitrous oxide and methane emissions from average performance calculations after model year 2020 or any other changes to the program. The standards continue to serve their intended purpose of capping emissions of those pollutants and providing for more-comprehensive regulation of emissions from light-duty vehicles. The standards were intended to prevent future emissions increases, and these standards were generally not expected to result in the application of new technologies or significant costs for manufacturers using current vehicle designs.226 The program flexibilities are working as intended and all manufacturers are successfully complying with the standards. Most vehicle models are well below the standards and for those that are above the standards, manufacturers have used the flexibilities to offset exceedances with CO2 improvements to demonstrate compliance. EPA did not receive any data in response to its request for comments supporting potential alternative levels of stringency. While the Alliance and several individual manufacturers recommended 221 MECA, NHTSA–2018–0067–11994, at 12. 222 Id. 223 CBD et al. at 48. 224 Id. 225 Washington State Department of Ecology, NHTSA–2018–0067–11926, at 6. 226 77 FR 62624, at 62799 (Oct 15, 2012). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 eliminating the standards altogether, EPA did not propose to eliminate the standards, but to eliminate the optional flexibilities, and solicited comment on adjusting the standards to be more or less stringent. Thus, EPA does not believe it would be appropriate to eliminate completely the standards in this final rule without providing an opportunity for comment on that idea. Furthermore, as noted above, EPA believes the standards are continuing to serve their intended purpose of capping emissions and remain appropriate. Manufacturers have been subject to the standards for several years, and the Alliance acknowledges in their comments that the exceedance of the standards, which is offset by manufacturers using compliance flexibilities, is very small and that most vehicles meet the standards. Regarding the Alliance comments that the standards should be based on a fleet average approach, EPA notes that the purpose of the standards is to cap emissions, not to achieve fleet-wide reductions.227 The fleet average emissions for N2O and CH4 are well below the numerical level of the cap standards and therefore the existing cap standards would not be an appropriate fleet average standard. Adopting a fleet average approach using the same numerical level as the established cap standards would not achieve the intended goal of capping emissions at current levels. If technologies lead to exceedances of the caps, automakers have the opportunity to apply appropriate flexibilities under the current program to achieve GHG emission neutrality. EPA is not aware of any manufacturer that has been prevented from bringing a technology to the marketplace because of the current cap levels or approach. EPA believes it would need to consider all options further, with an opportunity for public comment, before adopting such a significant change to the program. As explained above, the agencies have changed the alternatives considered for the final rule, partly in response to comments. The basic form of the standards represented by the 227 Relatedly, the Alliance and Global Automakers raised concerns in their comments regarding N2O measurement and testing burden. EPA did not propose any changes in testing requirements and at this time EPA is not adopting any changes. Manufacturers have been measuring N2O emissions and have successfully certified vehicles to the N2O standards for several years and EPA does not believe N2O measurement is an issue needing regulatory change. EPA continues to believe direct measurement is the best way for manufacturers to demonstrate compliance with the N2O standards and is more appropriate than an engineering statement without direct measurement. PO 00000 Frm 00074 Fmt 4701 Sfmt 4700 alternatives—footprint-based, defined by particular mathematical functions— remains the same and as described in the NPRM. For the EPA program, EPA has chosen in this final rule to retain the existing program for regulation of A/C refrigerant leakage, nitrous oxide, and methane emissions as part of the CO2 standard. This allows manufacturers to continue to rely on this flexibility which they describe as extremely important for compliance, although it results in continued differences between EPA’s and NHTSA’s programs. This approach also avoids the possibility of gaps in the regulation of HFCs, CH4, and N2O while EPA developed a different way of regulating the non-CO2 emissions as part of or concurrent with the NPRM, and thereby allows EPA to continue to regulate GHE emissions from light-duty vehicles on a more-comprehensive basis. Thus, all alternatives considered in this final rule reflect inclusion of CH4, N2O, and HFC in EPA’s overall ‘‘CO2’’ (more accurately, CO2equivalent, or CO2e) requirements. Besides this change, the alternatives considered for the final rule differ from the NPRM in two additional ways: First, alternatives reflecting the phase-out of the A/C efficiency and off-cycle programs have been dropped in response to certain comments and in recognition of the potential real-world benefits of those programs. And second, the preferred alternative for this final rule reflects a 1.5 percent year-over-year increase for both passenger cars and light trucks. These changes will be discussed further below, following a brief discussion of the form of the standards. A. Form of the Standards As in the CAFE and CO2 rulemakings in 2010 and 2012, NHTSA and EPA proposed in the NPRM to set attributebased CAFE and CO2 standards defined by a mathematical function of vehicle footprint, which has observable correlation with fuel economy and vehicle emissions. EPCA, as amended by EISA, expressly requires that CAFE standards for passenger cars and light trucks be based on one or more vehicle attributes related to fuel economy and be expressed in the form of a mathematical function.228 While the CAA includes no specific requirements regarding CO2 regulation, EPA has chosen to adopt attribute-based CO2 standards consistent with NHTSA’s EPCA/EISA requirements in the interest of harmonization and simplifying compliance. Such an approach is permissible under section 202(a) of the 228 49 E:\FR\FM\30APR2.SGM U.S.C. 32902(a)(3)(A). 30APR2 24247 CAA, and EPA has used the attributebased approach in issuing standards under analogous provisions of the CAA. Thus, both the proposed and final standards take the form of fuel economy and CO2 targets expressed as functions of vehicle footprint (the product of vehicle wheelbase and average track width). Section V.A.2 below discusses the agencies’ continued reliance on footprint as the relevant attribute. Under the footprint-based standards, the function defines a CO2 or fuel economy performance target for each unique footprint combination within a car or truck model type. Using the functions, each manufacturer thus will have a CAFE and CO2 average standard for each year that is almost certainly unique to each of its fleets,229 based upon the footprints and production volumes of the vehicle models produced by that manufacturer. A manufacturer will have separate footprint-based standards for cars and for trucks. The functions are mostly sloped, so that generally, larger vehicles (i.e., vehicles with larger footprints) will be subject to lower CAFE mpg targets and higher CO2 grams/mile targets than smaller vehicles. This is because, generally speaking, smaller vehicles are more capable of achieving higher levels of fuel economy/lower levels of CO2 emissions, mostly because they tend not to have to work as hard (and therefore require as much energy) to perform their driving task. Although a manufacturer’s fleet average standards could be estimated throughout the model year based on the projected production volume of its vehicle fleet (and are estimated as part of EPA’s certification process), the standards to which the manufacturer must comply are determined by its final model year production figures. A manufacturer’s calculation of its fleet average standards as well as its fleets’ average performance at the end of the model year will thus be based on the production-weighted average target and performance of each model in its fleet.230 For passenger cars, consistent with prior rulemakings, NHTSA is defining fuel economy targets as follows: where: TARGETFE is the fuel economy target (in mpg) applicable to a specific vehicle model type with a unique footprint combination, a is a minimum fuel economy target (in mpg), b is a maximum fuel economy target (in mpg), c is the slope (in gallons per mile per square foot, or gpm, per square foot) of a line relating fuel consumption (the inverse of fuel economy) to footprint, and d is an intercept (in gpm) of the same line. values, respectively, of the set of included values. For example, MIN[40,35] = 35 and MAX(40, 25) = 40, such that MIN[MAX(40, 25), 35] = 35. For light trucks, also consistent with prior rulemakings, NHTSA is defining fuel economy targets as follows: where: TARGETFE is the fuel economy target (in mpg) applicable to a specific vehicle model type with a unique footprint combination, a, b, c, and d are as for passenger cars, but taking values specific to light trucks, e is a second minimum fuel economy target (in mpg), f is a second maximum fuel economy target (in mpg), g is the slope (in gpm per square foot) of a second line relating fuel consumption (the inverse of fuel economy) to footprint, and h is an intercept (in gpm) of the same second line. Although the general model of the target function equation is the same for each vehicle category (passenger cars and light trucks) and each model year, the parameters of the function equation differ for cars and trucks. For MYs 2020–2026, the parameters are unchanged, resulting in the same stringency in each of those model years. Mathematical functions defining the CO2 targets are expressed as functions that are similar, with coefficients a-h corresponding to those listed above.231 For passenger cars, EPA is defining CO2 targets mathematically equivalent to the following: 229 EPCA/EISA requires NHTSA to separate passenger cars into domestic and import passenger car fleets whereas EPA combines all passenger cars into one fleet. 230 As discussed in prior rulemakings, a manufacturer may have some vehicle models that exceed their target and some that are below their target. Compliance with a fleet average standard is determined by comparing the fleet average standard (based on the production-weighted average of the target levels for each model) with fleet average performance (based on the production-weighted average of the performance of each model). 231 EPA regulations use a different but mathematically equivalent approach to specify targets. Rather than using a function with nested VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00075 Fmt 4701 Sfmt 4700 TARGETCO2 = MIN[b, MAX[a, c × FOOTPRINT + d]] where: TARGETCO2 is the is the CO2 target (in grams per mile, or g/mi) applicable to a specific vehicle model configuration, a is a minimum CO2 target (in g/mi), b is a maximum CO2 target (in g/mi), c is the slope (in g/mi, per square foot) of a line relating CO2 emissions to footprint, and d is an intercept (in g/mi) of the same line. For light trucks, CO2 targets are defined as follows: TARGETCO2 = MIN[MIN[b, MAX[a, c × FOOTPRINT + d]], MIN[f, MAX[e, g × FOOTPRINT + h]] minima and maxima functions, EPA regulations specify requirements separately for different ranges of vehicle footprint. Because these ranges reflect the combined application of the listed minima, maxima, and linear functions, it is mathematically equivalent and more efficient to present the targets as in this Section. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.052</GPH> Here, MIN and MAX are functions that take the minimum and maximum ER30AP20.051</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations where: CAFErequired is the CAFE level the fleet is required to achieve, i refers to specific vehicle model/ configurations in the fleet, PRODUCTIONi is the number of model configuration i produced for sale in the U.S., and TARGETFE,i the fuel economy target (as defined above) for model configuration i. given model year is determined by calculating the production-weighted average (not harmonic) of CO2 targets applicable to specific vehicle model configurations in the fleet, as follows: Similarly, the required average CO2 level applicable to a given fleet in a where: CO2required is the average CO2 level the fleet is required to achieve, i refers to specific vehicle model/ configurations in the fleet, PRODUCTIONi is the number of model configuration i produced for sale in the U.S., and TARGETCO2,i is the CO2 target (as defined above) for model configuration i. khammond on DSKJM1Z7X2PROD with RULES2 comply with all applicable emissions standards. When first mandating CAFE standards in the 1970s, Congress specified a more flexible averagingbased approach that allows some vehicles to ‘‘under comply’’ (i.e., fall short of the overall flat standard, or fall short of their target under attributebased standards) as long as a manufacturer’s overall fleet is in compliance. The required CAFE level applicable to a given fleet in a given model year is determined by calculating the production-weighted harmonic average of fuel economy targets applicable to specific vehicle model configurations in the fleet, as follows: Section VI.A.1 describes the advantages of attribute standards, generally. Section VI.A.2 explains the agencies’ specific decision to use vehicle footprint as the attribute over which to vary stringency for past and current rules. Section VI.A.3 discusses the policy considerations in selecting the specific mathematical function. Section VI.A.4 discusses the methodologies used to develop current attribute-based standards, and the agencies’ current proposal to continue to do so for MYs 2021–2026. Section VI.A.5 discusses the methodologies used to reconsider the mathematical function for the proposed standards. Here, i represents a given model 232 in a manufacturer’s fleet, Productioni represents the U.S. production of that model, and Targeti represents the target as defined by the attribute-based standards. This means no vehicle is required to meet its target; instead, manufacturers are free to balance improvements however they deem best 232 If a model has more than one footprint variant, here each of those variants is treated as a unique model, i, since each footprint variant will have a unique target. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00076 Fmt 4701 Sfmt 4700 1. Why attribute-based standards, and what are the benefits? Under attribute-based standards, every vehicle model has fuel economy and CO2 targets, the levels of which depend on the level of that vehicle’s determining attribute (for the MYs 2021–2026 standards, footprint is the determining attribute, as discussed below). The manufacturer’s fleet average CAFE performance is calculated by the harmonic production-weighted average of those targets, as defined below: within (and, given credit transfers, at least partially across) their fleets. Because CO2 is on a gram per mile basis rather a mile per gallon basis, E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.055</GPH> To be clear, as has been the case since the agencies began establishing attribute-based standards, no vehicle need meet the specific applicable fuel economy or CO2 targets, because compliance with either CAFE or CO2 standards is determined based on corporate average fuel economy or fleet average CO2 emission rates. In this respect, CAFE and CO2 standards are unlike, for example, safety standards and traditional vehicle emissions standards. CAFE and CO2 standards apply to the average fuel economy levels and CO2 emission rates achieved by manufacturers’ entire fleets of vehicles produced for sale in the U.S. Safety standards apply on a vehicle-by-vehicle basis, such that every single vehicle produced for sale in the U.S. must, on its own, comply with minimum FMVSS. Similarly, criteria pollutant emissions standards are applied on a per-vehicle basis, such that every vehicle produced for sale in the U.S. must, on its own, ER30AP20.054</GPH> where: TARGETCO2 is the is the CO2 target (in g/mi) applicable to a specific vehicle model configuration, a, b, c, and d are as for passenger cars, but taking values specific to light trucks, e is a second minimum CO2 target (in g/mi), f is a second maximum CO2 target (in g/mi), g is the slope (in g/mi per square foot) of a second line relating CO2 emissions to footprint, and h is an intercept (in g/mi) of the same second line. ER30AP20.053</GPH> 24248 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24249 harmonic averaging is not necessary when calculating required CO2 levels: 233 The 2002 NAS Report described at length and quantified the potential safety problem with average fuel economy standards that specify a single numerical requirement for the entire industry. See Transportation Research Board and National Research Council. 2002. Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards, Washington, DC: The National Academies Press (‘‘2002 NAS Report’’) at 5, finding 12, available at https://www.nap.edu/catalog/ 10172/effectiveness-and-impact-of-corporateaverage-fuel-economy-cafe-standards (last accessed June 15, 2018). Ensuing analyses, including by NHTSA, support the fundamental conclusion that standards structured to minimize incentives to downsize all but the largest vehicles will tend to produce better safety outcomes than flat standards. 234 Bento, A., Gillingham, K., & Roth, K. (2017). The Effect of Fuel Economy Standards on Vehicle Weight Dispersion and Accident Fatalities. NBER Working Paper No. 23340. Available at http:// www.nber.org/papers/w23340 (last accessed June 15, 2018). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 Second, attribute-based standards, if properly fitted, provide automakers with more flexibility to respond to consumer preferences than do singlevalued standards. As discussed above, a single-valued standard encourages a fleet mix with a larger share of smaller vehicles by creating incentives for manufacturers to use downsizing the average vehicle in their fleet (possibly through fleet mixing) as a compliance strategy, which may result in manufacturers building vehicles for compliance reasons that consumers do not want. Under a size-related, attributebased standard, reducing the size of the vehicle for compliance’s sake is a lessviable strategy because smaller vehicles have more stringent regulatory targets. As a result, the fleet mix under such standards is more likely to reflect aggregate consumer demand for the sizerelated attribute used to determine vehicle targets. Third, attribute-based standards provide a more equitable regulatory framework across heterogeneous manufacturers who may each produce different shares of vehicles along attributes correlated with fuel economy.235 An industry-wide singlevalue CAFE standard imposes disproportionate cost burden and compliance challenges on manufacturers who produce more vehicles with attributes inherently correlated with lower fuel economy— i.e. manufacturers who produce, on average, larger vehicles. As discussed above, retaining flexibility for manufacturers to produce vehicles which respect heterogeneous market preferences is an important consideration. Since manufacturers may target different markets as a part of their business strategy, ensuring that these manufacturers do not incur a disproportionate share of the regulatory cost burden is an important part of conserving consumer choices within the market. Industry commenters generally supported attribute-based standards, while other commenters questioned their benefits. IPI argued that preserving the current vehicle mix was not necessarily desirable or necessary for consumer welfare, and suggested that 235 2002 PO 00000 NAS Report at 4–5, finding 10. Frm 00077 Fmt 4701 Sfmt 4700 some vehicle downsizing in the fleet might be beneficial both for safety and for compliance.236 IPI also argued that compliance credit trading would ‘‘help smooth out any disproportionate impacts on certain manufacturers’’ and ‘‘ensure that manufacturers with relatively efficient fleets still have an incentive to continue improving fuel economy (in order to generate credits)’’ 237 Similarly, citing Ito and Sallee, Kathryn Doolittle commented that ‘‘. . . Ito and Sallee (2018) have found ABR [‘‘attribute-based regulations’’] inefficient in cost when juxtaposed with flat standard with compliance trading.’’ 238 The agencies have considered these comments. IPI incorrectly characterizes the agencies’ prior statements as claims that it is important to preserve the current vehicle mix. EPA and NHTSA have never claimed, and are not today claiming that it is important to preserve the current fleet mix. The agencies have said, and are today reiterating, that it is reasonable to expect that reducing the tendency of standards to distort the market should reduce at least part of the tendency of standards to reduce consumer welfare. Or, more concisely, it is better to work with the market than against it. Single-value (aka flat) CAFE standards in place from the 1970s through 2010 were clearly distortionary. Recognizing this, the National Academy of Sciences recommended in 2002 that NHTSA adopt attribute-based CAFE standards. NHTSA did so in 2006, for light trucks produced starting MY 2008. As mentioned above, in 2007, Congress codified the requirement for attributebased passenger car and light truck CAFE standards. Agreeing with this history, premise, and motivation, EPA has also adopted attribute-based CO2 standards. None of this is to say the agencies consider it important to hold fleet mix constant. Rather, the agencies expect that, compared to flat standards, attribute-based standards can allow the market—including fleet mix—to better 236 IPI, NHTSA–2018–0067–12362, at 14–15. NHTSA–2018–0067–12362, at 14. 238 Doolittle, K, NHTSA–2018–0067–7411. See also Ito, K and Sallee, J. ‘‘The Economics of Attribute-Based Regulation: Theory and Evidence from Fuel Economy Standards.’’ The Review of Economics and Statistics (2018), 100(2), pp. 319– 36. 237 IPI, E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.056</GPH> khammond on DSKJM1Z7X2PROD with RULES2 The idea is to select the shape of the mathematical function relating the standard to the fuel economy-related attribute to reflect the trade-offs manufacturers face in producing more of that attribute over fuel efficiency (due to technological limits of production and relative demand of each attribute). If the shape captures these trade-offs, every manufacturer is more likely to continue adding fuel-efficient technology across the distribution of the attribute within their fleet, instead of potentially changing the attribute—and other correlated attributes, including fuel economy—as a part of their compliance strategy. Attribute-based standards that achieve this have several advantages. First, assuming the attribute is a measurement of vehicle size, attributebased standards help to at least partially reduce the incentive for manufacturers to respond to CAFE and CO2 standards by reducing vehicle size in ways harmful to safety, as compared to ‘‘flat,’’ non-attribute based standards.233 Larger vehicles, in terms of mass and/or crush space, generally consume more fuel and produce more carbon dioxide emissions, but are also generally better able to protect occupants in a crash.234 Because each vehicle model has its own target (determined by a size-related attribute), properly fitted attribute-based standards reduce the incentive to build smaller vehicles simply to meet a fleet-wide average, because smaller vehicles are subject to more stringent compliance targets. 24250 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 follow its natural course, and all else equal, consumer acceptance is likely to be greater if the market does so. The agencies also disagree with comments implying that compliance credit trading can address all of the market distortion that flat standards would entail. Evidence thus far suggests that trading is fragmented, with some manufacturers apparently willing to trade only with some other specific manufacturers. The Ito and Sallee article cited by one commenter is a highly idealized theoretical construction, with the authors noting, inter alia, that their model ‘‘assumes perfect competition.’’ 239 Its findings regarding comparative economic efficiency of flatand attribute-based standards are, therefore, merely hypothetical, and the agencies find little basis in recent transactions to suggest the compliance credit trading market reflects the authors’ idealized assumptions. Even if the agencies did expect credit trading markets to operate as in an idealized textbook example, basing the structure of standards on the presumption of perfect trading would not be appropriate. FCA commented that ‘‘. . . when flexibilities are considered while setting targets, they cease to be flexibilities and become simply additional technology mandates,’’ and the Alliance commented, similarly, that ‘‘the Agencies should keep ‘flexibilities’ as optional ways to comply and not unduly assume that each flexibility allows additional stringency of footprint-based standards.’’ 240 Perhaps recognizing this reality, Congress has barred NHTSA from considering manufacturers’ ability to use compliance credits (even credits earned and used by the same OEM, much less credits traded between OEMs). As discussed further in Section VIII.A.2, EPA believes that while credit trading may be a useful flexibility to reduce the overall costs of the program, it is important to set standards in a way that does not rely on credit purchasing availability as a compliance mechanism. Considering these comments and realities, considering EPCA’s requirement for attribute-based CAFE standards, and considering the benefits of regulatory harmonization, the agencies are, again, finalizing attributebased CAFE and CO2 standards rather 239 Ito and Sallee, op. cit., Supplemental Appendix, at A–15, available at https:// www.mitpressjournals.org/doi/suppl/10.1162/ REST_a_00704/suppl_file/REST_a_00704esupp.pdf (accessed October 29, 2019). 240 FCA, NHTSA–2018–0067–11943, at 6; Alliance, NHTSA–2018–0067–12073, Full Comment Set, at 40, fn. 82. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 than, for either program, finalizing flat standards. Why footprint as the attribute? It is important that the CAFE and CO2 standards be set in a way that does not unnecessarily incentivize manufacturers to respond by selling vehicles that are less safe. Vehicle size is highly correlated with vehicle safety—for this reason, it is important to choose an attribute correlated with vehicle size (mass or some dimensional measure). Given this consideration, there are several policy and technical reasons why footprint is considered to be the most appropriate attribute upon which to base the standards, even though other vehicle size attributes (notably, curb weight) are more strongly correlated with fuel economy and tailpipe CO2 emissions. First, mass is strongly correlated with fuel economy; it takes a certain amount of energy to move a certain amount of mass. Footprint has some positive correlation with frontal surface area, likely a negative correlation with aerodynamics, and therefore fuel economy, but the relationship is less deterministic. Mass and crush space (correlated with footprint) are both important safety considerations. As discussed below and in the accompanying PRIA, NHTSA’s research of historical crash data indicates that holding footprint constant, and decreasing the mass of the largest vehicles, will result in a net positive safety impact to drivers overall, while holding footprint constant and decreasing the mass of the smallest vehicles will result in a net decrease in fleetwide safety. Properly fitted footprint-based standards provide little, if any, incentive to build smaller footprint vehicles to meet CAFE and CO2 standards, and therefore help minimize the impact of standards on overall fleet safety. Second, it is important that the attribute not be easily manipulated in a manner that does not achieve the goals of EPCA or other goals, such as safety. Although weight is more strongly correlated with fuel economy than footprint, there is less risk of artificial manipulation (i.e., changing the attribute(s) to achieve a more favorable target) by increasing footprint under footprint-based standards than there would be by increasing vehicle mass under weight-based standards. It is relatively easy for a manufacturer to add enough weight to a vehicle to decrease its applicable fuel economy target a significant amount, as compared to increasing vehicle footprint, which is a much more complicated change that PO 00000 Frm 00078 Fmt 4701 Sfmt 4700 typically takes place only with a vehicle redesign. Further, some commenters on the MY 2011 CAFE rulemaking were concerned that there would be greater potential for such manipulation under multi-attribute standards, such as those that also depend on weight, torque, power, towing capability, and/or off-road capability. As discussed in NHTSA’s MY 2011 CAFE final rule,241 it is anticipated that the possibility of manipulation is lowest with footprintbased standards, as opposed to weightbased or multi-attribute-based standards. Specifically, standards that incorporate weight, torque, power, towing capability, and/or off-road capability in addition to footprint would not only be more complex, but by providing degrees of freedom with respect to more easily adjusted attributes, they could make it less certain that the future fleet would actually achieve the projected average fuel economy and CO2 levels. This is not to say that a footprint-based system eliminates manipulation, or that a footprint-based system eliminates the possibility that manufacturers will change vehicles in ways that compromise occupant protection, but footprint-based standards achieve the best balance among affected considerations. Several stakeholders commented on whether vehicular footprint is the most suitable attribute upon which to base standards. IPI commented that ‘‘. . . footprint-based standards may be unnecessary to respect consumer preferences, may negatively impact safety, and may be overall inefficient. Several arguments call into question the footprint-based approach, but a particularly important one is that large vehicles can impose a negative safety externality on other drivers.’’ 242 IPI commented, further, that the agencies should consider the relative merits of other vehicle attributes, including vehicle fuel type, suggesting that it would be more difficult for manufacturers to manipulate a flatter standard or one ‘‘differentiated by fuel type.’’ 243 Similarly, Michalek and Whitefoot recommended ‘‘that the agencies reexamine automaker response to the footprint-based standards to determine if adjustments should be made to avoid inducing increases to vehicle size.’’ 244 241 See 74 FR at 14359 (Mar. 30, 2009). NHTSA–2018–0067–12362, at 12. 243 IPI, NHTSA–2018–0067–12362, at 13 et seq. 244 Michalek, J. and Whitefoot, K., NHTSA–2018– 0067–11903, at 13. 242 IPI, E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 Conversely, ICCT commented that ‘‘the switch to footprint-based CAFE and [CO2] standards has been widely credited with diminishing safety concerns with efficiency standards. Footprint standards encourage larger vehicles with wider track width, which reduces rollovers, and longer wheelbase, which increases the crush space and reduces deceleration forces for both vehicles in a two-vehicle collision.’’ 245 Similarly, BorgWarner commented that ‘‘the use of a footprint standard not only provides greater incentive for mass reduction, but also encourages a larger footprint for a given vehicle mass, thus providing increased safety for a given mass vehicle,’’ 246 and the Aluminum Association commented footprint based standards drive ‘‘fuel-efficiency improvement across all vehicle classes,’’ ‘‘eliminate the incentive to shift fleet volume to smaller cars which has been shown to slightly decrease safety in vehicle-to-vehicle collisions,’’ and provide ‘‘an incentive for reducing weight in the larger vehicles, where weight reduction is of the most benefit for societal safety,’’ citing Ford’s aluminum-intensive F150 pickup truck as an example.247 NADA urged the agencies to continue basing standards on vehicle footprint, as doing so ‘‘serves both to require and allow OEMs to build more fuel-efficient vehicles across the broadest possible light-duty passenger car and truck spectrum,’’ 248 and UCS commented that footprint-based standards ‘‘increase consumer choice, ensuring that the vehicles available for purchase in every vehicle class continue to get more efficient.’’ 249 Furthermore, regarding concerns that footprint-based standards may be susceptible to manipulation, the Alliance commented that ‘‘the data above [from Novation Analytics] shows there are no systemic footprint increases (or any type of target manipulation) occurring.’’ 250 While FCA’s comments supported this Alliance comment, FCA commented further that, lacking some utility-related vehicle attributes such as towing capability, 4-wheel-drive, and ride height, ‘‘it is clear the footprint standard does not fully account for pickup truck capability and the components needed such as larger powertrains, greater mass and frontal area,’’ and requested the agencies ‘‘correct LDT standards to 245 ICCT, NHTSA–2018–0067–11741, at B–4. NHTSA–2018–0067–11893, at 246 BorgWarner, 10. 247 Aluminum Association, NHTSA–2018–0067– 11952, at 3. 248 NADA, NHTSA–2018–0067–12064, at 13. 249 UCS, UCS, NHTSA–2018–0067–12039, at 46. 250 Alliance, NHTSA–2018–0067–12073, at 123. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 reflect the current market preference for capability over efficiency, and introduce mechanisms into the regulation that can adjust for efficiency and capability tradeoffs that footprint standards currently ignore.’’ 251 When first electing to adopt footprintbased standards, NHTSA carefully considered other alternatives, including vehicle mass and ‘‘shadow’’ (overall width multiplied by overall length). Compared to both of these other alternatives, footprint is much less susceptible to gaming, because while there is some potential to adjust track width, wheelbase is more expensive to change, at least outside a planned vehicle redesign. EPA agreed with NHTSA’s assessment, nothing has changed the relative merits of at least these three potential attributes, and nothing in the evolution of the fleet demonstrates that footprint-based standards are leading manufacturers to increase the footprint of specific vehicle models by more than they would in response to customer demand. Also, even if footprint-based standards are encouraging some increases in vehicle size, NHTSA continues to maintain, and EPA to agree, that such increases should tend to improve overall highway safety rather than degrading it. Regarding FCA’s request that the agencies adopt an approach that accounts for a wider range of vehicle attributes related to both vehicle fuel economy and customer-facing vehicle utility, the agencies are concerned that doing so could further complicate alreadycomplex standards and also lead to unintended consequences. For example, it is not currently clear how a multiattribute approach would appropriately balance emphasis between vehicle attributes (e.g., how much relative fuel consumption should be attributed to, respectively, vehicle footprint, towing capacity, drive type, and ground clearance). Also, basing standards on, in part, ground clearance would encourage manufacturers to increase ride height, potentially increasing the frequency of vehicle rollover crashes. Regarding IPI’s recommendation that fuel type be included as a vehicle attribute for attribute-based standards, the agencies note that both CAFE and CO2 standards already account for fuel type in the procedures for measuring fuel economy levels and CO2 emission rates, and for calculating fleet average CAFE and CO2 levels. Therefore, having considered public comments on the choice of vehicle attributes for CAFE and CO2 standards, the agencies are finalizing standards 251 FCA, PO 00000 NHTSA–2018–0067–11943, at 49. Frm 00079 Fmt 4701 Sfmt 4700 24251 that, as proposed, are defined in terms of vehicle footprint. 3. What mathematical function should be used to specify footprint-based standards? In requiring NHTSA to ‘‘prescribe by regulation separate average fuel economy standards for passenger and non-passenger automobiles based on 1 or more vehicle attributes related to fuel economy and express each standard in the form of a mathematical function,’’ EPCA/EISA provides ample discretion regarding not only the selection of the attribute(s), but also regarding the nature of the function. The CAA provides no specific direction regarding CO2 regulation, and EPA has continued to harmonize this aspect of its CO2 regulations with NHTSA’s CAFE regulations. The relationship between fuel economy (and CO2 emissions) and footprint, though directionally clear (i.e., fuel economy tends to decrease and CO2 emissions tend to increase with increasing footprint), is theoretically vague, and quantitatively uncertain; in other words, not so precise as to a priori yield only a single possible curve. The decision of how to specify this mathematical function therefore reflects some amount of judgment. The function can be specified with a view toward achieving different environmental and petroleum reduction goals, encouraging different levels of application of fuelsaving technologies, avoiding any adverse effects on overall highway safety, reducing disparities of manufacturers’ compliance burdens, and preserving consumer choice, among other aims. The following are among the specific technical concerns and resultant policy tradeoffs the agencies have considered in selecting the details of specific past and future curve shapes: • Flatter standards (i.e., curves) increase the risk that both the size of vehicles will be reduced, potentially compromising highway safety, and reducing any utility consumers would have gained from a larger vehicle. • Steeper footprint-based standards may create incentives to upsize vehicles, potentially oversupplying vehicles of certain footprints beyond what consumers would naturally demand, and thus increasing the possibility that fuel savings and CO2 reduction benefits will be forfeited artificially. • Given the same industry-wide average required fuel economy or CO2 standard, flatter standards tend to place greater compliance burdens on full-line manufacturers. • Given the same industry-wide average required fuel economy or CO2 E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations standard, dramatically steeper standards tend to place greater compliance burdens on limited-line manufacturers (depending of course, on which vehicles are being produced). • If cutpoints are adopted, given the same industry-wide average required fuel economy, moving small-vehicle cutpoints to the left (i.e., up in terms of fuel economy, down in terms of CO2 emissions) discourages the introduction of small vehicles, and reduces the incentive to downsize small vehicles in ways that could compromise overall highway safety. • If cutpoints are adopted, given the same industry-wide average required fuel economy, moving large-vehicle cutpoints to the right (i.e., down in terms of fuel economy, up in terms of CO2 emissions) better accommodates the design requirements of larger vehicles— especially large pickups—and extends the size range over which downsizing is discouraged. Here, Target is the fuel economy target applicable to vehicles of a given footprint in square feet (Footprint). The upper asymptote, a, and the lower asymptote, b, are specified in mpg; the reciprocal of these values represent the lower and upper asymptotes, respectively, when the curve is instead specified in gallons per mile (gpm). The slope, c, and the intercept, d, of the linear portion of the curve are specified as gpm per change in square feet, and gpm, respectively. upper asymptote, the upper asymptote is returned is returned from the min function, and the overall target in mpg is the reciprocal of the upper asymptote in gpm space, or b. In this way curves specified as constrained linear functions are specified by the following parameters: a = upper limit (mpg) b = lower limit (mpg) c = slope (gpm per sq.ft.) d = intercept (gpm) The slope and intercept are specified as gpm per sq. ft. and gpm instead of mpg per sq. ft. and mpg because fuel consumption and emissions appear roughly linearly related to gallons per mile (the reciprocal of the miles per gallon). 4. What mathematical functions have been used previously, and why? Notwithstanding the aforementioned discretion under EPCA/EISA, data should inform consideration of potential mathematical functions, but how relevant data is defined and interpreted, and the choice of methodology for The min and max functions will take the minimum and maximum values within their associated parentheses. Thus, the max function will first find the maximum of the fitted line at a given footprint value and the lower asymptote from the perspective of gpm. If the fitted line is below the lower asymptote it is replaced with the floor, which is also the minimum of the floor and the ceiling by definition, so that the target in mpg space will be the reciprocal of the floor in mpg space, or simply, a. If, however, the fitted line is not below the lower asymptote, the fitted value is returned from the max function and the min function takes the minimum value of the upper asymptote (in gpm space) and the fitted line. If the fitted value is below the upper asymptote, it is between the two asymptotes and the fitted value is appropriately returned from the min function, making the overall target in mpg the reciprocal of the fitted line in gpm. If the fitted value is above the a) NHTSA in MY 2008 and MY 2011 CAFE (Constrained Logistic) For the MY 2011 CAFE rule, NHTSA estimated fuel economy levels by footprint from the MY 2008 fleet after normalization for differences in technology,254 but did not make adjustments to reflect other vehicle attributes (e.g., power-to-weight ratios). Starting with the technology-adjusted passenger car and light truck fleets, NHTSA used minimum absolute deviation (MAD) regression without sales weighting to fit a logistic form as a starting point to develop mathematical functions defining the standards. 252 See 74 FR 14196, 14363–14370 (Mar. 30, 2009) for NHTSA discussion of curve fitting in the MY 2011 CAFE final rule. 253 The right cutpoint for the light truck curve was moved further to the right for MYs 2017–2021, so that more possible footprints would fall on the sloped part of the curve. In order to ensure that, for all possible footprints, future standards would be at least as high as MY 2016 levels, the final standards for light trucks for MYs 2017–2021 is the maximum of the MY 2016 target curves and the target curves for the give MY standard. This is defined further in the 2012 final rule. See 77 FR 62624, at 62699–700 (Oct. 15, 2012). 254 See 74 FR 14196, 14363–14370 (Mar. 30, 2009) for NHTSA discussion of curve fitting in the MY 2011 CAFE final rule. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00080 Fmt 4701 Sfmt 4700 fitting a curve to that data, can and should include some consideration of specific policy goals. This section summarizes the methodologies and policy concerns that were considered in developing previous target curves (for a complete discussion see the 2012 FRIA). As discussed below, the MY 2011 final curves followed a constrained logistic function defined specifically in the final rule.252 The MYs 2012–2021 final standards and the MYs 2022–2025 augural standards are defined by constrained linear target functions of footprint, as shown below: 253 NHTSA then identified footprints at which to apply minimum and maximum values (rather than letting the standards extend without limit) and transposed these functions vertically (i.e., on a gallons-per-mile basis, uniformly downward) to produce the promulgated standards. In the preceding rule, for MYs 2008–2011 light truck standards, NHTSA examined a range of potential functional forms, and concluded that, compared to other considered forms, the constrained logistic form provided the expected and appropriate trend (decreasing fuel economy as footprint increases), but avoided creating ‘‘kinks’’ the agency was concerned would provide distortionary incentives for vehicles with neighboring footprints.255 b) MYs 2012–2016 Standards (Constrained Linear) For the MYs 2012–2016 rule, potential methods for specifying mathematical functions to define fuel economy and CO2 standards were reevaluated. These methods were fit to the same MY 2008 data as the MY 2011 standard. Considering these further specifications, the constrained logistic form, if applied to post-MY 2011 standards, would likely contain a steep mid-section that would provide undue incentive to increase the footprint of midsize passenger cars.256 A range of 255 See 71 FR 17556, 17609–17613 (Apr. 6, 2006) for NHTSA discussion of ‘‘kinks’’ in the MYs 2008– 2011 light truck CAFE final rule (there described as ‘‘edge effects’’). A ‘‘kink,’’ as used here, is a portion of the curve where a small change in footprint results in a disproportionally large change in stringency. 256 75 FR at 25362. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.057</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24252 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations methods to fit the curves would have been reasonable, and a minimum absolute deviation (MAD) regression without sales weighting on a technology-adjusted car and light truck fleet was used to fit a linear equation. This equation was used as a starting point to develop mathematical functions defining the standards. Footprints were then identified at which to apply minimum and maximum values (rather than letting the standards extend without limit). Finally, these constrained/piecewise linear functions were transposed vertically (i.e., on a gpm or CO2 basis, uniformly downward) by multiplying the initial curve by a single factor for each MY standard to produce the final attribute-based targets for passenger cars and light trucks described in the final rule.257 These transformations are typically presented as percentage improvements over a previous MY target curve. c) MYs 2017 and Beyond Standards (Constrained Linear) The mathematical functions finalized in 2012 for MYs 2017 and beyond changed somewhat from the functions for the MYs 2012–2016 standards. These changes were made both to address comments from stakeholders, and to consider further some of the technical concerns and policy goals judged more preeminent under the increased uncertainty of the impacts of finalizing and proposing standards for model years further into the future.258 Recognizing the concerns raised by fullline OEMs, it was concluded that continuing increases in the stringency of the light truck standards would be more feasible if the light truck curve for MYs 2017 and beyond was made steeper than the MY 2016 truck curve and the right (large footprint) cut-point was extended only gradually to larger footprints. To accommodate these considerations, the 2012 final rule finalized the slope fit to the MY 2008 fleet using a salesweighted, ordinary least-squares regression, using a fleet that had technology applied to make the technology application across the fleet more uniform, and after adjusting the 256 75 FR at 25362. generally 74 FR at 49491–96; 75 FR at 25357–62. khammond on DSKJM1Z7X2PROD with RULES2 257 See VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 data for the effects of weight-tofootprint. Information from an updated MY 2010 fleet was also considered to support this decision. As the curve was vertically shifted (with fuel economy specified as mpg instead of gpm or CO2 emissions) upwards, the right cutpoint was progressively moved for the light truck curves with successive model years, reaching the final endpoint for MY 2021. 5. Reconsidering the Mathematical Functions for Today’s Rulemaking a) Why is it important to reconsider the mathematical functions? By shifting the developed curves by a single factor, it is assumed that the underlying relationship of fuel consumption (in gallons per mile) to vehicle footprint does not change significantly from the model year data used to fit the curves to the range of model years for which the shifted curve shape is applied to develop the standards. However, it must be recognized that the relationship between vehicle footprint and fuel economy is not necessarily constant over time; newly developed technologies, changes in consumer demand, and even the curves themselves could influence the observed relationships between the two vehicle characteristics. For example, if certain technologies are more effective or more marketable for certain types of vehicles, their application may not be uniform over the range of vehicle footprints. Further, if market demand has shifted between vehicle types, so that certain vehicles make up a larger share of the fleet, any underlying technological or market restrictions which inform the average shape of the curves could change. That is, changes in the technology or market restrictions themselves, or a mere re-weighting of different vehicles types, could reshape the fit curves. For the above reasons, the curve shapes were reconsidered in the proposal using the newest available data from MY 2016. With a view toward corroboration through different techniques, a range of descriptive statistical analyses were conducted that do not require underlying engineering models of how fuel economy and PO 00000 Frm 00081 Fmt 4701 Sfmt 4700 24253 footprint might be expected to be related, and a separate analysis that uses vehicle simulation results as the basis to estimate the relationship from a perspective more explicitly informed by engineering theory was conducted as well. Despite changes in the new vehicle fleet both in terms of technologies applied and in market demand, the underlying statistical relationship between footprint and fuel economy has not changed significantly since the MY 2008 fleet used for the 2012 final rule; therefore, EPA and NHTSA proposed to continue to use the curve shapes fit in 2012. The analysis and reasoning supporting this decision follows. b) What statistical analyses did EPA and NHTSA consider? In considering how to address the various policy concerns discussed above, data from the MY 2016 fleet was considered, and a number of descriptive statistical analyses (i.e., involving observed fuel economy levels and footprints) using various statistical methods, weighting schemes, and adjustments to the data to make the fleets less technologically heterogeneous were performed. There were several adjustments to the data that were common to all of the statistical analyses considered. With a view toward isolating the relationship between fuel economy and footprint, the few diesels in the fleet were excluded, as well as the limited number of vehicles with partial or full electric propulsion; when the fleet is normalized so that technology is more homogenous, application of these technologies is not allowed. This is consistent with the methodology used in the 2012 final rule. The above adjustments were applied to all statistical analyses considered, regardless of the specifics of each of the methods, weights, and technology level of the data, used to view the relationship of vehicle footprint and fuel economy. Table V–1, below, summarizes the different assumptions considered and the key attributes of each. The analysis was performed considering all possible combinations of these assumptions, producing a total of eight footprint curves. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations (1) Current Technology Level Curves (2) Maximum Technology Level Curves The ‘‘current technology’’ level curves exclude diesels and vehicles with electric propulsion, as discussed above, but make no other changes to each model year fleet. Comparing the MY 2016 curves to ones built under the same methodology from previous model year fleets shows whether the observed curve shape has changed significantly over time as standards have become more stringent. Importantly, these curves will include any market forces which make technology application variable over the distribution of footprint. These market forces will not be present in the ‘‘maximum technology’’ level curves: By making technology levels homogenous, this variation is removed. The current technology level curves built using both regression types and both regression weight methodologies from the MY 2008, MY 2010, and MY 2016 fleets, shown in more detail in Chapter 4.4.2.1 of the PRIA, support the curve slopes finalized in the 2012 final rule. The curves built from most methodologies using each fleet generally shift, but remain very similar in slope. This suggests that the relationship of footprint to fuel economy, including both technology and market limits, has not significantly changed. As in prior rulemakings, technology differences between vehicle models were considered to be a significant factor producing uncertainty regarding the relationship between fuel consumption and footprint. Noting that attribute-based standards are intended to encourage the application of additional technology to improve fuel efficiency and reduce CO2 emissions across the distribution of footprint in the fleet, approaches were considered in which technology application is simulated for purposes of the curve fitting analysis in order to produce fleets that are less varied in technology content. This approach helps reduce ‘‘noise’’ (i.e., dispersion) in the plot of vehicle footprints and fuel consumption levels and identify a more technologyneutral relationship between footprint and fuel consumption. The results of updated analysis for maximum technology level curves are also shown in Chapter 4.4.2.2 of the PRIA. Especially if vehicles progress over time toward more similar size-specific efficiency, further removing variation in technology application both better isolates the relationship between fuel consumption and footprint and further supports the curve slopes finalized in the 2012 final rule. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00082 Fmt 4701 Sfmt 4700 c) What other methodologies were considered? The methods discussed above are descriptive in nature, using statistical analysis to relate observed fuel economy levels to observed footprints for known vehicles. As such, these methods are clearly based on actual data, answering the question ‘‘how does fuel economy appear to be related to footprint?’’ However, being independent of explicit engineering theory, they do not answer the question ‘‘how might one expect fuel economy to be related to footprint?’’ Therefore, as an alternative to the above methods, an alternative methodology was also developed and applied that, using full-vehicle simulation, comes closer to answering the second question, providing a basis either to corroborate answers to the first, or suggest that further investigation could be important. As discussed in the 2012 final rule, several manufacturers have confidentially shared with the agencies what they described as ‘‘physics-based’’ curves, with each OEM showing significantly different shapes for the footprint-fuel economy relationships. This variation suggests that manufacturers face different curves given the other attributes of the vehicles in their fleets (i.e., performance E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.058</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24254 24255 characteristics) and/or that their curves reflected different levels of technology application. In reconsidering the shapes of the proposed MYs 2021–2026 standards, a similar estimation of physics-based curves leveraging thirdparty simulation work form Argonne National Laboratories (Argonne) was developed. Estimating physics-based curves better ensures that technology and performance are held constant for all footprints; augmenting a largely statistical analysis with an analysis that more explicitly incorporates engineering theory helps to corroborate that the relationship between fuel economy and footprint is in fact being characterized. Tractive energy is the amount of energy it will take to move a vehicle.259 Here, tractive energy effectiveness is defined as the share of the energy content of fuel consumed which is converted into mechanical energy and used to move a vehicle—for internal combustion engine (ICE) vehicles, this will vary with the relative efficiency of specific engines. Data from Argonne simulations suggest that the limits of tractive energy effectiveness are approximately 25 percent for vehicles with internal combustion engines which do not possess integrated starter generator, other hybrid, plug-in, pure electric, or fuel cell technology. A tractive energy prediction model was also developed to support today’s proposal. Given a vehicle’s mass, frontal area, aerodynamic drag coefficient, and rolling resistance as inputs, the model will predict the amount of tractive energy required for the vehicle to complete the Federal test cycle. This model was used to predict the tractive energy required for the average vehicle of a given footprint 260 and ‘‘body technology package’’ to complete the cycle. The body technology packages considered are defined in Table V–2, below. Using the absolute tractive energy predicted and tractive energy effectiveness values spanning possible ICE engines, fuel economy values were then estimated for different body technology packages and engine tractive energy effectiveness values. Chapter 6 of the PRIA show the resultant CAFE levels estimated for the vehicle classes Argonne simulated for this analysis, at different footprint values and by vehicle ‘‘box.’’ Pickups are considered 1-box, hatchbacks and minivans are 2-box, and sedans are 3box. These estimates are compared with the MY 2021 standards finalized in 2012. The general trend of the simulated data points follows the pattern of the previous MY 2021 standards for all technology packages and tractive energy effectiveness values presented in the PRIA. The tractive energy curves are intended to validate the curve shapes against a physics-based alternative, and the analysis suggests that the curve shapes track the physical relationship between fuel economy and tractive energy for different footprint values. Physical limitations are not the only forces manufacturers face; their success is dependent upon producing vehicles that consumers desire and will purchase. For this reason, in setting future standards, the analysis will continue to consider information from statistical analyses that do not homogenize technology applications in addition to statistical analyses which do, as well as a tractive energy analysis similar to the one presented above. The relationship between fuel economy and footprint remains directionally discernable but quantitatively uncertain. Nevertheless, each standard must commit to only one function. Approaching the question ‘‘how is fuel economy related to footprint’’ from different directions and applying different approaches has given EPA and NHTSA confidence that the function applied here appropriately and reasonably reflects the relationship between fuel economy and footprint. The agencies invited comments on this conclusion and the supporting analysis. IPI raised concerns that ‘‘. . . several dozen models (mostly subcompacts and sports cars) fall in the 30–40 square feet range, which are all subject to the same standards’’ and that ‘‘manufacturers of these models may have an incentive to decrease footprints as a compliance strategy, since doing so would not trigger more stringent standards.’’ 261 NHTSA and EPA agree that, all else equal, downsizing the smallest cars (e.g., Chevrolet Spark, Ford Fiesta, Mini Cooper, Mazda MX– 5, Porsche 911, Toyota Yaris) would most likely tend to degrade overall highway safety. At the same time, as discussed above, the agencies recognize that small vehicles do appear attractive to some market segments (although obviously the Ford Fiesta and Porsche 911 compete in different segments). 259 Thomas, J. ‘‘Drive Cycle Powertrain Efficiencies and Trends Derived from EPA Vehicle Dynamometer Results,’’ SAE Int. J. Passeng. Cars— Mech. Syst. 7(4):2014, doi:10.4271/2014–01–2562. Available at https://www.sae.org/publications/ technical-papers/content/2014-01-2562/ (last accessed June 15, 2018). 260 The mass reduction curves used elsewhere in this analysis were used to predict the mass of a vehicle with a given footprint, body style box, and mass reduction level. The ‘Body style Box’ is 1 for hatchbacks and minivans, 2 for pickups, and 3 for sedans, and is an important predictor of aerodynamic drag. Mass is an essential input in the tractive energy calculation. 261 IPI, NHTSA–2018–0067–12362, p. 14. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00083 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.059</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Therefore, there is a tension between on one hand, avoiding standards that unduly encourage safety-eroding downsizing and, on the other, avoiding standards that unduly penalize the market for small vehicles. The agencies examined this issue, and note that the market for the smallest vehicles has not evolved at all as estimated in the analysis supporting the 2012 final rule, and attribute this more to fuel prices and consumer demand for larger vehicles than to attribute-based CAFE and CO2 standards. For example, the market for vehicles with footprints less than 40 square foot was about 45 percent smaller in MY 2017 than in MY 2010. The agencies also found that among the smallest vehicle models produced throughout MYs 2010–2017, most have become larger, not smaller. For example, while the Mazda MX–5’s footprint decreased by 0.1 square foot (0.3 percent) during that time, the MY 2017 versions of the Mini Cooper, Smart fortwo, Porsche 911, and Toyota Yaris had larger footprints than in MY 2010. With the market for very small vehicles shrinking, and with manufacturers not evidencing a tendency to make the smallest vehicles even smaller, the agencies are satisfied that it would be unwise to change the target functions such that targets never stop becoming more stringent as vehicle footprint becomes ever smaller, because doing so 262 https://www.govinfo.gov/content/pkg/CFR– 2014-title40-vol19/pdf/CFR-2014-title40-vol19sec86-1818-12.pdf 263 EPA regulations use a different but mathematically equivalent approach to specify targets. Rather than using a function with nested minima and maxima functions, EPA regulations specify requirements separately for different ranges of vehicle footprint. Because these ranges reflect the combined application of the listed minima, VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00084 Fmt 4701 Sfmt 4725 could further impede an alreadyshrinking market. B. No-Action Alternative As in the proposal, the No-Action Alternative applies the augural CAFE and final CO2 targets announced in 2012 for MYs 2021–2025.262 For MY 2026, this alternative applies the same targets as for MY 2025. The carbon dioxide equivalent of air conditioning refrigerant leakage credits, nitrous oxide, and methane emissions are included for compliance with the EPA standards for all model years under the no-action alternative.263 maxima, and linear functions, it is mathematically equivalent and more efficient to present the targets as in this Section. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.060</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24256 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 264 CBD et al., NHTSA–2018–0067–12123, Attachment 1, at 13. 265 CARB, NHTSA–2018–0067–11873, at 124– 125. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 using 0%/year increases for both passenger cars and light trucks for MYs 2017–2026.’’ 267 Similarly, AVE argued that because previously-promulgated standards for MYs 2018–2021 already present a significant challenge that ‘‘will likely require almost every automaker to continue using credits for compliance, . . . AVE believes this rulemaking should reset . . . the current compliance baseline for cars and light trucks at MY 2018 . . .’’ 268 BorgWarner commented similarly that ‘‘Beginning in MY 2018, standards should be reset to the levels the industry actually achieved. For MY 2018 and beyond, succeeding model year targets should be set with an annual rate of improvement defined by the slope of improvement the industry has achieved over the last six years. . . . Based on these data, our analysis suggests the most reasonable and logical rate of improvement falls between 2.0% to 2.6% for cars and trucks. Additionally, a single rate of improvement for the combined fleet should be considered.’’ 269 The No-Action Alternative represents expectations regarding the world in the absence of a proposal, accounting for applicable laws already in place. Although manufacturers are already making significant use of compliance credits toward compliance with even MY 2017 standards, the agencies are at 12 and 29–30. W., NHTSA–2018–0067–0444, at 8. 268 AVE, NHTSA–2018–0067–11696, at 8–9. obligated to evaluate regulatory alternatives against the standards already in place through MY 2025. Similarly, even though manufacturers are already producing electric vehicles, EPA and NHTSA appropriately excluded California’s ZEV mandate from the No-Action alternative for the NPRM, for several reasons. First, the ZEV mandate is not Federal law; second, as described in the proposal and subsequently finalized in regulatory text, the ZEV mandate is expressly and impliedly preempted by EPCA; third, EPA proposed to withdraw the waiver of CAA preemption in the NPRM and subsequently finalized this withdrawal. Accordingly, the agencies have, therefore, appropriately excluded the ZEV mandate from the No-Action alternative. However, as discussed below, the agencies’ analysis does account for the potential that under every regulatory alternative, including the No-Action Alternative, vehicle electrification could increase in the future, especially if batteries become less expensive as gasoline becomes more expensive. C. Action Alternatives 1. Alternatives in Final Rule Table V–5 below shows the different alternatives evaluated in today’s notice. 266 SAB 267 Kreucher, PO 00000 Frm 00085 Fmt 4701 Sfmt 4725 269 BorgWarner, NHTSA–2018–0067–11895, at 3, 6. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.061</GPH> khammond on DSKJM1Z7X2PROD with RULES2 In comments on the DEIS, CBD et al. indicated that it was appropriate for NHTSA to use the augural CAFE standards as the baseline No Action regulatory alternative.264 However, CARB commented that the baseline regulatory alternative should include CARB’s ZEV mandate, in part because EPA must consider ‘‘other regulations promulgated by EPA or other government entities,’’ and, according to CARB, there will be much more vehicle electrification in the future as manufacturers respond to market demand and also work to comply with the ZEV mandate.265 Similarly, EPA’s Science Advisory Board recommended—despite the action taken in the One National Program Action— that the baseline include state ZEV mandates ‘‘to be consistent with policies that would prevail in the absence of the rule change.’’ 266 EPA’s Science Advisory Board further recommended including sensitivity analyses with different penetration rates of ZEVs. On the other hand, arguing for consideration of standards less stringent than those proposed in the NPRM, Walter Kreucher commented that rather than using the augural standards as the baseline, ‘‘a better approach would be to assume a clean sheet of paper and start from the existing 2016MY fleet and its associated standards as the baseline 24257 24258 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations With one exception, the alternatives considered in the NPRM included the changes in stringency for the above alternatives. Alternative 3, the preferred alternative, is newly included for today’s notice.270 Regulations regarding implementation of NEPA requires agencies to ‘‘rigorously explore and objectively evaluate all reasonable alternatives, and for alternatives which were eliminated from detailed study, briefly discuss the reasons for their having been eliminated.’’ 271 This does not amount to a requirement that agencies evaluate the widest conceivable spectrum of alternatives. For example, a State considering adding a single travel lane to a preexisting section of highway would not be required to consider adding three lanes, or to consider dismantling the highway altogether. Among thousands of individual comments that mentioned the proposed standards very generally, some comments addressed the range and definition of these regulatory alternatives in specific terms, and these specific comments include comments on the stringency, structure, and particular provisions defining the set of regulatory alternatives under consideration. As discussed throughout today’s notice, the agencies have updated and otherwise revised many aspects of the analysis. The agencies have also reconsidered whether the set of alternatives studied in detail should be expanded to include standards less stringent than the proposal’s preferred alternative, or to include standards more stringent than the proposal’s no-action alternative. On one hand, comments from Walter Kreucher and AVE cited above indicate the agencies should consider relaxing standards below MY 2020 levels, and CEI challenged the agencies’ failure to include lessstringent alternatives in the following comments on this question: khammond on DSKJM1Z7X2PROD with RULES2 DOT failed to consider the possibility of freezing CAFE at an even more lenient standard than currently exists, nor did it consider making its proposed freeze take effect sooner than MY 2020. However, as DOT’s own analysis strongly indicates, doing so would lead to even greater benefits and an even greater reduction in CAFE-related deaths and injuries. In short, DOT’s failure to 270 As the agencies indicated in the NPRM, they were considering and taking comment ‘‘on a wide range of alternatives and have specifically modeled eight alternatives.’’ 83 FR at 42990 (Aug. 24, 2018). The preferred alternative in this final rule was within the range of alternatives considered in the proposal, although it was not specifically modeled at that time. This issue is discussed in further detail below. 271 40 CFR 1502.14. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 consider this possibility is arbitrary and capricious. It has an opportunity to remedy this in its final rule, and it should do so by selecting a standard that is even more lenient than the one it proposed. . . . It should have gone beyond its original set of alternatives and examined less stringent ones as well— until it found one that, for some reason or another, failed to produce greater safety benefits or failed to meet the statutory factors.272 On the other hand, a coalition of ten environmental advocacy organizations stated that the agencies should consider alternatives more stringent than those defining the baseline no action alternative, arguing that in light of CEQ guidance and the 2018 IPCC report on climate change, ‘‘the increasing danger, increasing urgency, and increasing importance of vehicle emissions all rationally counsel for strengthening emission standards.’’ 273 CBD et al. observe that ‘‘none of these alternatives [considered in the NPRM] increases fuel economy in comparison with the No Action Alternative, none conserves energy . . .’’ and go on to assert that ‘‘none represents maximum feasible CAFE standards.’’ 274 Similarly, EDF commented that ‘‘. . . given its clear statutory directive to maximize fuel savings, NHTSA should have considered a range of alternatives that would be more protective than the existing standards,’’ 275 and three State agencies in Minnesota commented that ‘‘more stringent standards are consistent with EPCA’s purpose of energy conservation and the CAA’s purpose of reducing harmful air pollutants.’’ 276 The North Carolina Department of Environmental Quality acknowledged the agencies’ determination in the proposal that alternatives beyond the augural standards might be economically impracticable, but nevertheless argued that ‘‘alternatives that exceed the stringency of the current standards are consistent with EPCA’s purpose’’ 277 In oral testimony before the agencies, the New York State Attorney General also indicated that the agencies should consider alternatives more stringent than the augural standards.278 NHTSA–2018–0067–12015, at 1. et al., NHTSA–2018–0067–12057 p. 10. Also, see comments from Senator Tom Carper, NHTSA–2018–0067–11910, at 8–9, and from UCS, NHTSA–2018–0067–12039, at 3. 274 CBD, et al., NHTSA–2018–0067–12123, at 12– 13. 275 EDF, NHTSA–2018–0067–11996, at 20. 276 Minnesota Pollution Control Agency, Department of Transportation, and Department of Health, NHTSA–2018–0067–11706, at 5. 277 North Carolina Department of Environmental Quality, NHTSA–2018–0067–12025, at 37–38. 278 New York State Attorney General, Testimony of Austin Thompson, NHTSA–2018–0067–12305, at 13. A coalition of States and cities commented that ‘‘at a minimum, the existing standards should be left in place, but EPA should also consider whether to make the standards more stringent, not less, just as it has done in prior proposals.’’ 279 More specifically, through International Mosaic, some individuals commented that the agencies must ‘‘fully and publicly consider a few options that require at least a seven annual percent [sic] improvement in vehicle fleet mileage.’’ 280 In comments on the DEIS, CBD, et al. went further, commenting that ‘‘NHTSA’s most stringent alternative must be set at no lower than a 9 percent improvement per year.’’ 281 Most manufacturers who commented on stringency did not identify specific regulatory alternatives that the agencies should consider, although Honda suggested that standards be set to increase in stringency at 5 percent annually for both passenger cars and light trucks throughout model years 2021–2026.282 283 The agencies carefully considered these comments to expand the range of stringencies to be evaluated as possible candidates for promulgation. To inform this consideration, the agencies used the CAFE model to examine a progression of stringencies extending outside the range presented in the proposal and draft EIS, and as a point of reference, using a case that reverts to MY 2018 standards starting in MY 2021. Scenarios included in this initial screening exercise ranged as high as increasing annually at 9.5 percent during MYs 2021–2026, reaching average CAFE and CO2 requirements of 66 mpg and 120 g/mi, respectively. Results of this analysis are presented in the following tables and charts. Focusing on MY 2029, the tables show average required and achieved CAFE (as mpg) and CO2 (as g/mi) levels for each scenario, along with average per-vehicle costs (in 2018 dollars, relative to retaining MY 2017 technologies). The proposed (0%/0%), final (1.5%/1.5%), and baseline augural standards are shown in bold type. The charts present 272 CEI, 273 CBD, PO 00000 Frm 00086 Fmt 4701 Sfmt 4700 279 NHTSA–2018–0067–11735, 280 International at 49. Mosaic NHTSA–2018–0067– 11154, at 1 281 CBD, et al., NHTSA–2018–0067–12123, at 17. 282 Honda, NHTSA–2018–0067–12019, EPA–HQ– OAR–2018–0283, at 54. 283 In model year 2021, the baseline standards for passenger cars and light trucks increase by about 4% and 6.5%, respectively, relative to standards for model year 2020. Depending on the composition of the future new vehicle fleet (i.e., the footprints and relative market shares of passenger cars and light trucks), this amounts to an overall average stringency increase of about 5.5% relative to model year 2020. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 clearly applies in both directions. On one hand, relaxing stringency below the proposed standards by reverting to MY 2018 or MY 2019 standards reduces average MY 2029 costs by only modest amounts ($54-$121). As discussed in Section VIII, the agencies’ updated analysis indicates that the proposed standards would not be maximum feasible considering the EPCA/EISA statutory factors, and would not be appropriate under the CAA after considering the appropriate factors. If further relaxation of standards appeared likely to yield more significant cost reductions, it is conceivable that such savings could outweigh further foregoing of energy and climate benefits. However, this screening analysis does not show dramatic cost reductions. Therefore, the agencies did not include these two less stringent alternatives in the detailed analysis presented in Section VII. PO 00000 Frm 00087 Fmt 4701 Sfmt 4725 On the other hand, increases in stringency beyond the baseline augural standards show relative costs continuing to accrue much more rapidly than relative CAFE and CO2 improvements. As discussed below in Section VIII, even the no action alternative is already well beyond levels that can be supported under the CAA and EPCA. If further stringency increases appeared likely to yield more significant additional energy and environmental benefits, it is conceivable that these could outweigh these significant additional cost increases. However, this screening analysis shows no dramatic relative acceleration of energy and environmental benefits. Therefore, the agencies did not include stringencies beyond the augural standards in the detailed analysis presented in Section VII. BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.062</GPH> khammond on DSKJM1Z7X2PROD with RULES2 the same results on a percentage basis, relative to values shown below for the scenario that reverts to MY 2018 standards starting in MY 2021. For example, reverting to the MY 2018 CAFE standards starting in MY 2021 yields an average CAFE requirement of 35 mpg by MY 2029, with the industry exceeding that standard by 5 mpg at an average cost of $1,255 relative to MY 2017 technology. Under the augural standards, the MY 2029 requirement increases to 47 mpg, the average compliance margin falls to 1 mpg, and the average cost increases to $2,770. In other words, compared to the scenario that reverts to MY 2018 stringency starting in MY 2021, the augural standards increase stringency by 34 percent (from 35 to 47 mpg), increase average fuel economy by 20 percent (from 40 to 48 mpg), and increase costs by 121 percent (from $1,255 to $2,770). As indicated in the following two charts, the reality of diminishing returns 24259 ER30AP20.064</GPH> Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00088 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.063</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24260 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Specific to model year 2021, some commenters argued that EPCA’s lead time requirement prohibits NHTSA from revising CAFE standards for model year 2021.284 Regarding the revision of standards for model year 2021, NHTSA did consider EPCA’s lead time requirement, and determined that while the agency would need to finalize a stringency increase at least 18 months before the beginning of the first affected model year, the agency can finalize a stringency decrease closer (or even after) the beginning of the first affected model year. The agency’s reasoning is explained further in Section VIII. Therefore, NHTSA did not change regulatory alternatives to avoid any relaxation of stringency in model year 2021. The Auto Alliance stated that ‘‘the truck increase rate should be no greater than the car rate of increase and should be the ‘equivalent task’ per fleet.’’ 285 284 State of California, et al., NHTSA–2018–0067– 11735, at 78.; CBD, et al., NHTSA–2018–0067– 12000, Appendix A, at 66.; National Coalition for Advanced Transportation, NHTSA–2018–0067– 11969, at 46. 285 Alliance, NHTSA–2018–0067–12073, at 7–8 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 Supporting these Alliance comments, FCA elaborated by commenting that ‘‘(1) in MY2017, the latest data we have available, most trucks have a larger gap to standards than cars, and (2) all of the truck segments are challenged because consumers are placing a greater emphasis on capability than fuel economy.’’ 286 Similarly, Ford commented that ‘‘. . . the rates of increase in the stringency of the standards should remain equivalent between passenger cars and light duty trucks.’’ 287 Other commenters expressed general support for equalizing the rates at which the stringencies of passenger car and light truck standards increase.288 For the final rule, the agencies have added an alternative in which stringency for both cars and trucks increases at 1.5 percent. This is consistent with comments received requesting that both fleets’ standards increase in stringency by the same 286 FCA, NHTSA–2018–0067–11943, at 46–47. NHTSA–2018–0067–11928, at 3. 288 See, e.g., Global, NHTSA–2018–0067–12032, at 4; NADA, NHTSA–2018–0067–12064, at 13; BorgWarner, NHTSA–2018–0067–11895, at 6. 287 Ford, PO 00000 Frm 00089 Fmt 4701 Sfmt 4700 amount, and 1.5 percent represents a rate of increase within the range of rates of increase considered in the NPRM. Throughout the NPRM, the agencies described their consideration as covering a range of alternatives.289 The preferred alternative for this final rule, an increase in stringency of 1.5 percent for both cars and trucks, falls squarely 289 83 FR at 42986 (Aug. 24, 2018) (explaining, in ‘‘Summary’’ section of NPRM, that ‘‘comment is sought on a range of alternatives discussed throughout this document’’); id. at 42988 (stating that the agencies are ‘‘taking comment on a wide range of alternatives, including different stringencies and retaining existing CO2 standards and the augural CAFE standards’’); 42990 (‘‘As explained above, the agencies are taking comment on a wide range of alternatives and have specifically modeled eight alternatives (including the proposed alternative) and the current requirements (i.e., baseline/no action).’’); 43197 (‘‘[T]oday’s notice also presents the results of analysis estimating impacts under a range of other regulatory alternatives the agencies are considering.’’); 43229 (explaining that ‘‘technology availability, development and application, if it were considered in isolation, is not necessarily a limiting factor in the Administrator’s selection of which standards are appropriate within the range of the Alternatives presented in this proposal.’’); 43369 (‘‘As discussed above, a range of regulatory alternatives are being considered.’’). E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.065</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C 24261 24262 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 within the range of alternatives proposed by the agencies. The NPRM alternatives were bounded on the upper end by the baseline/no action alternative, and the proposed alternative on the lower end (0 percent per year increase in stringency for both cars and trucks). For passenger cars, the agencies considered a range of stringency increases between 0 percent and 2 percent per year for passenger cars, in addition to the baseline/no action alternative. For light trucks, the agencies considered a range of stringency increases between 0 percent and 3 percent per year, in addition to the baseline/no action alternative. The agencies considered the same range of alternatives for this final rule. As with the proposal, the alternatives for stringency are bounded on the upper end by the baseline/no action alternative and on the lower end by 0 percent per year increases for both passenger cars and light trucks. Consistent with the proposal, for this final rule, the agencies considered stringency increases of between 0 and 2 percent per year for passenger cars and between 0 and 3 percent per year for light trucks, in addition to the baseline/ no action alternative. While it was not specifically modeled in the NPRM, the new preferred alternative of an increase in stringency of 1.5 percent for both cars and trucks was well within the range of alternatives considered. The proposal described the alternatives specifically modeled as options for the agencies, but also gave notice that they did not limit the agencies in selecting from among the range of alternatives under consideration.290 The agencies explained in the proposal that they were ‘‘taking comment on a wide range of alternatives and have specifically modeled eight alternatives.’’ 291 As with the proposal, for the final rule, the agencies specifically modeled the upper and lower bounds of the baseline/no action alternative and 0 percent per year stringency increases for both passenger cars and light trucks. In both the 290 See, e.g., 83 FR at 43003 (Aug. 24, 2018) (‘‘These alternatives were examined because they will be considered as options for the final rule. The agencies seek comment on these alternatives, seek any relevant data and information, and will review responses. That review could lead to the selection of one of the other regulatory alternatives for the final rule or some combination of the other regulatory alternatives (e.g., combining passenger cars standards from one alternative with light truck standards from a different alternative).’’); id. at 43229 (describing a factor relevant to ‘‘the Administrator’s selection of which standards are appropriate within the range of the Alternatives presented in this proposal’’). 291 83 FR at 42990 (Aug. 24, 2018). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 proposal and the final rule, the agencies also modeled a stringency increase of 2 percent per year for passenger cars and 3 percent per year for light trucks, as well as a variety of other specific increases between 0 and 2 percent for passenger cars and 0 and 3 percent for light trucks. The specific alternatives the agencies modeled for the final rule reflect their consideration of public comments. As discussed above, multiple commenters expressed support for equalizing the rates at which the stringencies of passenger car and light truck standards increase. To help the agencies evaluate alternatives that include the same stringency increase for passenger cars and light trucks, three of the seven alternatives (in addition to the baseline/ no action alternative) that the agencies specifically modeled for the final rule included the same stringency increase for passenger cars and light trucks. This includes the new preferred alternative of an increase in stringency of 1.5 percent for both cars and trucks. This alternative, and all others specifically modeled for the final rule, falls within the range of alternatives for stringency considered by the agencies in the proposal. Beyond these stringency provisions discussed in the NPRM, the agencies also sought comment on a number of additional compliance flexibilities for the programs, as discussed in Section IX. 2. Additional Alternatives Suggested by Commenters Beyond the comments discussed above regarding the shapes of the functions defining fuel economy and CO2 targets, regarding the inclusion of non-CO2 emissions, and regarding the stringencies to be considered, the agencies also received a range of other comments regarding regulatory alternatives. Some of these additional comments involved how CAFE and CO2 standards compare to one another for any given regulatory alternative. With a view toward maximizing harmonization of the standards, the Alliance, supported by some of its members’ individual comments, indicated that ‘‘to the degree flexibilities and incentives are not completely aligned between the CAFE and [CO2] programs, there must be an offset in the associated footprint-based targets to account for those differences. Some areas of particular concerns are air conditioning refrigerant credits, and incentives for advanced technology vehicles. The Alliance urges the Agencies to seek harmonization of the PO 00000 Frm 00090 Fmt 4701 Sfmt 4700 standards and flexibilities to the greatest extent possible. . . .’’ 292 On the other hand, discussing consideration of compliance credits but making a more general argument, the NYU Institute for Policy Integrity commented that ‘‘. . . EPA is not allowed to set lower standards just for the sake of harmonization; to the contrary, full harmonization may be inconsistent with EPA’s statutory responsibilities.’’ 293 Similarly, ACEEE argued that ‘‘any consideration of an extension or expansion of credit provisions under the [carbon dioxide] or CAFE standards program should take as a starting point the assumption that the additional credits will allow the stringency of the standards to be increased.’’ 294 EPCA’s requirement that NHTSA set standards at the maximum feasible levels is separate and ‘‘wholly independent’’ from the CAA’s requirement, per Massachusetts v. EPA, that EPA issue regulations addressing pollutants that EPA has determined endanger public health and welfare.295 Nonetheless, as recognized by the Supreme Court, ‘‘there is no reason to think the two agencies cannot both administer their obligations and yet avoid inconsistency.’’ 296 This conclusion was reached despite the fact that EPCA has a range of very specific requirements about how CAFE standards are to be structured, how manufacturers are to comply, what happens when manufacturers are unable to comply, and how NHTSA is to approach setting standards, and despite the fact that the CAA has virtually no such requirements. This means that while nothing about either EPCA or the CAA, much less the combination of the two, guarantees ‘‘harmonization’’ defining ‘‘One National Program,’’ the agencies are expected to be able to work out the differences. Since tailpipe CO2 standards are de facto fuel economy standards, the more differences there are between CO2 and CAFE standards and compliance provisions, the more challenging it is for manufacturers to plan year-by-year production that responses to both, and the more difficult it is for affected stakeholders and the general public to understand regulation in this space. Therefore, even if the two statutes, taken together, do not guarantee ‘‘full harmonization,’’ steps toward greater 292 Alliance, NHTSA–2018–0067–12073, at 40. See also FCA, NHTSA–2018–0067–11943, at 6–7. 293 IPI, NHTSA–2018–0067–12213, at 21. 294 ACEEE, NHTSA–2018–0067–12122, at 3. 295 Massachusetts v. EPA, 549 U.S. 497, 532 (2007). 296 Id. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations harmonization help with compliance planning and transparency—and meet the expectations set forth by the Supreme Court that the agencies avoid inconsistencies. The agencies have taken important steps toward doing so. For example, EPA has adopted separate footprintbased CO2 standards for passenger cars and light trucks, and has redefined CAFE calculation procedures to introduce recognition for the application of real-world fuel-saving technology that is not captured with traditional EPA two-cycle compliance testing. Detailed aspects of both sets of standards and corresponding compliance provisions are discussed at length in Section IX. The agencies never set out with the primary goal of achieving ‘‘full harmonization,’’ such that both sets of standards would lead each manufacturer to respond in exactly the same way in every model year.297 For example, EPA did not adopt the EPCA requirement that domestic passenger car fleets each meet a minimum standard, or the EPCA cap on compliance credit transfers between passenger car fleets. On the other hand, EPA also did not adopt the EPCA civil penalty provisions that have allowed some manufacturers to pay civil penalties as an alternative method of meeting EPCA obligations. These and other differences provide that even if CAFE and CO2 standards are ‘‘mathematically’’ harmonized, for any given manufacturer, the two sets of standards will not be identically burdensome in each model year. Inevitably, one standard will be more challenging than the other, varying over time, between manufacturers, and between fleets. This means manufacturers need to have compliance plans for both sets of standards. In 2012, recognizing that EPCA provides no clear basis to address HFC, CH4, or N2O emissions directly, the agencies ‘‘offset’’ CO2 targets from fuel economy targets (after converting the latter to a CO2 basis) by the amounts of credit EPA anticipated manufacturers would, on average, earn in each model years by reducing A/C leakage and adopting refrigerants with reduced GWPs. In 2012, EPA assumed that by 2021, all manufacturers would be earning the maximum available credit, and EPA’s analysis assumed that all manufacturers would make progress at the same rate. However, as discussed 297 Full harmonization would mean that, for example, if Ford would do some set of things over time in response to CAFE standards in isolation, it would do exactly the same things on exactly the same schedule in response to CO2 standards in isolation. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 above, data highlighted in comments by Chemours, Inc., demonstrate that actual manufacturers’ adoption of lower-GWP refrigerants thus far ranges widely, with some manufacturers (e.g., Nissan) having taken no such steps to move toward lower-GWP refrigerants, while others (e.g., JLR) have already applied lower-GWP refrigerants to all vehicles produced for sale in the U.S. Therefore, at least in practice, HFC provisions thus far continue to leave a gap (in terms of harmonization) between the two sets of standards. The proposal would have taken the additional step of decoupling provisions regarding HFC (i.e., A/C leakage credits), CH4, and N2O emissions from CO2 standards, addressing these in separate regulations to be issued in a new proposal. As discussed above, EPA did not finalize this proposal. Accordingly, for the regulatory alternatives considered today, EPA has reinstated offsets of CO2 targets from fuel economy targets, reflecting the assumption that all manufacturers will be earning the maximum available A/C leakage credit by MY 2021. In addition to general comments on harmonization, the agencies received a range of comments on specific provisions—especially involving ‘‘flexibilities’’—that may or may not impact harmonization. With a view toward encouraging further electrification, NCAT proposed that EPA extend indefinitely the exclusion of upstream emissions from electricity generation, and also extend and potentially restructure production multipliers for PHEVs, EVs, and FCVs.298 On the other hand, connecting its comments back to the stringency of standards, NCAT also commented that ‘‘. . . expansion of compliance flexibilities in the absence of any requirement to improve [CO2] reduction or fuel economy (as under the agencies’ preferred option) could result in an effective deterioration of existing [CO2] and fuel economy performance, as well as little or no effective support for advanced vehicle technology development or deployment.’’ 299 Global Automakers indicated that the final rule ‘‘should include a package of programmatic elements that provide automakers with flexible compliance options that promote the full breadth of vehicle technologies,’’ such options to include the extension of ‘‘advanced technology’’ production multipliers through MY 2026, the indefinite exclusion of emissions from electricity generation, the extension to passenger 298 NCAT, NHTSA–2018–0067–11969, at 3–5. 299 Id. PO 00000 Frm 00091 Fmt 4701 Sfmt 4700 24263 cars of credits currently granted for the application of ‘‘game changing’’ technologies (e.g., HEVs) only to fullsize pickup trucks, an increase (to 15 g/ mi) of the cap on credits for off-cycle technologies, an updated credit ‘‘menu’’ of off-cycle technologies, and easier process for handling applications for off-cycle credits.300 The Alliance also called for expanded sales multipliers and a permanent exclusion of emissions from electricity generation.301 Walter Kreucher recommended the agencies consider finalizing the proposed standards but also keeping the augural standards as ‘‘voluntary targets’’ to ‘‘provide compliance with the statutes and an aspirational goal for manufacturers.’’ 302 The agencies have carefully considered these comments, and have determined that the current suite of ‘‘flexibilities’’ generally provide ample incentive more rapidly to develop and apply advanced technologies and technologies that produce fuel savings and/or CO2 reductions that would otherwise not count toward compliance. The agencies also share some stakeholders’ concern that expanding these flexibilities could increase the risk of ‘‘gaming’’ that would make compliance less transparent and would unduly compromise energy and environmental benefits. Nevertheless, as discussed in Section IX, EPA is adopting new multiplier incentives for natural gas vehicles. EPA is also finalizing some changes to procedures for evaluating applications for off-cycle credits, and expects these changes to make this process more accurate and more efficient. Also, EPA is revising its regulations to not require manufacturers to account for upstream emissions associated with electricity use for electric vehicles and plug-in hybrid electric vehicles through model year 2026; compliance will instead be based on tailpipe emissions performance only and not include emissions from electricity generation until model year 2027. As discussed below, even with this change, and even accounting for continued increases in fuel prices and reductions in battery prices, BEVs are projected in this final rule analysis to continue to account for less than 5 percent of new light vehicle sales in the U.S. through model year 2026. To the extent that this projection turns out to reflect reality, this means that the impact of upstream emissions from electricity use on the projected CO2 300 Global Automakers, NHTSA–2018–0067– 12032, at 4 et seq. 301 Alliance, NHTSA–2018–0067–12073, at 8. 302 Kreucher, W., NHTSA–2018–0067–0444, at 9. E:\FR\FM\30APR2.SGM 30APR2 24264 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations reductions associated with these standards would likely remain small. Regarding comments suggesting that the augural standards should be finalized as ‘‘voluntary targets,’’ the agencies have determined that having such targets exist alongside actual regulatory requirements would be, at best, unnecessary and confusing. Beyond these additional proposals, some commenters’ proposals clearly fell outside authority provided under EPCA or the CAA. Ron Lindsay recommended the agencies ‘‘consider postponing the rule changes until the U.S. can establish a legally binding national and international carbon budget and a binding mechanism to adhere to it.’’ 303 EPCA requires NHTSA to issue standards for MY 2022 by April 1, 2020, and previously-issued EPA regulations commit EPA to revisiting MY 2021– 2025 standards on a similar schedule. These statutory and regulatory provisions do not include a basis to delay decisions pending an international negotiation for which prospects and schedules are both unknown. SCAQMD, supported by Shyam Shukla, indicated that the agencies khammond on DSKJM1Z7X2PROD with RULES2 303 Ron Lindsay, EPA–HQ–OAR–2018–0283– 1414, at 6. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 should consider an alternative that keeps the waiver for California’s CO2 standards in place.304 NCAT and the North Carolina DEQ offered similar comments and CBD, et al. commented that ‘‘among the set of more stringent alternatives that NEPA requires the agency to consider, NHTSA must include action alternatives that retain the standards California and other states have lawfully adopted.’’ 305 As discussed above, the agencies recently issued a final rule addressing the issue of California’s authority. NEPA does not require NHTSA to include action alternatives that cannot be lawfully realized. International Mosiac commented that NHTSA’s DEIS ‘‘is fatally flawed . . . because it does not consider any marketbased alternatives (e.g., a ‘cap and trade’ type option).’’ 306 While EPCA/EISA does include very specific provisions regarding trading of CAFE compliance credits, the statute provides no authority for a broad-based cap-and-trade program 304 SCAQMD, NHTSA–2018–0067–5666, at 1–2; Shyam Shukla, NHTSA–2018–0067–5793, at 1–2. 305 NCAT, NHTSA–2018–0067–11969, at 64; NCDEQ, NHTSA–2018–0067–12025, at 38; CBD et al., NHTSA–2018–0067–12123, Attachment 1, at 18. 306 International Mosaic, NHTSA–2018–0067– 11154, at 1–2. PO 00000 Frm 00092 Fmt 4701 Sfmt 4700 involving other sectors. Similarly, Michalek, et al. wrote that ‘‘a more economically efficient approach of, taxing emissions and fuel consumption at socially appropriate levels would allow households to determine whether to reduce fuel consumption and emissions by driving less, by buying a vehicle with more fuel saving technologies, or by buying a smaller vehicle—or, alternatively, not to reduce fuel consumption and emissions at all but rather pay a cost based on the damages they cause. Forcing improvements only through one mechanism (fuel-saving technologies) increases the cost of achieving these outcomes.’’ 307 While some economists would agree with these comments, Congress has provided no clear authority for NHTSA or EPA to implement either an emissions tax or a broad-based cap-and-trade program in which motor vehicles could participate. 3. Details of Alternatives Considered in Final Rule a) Alternative 1 Alternative 1 holds the stringency of targets constant and MY 2020 levels through MY 2026. 307 Michalek, 13. E:\FR\FM\30APR2.SGM 30APR2 et al., NHTSA–2018–0067–11903, at Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 Alternative 2 increases the stringency of targets annually during MYs 2021– VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 2026 (on a gallon per mile basis, starting from MY 2020) by 0.5 percent for PO 00000 Frm 00093 Fmt 4701 Sfmt 4700 passenger cars and 0.5 percent for light trucks. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.066</GPH> b) Alternative 2 24265 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations c) Alternative 3 khammond on DSKJM1Z7X2PROD with RULES2 Alternative 3; the final standards promulgated today, increases the VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 stringency of targets annually during MYs 2021–2026 (on a gallon per mile basis, starting from MY 2020) by 1.5 PO 00000 Frm 00094 Fmt 4701 Sfmt 4700 percent for passenger cars and 1.5 percent for light trucks. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.067</GPH> 24266 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 Alternative 4 increases the stringency of targets annually during MYs 2021– VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 2026 (on a gallon per mile basis, starting from MY 2020) by 1.0 percent for PO 00000 Frm 00095 Fmt 4701 Sfmt 4700 passenger cars and 2.0 percent for light trucks. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.068</GPH> d) Alternative 4 24267 24268 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations e) Alternative 5 ER30AP20.070</GPH> passenger cars and 2.0 percent for light trucks. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00096 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.069</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Alternative 5 increases the stringency of targets annually during MYs 2022– 2026 (on a gallon per mile basis, starting from MY 2021) by 1.0 percent for Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations f) Alternative 6 passenger cars and 3.0 percent for light trucks. ER30AP20.072</GPH> ER30AP20.073</GPH> 2026 (on a gallon per mile basis, starting from MY 2020) by 2.0 percent for VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00097 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.071</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Alternative 6 increases the stringency of targets annually during MYs 2021– 24269 24270 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations g) Alternative 7 2026 (on a gallon per mile basis, starting from MY 2021) by 2.0 percent for passenger cars and 3.0 percent for light trucks. manufactured for sale in the U.S. by all manufacturers in the model year, which projection shall be published in the Federal Register when the standard for that model year is promulgated in accordance with 49 U.S.C. 32902(b).308 Any time NHTSA establishes or changes a passenger car standard for a model year, the MDPCS for that model year must also be evaluated or re-evaluated and established accordingly. Thus, this final rule establishes the applicable MDPCS for MYs 2021–2026. Table V–22 lists the minimum domestic passenger car standards. Alternative 7 increases the stringency of targets annually during MYs 2022– ER30AP20.076</GPH> ER30AP20.075</GPH> 308 49 U.S.C. 32902(b)(4). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00098 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.074</GPH> khammond on DSKJM1Z7X2PROD with RULES2 EPCA, as amended by EISA, requires that any manufacturer’s domesticallymanufactured passenger car fleet must meet the greater of either 27.5 mpg on average, or 92 percent of the average fuel economy projected by the Secretary for the combined domestic and nondomestic passenger automobile fleets Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VI. Analytical Approach as Applied to Regulatory Alternatives khammond on DSKJM1Z7X2PROD with RULES2 A. Overview of Methods Like analyses accompanying the NPRM and past CAFE and CAFE/CO2 rulemakings, the analysis supporting today’s notice spans a range of technical topics, uses a range of different types of data and estimates, and applies several different types of computer models. The purpose of the analysis is not to determine the standards, but rather to provide information for consideration in doing so. The analysis aims to answer the question ‘‘what impacts might each of these regulatory alternatives have?’’ Over time, NHTSA’s and, more recently, NHTSA’s and EPA’s analyses have expanded to address an increasingly wide range of types of impacts. Today’s analysis involves, among other things, estimating how the application of various combinations of technologies could impact vehicles’ costs and fuel economy levels (and CO2 emission rates), estimating how vehicle manufacturers might respond to standards by adding fuel-saving technologies to new vehicles, estimating how changes in new vehicles might impact vehicle sales and operation, and estimating how the combination of these changes might impact national-scale energy consumption, emissions, highway safety, and public health. In addition, the EIS accompanying today’s notice addresses impacts on air quality and climate. The analysis of these factors informs and supports both NHTSA’s application of the statutory requirements governing the setting of ‘‘maximum feasible’’ fuel-economy standards under EPCA, including, among others, technological feasibility and economic practicability, and EPA’s application of the CAA requirements for tailpipe emissions. Supporting today’s analysis, the agencies have brought to bear a variety of different types of data, a few examples of which include fuel economy compliance reports, historical sales and average characteristics of light-duty vehicles, historical economic and demographic measures, historical travel demand and energy prices and consumption, and historical measures of highway safety. Also supporting today’s analysis, the agencies have applied several different types of estimates, a few examples of which include projections of the future cost of different fuel-saving technologies, projections of future GDP and the number of households, estimates of the ‘‘gap’’ between ‘‘laboratory’’ and on-road fuel economy, and estimates of the social VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 cost of CO2 emissions and petroleum ‘‘price shocks.’’ With a view toward transparency, repeatability, and efficiency, the agencies have used a variety of computer models to conduct the majority of today’s analysis. For example, the agencies have applied DOE/EIA’s National Energy Modeling System (NEMS) to estimate future energy prices, EPA’s MOVES model to estimate tailpipe emission rates for ozone precursors and other criteria pollutants, DOE/Argonne’s GREET model to estimate emission rates for ‘‘upstream’’ processes (e.g., petroleum refining), and DOE/Argonne’s Autonomie simulation tool to estimate the fuel consumption impacts of different potential combinations of fuelsaving technology. In addition, the EIS accompanying today’s notice applies photochemical models to estimate air quality impacts, and applies climate models to estimate climate impacts of overall emissions changes. Use of these different types of data, estimates, and models is discussed further below in the most closely relevant sections. For example, the agencies’ use of NEMS is discussed below in the portion of Section VI that addresses the macroeconomic context, which includes fuel prices, and the agencies use of Autonomie is discussed in the portion of Section VI.B.3 that addresses the agencies’ approach to estimating the effectiveness of various technologies (in reducing fuel consumption and CO2 emissions). Providing an integrated means to estimate both vehicle manufacturers’ potential responses to CAFE or CO2 standards and, in turn, many of the different potential direct results (e.g., changes in new vehicle costs) and indirect impacts (e.g., changes in rates of fleet turnover) of those responses, the CAFE Model plays a central role in the agencies’ analysis supporting today’s notice. The agencies used the specific models mentioned above to develop inputs to the CAFE model, such as fuel prices and emission factors. Outputs from the CAFE Model are discussed in Sections VII and VIII of today’s notice, and in the accompanying RIA. The EIS accompanying today’s notice makes use of the CAFE Model’s estimates of changes in total emissions from lightduty vehicles, as well as corresponding changes in upstream emissions. These changes in emissions are included in the set of inputs to the models used to estimate air quality and climate impacts. The remainder of this overview focuses on the CAFE Model. The purpose of this overview is not to provide a comprehensive technical PO 00000 Frm 00099 Fmt 4701 Sfmt 4700 24271 description of the model,309 but rather to give an overview of the model’s functions, to explain some specific aspects not addressed elsewhere in today’s notice, and to discuss some model aspects that were the subject of significant public comment. Some model functions and related comments are addressed in other parts of today’s notice. For example, the model’s handling of Autonomie-based fuel consumption estimates is addressed in the portion of Section VI.B.3 that discusses the agencies’ application of Autonomie. The model documentation accompanying today’s notice provides a comprehensive and detailed description of the model’s functions, design, inputs, and outputs. 1. Overview of CAFE Model The basic design of the CAFE Model is as follows: The system first estimates how vehicle manufacturers might respond to a given regulatory scenario, and from that potential compliance solution, the system estimates what impact that response will have on fuel consumption, emissions, and economic externalities. A regulatory scenario involves specification of the form, or shape, of the standards (e.g., flat standards, or linear or logistic attributebased standards), scope of passenger car and truck regulatory classes, and stringency of the CAFE and CO2 standards for each model year to be analyzed. Manufacturer compliance simulation and the ensuing effects estimation, collectively referred to as compliance modeling, encompass numerous subsidiary elements. Compliance simulation begins with a detailed userprovided initial forecast of the vehicle models offered for sale during the simulation period. The compliance simulation then attempts to bring each manufacturer into compliance with the standards defined by the regulatory scenario contained within an input file developed by the user. For example, a regulatory scenario may define CAFE or CO2 standards that increase in stringency by 4 percent per year for 5 consecutive years. The model applies various technologies to different vehicle models in each manufacturer’s product line to simulate how each manufacturer might make progress toward compliance with the specified standard. Subject to a variety of user-controlled constraints, the model applies technologies based on 309 The CAFE Model is available at https:// www.nhtsa.gov/corporate-average-fuel-economy/ compliance-and-effects-modeling-system with documentation and all inputs and outputs supporting today’s notice. E:\FR\FM\30APR2.SGM 30APR2 24272 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 their relative cost-effectiveness, as determined by several input assumptions regarding the cost and effectiveness of each technology, the cost of compliance (determined by the change in CAFE or CO2 credits, CAFErelated civil penalties, or value of CO2 credits, depending on the compliance program being evaluated and the effective-cost mode in use), and the value of avoided fuel expenses. For a given manufacturer, the compliance simulation algorithm applies technologies either until the manufacturer runs out of cost-effective technologies, until the manufacturer exhausts all available technologies, or, if the manufacturer is assumed to be willing to pay civil penalties, until paying civil penalties becomes more cost-effective than increasing vehicle fuel economy. At this stage, the system assigns an incurred technology cost and updated fuel economy to each vehicle model, as well as any civil penalties incurred by each manufacturer. This compliance simulation process is repeated for each model year available during the study period. This point marks the system’s transition between compliance simulation and effects calculations. At the conclusion of the compliance simulation for a given regulatory scenario, the system contains multiple copies of the updated fleet of vehicles corresponding to each model year analyzed. For each model year, the vehicles’ attributes, such as fuel types (e.g., diesel, electricity), fuel economy values, and curb weights have all been updated to reflect the application of technologies in response to standards VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 throughout the study period. For each vehicle model in each of the model year specific fleets, the system then estimates the following: Lifetime travel, fuel consumption, carbon dioxide and criteria pollutant emissions, the magnitude of various economic externalities related to vehicular travel (e.g., noise), and energy consumption (e.g., the economic costs of short-term increases in petroleum prices). The system then aggregates model-specific results to produce an overall representation of modeling effects for the entire industry. Different categorization schemes are relevant to different types of effects. For example, while a fully disaggregated fleet is retained for purposes of compliance simulation, vehicles are grouped by type of fuel and regulatory class for the energy, carbon dioxide, criteria pollutant, and safety calculations. Therefore, the system uses model-by-model categorization and accounting when calculating most effects, and aggregates results only as required for efficient reporting. 2. Representation of the Market As a starting point, the model needs enough information to represent each manufacturer covered by the program. As discussed below in Section VI.B.1, the MY 2017 analysis fleet contains information about each manufacturer’s: • Vehicle models offered for sale— their current (i.e., MY 2017) production volumes, manufacturer suggested retail prices (MSRPs), fuel saving technology content and other attributes (curb weight, drive type, assignment to technology class and regulatory class); PO 00000 Frm 00100 Fmt 4701 Sfmt 4700 • Production considerations— product cadence of vehicle models (i.e., schedule of model redesigns and ‘‘freshenings’’), vehicle platform membership, degree of engine and/or transmission sharing (for each model variant) with other vehicles in the fleet; and • Compliance constraints and flexibilities—preference for full compliance or penalty payment/credit application, willingness to apply additional cost-effective fuel saving technology in excess of regulatory requirements, projected applicable flexible fuel credits, and current credit balance (by model year and regulatory class) in first model year of simulation. Representation of Fuel-Saving Technologies The modeling system defines technology pathways for grouping and establishing a logical progression of technologies that can be applied to a vehicle. Technologies that share similar characteristics form cohorts that can be represented and interpreted within the CAFE Model as discrete entities. The following Table VI–1 shows the technologies available within the modeling system used for this final rule. Each technology is discussed in detail below. However, an understanding of the technologies considered and how they are defined in the model (e.g., a 6speed manual transmission is defined as ‘‘MT6’’) is helpful for the following explanation of the compliance simulation and the inputs required for that simulation. BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00101 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24273 ER30AP20.077</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations BILLING CODE 4910–59–C khammond on DSKJM1Z7X2PROD with RULES2 These entities are then laid out into pathways (or paths), which the system uses to define relations of mutual exclusivity between conflicting sets of technologies. For example, as presented in the next section, technologies on the Turbo Engine path are incompatible with those on the HCR Engine or the Diesel Engine paths. As such, whenever a vehicle uses a technology from one pathway (e.g., turbo), the modeling system immediately disables the incompatible technologies from one or more of the other pathways (e.g., HCR and diesel). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 In addition, each path designates the direction in which vehicles are allowed to advance as the modeling system evaluates specific technologies for application. Enforcing this directionality within the model ensures that a vehicle that uses a more advanced or more efficient technology (e.g., AT8) is not allowed to ‘‘downgrade’’ to a less efficient option (e.g., AT5). Visually, as portrayed in the charts in the sections that follow, this is represented by an arrow leading from a preceding technology to a succeeding one, where vehicles begin at the root of each path, PO 00000 Frm 00102 Fmt 4701 Sfmt 4700 and traverse to each successor technology in the direction of the arrows. The modeling system incorporates twenty technology pathways for evaluation as shown below. Similar to individual technologies, each path carries an intrinsic application level that denotes the scope of applicability of all technologies present within that path, and whether the pathway is evaluated on one vehicle at a time, or on a collection of vehicles that share a common platform, engine, or transmission. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.078</GPH> 24274 Even though technology pathways outline a logical progression between related technologies, all technologies available to the system are evaluated concurrently and independently of each other. Once all technologies have been examined, the model selects a solution deemed to be most cost-effective for application on a vehicle. If the modeling system applies a technology that resides later in the pathway, it will subsequently disable all preceding technologies from further consideration to prevent a vehicle from potentially downgrading to a less advanced option. Consequently, the system skips any technology that is already present on a vehicle (either those that were available on a vehicle from the input fleet or those that were previously applied by the model). This ‘‘parallel technology’’ approach, unlike the ‘‘parallel path’’ methodology utilized in the preceding versions of the model, allows the system always to consider the entire set of available technologies instead of foregoing the application of potentially more cost-effective options that happen to reside further down the pathway.310 This revised approach addresses 310 Previous versions of the CAFE Model followed a ‘‘low-cost’’ first approach where the system would stop evaluating technologies residing within a given pathway as soon as the first cost-effective option within that path was reached. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 comments summarized below, and allows the system to analyze all available technology options concurrently and independently of one other without having to first apply one or more ‘‘predecessor’’ technologies. For example, if model inputs are such that a 7-speed transmission is cost-effective, but not as cost-effective as an 8-speed transmission, the revised approach enables the model to skip over the 7speed transmission entirely, whereas the NPRM version of the model might first apply the 7-speed transmission and then consider whether to proceed immediately to the 8-speed transmission. As such, the model’s choices for evaluation of new technology solutions becomes slightly less restrictive, allowing it immediately to consider and apply more advanced options, and increasing the likelihood that the a globally optimum solution is selected. Some commenters supported the agencies’ use of such pathways in the simulation of manufacturers’ potential application of technologies. As one of a dozen examples of CAFE model design elements that lead to the transparent representation of real-world factors, the Alliance highlighted ‘‘recognition of the need for manufacturers to follow ‘technology’ pathways that retain capital and implementation expertise, such as PO 00000 Frm 00103 Fmt 4701 Sfmt 4700 24275 specializing in one type of engine or transmission instead of following an unconstrained optimization that would cause manufacturers to leap to unrelated technologies and show overly optimistic costs and benefits.’’ 311 Similarly, Toyota commented that ‘‘the inertia of capital investments and engineering expertise dedicated to one compliance technology or set of technologies makes it unreasonable for manufacturers to immediately switch to another technology path.’’ 312 Other commenters cited the use of technology pathways as inherently overly restrictive. For example, as an example of ‘‘arbitrary model constraints,’’ a coalition of commenters cited the fact the model ‘‘prohibit[s] manufacturers from switching vehicle technology pathways.’’ 313 Also, EDF, UCS, and CARB cited the combination of technology pathways, decision making criteria, and model inputs as producing unrealistic results.314 Regarding the technology pathways, specifically, EDF’s consultant argued that the technology paths are not 311 Alliance, NHTSA–2018–0067–12073, at 9. NHTSA–2018–0067–12098, at 7. 313 CBD, et al., NHTSA–2018–0067–12057, at 3. 314 EDF, NHTSA–2018–0067–12108, Appendix A, at 57 et seq.; UCS, NHTSA–2018–0067–12039, Appendix, at 25 et seq.; Roush Industries, NHTSA– 2018–0067–11984, at 5. 312 Toyota, E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.079</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24276 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations transparent, and cited the potential that specific paths may not necessarily be arranged in progression from least to most cost-effective—that ‘‘NHTSA ignores the cost of the technology when developing this list.’’ 315 Relatedly, as EDF’s consultant commented: khammond on DSKJM1Z7X2PROD with RULES2 [T]he Volpe Model is not designed to look backwards along its technology paths. Thus, the opportunity to recover the expenditure of inefficient technology is missed. NHTSA might argue that a manufacturer will not invest in 10-speed transmissions, for example, and then return to an older design. Whether or not this is true in real life, such a view would put too much stake in the Volpe Model projections. The model simply projects what could be done, not what will be. Anyone examining the progression of technology and noting the reversion of transmission technology could easily modify the model inputs to avoid this. Also, if NHTSA evaluated combinations of technologies prior to entering them in the model piecemeal, it would automatically avoid such apparent problems.316 The agencies also received additional public comments on specific paths and specific interactions between paths (e.g., involving engines and hybridization). These comments are addressed below. The agencies have carefully considered these comments and the approach summarized below reflects some corresponding revision. As mentioned above, the CAFE model now approaches the technology paths in a such way that, faced with two costeffective technologies on the same path, the model can proceed directly to the more advanced technology if that technology is the more cost effective of the two. However, the agencies reject assertions that the model’s use of technology paths is not transparent. The agencies provided extensive explanatory text, figures, model documentation, and model source code specifically addressing these paths (and other model features). This transparency appears evident in that commenters (sometimes while claiming that a specific feature of the model is not transparent) presented analytical results involving changes to corresponding inputs that required a detailed understanding of that feature’s operation. Regarding comments that the technology paths should be arranged in order of cost-effectiveness, the agencies note that such comments presume, without merit, that costs, fuel consumption impacts, and other inputs (e.g., fuel prices) that logically impact manufacturers’ decision-making are not 315 EDF, NHTSA–2018–0067–12108, Appendix B, at 69. 316 Ibid., at 70. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 subject to uncertainty. These inputs are all subject to uncertainty, and the CAFE Model’s arrangement of technologies into several paths is responsive to these uncertainties. Nevertheless, the agencies maintain that some technologies do reflect a higher level of advancement than others (e.g., 10-speed transmissions vs. 5-speed transmissions), and while manufacturers may, in practice, occasionally revert to less advanced technologies, it is appropriate and reasonable to conduct the agencies’ analysis in a manner that assumes manufacturers will continue to make forward progress. As observed by EDF’s consultant’s remarks, the CAFE Model ‘‘simply projects what could be done, not what will be.’’ While no model, much less any model relying on information that can be made publicly available, can hope to represent precisely each manufacturers’ actual detailed constrains related to product development and planning, such constraints are real and important. The agencies agree that the CAFE Model’s representation of such constraints— including the Model’s use of technology paths—provides a reasonable means of accounting for them. 4. Compliance Simulation The CAFE model provides a way of estimating how vehicle manufacturers could attempt to comply with a given CAFE standard by adding technology to fleets that the agencies anticipate they will produce in future model years. This exercise constitutes a simulation of manufacturers’ decisions regarding compliance with CAFE or CO2 standards. This compliance simulation begins with the following inputs: (a) The analysis fleet of vehicles from model year 2017 discussed below in Section VI.B.1, (b) fuel economy improving technology estimates discussed below in Section VI.C, (c) economic inputs discussed below in Section VI.D, and (d) inputs defining baseline and potential new CAFE or CO2 standards discussed above in Section V. For each manufacturer, the model applies technologies in both a logical sequence and a cost-optimizing strategy in order to identify a set of technologies the manufacturer could apply in response to new CAFE or CO2 standards. The model applies technologies to each of the projected individual vehicles in a manufacturer’s fleet, considering the combined effect of regulatory and market incentives while attempting to account for manufacturers’ production constraints. Depending on how the model is exercised, it will apply PO 00000 Frm 00104 Fmt 4701 Sfmt 4700 technology until one of the following occurs: (1) The manufacturer’s fleet achieves compliance 317 with the applicable standard and adding additional technology in the current model year would be attractive neither in terms of stand-alone (i.e., absent regulatory need) cost-effectiveness nor in terms of facilitating compliance in future model years; (2) The manufacturer ‘‘exhausts’’ available technologies; 318 or (3) For manufacturers assumed to be willing to pay civil penalties (in the CAFE program), the manufacturer reaches the point at which doing so would be more cost-effective (from the manufacturer’s perspective) than adding further technology. The model accounts explicitly for each model year, applying technologies when vehicles are scheduled to be redesigned or freshened and carrying forward technologies between model years once they are applied (until, if applicable, they are superseded by other technologies). The model then uses these simulated manufacturer fleets to generate both a representation of the U.S. auto industry and to modify a representation of the entire light-duty registered vehicle population. From these fleets, the model estimates changes in physical quantities (gallons of fuel, pollutant emissions, traffic fatalities, etc.) and calculates the relative costs and benefits of regulatory alternatives under consideration. The CAFE model accounts explicitly for each model year, in turn, because manufacturers actually ‘‘carry forward’’ most technologies between model years, tending to concentrate the application of new technology to vehicle redesigns or mid-cycle ‘‘freshenings,’’ and design cycles vary widely among manufacturers and specific products. 317 When determining whether compliance has been achieved in the CAFE program, existing CAFE credits that may be carried over from prior model years or transferred between fleets are also used to determine compliance status. For purposes of determining the effect of maximum feasible CAFE standards, however, EPCA prohibits NHTSA from considering these mechanisms for years being considered (though it does so for model years that are already final) and the agency runs the CAFE model without enabling these options. 49 U.S.C. 32902(h)(3). 318 In a given model year, it is possible that production constraints cause a manufacturer to ‘‘run out’’ of available technology before achieving compliance with standards. This can occur when: (a) An insufficient volume of vehicles are expected to be redesigned, (b) vehicles have moved to the ends of each (relevant) technology pathway, after which no additional options exist, or (c) engineering aspects of available vehicles make available technology inapplicable (e.g., secondary axle disconnect cannot be applied to two-wheel drive vehicles). E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Comments by manufacturers and model peer reviewers strongly support explicit year-by-year simulation. Year-by-year accounting also enables accounting for credit banking (i.e., carry-forward), as discussed above, and at least four environmental organizations recently submitted comments urging the agencies to consider such credits, citing NHTSA’s 2016 results showing impacts of carried-forward credits.319 Moreover, EPCA/EISA requires that NHTSA make a year-by-year determination of the appropriate level of stringency and then set the standard at that level, while ensuring ratable increases in average fuel economy through MY 2020. The multi-year planning capability, simulation of ‘‘market-driven overcompliance,’’ and EPCA credit mechanisms (again, for purposes of modeling the CAFE program) increase the model’s ability to simulate manufacturers’ real-world behavior, accounting for the fact that manufacturers will seek out compliance paths for several model years at a time, while accommodating the year-by-year requirement. This same multi-year planning structure is used to simulate responses to standards defined in grams CO2/mile, and utilizing the set of specific credit provisions defined under EPA’s program. After the light-duty rulemaking analysis accompanying the 2012 final rule that finalized NHTSA’s standards through MY 2021, NHTSA began work on changes to the CAFE model with the intention of better reflecting constraints of product planning and cadence for which previous analyses did not account. This involves accounting for expected future schedules for redesigning and ‘‘freshening’’ vehicle models, and accounting for the fact that a given engine or transmission is often shared among more than one vehicle model, and a given vehicle production platform often includes more than one vehicle model. These real product planning considerations are explained below. Like earlier versions, the current CAFE model provides the capability for integrated analysis spanning different regulatory classes, accounting both for standards that apply separately to different classes and for interactions between regulatory classes. Light vehicle CAFE and CO2 standards are specified separately for passenger cars and light trucks. However, there is considerable sharing between these two regulatory classes, where a single engine, transmission, or platform can appear in both the passenger car and light truck regulatory class. For example, some sport-utility vehicles are offered in 2WD versions (classified as passenger cars for compliance purposes) and 4WD versions (classified as light trucks for compliance purposes). Integrated analysis of manufacturers’ passenger car and light truck fleets provides the ability to account for such sharing and reduces the likelihood of finding solutions that could involve introducing impractical and unrealistic levels of complexity in manufacturers’ product lines. In addition, integrated fleet analysis provides the ability to simulate the potential that manufacturers could earn CAFE and CO2 credits by over complying with the standard in one fleet and use those credits toward compliance with the standard in another fleet (i.e., to simulate credit transfers between regulatory classes).320 The CAFE model also accounts for EPCA’s requirement that compliance be determined separately for fleets of domestic passenger cars and fleets of imported passenger cars. The model accounts for all three CAFE regulatory classes simultaneously (i.e., in an integrated way) yet separately: Domestic passenger cars, imported passenger cars, and light trucks. The model further accounts for two related specific statutory requirements specifically involving this distinction between domestic and imported passenger cars. First, EPCA/EISA requires that any given fleet of domestic passenger cars meet a minimum standard, irrespective of any available compliance credits. Second, EPCA/EISA requires compliance with the standards applicable to the domestic passenger car fleet without regard to traded or transferred credits.321 However, the CAA has no such limitation regarding compliance by domestic and imported vehicles; EPA did not adopt provisions similar to the aforementioned EPCA/EISA requirements and is not doing so today. Therefore, the CAFE model determines compliance for manufacturers’ overall passenger car and light truck fleets for EPA’s program. Each manufacturer’s regulatory requirement represents the productionweighted harmonic mean of their vehicle’s targets in each regulated fleet. 320 Note, 319 Comment by Environmental Law & Policy Center, Natural Resources Defense Council (NRDC), Public Citizen, and Sierra Club, Docket ID EPA– HQ–OAR–2015–0827–9826, at 28–29. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 however, that EPCA prohibits NHTSA from considering the availability of such credit trading when setting maximum feasible fuel economy standards. 49 U.S.C. 32902(h)(3). 321 49 U.S.C. 32903(f)(2) and (g)(4). PO 00000 Frm 00105 Fmt 4701 Sfmt 4700 24277 This means that no individual vehicle has a ‘‘standard,’’ merely a target, and each manufacturer is free to identify a compliance strategy that makes the most sense given its unique combination of vehicle models, consumers, and competitive position in the various market segments. As the CAFE model provides flexibility when defining a set of regulatory standards, each manufacturer’s requirement is dynamically defined based on the specification of the standards for any simulation and the distribution of footprints within each fleet. Given this information, the model attempts to apply technology to each manufacturer’s fleet in a manner that, given product planning and engineering-related considerations, optimizes the selected cost-related metric. The metric supported by the NPRM version of the model is termed ‘‘effective cost.’’ The effective cost captures more than the incremental cost of a given technology; it represents the difference between their incremental cost and the value of fuel savings to a potential buyer over the first 30 months of ownership.322 In addition to the technology cost and fuel savings, the effective cost also includes the change in CAFE civil penalties from applying a given technology and any estimated welfare losses associated with the technology (e.g., earlier versions of the CAFE model simulated low-range electric vehicles that produced a welfare loss to buyers who valued standard operating ranges between re-fueling events). Comments on this metric are discussed below, as are model changes responding to these comments. This construction allows the model to choose technologies that both improve a manufacturer’s regulatory compliance position and are most likely to be attractive to its consumers. This also means that different assumptions about future fuel prices will produce different rankings of technologies when the model evaluates available technologies for application. For example, in a high fuel price regime, an expensive but very efficient technology may look attractive to manufacturers because the value of the fuel savings is sufficiently high both to counteract the higher cost of the technology and, implicitly, to satisfy consumer demand to balance price increases with reductions in operating cost. 322 The length of time over which to value fuel savings in the effective cost calculation is a model input that can be modified by the user. This analysis uses 30 months’ worth of fuel savings in the effective cost calculation, using the price of fuel at the time of vehicle purchase. E:\FR\FM\30APR2.SGM 30APR2 24278 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations In general, the model adds technology for several reasons but checks these sequentially. The model then applies any ‘‘forced’’ technologies. Currently, only variable valve timing (VVT) is forced to be applied to vehicles at redesign since it is the root of the engine path and the reference point for all future engine technology applications.323 The model next applies any inherited technologies that were applied to a leader vehicle on the same vehicle platform and carried forward into future model years where follower vehicles (on the shared system) are freshened or redesigned (and thus eligible to receive the updated version of the shared component). In practice, very few vehicle models enter without VVT, so inheritance is typically the first step in the compliance loop. Next, the model evaluates the manufacturer’s compliance status, applying all costeffective technologies regardless of compliance status.324 Then the model applies expiring overcompliance credits (if allowed to do so under the perspective of either the ‘‘unconstrained’’ or ‘‘standard setting’’ analysis, for CAFE purposes).325 At this point, the model checks the manufacturer’s compliance status again. If the manufacturer is still not compliant (and is unwilling to pay civil penalties, again for CAFE modeling), the model will add technologies that are not costeffective until the manufacturer reaches compliance. If the manufacturer exhausts opportunities to comply with the standard by improving fuel economy/reducing emissions (typically due to a limited percentage of its fleet being redesigned in that year), the model will apply banked CAFE or CO2 credits to offset the remaining deficit. If no credits exist to offset the remaining deficit, the model will reach back in time to alter technology solutions in earlier model years. The CAFE model implements multiyear planning by looking back, rather than forward. When a manufacturer is unable to comply through cost-effective (i.e., producing effective cost values less than zero) technology improvements or credit application in a given year, the khammond on DSKJM1Z7X2PROD with RULES2 323 As a practical matter, this affects very few vehicles. More than 95 percent of vehicles in the market file either already have VVT present or have surpassed the basic engine path through the application of hybrids or electric vehicles. 324 For further explanation of how the CAFE model considers the effective cost of applying different technologies see the CAFE Model Documentation for the final rule, at S5.3 Compliance Simulation Algorithm. 325 As mentioned above, EPCA prohibits consideration of available credits when setting maximum feasible fuel economy standards. 49 U.S.C. 32902(h)(3). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 model will ‘‘reach back’’ to earlier years and apply the most cost-effective technologies that were not applied at that time and then carry those technologies forward into the future and re-evaluate the manufacturer’s compliance position. The model repeats this process until compliance in the current year is achieved, dynamically rebuilding previous model year fleets and carrying them forward into the future, and accumulating CAFE or CO2 credits from over-compliance with the standard wherever appropriate. In a given model year, the model determines applicability of each technology to each vehicle platform, model, engine, and transmission. The compliance simulation algorithm begins the process of applying technologies based on the CAFE or CO2 standards specified during the current model year. This involves repeatedly evaluating the degree of noncompliance, identifying the next ‘‘best’’ technology (ranked by the effective cost discussed earlier) available on each of the parallel technology paths described above and applying the best of these. The algorithm combines some of the pathways, evaluating them sequentially instead of in parallel, to ensure appropriate incremental progression of technologies. The algorithm first finds the best next applicable technology in each of the technology pathways and then selects the best among these. For CAFE purposes, the model applies the technology to the affected vehicles if a manufacturer is either unwilling to pay penalties or if applying the technology is more cost-effective than paying penalties. Afterwards, the algorithm reevaluates the manufacturer’s degree of noncompliance and continues application of technology. Once a manufacturer reaches compliance (i.e., the manufacturer would no longer need to pay penalties), the algorithm proceeds to apply any additional technology determined to be costeffective (as discussed above). Conversely, if a manufacturer is assumed to prefer to pay penalties, the algorithm only applies technology up to the point where doing so is less costly than paying penalties. The algorithm stops applying additional technology to this manufacturer’s products once no more cost-effective solutions are encountered. This process is repeated for each manufacturer present in the input fleet. It is then repeated for each model year. Once all model years have been processed, the compliance simulation algorithm concludes. The process for CO2 standard compliance simulation is similar, but without the PO 00000 Frm 00106 Fmt 4701 Sfmt 4700 option of penalty payment, such that technologies are applied until compliance (accounting for any modeled application of credits) is achieved. For both CAFE and CO2 standards, the model also applies any additional (i.e., beyond required for compliance) technology that ‘‘pays back’’ within a specified period (for the NPRM and today’s analysis, 30 months). Some commenters argued that the CAFE model applies constraints that excessively limit options manufacturers have to add technology, causing the model to overestimate costs to achieve a given level of improvement.326 Some of these commenters further argued that the agencies should assume greater potential to apply technologies that contribute to compliance by improving air conditioner efficiency or otherwise reducing ‘‘off cycle’’ fuel consumption and CO2 emissions.327 Other commenters argued that such constraints, while warranting some refinements, help the model to simulate manufacturers’ decision making realistically and to estimate technology effectiveness and costs reasonably.328 329 Some commenters questioned the ‘‘effective cost’’ metric the model uses to decide among available options, claiming that the metric also causes the model to avoid selection of pathways that are not always economically optimal.330 One of these commenters recommended the agencies modify the effective cost metric for CO2 compliance by removing the term placing a monetary value on progress toward compliance, and instead dividing the remaining net cost (i.e., the increase in technology costs minus a portion of the fuel outlays expected to be avoided) by the additional CO2 credits earned.331 Another of these commenters claimed on one hand, that the effective cost metric ‘‘does not include a measurement of the technology’s reduction in fuel consumption or CO2 emissions’’ and, on the other, that the metric inappropriately places a value on avoided fuel consumption.332 One commenter claimed that the model inappropriately allows earned 326 NHTSA–2018–0067–12057, 327 NHTSA–2018–0067–11741, CBD, et. al, p. 3. ICCT, Attachment 2, p. 4. 328 NHTSA–2018–0067–12073, Alliance of Automobile Manufacturers, pp. 134–36. 329 American Honda Motor Co., ‘‘Honda Comments on the NPRM and various proposals contained therein—Prepared for NHTSA, EPA and ARB,’’ October 17, 2018, pp. 12–16. 330 NHTSA–2018–0067–11741, ICCT, Attachment 3, p. I–62. 331 NHTSA–2018–0067–12039, UCS, Technical Appendix, pp. 28–32. 332 NHTSA–2018–0067–12108, EDF, Appendix B, p. 67. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 credits (including CO2 program credits for which EPA has granted a one-time exemption from carry-forward limits) to expire while also showing undue degrees overcompliance with standards, and further proposed that the model be modified to simulate both credit ‘‘carry back’’ (aka ‘‘borrowing’’) and credit trading between manufacturers.333 In addition, some commenters indicated that the agencies’ analysis (impliedly, its modeling) should account for some States’ mandates that manufacturers sell minimum quantities of ‘‘Zero Emission Vehicles’’ (ZEVs).334 335 Regarding the model’s representation of engineering and product planning constraints, the agencies maintain that having such constraints produces more realistic potential (as mentioned above, not ‘‘predicted’’) pathways forward from manufacturers’ current fleets than would be the case were these constraints removed. For example, while manufacturers’ product plans are protected as confidential business information (CBI), some manufacturers’ public comments demonstrate year-byyear balancing such as the CAFE model emulates.336 Also, even manufacturers that have invested in technologies such as hybrid electric powertrains and Atkinson cycle engines have commented that a manufacturers’ past investments will constrain the pathways it can practicably take.337 Therefore, the agencies have retained the model’s basic structural constraints, have updated and expanded the model’s technology paths (and, as discussed, the model’s logic for approaching these paths), and have updated inputs defining the range of manufacturer-, technology-, and product-specific constraints. These updates are discussed below at greater length. The agencies have also reconsidered opportunities manufacturers may have to expand the application of technologies that contribute to compliance by improving air conditioner efficiency or otherwise reducing ‘‘off cycle’’ fuel consumption and CO2 emissions, or to earn credit toward CO2 compliance by using refrigerants with lower global warming potential (GWP) or reducing the potential for refrigerant leaks. The version of the model used for the proposal accommodates inputs that, for 333 NHTSA–2018–0067–12039, UCS, Technical Appendix, pp. 36–40. 334 NHTSA–2018–0067–12036, Volvo, p. 5. 335 NHTSA–2018–0067–11813, South Coast AQMD, Attachment 1, p. 4 and EIS comments, p. 9. 336 See, e.g., FCA, pp. 5–6. 337 Toyota, Attachment 1, p. 10. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 each of these adjustments or credits, applies the same value to every model year. The agencies have revised the model to accommodate inputs that specify the degree of adjustment or credit separately for each model year, and have applied inputs that assume manufacturers will increase application of these improvements to the highest levels reported within the industry. Regarding comments on the effective cost metric the model uses to compare and select among available options to add technology, the agencies have considered changes such as those mentioned above. Given the myriad of factors that manufacturers can consider, any weighing to be conducted using publicly-available information will constitute a simplified representation. Nevertheless, within the model’s context, it is obvious that any weighing of options should, at a minimum, consider some measure of each option’s costs and benefits. Since this aspect of the model involves simulating manufacturers’ decisions, it is also clearly appropriate that these costs and benefits be considered from a manufacturer perspective rather than a social perspective. The effective cost metric used for the NPRM version of the model represents the cost of a given option as the cost to apply a given technology to a given set of vehicles, and represents the benefit of the same option as the extent to which the manufacturer might expect buyers would be willing to pay for fuel economy (as represented by a portion of the projected fuel savings), combined with any reduction in CAFE civil penalties that the manufacturer might ultimately need to pass along to buyers. The reduction in CAFE civil penalties places a value on progress made toward compliance with CAFE standards. The CAA provides no direction regarding CO2 standards, so the model accepts inputs specifying an analogous basis for valuing changes in the quantity of CO2 credits earned from (or required by) a manufacturer’s fleet. Because each of these three components (technology cost, fuel benefit, and compliance benefit) is expressed in dollars, subtracting benefits from costs produces a net cost, and after dividing net costs by the number of affected vehicles, it is logical to, at each step, select the option that produces the most negative net unit cost. This approach can be interpreted as maximizing net benefits (to the manufacturer). As an alternative, the agencies considered a simpler metric that considers only the cost of the option and the extent to which the option increases the quantity of earned credits, PO 00000 Frm 00107 Fmt 4701 Sfmt 4700 24279 and does not require input assumptions regarding how to value progress toward compliance. Such a metric is expressed in dollars per ton or dollars per gallon such that seeking options that produce the smallest (positive) values can be interpreted as maximizing cost effectiveness (of progress toward compliance). However, simply comparing technology costs to corresponding compliance improvements would implicitly assume that manufacturers do not respond at all to fuel prices. This assumption is clearly unrealistic. For example, if diesel fuel costs $5 per gallon and gasoline costs $2 per gallon, manufacturers will be reluctant to respond to stringent CAFE or CO2 standards by replacing gasoline engines with diesel engines. Manufacturers’ comments credibly assert that fuel prices matter, and in the agencies’ judgment, simulations of decisions between available options should continue to account for avoided fuel outlays. On the other hand, while any metric should incorporate some measure of progress toward compliance, it is not obvious that this progress must be expressed in monetary terms. While the CAFE civil penalty provisions provide a logical basis for doing so with respect to CAFE, the recently-introduced (through EISA) option to trade credit between manufacturers adds an alternative basis that is undefined and uncertain, in part because terms of past trades are not known to the agencies. Also, as mentioned above, EPCA/EISA’s civil penalty provisions are not applicable to noncompliance with CO2 standards. Therefore, for the purpose of selecting among available options to add technology, the agencies consider it reasonable to use the degree of compliance improvement in ‘‘raw’’ (i.e., not monetized) form, and to divide net costs (i.e., technology costs minus a portion of expected avoided fuel outlays) by this improvement. Under a range of side-by-side tests, this change to the effective cost metric most frequently produced lower overall estimates of compliance costs. However, differences vary among manufacturers, model years, and regulatory alternatives, and also depend on other model inputs. For example, at high fuel prices, the new metric tends to select more expensive pathways than the NPRM’s metric, and with the new metric, a case simulating ‘‘perfect trading’’ of CO2 compliance credits tends to show such trading increasing compliance costs rather than, as expected, decreasing such costs. The version of the model used for the proposal simulates the potential that, for E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24280 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations a given fleet in a given model year, a manufacturer might be able to use credits from an earlier model year or a different fleet. This version of the model did not explicitly simulate the potential that, for a given fleet in a given model year, a manufacturer might be to use credits from a future model year or a different manufacturer. However, the agencies did apply model inputs that reflected assumptions regarding possible trading of credits actually earned prior to model year 2016 (the earliest represented in detail in the agencies’ analysis), and the agencies did examine a case (included in the sensitivity analysis) involving hypothetical ‘‘perfect’’ trading of CO2 credits among manufacturers by treating the industry as a single ‘‘manufacturer.’’ Although past versions of the CAFE Model had included code under development with a view toward eventually simulating one or both of these provisions, this code had never proceeded beyond preliminary experimentation, and had never been the focus of peer reviews or application in published analyses. Nevertheless, the agencies considered expanding the model to simulate credit ‘‘carry back’’ (or ‘‘borrowing’’) and trading (explicitly, rather than in an idealized hypothetical way). The agencies closely examined the corresponding model revisions proposed by UCS and determined that such methods would not produce repeatable results. This is because the approach proposed by UCS ‘‘randomly swaps items in list to minimize trading bias.’’ 338 Even if such revisions could be modified to produce non-random results, including credit banking and trading would introduce highly speculative elements into the agencies’ analysis. While manufacturers have occasionally indicated plans to carry back credits from future model years, those plans have sometimes backfired when projected credits have failed to materialize, e.g., by misjudging consumer demand for more efficient vehicles. In the agencies’ judgment, it would be inappropriate to set standards based on an analysis that relies on the type of borrowing that has been known to fail. To rely also on credit trading during the model years included in the analysis would compound this undue speculation. For example, including credit borrowing and trading throughout the analysis, as some commenters proposed, would lead to an analysis that depends on the potential that, in order 338 UCS, NHTSA–2018–0067–12039, Technical Appendix, at 84–87. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 to comply with the MY 2022 standard for light trucks, FCA could use credits it expects to be able to buy from another manufacturer in MY 2025. Even if the agencies’ analysis had knowledge of and made use of manufacturers’ actual product plans, expectations about the ability to borrow others’ unearned credits would necessarily be considered risky and unreliable. Within an analysis that, to provide for public disclosure, extrapolates forward many years from the most recent observed fleet, such transactions would add an unreasonable level of speculation. Therefore, the agencies have declined to introduce credit borrowing and trading into the model’s logic. The analysis presented in the proposal applied inputs reflecting potential application of credits earned earlier than the first year modeled explicitly. However, as observed by some commenters, those inputs did not fully account for the one-time exemption from the 5-year limit on the extent to which manufacturers may carry forward CO2 credits. The agencies have updated the analysis fleet to MY 2017 and, in doing so, have updated inputs specifying how credits earned to MY 2017 might be applied. These updates implement a reasonably full accounting of these ‘‘legacy’’ credits, including of the one-time exemption from the credit life limit. As mentioned above, some commenters also indicated that the model is unrealistically ‘‘reluctant’’ to apply credits carried forward from early model years. As explained in the proposal and in the model documentation, the model’s application of carried-forward credits is partially controlled by model inputs, which, for the proposal, were set to assume that manufacturers would tend to retain credits as long as possible. This assumption is entirely consistent with manufacturers’ past practice and logical in a context wherein the stringency of standards is generally increasing over time. Even though using credits in some model years might seem initially advantageous, doing so means foregoing actual improvements likely to be needed in later model years. Regarding the model’s treatment of mandates and credits for the sale of ZEVs, as indicated in the model documentation accompanying the proposal, these capabilities were experimental in that version of the model. The reference case analysis for today’s notice, like that for the proposal, PO 00000 Frm 00108 Fmt 4701 Sfmt 4700 does not simulate compliance with ZEV mandates.339 For the NPRM, the CAFE model was exercised with inputs extending this explicit simulation of technology application through MY 2032, as the agencies anticipated this was sufficiently beyond MY 2026 that nearly all multiyear planning attributable to MY 2026 standards should be accounted for, and any compliance credits carried forward from MY 2026 would have expired. The analysis met this expectation, and the agencies presented analysis of the resultant estimated impacts over the useful lives of vehicles produced prior to MY 2030. The agencies invited comment on all aspects of the analysis, and relevant to this aspect of the analysis—i.e., its perspective and temporal span—EDF stated that that these led the agencies to overstate the proposal’s positive impacts on safety, in part because by explicitly representing vehicle model years only through 2032, the agencies had failed to account for the impact of distant model years prices and fuel economy levels on the retention and scrappage of vehicles produced through MY 2029.340 For example, some vehicles produced in MY 2026 will likely still be on the road during calendar years (CY) 2033–2050 and the rates at which these MY 2026 vehicles will be scrapped during CYs 2033–2050 will be impacted by the prices and fuel economy levels of vehicles produced during MYs 2033– 2050. The agencies have addressed this comment by expanding model inputs to extend the explicit simulation of technology application through MY 2050. Most of these expanded model inputs involve the analysis fleet and inputs defining the cost and availability of various fuel-saving technologies. These inputs are discussed below. The agencies also made minor modifications to the model in order to extend model outputs to cover this wider span and to carry forward each regulatory alternative’s standards automatically through the last year to be modeled (e.g., extending standards without change from MY 2032 through MY 2050). The model documentation discusses these 339 The agencies note their finalization of the One National Program Final Action, in which EPA partially withdrew a waiver of CAA preemption previously granted to the State of California relating to its ZEV mandate, and NHTSA finalized regulations providing that State ZEV mandates are impliedly and expressly preempted by EPCA. This joint action is available at 84 FR 51310. 340 EDF, NHTSA–2018–0067–12108, Attachment A at 11 and Attachment B at 11–28. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 minor changes.341 In addition, although the agencies published detailed model output files documenting all estimated annual impacts through calendar year 2089, the notice and PRIA both emphasized the above-mentioned ‘‘model year’’ perspective, as in past regulatory analyses supporting CAFE and CO2 standards. Recognizing that an alternative ‘‘calendar year’’ perspective is of interest to EDF and, perhaps other stakeholders, the agencies have expanded the presentation of results in today’s notice and FRIA by presenting some physical impacts (e.g., fuel consumption and CO2 emissions) as well as monetized benefits, costs, and net benefits for each of CYs 2017–2050. All of these results appear in the model output files published with today’s notice, as do corresponding results for more specific impacts (e.g., year-by-year components of monetized social costs).342 5. Calculation of Physical Impacts Once it has completed the simulation of manufacturers’ potential application of technology in response to CAFE/CO2 standards and fuel prices, the CAFE Model calculates impacts of the resultant changes in new vehicle fuel economy levels and prices. This involves several steps. The model calculates changes in the total quantity of new vehicles sold in each model year as well as the relative shares passenger cars and light trucks comprise of the overall new vehicle market. The agencies received many comments on the estimation of sales impacts, and as discussed below, today’s analysis applies methods and corresponding estimates that reflect careful consideration of these comments. Related to these calculations, the model now operates in an iterated fashion with a view toward obtaining sales impacts that are balanced with changes in vehicle prices and fuel economy levels. This involves solving for compliance, calculating sales impacts, re-solving for compliance, and repeating these steps as many times as specified in model inputs. For today’s analysis, the agencies operated the model with four iterations, as early testing suggested three iterations should be sufficient for fleetwide results to converge between iterations. The model documentation describes the procedures for iteration in detail. 341 The model and documentation are available at https://www.nhtsa.gov/corporate-average-fueleconomy/compliance-and-effects-modeling-system. 342 Detailed model inputs and outputs are available at https://www.nhtsa.gov/corporateaverage-fuel-economy/compliance-and-effectsmodeling-system. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 The impacts on outlays for new vehicles occur coincident with the sale of these vehicles so the model can simply calculate and record these for each model year included in the analysis. However, virtually all other impacts result from vehicle operation that extends long after a vehicle is produced. Like other models (including, e.g., NEMS), the CAFE Model includes procedures (sometimes referred to as ‘‘stock models’’ or as models of fleet turnover) to estimate annual rates at which new vehicles are used and subsequently scrapped. The agencies received many comments on procedures for estimating vehicle scrappage and on procedures for estimating annual quantities of highway travel, accounting for the elasticity of travel demand with respect to per-mile costs for fuel. Below, Section VI.D.1 discusses these comments and reviews procedures and corresponding estimates that also reflect careful consideration of these comments. For each vehicle model in each model year, these procedures result in estimates of the number of vehicles remaining in service in each calendar year, as well as the annual mileage accumulation (i.e., vehicle miles traveled, or VMT) in each calendar year. As mentioned above, most of the physical impacts of interest derive from this vehicle operation. Also discussed above, the simulated application of technology results in ‘‘initial’’ and ‘‘final’’ estimates of the cost, fuel type, fuel economy, and fuel share (for, in particular, PHEVs that can run on gasoline or electricity) applicable to each vehicle model in each model year. Together with quantities of travel, and with estimates of the ‘‘gap’’ between ‘‘laboratory’’ and ‘‘on-road’’ fuel economy, these enable calculation of quantities of fuel consumed in each year during the useful life of each vehicle model produced in each model year.343 The model documentation provides specific procedures and formulas implementing these calculations. As for the NPRM, the model calculates emissions of CO2 and other air pollutants, reporting emissions both from vehicle tailpipes and from upstream processes (e.g., petroleum refining) involved in producing and supplying fuels. Section VI.D.3 below reviews methods, models, and estimates used in performing these calculations. The model also calculates impacts on highway safety, accounting for changes 343 The agencies have applied the same estimates of the ‘‘on road gap’’ as applied for the analysis supporting the NPRM. For operation on gasoline, diesel, E85, and CNG, this gap is 20 percent; for electricity and hydrogen, 30 percent. PO 00000 Frm 00109 Fmt 4701 Sfmt 4700 24281 in travel demand, changes in vehicle mass, and continued past and expected progress in vehicle safety (through, e.g., the application of new crash avoidance systems). Section VI.D.2 discusses methods, data sources, and estimates involved in estimating safety impacts, comments on the same, and changes included in today’s analysis. In response to the NPRM, some comments urged the agencies also to quantify different types of health impacts from changes in air pollution rather than only accounting for such impacts in aggregate estimates of the social costs of air pollution. Considering these comments, the agencies added such calculations to the model, as discussed in Section VI.D.3. 6. Calculation of Benefits and Costs Having estimated how technologies might be applied going forward, and having estimated the range of resultant physical impacts, the CAFE Model calculates a variety of private and social benefits and costs, reporting these from the consumer, manufacturer, and social perspectives, both in undiscounted and discounted present value form (given inputs specifying the corresponding discount rate and present year). Estimates of regulatory costs are among the direct outputs of the simulation of manufacturers’ potential responses to new standards. Other benefits and costs are calculated based on the abovementioned estimates of travel demand, fuel consumption, emissions, and safety impacts. The agencies received many comments on the NPRM’s calculation of benefits and costs, and Section VI.D.1 discusses these comments and presents the methods, data sources, and estimates used in calculating benefits and costs reported here. 7. Structure of Model Inputs and Outputs All CAFE Model inputs and outputs described above are specified in Microsoft Excel format, and the user can define and edit all inputs to the system. Table VI–3 describes (non-exhaustively) which inputs are contained within each input file and Table VI–4 describes which outputs are contained in each output file. This is important for three reasons: (1) Each file is discussed throughout the following sections; (2) several commenters conflated aspects of the model with its inputs; and (3) several commenters seemed confused about where to find specific information in the output files. This information was described in detail in the NPRM CAFE Model Documentation, but is reproduced here for quick reference. When specifically referencing the input E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations or FRM, respectively, will precede the file name. A catalog of the Argonne National Laboratory Autonomie fuel economy technology effectiveness value output files are reproduced in the following Table VI–5 as well. The left column shows the terminology used in this text to refer to the file, while the right column describes each file. NPRM or FRM, respectively, may precede the terminology in the text as appropriate. ER30AP20.081</GPH> or output file used for the NPRM or final rule in the following discussion, NPRM VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00110 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.080</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24282 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 referred to frequently throughout the text. NPRM or FRM, respectively, may PO 00000 Frm 00111 Fmt 4701 Sfmt 4700 precede the terminology in the text as appropriate. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.082</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Finally, Table VI–6 lists the terminologies used to refer to other model-related documents which are 24283 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 B. What inputs does the compliance analysis require? 1. Analysis Fleet The starting point for the evaluation of the potential feasibility of different stringency levels for future CAFE and CO2 standards is the analysis fleet, which is a snapshot of the recent vehicle market. The analysis fleet provides a baseline from which to project what and how additional technologies could feasibly be applied to vehicles in a cost-effective manner to raise those vehicles’ fuel economy and lower their CO2 emission levels.344 The fleet characterization also provides a reference point with data for other factors considered in the analysis, including environmental effects and effects estimated by the economic modules (i.e., sales, scrappage, and labor utilization). When the scope of the analysis widens, another piece of data must be included for each vehicle in the analysis fleet to map a given element of the fleet appropriately onto an analysis module. For the analysis presented in this final rule, the analysis fleet includes information about vehicles that is essential for each analysis module. The first part of projecting how additional technologies could be applied to vehicles is knowing which vehicles are produced by which manufacturers, the fuel economies of those vehicles, how many of each are sold, whether they are passenger cars or light trucks, and their 344 The CAFE model does not generate compliance paths a manufacturer should, must, or will deploy. It is intended as a tool to demonstrate a compliance pathway a manufacturer could choose. It is almost certain all manufacturers will make compliance choices differing from those projected by the CAFE model. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 footprints. This is important because it improves understanding of the overall impacts of different levels of CAFE and CO2 standards; overall impacts that result from industry’s response to standards, and industry’s response, is made up of individual manufacturer responses to the standards in light of the overall market and their individual assessment of consumer acceptance. Establishing an accurate representation of manufacturers’ existing fleets (and the vehicle models in them) that will be subject to future standards helps in predicting potential individual manufacturer responses to those future standards in addition to potential changes in those standards. Another part of projecting how additional fuel economy improving technologies could be applied to vehicles is knowing which fuel saving technologies manufacturers have equipped on which vehicles. In many cases, the agencies also collect and reference additional information on other vehicle attributes to help with this process.345 Accounting for technologies already applied to vehicles helps avoid ‘‘double-counting’’ the value of those technologies, by assuming they are still available to be applied to improve fuel economy and reduce CO2 emissions. It also promotes more realistic determinations of what additional technologies can feasibly be applied to those vehicles: If a manufacturer has already started down a technological path to fuel economy or performance improvements, the agencies do not 345 For instance, curb weight, horsepower, drive configuration, pickup bed length, oil type, body style, aerodynamic drag coefficients, and rolling resistance coefficients, and (if applicable) battery sizes are all required to assign technology content properly. PO 00000 Frm 00112 Fmt 4701 Sfmt 4700 assume it will completely abandon that path because doing so would be unrealistic and fails to represent accurately manufacturer responses to standards. Each vehicle model (and configurations of each model) in the analysis fleet, therefore, has a comprehensive list of its technologies, which is important because different configurations may have different technologies applied to them.346 In addition, to properly account for technology costs, the agencies assign each vehicle to a technology class and an engine class. Technology classes reference each vehicle to a set of full vehicle simulations, so that the agencies may project fuel efficiency with combinations of additional fuel saving equipment and hybrid and electric vehicle battery costs. Yet another part of projecting which vehicles might exist in future model years is developing reasonable realworld assumptions about when and how manufacturers might apply certain technologies to vehicles. The analysis fleet accounts for links between vehicles, recognizing vehicle platforms will share technologies, and the vehicles that make up that platform should receive (or not receive) additional technological improvements together. Shared engines, shared transmissions, and shared vehicle platforms for mass reduction technology are considered. In addition, each vehicle model/ configuration in the analysis fleet also has information about its redesign 346 Considering each vehicle model/configuration also improves the ability to consider the differential impacts of different levels of potential standards on different manufacturers, since all vehicle model/ configurations ‘‘start’’ at different places, in terms of technologies already used and how those technologies are used. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.083</GPH> 24284 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations schedule, i.e., the last year it was redesigned and when the agencies expect it to be redesigned again. Redesign schedules are a key part of manufacturers’ business plans, as each new product can cost more than $1B, and involve a significant portion of a manufacturer’s scarce research, development, and manufacturing and equipment budgets and resources.347 Manufacturers have repeatedly told the agencies that sustainable business plans require careful management of resources and capital spending, and that the length of time each product remains in production is crucial to recouping the upfront product development and plant/ equipment costs, as well as the capital needed to fund the development and manufacturing equipment needed for future products. Because the production volume of any given vehicle model varies within a manufacturer’s product line, and varies among different manufacturers, redesign schedules typically vary for each model and manufacturer. Some (relatively few) technological improvements are small enough that they can be applied in any model year; a few other technological improvements may be applied during a refreshening (when a few additional changes are made, but well short of a full redesign), but others are major enough that they can only be costeffectively applied at a vehicle redesign, when many other things about the vehicle are already changing. Ensuring the CAFE model makes technological improvements to vehicles only when it is feasible to do so also helps the analysis better represent manufacturer responses to different levels of standards. Finally, the agencies restrict the applications of some technologies on some vehicles upon determining the technology is not compatible with the functional and performance requirements of the vehicle, or if the manufacturers are unlikely to apply a specific technology to a specific vehicle for reasons articulated with confidential business information that the agencies found credible. Other data important for the analysis that are referenced to the analysis fleet include baseline economic, environmental, and safety information. Vehicle fuel tank size is required to estimate range and refueling benefit while curb weights and safety class assignments help the agencies consider how changes in vehicle mass may affect safety. The agencies identify the final assembly location for each vehicle, engine, and transmission, as well as the percent of U.S. content to support the labor impact analysis. In addition, the aforementioned accounting for first-year vehicle production volumes (i.e., the number of vehicles of each new model sold in MY 2017, for this analysis) is the foundation for estimating how future vehicle sales might change in response to different potential standards. The input file for the CAFE model characterizing the analysis fleet, referred to as the ‘‘market inputs’’ file or ‘‘market data’’ file, accordingly includes a large amount of data about vehicles, their technological characteristics, the manufacturers and fleets to which they belong, and initial prices and production volumes, which provide the starting points for projection (by the sales model) to ensuing model years. In the Draft TAR (which utilized a MY 2015 analysis fleet) and NPRM (which utilized a MY 2016 analysis fleet), the agencies needed to populate about 230,000 cells in the market data file to characterize the fleet. For this final rule (which utilized a MY 2017 analysis fleet), the agencies populated more than 400,000 cells to characterize the fleet. While the fleet is not actually much more heterogeneous in reality,348 the agencies have provided and collected more data to justify the characterization of the analysis fleet, and to support the functionality of modules in the CAFE model. A solid characterization of a recent model year as an analytical starting point helps realistically estimate ways manufacturers could potentially respond to different levels of standards, and the modeling strives to simulate realistically how manufacturers could progress from that starting point. While manufacturers can respond in many ways beyond those represented in the analysis (e.g., applying other technologies, shifting production volumes, changing vehicle footprint), such that it is impossible to predict with any certainty exactly how each manufacturer will respond, it is still important to establish a solid foundation from which to estimate potential costs and benefits of potential future standards. The following sections discuss aspects of how the analysis fleet was built for this analysis, and includes discussion of the comments on fleet that the agencies received on the proposed rule. 347 Shea, T., Why Does It Cost So Much For Automakers To Develop New Models? Autoblog (Jul. 27, 2010), https://www.autoblog.com/2010/07/ 27/why-does-it-cost-so-much-for-automakers-todevelop-new-models/. 348 The expansion of cells is primarily due to (1) considering more technologies, and (2) listing trim levels separately, which often yields more precise curb weights and more accurate manufacturer suggested retail prices. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00113 Fmt 4701 Sfmt 4700 24285 a) Principles on Data Sources Used To Populate the Analysis Fleet The source data for vehicles in the analysis fleet and their technologies is a central input for the analysis. The sections below discuss pros and cons of different potential sources and what the agencies used for this analysis, and responds to comments the agencies received on data sources in the proposal. (1) Use of Confidential Business Information Versus Publicly-Releasable Sources Since 2001, CAFE analysis has used either confidential, forward-estimating product plans from manufacturers, or publicly available data on vehicles already sold as a starting point for determining what technologies can be applied to what vehicles in response to potential different levels of standards. The use of either data source requires certain tradeoffs. Confidential product plans comprehensively represent what vehicles a manufacturer expects to produce in coming years, accounting for plans to introduce new vehicles and fuel-saving technologies and, for example, plans to discontinue other vehicles and even brands. This information can be very thorough and can improve the accuracy of the analysis, but cannot be publicly released. This makes it difficult for public commenters to reproduce the analysis for themselves as they develop their comments. Some non-industry commenters have also expressed concern about manufacturers having an incentive in the submitted plans to underestimate (deliberately or not) their future fuel economy capabilities and overstate their expectations about, for example, the levels of performance of future vehicle models in order to affect the analysis. Accordingly, since 2010, EPA and NHTSA have based analysis fleets almost exclusively on information from commercial and public sources, starting with CAFE compliance data and adding information from other sources. An analysis fleet based primarily on public sources can be released to the public, solving the issue of commenters being unable to reproduce the overall analysis. However, industry commenters have argued such an analysis fleet cannot accurately reflect manufacturers’ actual plans to apply fuel-saving technologies (e.g., manufacturers may apply turbocharging to improve not just fuel economy, but also to improve vehicle performance) or manufacturers’ plans to change product offerings by introducing some vehicles and brands and discontinuing other E:\FR\FM\30APR2.SGM 30APR2 24286 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 vehicles and brands, precisely because that information is typically confidential business information (CBI). A fully-publicly-releasable analysis fleet holds vehicle characteristics unchanged over time and lacks some level of accuracy when projected into the future. For example, over time, manufacturers introduce new products and even entire brands. On the other hand, plans announced in press releases do not always ultimately bear out, nor do commercially available third-party forecasts. Assumptions could be made about these issues to improve the accuracy of a publicly releasable analysis fleet, but concerns include that this information would either be largely incorrect, or, if the assumptions were correct, information would be released that manufacturers would consider CBI. Furthermore, some technologies considered in the rulemaking are difficult to observe in the analysis fleet without expensive teardown study and time-consuming benchmarking. Not giving credit for these technologies puts the analysis at significant risk of doublecounting the effectiveness of these technologies, as manufacturers cannot equip technologies twice to the same vehicle for double the fuel economy benefit. As discussed in the Draft TAR, the agencies assigned little (if any) technology application in the baseline fleet for some of these technologies.349 For the NPRM MY 2016 fleet development process, the agencies again offered the manufacturers the opportunity to volunteer CBI to the agencies to help inform the technology content of the analysis fleet, and many manufacturers did. The agencies were able to confirm that many manufacturers had already included many hard-to-observe technologies in the MY 2016 fleet (which they were not properly given credit for in the characterization of the MY 2014 and MY 2015 fleets presented in Draft TAR) so the agencies reflected this new information in the NPRM analysis and in the analysis presented today. In addition, many manufacturers provided confidential comment on the potential applicability of fuel-saving technologies to their fleet. In particular, many manufacturers confidentially identified specific engine technologies 349 These technologies include low rolling resistance technology (incorrectly applied to zero baseline vehicles in Draft TAR), low-drag brakes (incorrectly applied to zero baseline vehicles in Draft TAR), electric power steering (incorrectly applied to too few vehicles in Draft TAR), accessory drive improvements (incorrectly applied to zero baseline vehicles in Draft TAR), engine friction reduction (previously named LUBEFR1, LUBEFR2, and LUBEFR3), secondary axle disconnect and transmission improvements. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 that they will not use in the near term, either on specific vehicles, or at all. Reasons varied: Some manufacturers cited intellectual property concerns, and others stated functional performance concerns for some engine types on some vehicles. Other manufacturers shared forward-looking product plans, and explained that it would be cost prohibitive to scrap significant investments in one technology in favor of another. This topic is discussed in more detail in Section VI.B.1.b)(6), below. The agencies sought comment on how to address this issue going forward, recognizing both the competing interests involved and the typical timeframes for CAFE and CO2 standards rulemakings. Many commenters expressed concern with the agencies using any CBI as part of the rulemaking process. Some commenters expressed concern that use of CBI would make the CAFE model subject to inaccuracies because manufacturers would only provide additional information in situations in which a correction to the agencies’ baseline assumptions would favor the manufacturers.350 The agencies recognize this as a reasonable concern, but the analysis presented in the Draft TAR consistently assumed very little (if any) technology had been applied in the baseline. In addition, many manufacturers shared information on advanced technologies that were not yet in production in MY 2017, but could be used in the future; manufacturer contributions helped the agencies better model many advanced engine technologies and to include them in today’s analysis, and inclusion of these technologies (and costs) in the analysis sometimes lowered the projected cost of compliance for stringent alternatives. Other commenters expressed concern that automakers would supply false or incomplete information that would unduly restrict what technologies can be deployed.351 When possible, the agencies sought independently to verify manufacturer CBI (or claims made by other stakeholders) through lab testing and benchmarking.352 The agencies found no evidence of misrepresentation of engineering specifications in the MY 2017 fleet in manufacturer CBI; instead, the agencies were able to verify independently many CBI submissions, and confirm the credibility of 350 NHTSA–2018–0067–12039, Union of Concerned Scientists. 351 NHTSA–2018–0067–11741, ICCT. 352 For instance, the agencies continue to evaluate tire rolling resistance on production vehicles via independent lab testing, and the agencies benchmarked the operating behavior and calibration of many engines and transmissions. PO 00000 Frm 00114 Fmt 4701 Sfmt 4700 information provided from those sources. Some commenters requested that more CBI be used in the analysis. For instance, some commenters suggested that the agencies should return to the use of product plans and announcements regarding future fleets because manufacturers had already committed investments to bring announced products to market.353 However, if the agencies were to assume that these commitments were already in the baseline, the agencies would underestimate the cost of compliance for stringent alternatives. Moreover, while upfront investments to bring technologies to market are significant, the total marginal costs of components are typically large in comparison over the entire product life-cycle, and these costs have not yet been realized in vehicles not yet produced. The agencies did make use of some forward-looking CBI in the analysis. The agencies received many comments from manufacturers on the technological feasibility, or functional applicability of some fuel saving technologies to certain vehicles, or certain vehicle applications, and the agencies took this information into consideration when projecting compliance pathways. These cases are discussed generally in Section VI.B.1.b)(6), below, and specifically for each technology in those technology sections. Some commenters expressed that the use of CBI for future product plans would be acceptable, but only if the agencies disclosed the CBI affecting all vehicles through MY 2025 at the time of publication.354 Functionally, this is not possible. Manufacturer’s confidential product plans cannot be made public, as prohibited under NHTSA’s regulations at 49 CFR part 512, and if the information meets the requirements of section 208(c) of the Clean Air Act. If the agencies disclosed confidential information, it would not only violate the terms on which the agencies obtained the CBI, but it is unlikely that manufacturers would continue to offer CBI, which in turn would likely degrade the quality of the analysis. The agencies believe that the use of CBI in the NPRM and final rule analysis—to confirm, reference, or to otherwise modify aspects of the analysis that can be made public—threads the needle between a more accurate but less transparent analysis (using more CBI) and a less accurate but more transparent analysis (using less CBI). 353 NHTSA–2018–0067–11956, PA Department of Environmental Protection. 354 NHTSA–2018–0067–11741. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations (2) Source Data and Vintage Used in the Analysis Based on the assumption that a publicly-available analysis fleet continued to be desirable, manufacturer compliance submissions to EPA and NHTSA were used as a starting point for the NPRM and final rule analysis fleets. Generally, manufacturer compliance submissions break down vehicle fuel economy and production volume by regulatory class, and include some very basic product information (typically including vehicle nameplate, engine displacement, basic transmission information, and drive configuration). Many different trim levels of a product are typically rolled up and reported in an aggregated fashion, and these groupings can make decomposition of different fuel-saving, road load reducing technologies extremely difficult. For instance, vehicles in different test weight classes, with different tires or aerodynamic profiles may be aggregated and reported together.355 A second portion of the compliance submission summarizes production volume by vehicle footprints (a key compliance measure for standard setting) by nameplate, and includes some basic information about engine displacement, transmission, and drive configuration. Often these production volumes by footprint do not fit seamlessly together with the production volumes for fuel economy, so the agencies must reconcile this information. Information from the MY 2016 fleet was chosen as the foundation for the NPRM analysis fleet because, at the time the rulemaking analysis was initiated, the 2016 fleet represented the most upto-date information available in terms of individual vehicle models and configurations, production technology levels, and production volumes. If MY 2017 data had been used while this analysis was being developed, the agencies would have needed to use product planning information that could not be made available to the public until a later date. The NPRM analysis fleet was initially developed with 2016 mid-model year compliance data because final compliance data was not available at that time, and the timing provided manufacturers the opportunity to review and comment on the characterization of their vehicles in the fleet. With a view toward developing an accurate characterization of the 2016 fleet to serve as an analytical starting point, 355 Some fuel-economy compliance information for pickup trucks span multiple cab and box configurations, but manufacturers reported these disparate vehicles together. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 corrections and updates to mid-year data (e.g., to production estimates) were sought, in addition to corroboration or correction of technical information obtained from commercial and other sources (to the extent that information was not included in compliance data), although future product planning information from manufacturers (e.g., future product offerings, products to be discontinued) was not requested, as most manufacturers view such information as CBI. Manufacturers offered a range of corrections to indicate engineering characteristics (e.g., footprint, curb weight, transmission type) of specific vehicle model/ configurations, as well as updates to fuel economy and production volume estimates in mid-year reporting. After following up on a case-by-case basis to investigate significant differences, the analysis fleet was updated. Sales, footprint, and fuel economy values with final compliance data were also updated if that data was available. In a few cases, final production and fuel economy values were slightly different for specific MY 2016 vehicle models and configurations than were indicated in the NPRM analysis; however, other vehicle characteristics (e.g., footprint, curb weight, technology content) important to the analysis were reasonably accurate. While some commenters have, in the past, raised concerns that non-final CAFE compliance data is subject to change, the potential for change is likely not significant enough to merit using final data from an earlier model year reflecting a more outdated fleet. Moreover, even ostensibly final CAFE compliance data is frequently subject to later revision (e.g., if errors in fuel economy tests are discovered), and the purpose of the analysis was not to support enforcement actions but rather to provide a realistic assessment of manufacturers’ potential responses to future standards. Manufacturers integrated a significant amount of new technology in the MY 2016 fleet, and this was especially true for newly-designed vehicles launched in MY 2016. While subsequent fleets will involve even further application of technology, using available data for MY 2016 provided the most realistic detailed foundation for analysis that could be made available publicly in full detail, allowing stakeholders to reproduce the analysis presented in the proposal independently. Insofar as future product offerings are likely to be more similar to vehicles produced in 2016 than to vehicles produced in earlier model years, using available data regarding the 2016 model year provided PO 00000 Frm 00115 Fmt 4701 Sfmt 4700 24287 the most realistic, publicly releasable foundation for constructing a forecast of the future vehicle market for this proposal. Many comments responding to the Draft TAR, EPA’s Proposed Determination, EPA’s 2017 Request for Comment, and the NPRM preceding today’s notice stated that the most upto-date analysis fleet possible should be used, because a more up-to-date analysis fleet will better capture how manufacturers apply technology and will account better for vehicle model/ configuration introductions and deletions.356 357 On the other hand, some commenters suggested that because manufacturers continue improving vehicle performance and utility over time, an older analysis fleet should be used to estimate how the fleet could have evolved had manufacturers applied all technological potential to fuel economy rather than continuing to improve vehicle performance and utility.358 Because manufacturers change and improve product offerings over time, conducting analysis with an older analysis fleet (or with a fleet using fuel economy levels and CO2 emissions rates that have been adjusted to reflect an assumed return to levels of performance and utility typical of some past model year) would miss this real-world trend. While such an analysis could project what industry could do if, for example, manufacturers devoted all technological improvements toward raising fuel economy and reducing CO2 emissions (and if consumers decided to purchase these vehicles), the agencies do not believe it would be consistent with a transparent examination of what effects different levels of standards would have 356 82 FR 39551 (Aug. 21, 2017). example, in 2016 comments to dockets EPA–HQ–OAR–2015–0827 and NHTSA–2016– 0068, the Alliance of Automobile Manufacturers commented that ‘‘the Alliance supports the use of the most recent data available in establishing the baseline fleet, and therefore believes that NHTSA’s selection [of, at the time, model year 2015] was more appropriate for the Draft TAR.’’ Alliance at 82, Docket ID. EPA–HQ–OAR–2015–0827–4089. Global Automakers commented that ‘‘a one-year difference constitutes a technology change-over for up to 20% of a manufacturer’s fleet. It was also generally understood by industry and the agencies that several new, and potentially significant, technologies would be implemented in MY 2015. The use of an older, outdated baseline can have significant impacts on the modeling of subsequent Reference Case and Control Case technologies.’’ Global Automakers at A–10, Docket ID EPA–HQ– OAR–2015–0827–4009. 358 For example, in 2016 comments to dockets EPA–HQ–OAR–2015–0827 and NHTSA–2016– 0068, UCS stated ‘‘in modeling technology effectiveness and use, the agencies should use 2010 levels of performance as the baseline.’’ UCS at 4, Docket ID. EPA–HQ–OAR–2015–0827–4016. 357 For E:\FR\FM\30APR2.SGM 30APR2 24288 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 on individual manufacturers and the fleet as a whole. All else being equal, using a newer analysis fleet will produce more realistic estimates of impacts of potential new standards than using an outdated analysis fleet. However, among relatively current options, a balance must be struck between input freshness, and input completeness and accuracy.359 During assembly of the inputs for the NPRM analysis, final compliance data was available for the MY 2015 model year but not, in a few cases, for MY 2016. However, between mid-year compliance information and manufacturers’ specific updates discussed above, a robust and detailed characterization of the MY 2016 fleet was developed. While information continued to develop regarding the MY 2017 and, to a lesser extent MY 2018 and even MY 2019 fleets, this information was—even in mid-2017— too incomplete and inconsistent to be assembled with confidence into an analysis fleet for modeling supporting deliberations regarding the NPRM analysis. Manufacturers requested that the baseline fleet supporting the final rule incorporate the MY 2018 or most recent information available.360 Other commenters expressed desire for multiple fleets of various vintages to compare the updated model outputs with those of previous rule-makings. Specifically, some commenters requested that older fleet vintages (MY 2010, for instance) be developed in parallel with the MY 2017 fleet so that those too may be used as inputs for the model.361 Between the NPRM and this final rule, manufacturers submitted final compliance data for the MY 2017 fleet. When the agencies pulled together information for the fleet for the final rule, the agencies decided to use the 359 Comments provided through a recent peer review of the CAFE model recognize the competing interests behind this balance. For example, referring to NHTSA’s 2016 Draft TAR analysis, one of the peer reviewers commented as follows: ‘‘The NHTSA decision to use MY 2015 data is wise. In the TAR they point out that a MY 2016 foundation would require the use of confidential data, which is less desirable. Clearly they would also have a qualitative vision of the MY 2016 landscape while employing MY 2015 as a foundation. Although MY 2015 data may still be subject to minor revision, this is unlikely to impact the predictive ability of the model . . . A more complex alternative approach might be to employ some 2016 changes in technology, and attempt a blend of MY 2015 and MY 2016, while relying of estimation gained from only MY 2015 for sales. This approach may add some relevancy in terms of technology, but might introduce substantial error in terms of sales.’’ 360 NHTSA–2018–0067–12150, Toyota North America. 361 NHTSA–2018–0067–11741, ICCT. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 highest-quality, most up-to-date information available. Given that pulling this information together takes some time, and given that ‘‘final’’ compliance submissions often lag production by a few years, the agencies decided to use 2017 model year as the base year for the analysis fleet, as the agencies stated in the NPRM.362 While the agencies could have used preliminary 2018 data or even very early 2019 data, this information was not available in time to support the final rulemaking. Likewise, the agencies chose not to revert to a previous model year (for instance 2016 or 2012) because many manufacturers have incorporated fuel savings technologies over the last few years, realized some benefits for fuel economy, and adjusted the performance or sales mix of vehicles to remain competitive in the market. Also, using an earlier model year would provide less accurate projections because the analysis would be based on what manufacturers could have done in past model years and would have estimated the fuel economy improvements instead of using known information on the technologies that were employed and the actual fuel economy that resulted from applying those technologies. Some additional information (about off-cycle technologies, for instance) was often not reported by manufacturers in MY 2017 formal compliance submissions in a way that provided clear information on which technologies were included on which products. As part of the formal compliance submission, some manufacturers voluntarily submitted additional information (about engine technologies, for instance). While this data was generally of very high quality, there were some mistakes or inconsistencies with publicly available information, causing the agencies to contact the manufacturers to understand and correct identified issues. In most cases, however, the formal compliance data was very limited in nature, and the agencies collected additional information necessary to characterize fully the fleet from other sources, and scrutinized additional information submitted by manufacturers carefully, independently verifying when possible. Specifically, the agencies downloaded and reviewed numerous marketing brochures and product launch press releases to confirm information 362 83 FR 43006 (‘‘If newer compliance data (i.e., MY 2017) becomes available and can be analyzed during the pendency of this rulemaking, and if all other necessary steps can be performed, the analysis fleet will be updated, as feasible, and made publicly available.’’). PO 00000 Frm 00116 Fmt 4701 Sfmt 4700 submitted by manufacturers and to fill in information necessary for the analysis fleet that was not provided in the compliance data. Product brochures often served as the basis for the curb weights used in the analysis. This publicly available manufacturer information sometimes also included aerodynamic drag coefficients, information about steering architecture, start-stop systems, pickup bed lengths, fuel tank capacities, and high-voltage battery capacities. The agencies recorded vehicle horsepower, compression ratio, fuel-type, and recommended oil weight rating from a combination of manufacturer product brochures and owner’s manuals. The product brochures, as well as online references such as Autobytel, informed which combinations of fuel saving technologies were available on which trim levels, and what the manufacturer suggested retail price was for many products. Overall this information proved helpful for assigning technologies to vehicles, and for getting data (such as fuel tank size 363) necessary for the analysis. These reference materials have been included in the rulemaking documentation.364 The agencies elected not to develop fleets of previous model year vintages that could be used in parallel as an input to the CAFE model. Developing a detailed characterization of the fleet of any vintage would be a huge undertaking with few benefits. As the scope has increased, and as additional modules are added, going back in time to re-characterize a previous fleet in a format that works with CAFE model updates can be time- and resourceprohibitive for the agencies, even if that work is adapting a fleet that was used in previous rule-making analysis. Doing so also offers little value in determining what potential fuel saving technology can be added to a more recent fleet during the rulemaking timeframe. The MY 2017 manufacturer-submitted data, verified and supplemented by the agencies with publicly-available information, therefore presented the fullest, most up-to-date data set that the agencies could have used to support this analysis. 363 The quality of data for today’s analysis fleet is notably improved for fuel tank capacity, which factors into the calculation of refueling time benefits. In many previous analyses, fuel tank sizes were often stated as estimates or proxies, and not sourced so carefully. 364 Publicly available data used to supplement analysis fleet information is available in the docket. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 b) Characterizing Vehicles and Their Technology Content The starting point for projecting what additional fuel economy improving technologies could feasibly be applied to vehicles is knowing what vehicles are produced by which manufacturers and what technologies exist on those vehicles. Rows in the market data file are the smallest portion of the fleet to which technology may be applied as part of a projected compliance pathway. For the analysis presented in this final rule, the agencies, when possible, attempted to include vehicle trim level information in discrete rows. A manufacturer, for example GM, may produce one or more vehicle makes (or brands), for example Chevrolet, Buick and others. Each vehicle make may offer one or more vehicle models, for example Malibu, Traverse and others. And each vehicle model may be available in one or more trim levels (or standard option levels), for example ‘‘RS,’’ ‘‘Premier’’ and others, which have different levels of standard options, and in some cases, different engines and transmissions. Manufacturer compliance submissions, discussed above, were used as a starting point to define working rows in the market data file; however, often the rows needed to be further disaggregated to correctly characterize vehicle information covered in the scope of the analysis, and analysis fleet. Manufacturers often grouped vehicles with multiple trim levels together because they often included the same fuel-saving technologies and may be aggregated to simplify reporting. However, the manufacturer suggested retail prices of different trim levels are certainly VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 different, and other features relevant to the analysis are occasionally different. As a result of further disaggregating compliance information, the number of rows in the market data file increased from 1,667 rows used in the NPRM to 2,952 rows for this final rule analysis. The agencies do not have data on sales volumes for each nameplate by trim level, and used an approach that evenly distributed volume across offered trim levels, within the defined constraints of the compliance data.365 Evenly distributing the volume across trim levels is a simplification, but this action should (1) highlight some difficulties that could be encountered when acquiring data for a full-vehicle consumer choice model should the agencies pursue developing one in the future (discussed further, below), and (2) lower the average sales volume per row in the market data file, thereby allowing the application of very advanced electrification technologies in smaller lumps. The latter effect is responsive to comments (discussed below) that suggested electrification technologies could be more costeffectively deployed in lower volumes, and that the CAFE model artificially constrains cost effective technologies that may be deployed, resulting in higher costs and large over-compliance. (1) Assigning Vehicle Technology Classes While each vehicle in the analysis fleet has its list of observed technologies and equipment, the ways in which manufacturers apply technologies and equipment do not always coincide 365 The sum of volumes by nameplate configuration, for fuel economy value, and for footprint value remains the same. PO 00000 Frm 00117 Fmt 4701 Sfmt 4700 24289 perfectly with how the analysis characterizes the various technologies that improve fuel economy and reduce CO2 emissions. To improve how the observed vehicle fleet ‘‘fits into’’ the analysis, each vehicle model/ configuration is ‘‘mapped’’ to the fullvehicle simulation modeling by Argonne National Laboratory that is used to estimate the effectiveness of the fuel economy-improving/CO2 emissions-reducing technologies considered. Argonne produces fullvehicle simulation modeling for many combinations of technologies, on many types of vehicles, but it did not simulate literally every single manufacturer’s vehicle model/configuration in the analysis fleet because it would be impractical to assemble the requisite detailed information—much of which would likely only be provided on a confidential basis—specific to each vehicle model/configuration and because the scale of the simulation effort would correspondingly increase by at least two orders of magnitude. Instead, Argonne simulated 10 different vehicle types corresponding to the ‘‘technology classes’’ generally used in CAFE analysis over the past several rulemakings (e.g., small car, small performance car, pickup truck, etc.). Each of those 10 different vehicle types was assigned a set of ‘‘baseline characteristics’’ to which Argonne added combinations of fuel-saving technologies and then ran simulations to determine the fuel economy achieved when applying each combination of technologies to that vehicle type given its baseline characteristics. BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations BILLING CODE 4910–59–C In the analysis fleet, inputs assign each specific vehicle model/ configuration to a technology class, and once there, map to the simulation within that technology class most closely matching the combination of observed technologies and equipment on that vehicle. This mapping to a specific simulation result most closely representing a given vehicle model/ configuration’s initial technology ‘‘state’’ enables the CAFE model to estimate the same vehicle model/ configuration’s fuel economy after application of some other combination of technologies, leading to an alternative technology state. (2) Assigning Vehicle Technology Content khammond on DSKJM1Z7X2PROD with RULES2 As explained above, the analysis fleet is defined not only by the vehicles it 366 These technologies are generally grouped into the following categories: Vehicle technologies include mass reduction, aerodynamic drag reduction, low rolling resistance tires, and others. Engine technologies include engine attributes describing fuel type, engine aspiration, valvetrain configuration, compression ratio, number of cylinders, size of displacement, and others. Transmission technologies include different transmission arrangements like manual, 6-speed automatic, 10-speed automatic, continuously variable transmission, and dual-clutch transmissions. Hybrid and electric powertrains may complement traditional engine and transmission designs or replace them entirely. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 contains, but also by the technologies on those vehicles. Each vehicle in the analysis fleet has an associated list of observed technologies and equipment that can improve fuel economy and reduce CO2 emissions.366 With a portfolio of descriptive technologies arranged by manufacturer and model, the analysis fleet can be summarized and project how vehicles in that fleet may increase fuel economy over time via the application of additional technology. In many cases, vehicle technology is clearly observable from the 2017 compliance data (e.g., compliance data indicates clearly which vehicles have turbochargers and which have continuously variable transmissions), but in some cases technology levels are less observable. For the latter, like levels of mass reduction, the analysis categorized levels of technology already used in a given vehicle. Similarly, engineering judgment was used to determine if higher mass reduction levels may be used practicably and safely for a given vehicle. Either in mid-year compliance data for MY 2016, final compliance data for MY 2017, or separately and at the agencies’ invitation prior to the NPRM or in comments in responses to the NPRM, most manufacturers provided guidance on the technology already present in each of their vehicle model/ configurations. This information was not as complete for all manufacturers’ products as needed for the analysis, so, in some cases, information was supplemented with publicly available data, typically from manufacturer media sites. In limited cases, manufacturers did not supply information, and 367 NHTSA–2018–0067–11741. PO 00000 Frm 00118 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.089</GPH> 24290 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations information from commercial and publicly available sources was used. The agencies continued to evaluate emerging technologies in the analysis. In response to comments,367 and given recent product launches for MY 2020, and some very recently announced future product offerings, the agencies elevated some technologies that were discussed in the NPRM to the compliance simulation. As a result, khammond on DSKJM1Z7X2PROD with RULES2 367 NHTSA–2018–0067–11741. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 several additional engine technologies, expanded levels of mass reduction technology, and some additional combinations of engines with plug-in hybrid, or strong hybrid technology are available in the compliance pathways for the final rule analysis. In addition, some redundant technologies, or technologies that were inadvertently represented on the technology tree as being available to be applied twice, have been consolidated. PO 00000 Frm 00119 Fmt 4701 Sfmt 4700 24291 For instance, previous basic versions of engine friction reduction were layered on top of basic engine maps, but the efficiency in many modern engine maps already include the benefits of that engine friction reduction technology. The following Table VI–8 lists the technologies considered in the final rule analysis, with the data sources used to map those technologies to vehicles in the analysis fleet. BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00120 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.090</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24292 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00121 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24293 ER30AP20.091</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations BILLING CODE 4910–59–C Industry commenters generally stated the MY 2016 baseline technology content presented in the NPRM as an improvement over previous analyses because it more accurately accounted for technology already used in the VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 fleet.368 369 In contrast, some commenters expressed preference for EPA’s baseline technology assignment 368 NHTSA–2018–0067–12073, Alliance of Automobile Manufacturers. 369 NHTSA–2018–0067–12150, Toyota North America. PO 00000 Frm 00122 Fmt 4701 Sfmt 4700 assumptions presented in the Draft TAR for mass reduction, tire rolling resistance, and aerodynamic drag because those assumptions projected very few technology improvements were present in the baseline fleet. In assessing the comments, the agencies found that E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.092</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24294 24295 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations using the EPA Draft TAR approach would lead to projected compliance pathways with overestimated fuel economy improvements and underestimated costs.370 Many of those assumptions were neither scientifically meritorious, nor isolated examples. For instance, for the EPA Draft TAR and Proposed Determination analyses, the BMW i3, a vehicle with full carbon fiber bodysides and downsized, mass-reduced wheels and tires (some of the most advanced mass reducing technologies commercialized in the automotive industry), was assumed to have 1.0 percent mass reduction (a very minor level of mass reduction). Similarly, previous analyses assigned the Chevrolet Corvette, a performance vehicle that has long been a platform for commercializing advanced weight saving technologies,371 with zero mass reduction. For aerodynamic drag, previous EPA analysis assumed that pickup trucks could achieve the aerodynamic drag profile typical of a sedan, with little regard for form drag constraints or frontal area (and headroom, or ground clearance) considerations. These assumptions commonly led to projections of a 20 percent improvement in mass, aerodynamic drag, and tire rolling resistance, even when a large portion of those improvements had either already been implemented, or were not technologically feasible. On the other hand, in the Draft TAR, NHTSA presented methodologies to evaluate content for mass reduction technology, aerodynamic drag improvements, and rolling resistance technologies that better accounted for the actual level of technologies in the analysis fleet. Throughout the rulemaking process, the agencies reconciled these differences, jointly presented improved approaches in the NPRM similar to what NHTSA presented in the Draft TAR, and again used those reconciled approaches in today’s analysis.372 Many commenters correctly observed that the analysis fleet in the NPRM recognized more technology content in the baseline than in the Draft TAR (with higher penetration rates of tire rolling 370 NHTSA–2018–0067–11741, ICCT. e.g., Fiberglass to Carbon Fiber: Corvette’s Lightweight Legacy, GM (August 2012), https:// media.gm.com/media/us/en/gm/news.detail.html/ content/Pages/news/us/en/2012/Aug/0816_ corvette.html. 372 Because these road load technologies are no longer double counted, the projected compliance pathway in the NPRM, and in today’s analysis for stringent alternatives, often requires more advanced fuel saving technologies than previously projected, including higher projected penetration rates of hybrid and electric vehicle technologies. khammond on DSKJM1Z7X2PROD with RULES2 371 See, VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 resistance and aerodynamic drag improvements, for instance), but also that the fuel economy values of the fleet had not improved all that much from the previous year. Some commenters concluded that the NPRM baseline technology assignment process was arbitrary and overstated the technology content already present in the baseline fleet.373 374 The agencies agree that there was a large increase in the amount of road load technology credited in the baseline fleet between EPA’s Draft TAR and the jointly produced NPRM, and clarify that this change was largely due to a recognition of technologies that were actually present in the fleet, but not properly accounted for in previous analyses. The change in penetration rates of road load technologies (after accounting for glider share updates, which is discussed in more detail in the mass reduction technology section) between the NPRM and today’s analysis is relatively small. Many commenters noted that the different baseline road load assumptions (and other technology modeling) materially affect compliance pathways, and projected costs.375 ICCT commented that the agencies should conduct sensitivity analyses assuming every vehicle in the analysis fleet is set to zero percent road load technology improvement, to demonstrate how the technology content of the analysis fleet affected the compliance scenarios.376 While the agencies have clearly described the methods by which initial road load technologies are assigned in Section VI.C.4 Mass Reduction, Section VI.C.5 Aerodynamics, and Section VI.C.6 Tire Rolling Resistance below, the agencies considered a sensitivity case that assumed no mass reduction, rolling resistance, or aerodynamic improvements had been made to the MY 2017 fleet (i.e., setting all vehicle road levels to zero—MRO, AERO and ROLL0). While this is an unrealistic characterization of the initial fleet, the agencies conducted a sensitivity analysis to understand any affect it may have on technology penetration along other paths (e.g. engine and hybrid technology). Under the CAFE program, the sensitivity analysis shows a slight decrease in reliance on engine technologies (HCR engines, turbocharge engines, and engines utilizing cylinder deactivation) and hybridization (strong hybrids and plug-in hybrids) in the 373 NHTSA–2018–0067–11741, 374 NHTSA–2018–0067–12039, ICCT. Union of Concerned Scientists. 375 NHTSA–2018–0067–11928, Ford Motor Company. 376 NHTSA–2018–0067–11741, ICCT. PO 00000 Frm 00123 Fmt 4701 Sfmt 4700 baseline (relative to the central analysis). The consequence of this shift to reliance on lower-level road load technologies is a reduction in compliance cost in the baseline of about $300 per vehicle (in MY 2026). As a result, cost savings in the preferred alternative are reduced by about $200 per vehicle. Under the CO2 program, the general trend in technology shift is less dramatic (though the change in BEVs is larger) than the CAFE results. The cost change is also comparable, but slightly smaller ($200 per vehicle in the baseline) than the CAFE program results. Cost savings under the preferred alternative are further reduced by about $100. With the lower technology costs in all cases, the consumer payback periods decreased as well. These results are consistent with the approach taken by manufacturers who have already deployed many of the low-level road load reduction opportunities to improve fuel economy. Some commenters preferred that the agencies develop a different methodology based on reported road load coefficients (‘‘A,’’ ‘‘B’’ and ‘‘C’’ coastdown coefficients) to estimate levels of aerodynamic drag improvement and rolling resistance in the baseline fleet that did not rely on CBI.377 The agencies considered this, but determined that using CBI to assign baseline aerodynamic drag levels and rolling resistance values was more accurate and appropriate. Estimating aerodynamic drag levels and rolling resistance levels from coastdown coefficients is not straightforward, and to do it well would require information the agencies do not have (much of which is also CBI). For instance, rotational inertias of wheel, tire, and brake packages can affect coastdown, so mass of the vehicle is not sufficient. The frontal area of the vehicles, a key component for calculating aerodynamic drag, is rarely known, and often requires manufacturer input to get an accurate value. Other important vehicle features like all-wheel-drive should also be accounted for, and the agencies would struggle to correctly identify improvements in rolling resistance, lowdrag brakes, and secondary axle disconnect, because all of these technologies would present similar signature on a coast down test. All of these technologies are represented as technology pathways in today’s analysis. Manufacturers acknowledged the possibility of using road load coefficients to estimate rolling resistance and aerodynamic features, but warned that the process ‘‘required 377 NHTSA–2018–0067–11741, E:\FR\FM\30APR2.SGM 30APR2 ICCT. 24296 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations various assumptions and is not very accurate,’’ and stated that the use of CBI to assess aerodynamic and rolling resistance technologies is an ‘‘accurate and practical solution’’ to assign these difficult to observe technologies.378 khammond on DSKJM1Z7X2PROD with RULES2 (3) Assigning Engine Configurations Engine technology costs can vary significantly by the configuration of the engine. For instance, adding variable valve lift to each cylinder on an engine would cost more for an engine with eight cylinders than an engine with four cylinders. Similarly, the cost of adding a turbocharger to an engine and downsizing the engine would be different going from a naturally aspirated V8 to a turbocharged V6 than going from a naturally aspirated V6 to a turbocharged I4. As discussed in detail in the engine technology section of this document, the cost files for the CAFE model account for instances such as these examples. Information in the analysis fleet enables the CAFE model to reference the intended engine costs. The ‘‘Engine Technology Class (Observed)’’ lists the architecture of the observed engine. Notably, the analysis assumes that nearly all turbo charged engines take advantage of downsizing to optimize fuel efficiency, minimize the cost of turbo charging, and to maintain performance (to the extent practicable) with the naturally aspirated counterpart engine. Therefore, engines observed in the fleet that have already been downsized must reference costs for a larger basic engine, which assumes downsizing with the application of turbo technology. In these cases, the ‘‘Engine Technology Class’’ which is used to reference costs will be larger than the ‘‘Engine Technology Class (Observed).’’ This is the same process agencies used in the NPRM, and it corrects a previous error in the Draft TAR analysis, which incorrectly underestimated turbocharged engine costs.379 Some commenters expressed confusion and disagreement with this correction, with some even commenting that the analysis baselessly inflated costs of turbocharging technologies between the Draft TAR and the NPRM.380 To be clear, this was a correction so that the costs used to calculate turbocharged engine costs accurately reflected the total costs for a turbocharged engine. 378 NHTSA–2018–0067–12073, Alliance of Automobile Manufacturers. 379 For instance, the Draft TAR engine costs would map an observed V6 Turbo engine to I4 Turbo engine costs, by referencing a 4C1B engine cost. 380 NHTSA–2018–0067–11741, ICCT. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 (4) Characterizing Shared Vehicle Platforms, Engines, and Transmissions Another aspect of characterizing vehicle model/configurations in the analysis fleet is based on whether they share a ‘‘platform’’ with other vehicle model/configurations. A ‘‘platform’’ refers to engineered underpinnings shared on several differentiated products. Manufacturers share and standardize components, systems, tooling, and assembly processes within their products (and occasionally with the products of another manufacturer) to manage complexity and costs for development, manufacturing, and assembly. The concept of platform sharing has evolved over time. Years ago, manufacturers rebadged vehicles and offered luxury options only on premium nameplates (and manufacturers shared some vehicle platforms in limited cases). Today, manufacturers share parts across highly differentiated vehicles with different body styles, sizes, and capabilities that may share the same platform. For instance, the Honda Civic and Honda CR–V share many parts and are built on the same platform. Engineers design chassis platforms with the ability to vary wheelbase, ride height, and even driveline configuration. Assembly lines can produce hatchbacks and sedans to costeffectively utilize manufacturing capacity and respond to shifts in market demand. Engines made on the same line may power small cars or mid-size sport utility vehicles. In addition, although the agencies’ analysis, like past CAFE analyses, considers vehicles produced for sale in the U.S., the agency notes these platforms are not constrained to vehicle models built for sale in the U.S.; many manufacturers have developed, and use, global platforms, and the total number of platforms is decreasing across the industry. Several automakers (for example, General Motors and Ford) either plan to, or already have, reduced their number of platforms to less than 10 and account for the overwhelming majority of their production volumes on that small number of platforms. Vehicle model/configurations derived from the same platform are so identified in the analysis fleet. Many manufacturers’ use of vehicle platforms is well documented in the public record and widely recognized among the vehicle engineering community. Engineering knowledge, information from trade publications, and feedback from manufacturers and suppliers was also used to assign vehicle platforms in the analysis fleet. PO 00000 Frm 00124 Fmt 4701 Sfmt 4700 When the CAFE model is deciding where and how to add technology to vehicles, if one vehicle on the platform receives new technology, other vehicles on the platform also receive the technology as part of their next major redesign or refresh.381 Similar to vehicle platforms, manufacturers create engines that share parts. For instance, manufacturers may use different piston strokes on a common engine block, or bore out common engine block castings with different diameters to create engines with an array of displacements. Head assemblies for different displacement engines may share many components and manufacturing processes across the engine family. Manufacturers may finish crankshafts with the same tools to similar tolerances. Engines on the same architecture may share pistons, connecting rods, and the same engine architecture may include both six and eight cylinder engines. One engine family may appear on many vehicles on a platform, and changes to that engine may or may not carry through to all the vehicles. Some engines are shared across a range of different vehicle platforms. Vehicle model/configurations in the analysis fleet that share engines belonging to the same platform are also identified as such. It is important to note that manufacturers define common engines differently. Some manufacturers consider engines as ‘‘common’’ if the engines shared an architecture, components, or manufacturing processes. Other manufacturers take a narrower definition, and only assume ‘‘common’’ engines if the parts in the engine assembly are the same. In some cases, manufacturers designate each engine in each application as a unique powertrain. For example, a manufacturer may have listed two engines separately for a pair that share designs for the engine block, the crank shaft, and the head because the accessory drive components, oil pans, and engine calibrations differ between the two. In practice, many engines share parts, tooling, and assembly resources, and manufacturers often coordinate design updates between two similar engines. Engine families, designated in the analysis using ‘‘engine codes,’’ for each manufacturer were tabulated and assigned based on data-driven criteria. If engines shared a common cylinder count and configuration, displacement, valvetrain, and fuel type, those engines 381 The CAFE model assigns mass reduction technology at a platform level, but many other technologies may be assigned and shared at a vehicle nameplate or vehicle model level. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations may have been considered together. In addition, if the compression ratio, horsepower, and displacement of engines were only slightly different, those engines were considered the same for the purposes of redesign and sharing. Vehicles in the analysis fleet with the same engine family will, therefore, adopt engine technology in a coordinated fashion. Specifically, if such vehicles have different design schedules (i.e., refresh and redesign schedules), and a subset of vehicles using a given engine add engine technologies during of a redesign or refresh that occurs in an early model year (e.g., 2018), other vehicles using the same engine ‘‘inherit’’ these technologies at the soonest ensuing refresh or redesign. This is consistent with a view that, over time, most manufacturers are likely to find it more practicable to shift production to a new version of an engine than to continue production of both the new engine and a ‘‘legacy’’ engine indefinitely. By grouping engines together, the CAFE model controls future engine families to ensure reasonable powertrain complexity. This means, however, that for manufacturers that submitted highly atomized engine and transmission portfolios, there is a practical cap on powertrain complexity and the ability of the manufacturer to optimize the displacement of (i.e., ‘‘right size’’) engines perfectly for each vehicle configuration. This concept is discussed further in Section VI.B.4.a), below. Like with engines, manufacturers often use transmissions that are the same or similar on multiple vehicles. Manufacturers may produce transmissions that have nominally different machining to castings, or manufacturers may produce transmissions that are internally identical, except for the final gear ratio. In some cases, manufacturers subcontract with suppliers that deliver whole transmissions. In other cases, manufacturers form joint ventures to develop shared transmissions, and these transmission platforms may be offered in many vehicles across manufacturers. Manufacturers use supplier and jointventure transmissions to a greater extent than they do with engines. To reflect this reality, shared transmissions were considered for manufacturers as appropriate. Transmission configurations are referred to in the analysis as ‘‘transmission codes.’’ Like the inheritance approach outlined for engines, if one vehicle application of a shared transmission family upgraded the transmission, other vehicle applications also upgraded the VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 transmission at the next refresh or redesign year. To define common transmissions, the agencies considered transmission type (manual, automatic, dual-clutch, continuously variable), number of gears, and vehicle architecture (front-wheel-drive, rearwheel-drive, all-wheel-drive based on a front-wheel drive platform, or all-wheeldrive based on a rear-wheel-drive platform). If vehicles shared these attributes, these transmissions were grouped for the analysis. Vehicles in the analysis fleet with the same transmission configuration will adopt transmission technology together, as described above. Having all vehicles that share a platform (or engines that are part of a family) adopt fuel economy-improving/ CO2 emissions-reducing technologies together, subject to refresh/redesign constraints, reflects the real-world considerations described above, but also overlooks some decisions manufacturers might make in the real world in response to market pull. Accordingly, even though the analysis fleet is incredibly complex, it is also oversimplified in some respects compared to the real world. For example, the CAFE model does not currently attempt to simulate the potential for a manufacturer to shift the application of technologies to improve performance rather than fuel economy. Therefore, the model’s representation of the ‘‘inheritance’’ of technology can lead to estimates a manufacturer might eventually exceed fuel economy standards as technology continues to propagate across shared platforms and engines. While the agencies have previously seen examples of extended periods during which some manufacturers exceeded one or both CAFE and/or CO2 standards, in plenty of other examples, manufacturers chose to introduce (or even reintroduce) technological complexity into their vehicle lineups in response to buyer preferences. Going forward, and recognizing the recent trend for consolidating platforms, it seems likely manufacturers will be more likely to choose efficiency over complexity in this regard; therefore, the potential should be lower that today’s analysis turns out to be oversimplified compared to the real world. Manufacturers described shared engines, transmissions, and vehicle platforms as ‘‘standard business practice’’ and they were encouraged that the NHTSA analysis in the Draft TAR, and the jointly issued NPRM placed realistic limits on the number of unique engines and transmissions in a PO 00000 Frm 00125 Fmt 4701 Sfmt 4700 24297 powertrain portfolio.382 In previous rulemakings, stakeholders pointed out that shared parts and portfolio complexity should be considered (but were not), and that the proliferation of unique technology combinations resulting from unconstrained compliance pathways would jeopardize economies of scale in the real world.383 HD Systems acknowledged that previous rulemakings did not appropriately consider part sharing, but contended that in today’s global marketplace, manufacturers have flexibility to compete in new ways that break old part sharing rules.384 The agencies acknowledge that some transmissions are now sourced through suppliers, and that economies of scale could, in the future be achieved at an industry level instead of a manufacturer level; however, even when manufacturers outsource a transmission, recent history suggests they apply that transmission to multiple vehicles to control assembly plant and service parts complexity, as they would if they were making the transmission themselves. Similarly, even for global platforms, or global powertrains, there is little evidence that manufacturers fragment powertrain line-ups for a vehicle, or a set of vehicles that have typically used the same engine. The agencies will continue to consider how to capture more accurately the ways vehicles share engines, transmissions, and platforms in future rulemakings, but the part-sharing and modeling approach presented in the NPRM and this final rule represents a marked improvement over previous analysis. (5) Characterizing Production Design Cycles Another aspect of characterizing vehicles in the analysis fleet is based on when they can next be refreshed or redesigned. Redesign schedules play an important role in determining when new technologies may be applied. Many technologies that improve fuel economy and reduce CO2 emissions may be difficult to incorporate without a major product redesign. Therefore, each vehicle model in the analysis fleet has an associated redesign schedule, and the CAFE model uses that schedule to implement significant advances in some technologies (like major mass reduction) to redesign years, while allowing manufacturers to include minor advances (such as improved tire rolling 382 NHTSA–2018–0067–12150, Toyota North America. 383 Alliance of Automobile Manufacturers, EPA– HQ–OAR–0827 and NHTSA–2016–0068. 384 NHTSA–2018–0067–11985, HD Systems. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations schedule across the industry would likely reduce consistency with the real world, especially for light trucks, which are redesigned, on average, no less than every six years (see Table VI–9, below). Moreover, if some manufacturers accelerate redesigns in response to new standards, doing so would likely involve costs (greater levels of stranded capital, reduced opportunity to benefit from ‘‘learning’’-related cost reductions) greater than reflected in other inputs to the analysis. As discussed in the NPRM, manufacturers use diverse strategies with respect to when, and how often they update vehicle designs. While most vehicles have been redesigned sometime in the last five years, many vehicles have not. In particular, vehicles with lower annual sales volumes tend to be redesigned less frequently, perhaps giving manufacturers more time to recoup the investment needed to bring the product to market. In some cases, manufacturers continue to produce and sell vehicles designed more than a decade ago. resistance) during a vehicle ‘‘refresh,’’ or a smaller update made to a vehicle, which can happen between redesigns. In addition to refresh and redesign schedules associated with vehicle model/configurations, vehicles that share a platform subsequently have platform-wide refresh and redesign schedules for mass reduction technologies. To develop the refresh/redesign cycles used for the NPRM vehicles in the analysis fleet, information from commercially available sources was used to project redesign cycles through MY 2022, as was done for NHTSA’s analysis for the 2016 Draft TAR.385 Commercially available sources’ estimates through MY 2022 are generally supported by detailed consideration of public announcements plus related intelligence from suppliers and other sources, and recognize that uncertainty increases considerably as the forecasting horizon is extended. For MYs 2023–2035, in recognition of that uncertainty, redesign schedules were extended considering past pacing for each product, estimated schedules through MY 2022, and schedules for other products in the same technology classes. As mentioned above, potentially confidential forward-looking information was not requested from manufacturers; nevertheless, all manufacturers had an opportunity to review the estimates of product-specific redesign schedules. A few manufacturers provided related forecasts and, for the most part, that information corroborated the estimates. Some commenters suggested supplanting these estimated redesign schedules with estimates applying faster cycles (e.g., four to five years), and this approach was considered for the analysis. Some manufacturers tend to operate with faster redesign cycles and may continue to do so, and manufacturers tend to redesign some products more frequently than others. However, especially considering that information presented by manufacturers largely supports estimates discussed above, applying a ‘‘one size fits all’’ acceleration of redesign cycles would not improve the analysis; instead, assuming a fixed, shortened redesign BILLING CODE 4910–59–P 385 In some cases, data from commercially available sources was found to be incomplete or inconsistent with other available information. For instance, commercially available sources identified some newly imported vehicles as new platforms, but the international platform was midway through the product lifecycle. While new to the U.S. market, treating these vehicles as new entrants would have resulted in artificially short redesign cycles if carried forward, in some cases. Similarly, commercially available sources labeled some product refreshes as redesigns, and vice versa. In these limited cases, the data was revised to be consistent with other available information or typical redesign and refresh schedules for CAFE modeling. In these limited cases, the forecast time between redesigns and refreshes was updated to match the observed past product timing. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00126 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.093</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24298 24299 Each manufacturer may use different strategies throughout their product portfolio, and a component of each strategy may include the timing of refresh and redesign cycles. Table VI–10 summarizes the average time between redesigns, by manufacturer, by vehicle technology class. Dashes mean the manufacturer has no volume in that vehicle technology class in the MY 2017 analysis fleet. Across the industry, manufacturers average 6.6 years between product redesigns. Trends on redesign schedules identified in the NPRM remain in place for today’s analysis. Pick-up trucks have much longer redesign schedules than small cars. Some manufacturers redesign vehicles often, while other manufacturers redesign vehicles less often. Even if two manufacturers have similar redesign cadence, the model years in which the redesigns occur may still be different and dependent on where each of the manufacturer’s products are in their life cycle. Table VI–11 summarizes the average age of manufacturers’ offering by vehicle technology class. A value of ‘‘0.0’’ means that every vehicle for a manufacturer in the vehicle technology class, represented by the MY 2017 analysis fleet was new in MY 2017. Across the industry manufacturers redesigned MY 2017 vehicles an average of 3.5 years earlier, meaning the average MY 2017 vehicle was last redesigned in approximately MY 2013, also on average near a midpoint in their product lifecycle. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00127 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.094</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C Some commenters cited examples of vehicles in the NPRM analysis fleet where the redesign years were off by a year here or there in the 2017–2022 timeframe relative to the most recent public announcements, or that the extended forecasts were too rigid.386 The CAFE model structurally requires an input for the redesign years, and the agencies worked to make these generally representative without disclosing precise CBI product plans. Many of the redesign schedules were carried over from the NPRM, with a few minor updates. Some commenters contended that the agencies should not look at the historical data to project the timing between redesigns (‘‘business as usual’’), but should instead adopt a ‘‘policy case’’ with an accelerated pace of redesigns and refreshes.387 Some 386 NHTSA–2018–0067–11723, Natural Resources Defense Council. 387 NHTSA–2018–0067–11723, Natural Resources Defense Council. VerDate Sep<11>2014 commenters suggested that the agencies use a standard 5 or 6 year redesign schedule for all manufacturers and all products as a way to lower projected costs.388 Other stakeholders commented that the entire industry should be modeled with the ability to redesign everything at one time in the near term because that would not presuppose precisely how manufacturers may adjust their fleet.389 If the agencies were to implement any such approaches, the agencies would need to more precisely account for tooling costs, research and development costs, and product lifecycle marketing costs, or risk missing ‘‘hidden costs’’ of a shortened cadence. To account properly for these, the CAFE model would require major changes, and would require specific inputs that are currently covered generically under the 23:30 Apr 30, 2020 Jkt 250001 388 NHTSA–2018–0067–11985, 389 NHTSA–2018–0067–12039, HD Systems. Union of Concerned Scientists. PO 00000 Frm 00128 Fmt 4701 Sfmt 4700 retail price equivalency (RPE) factor.390 The agencies considered these comments, and decided the process for refresh and redesign outlined in the NPRM was a reasonable and realistic approach to characterize product changes. The agencies conducted sensitivity analysis with compressed redesign and refresh schedules, though these ignore the resulting compressed amortization schedules, missing important costs that are incorporated in the current RPE assumptions. Some commenters claimed that the agency had extraordinarily extended redesign schedule of 17.7 years for FCA between 2021–2025, and an average redesign time of 25.8 years for Ford between 2022–2025.391 The agencies found these claims inaccurate and without basis. Table VI–10, ‘‘Summary of Sales Weighted Average Time 390 Shorter redesign schedules are likely to put upward pressure on RPE, as the manufacturers would have less time to recoup investments. 391 NHTSA–2018–0067–11723, Natural Resources Defense Council. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.095</GPH> 24300 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations between Engineering Redesigns, by Manufacturer, by Vehicle Technology Class’’ summarizes the data used in today’s analysis (which is very similar to the information used in the NPRM, with some minor adjustments and updates to the fleet), and the detailed information vehicle-by-vehicle is reported in the ‘‘market data’’ file. The agencies recognize that the natural sequence of redesigns for some manufacturers and some products is not ideal to meet stringent alternatives, which is part of the consideration for economic practicability and technological feasibility. Manufacturers commented supportively on the idea of vehicle specific redesign schedules, and the redesign cadence used in the NPRM, as these contribute to realistic assessments of new technology penetration within the fleet, and acknowledge the heterogeneity in the product development approaches and business practices for each manufacturer.392 One commenter recognized that redesign and refresh schedules represented a vast improvement over phase-in caps to model the adoption of mature technologies.393 Other commenters argued that the structural construct of technologies only being available at redesign or at refresh (via inheritance) did not reflect real world actions and was not supported by any actual data.394 Other commenters acknowledged the inheritance of engine and transmission technologies at refresh as an important, positive feature of the CAFE model.395 HD Systems argued that an engine or transmission package available in other markets on a global platform could be imported to the U.S. khammond on DSKJM1Z7X2PROD with RULES2 392 NHTSA–2018–0067–11928, Ford Motor Company. 393 NHTSA–2018–0067–0444, Walter Kreucher. 394 NHTSA–2018–0067–11985, HD Systems. 395 NHTSA–2018–0067–11723, Natural Resources Defense Council. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 market during refresh, and did not require a ‘‘leader’’ at redesign in the U.S. market to seed adoption. HDS cited a few examples where manufacturers have introduced strong hybrid powertrains on an existing vehicle a year or two after the product launch, not associated with any particular vehicle redesign or refresh. The agencies carefully considered these comments, and observed that some relatively low volume hybrid options may appear after launch, or that some transmissions were quickly replaced shortly after a major redesign. In many of these cases, launch delays, warranty claims, or other external factors contributed to, at least in part, an atypically timed introduction of fuel saving technology to the fleet.396 At this point, this does not appear to be a mainstream, or preferred industry practice. However, the agencies will continue to evaluate this. For future rulemaking, the agencies may consider engine refresh and redesign cycles for engines and transmissions. These may be separate from vehicle redesign and refresh schedules because the powertrain product lifecycles may be longer on average than the typical vehicle redesign schedules. This approach, if researched and implemented in future analysis, could provide some opportunity for manufacturers to introduce new powertrain technologies independent of the vehicle redesign schedules, in addition to inheriting advanced powertrain technology as refresh as already modeled in the NPRM and today’s analysis. For today’s analysis, the agencies, with a few exceptions based on updated publicly available information, carried over redesign cadences for each vehicle nameplate as presented in the NPRM. 396 Such instances are observable in detailed CAFE and CO2 compliance data submitted to EPA and NHTSA. PO 00000 Frm 00129 Fmt 4701 Sfmt 4700 24301 The agencies do not claim that the projected redesign years will perfectly match what industry does—notably because refresh and redesign information is CBI and the agencies have applied more generalized schedules to protect the CBI. Also, what any individual manufacturer may choose to do today could be completely different than what it chooses to do tomorrow due to changing business circumstances and plans—but the agencies have worked to ensure the timing of redesigns will be roughly correct (especially in the near term), and that the time between redesigns will continue forward for each manufacturer as it has based on recent history. The agencies have also increased the frequency of refreshes in response to comments about the proliferation of some engine and transmission families through manufacturers’ product portfolios. Also for today’s analysis, the agencies now explicitly model CAFE compliance pathways out through 2050. For the model to work as intended, the agencies must project refresh and redesign schedules out through 2050. The agencies recognize that the accuracy of predictions about the distant future, particularly about refresh and redesign cycles through the 2030–2050 timeframe, are likely to be poor. If historical evolution of the industry continues, many of the nameplates carried forward in the fleet are likely to be out of production, and new nameplates not considered in the analysis are sure to emerge. Still, carrying forward the MY 2017 fleet with the current refresh and redesign cadences is consistent with the current analysis, and imposing an alternative schedule on the fleet, or making up new nameplates and retiring older nameplates without a clear basis, would lack proper foundation. BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations BILLING CODE 4910–59–C khammond on DSKJM1Z7X2PROD with RULES2 (6) Defining Technology Adoption Features In some circumstances, the agencies may reference full vehicle simulation effectiveness data for technology combinations that are not able to be, or are not likely to be applied to all vehicles. In some cases, a specific technology as modeled only exists on paper, and questions remain about the technological feasibility of the efficiency characterization.397 Or, a technology may perform admirably on the test cycle, but fail to meet all functional, or performance requirements for certain vehicles.398 In other cases, the intellectual property landscape may 397 High levels of aerodynamic drag reduction for some body styles, or EPA’s previous, speculative characterization of ‘‘HCR2’’ engines, for example. 398 Examples of applications that are unsuitable for certain technologies include low end torque requirements for HCR engines on high load vehicles, or towing and trailering applications, continuously variable transmissions in high torque applications, and low rolling resistance tires on vehicles built for precision cornering and high lateral forces, or instant acceleration from a stand still. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 make commercialization of one technology risky for a manufacturer without the consent of the intellectual property owner.399 In such cases, the agencies may not allow a technology to be applied to a certain vehicle. The agencies designate this in the ‘‘market data’’ file with a ‘‘SKIP’’ for the technology and vehicle. The logic is explained technology by technology in this document, as the logic was explained in the PRIA for this rule. Some commenters argued that the restrictions of technologies on a case-bycase basis required case-by-case explanation (and not objective specification defined cut-offs), and that the use of CBI for performance considerations was unacceptable unless fully disclosed.400 As discussed above, the agencies are not able to disclose CBI. Stakeholders have had plenty of opportunities to comment on the applicability of technologies, including 399 Variable compression ratio engines, for example. 400 NHTSA–2018–0067–11741, ICCT. PO 00000 Frm 00130 Fmt 4701 Sfmt 4700 the few that have used SKIP logic restrictions for a portion of the fleet. Other commenters suggested an optimistic and wholly unfounded approach to manufacturer innovation, arguing that costs would continue to come down (beyond what is currently modeled with cost learning), and the list of fuel-saving technologies would continually regenerate itself (even if the technological mechanism for fuel saving technologies was not yet identified).401 Therefore, the argument goes that people will figure out new ways to improve fuel saving technologies to increase their applicability, and the current technology characterization should be enabled for selection with no restriction—not because the commenter knows how the technology will be adapted, but that the commenter believes the technology could, eventually, within the timeline of the rulemaking, be adapted, brought to market, and be accepted by consumers. While the agencies recognize the improvements that many manufacturers 401 NHTSA–208–0067–12122–33, American Council for an Energy-Efficient Economy. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.096</GPH> 24302 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations have achieved in fuel saving technologies, some of which were difficult to foresee, the agencies have an obligation under the law to be judicious and specific about technological feasibility, and to avoid speculative conclusions about technologies to justify the rulemaking. c) Other Analysis Fleet Data (1) Safety Classes The agencies referenced the masssize-safety analysis to project the effects changes in weight may have on crash fatalities. That analysis, discussed in more detail in Section VI.D.2, considers how weight changes may affect safety for cars, crossover utility vehicles and sport utility vehicles, and pick-up trucks. To consider these effects, the agencies mapped each vehicle in the analysis fleet to the appropriate ‘‘Safety Class.’’ khammond on DSKJM1Z7X2PROD with RULES2 (2) Labor Utilization The analysis fleet summarizes components of direct labor for each vehicle considered in the analysis. The labor is split into three components: (1) Dealership hours worked on sales functions per vehicle, (2) direct assembly labor for final assembly, engine, and transmission, and (3) percent U.S. content. In the MY 2016 fleet for the NPRM, the agencies catalogued production locations and plant employment, reviewed annual reports from the North American Dealership Association to estimate dealership employment (27.8 hours per vehicle sold), and estimated the industry average labor hours for final assembly of vehicles (30 hours per vehicle produced), engine machining and assembly (4 hours per engine produced), and transmission production (5 hours per transmission produced). Today’s analysis fleet carries over the estimated labor coefficients for sales and production, but references the most recent Part 583 American Automobile Labeling Act Report for percent U.S. content and for the location of vehicle assembly, engine assembly, and transmission assembly.402 (3) Production Volumes for Sales Analysis A final important aspect of projecting what vehicles will exist in future model years and potential manufacturer responses to standards is estimating how future sales might change in response to different potential standards. If potential future standards 402 Part 583 American Automobile Labeling Act Report, available at https://www.nhtsa.gov/part583-american-automobile-labeling-act-reports. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 appear likely to have major effects in terms of shifting production from cars to trucks (or vice versa), or in terms of shifting sales between manufacturers or groups of manufacturers, that is important for the agencies to consider. For previous analyses, the CAFE model used a static forecast contained in the analysis fleet input file, which specified changes in production volumes over time for each vehicle model/ configuration. This approach yielded results that, in terms of production volumes, did not change between scenarios or with changes in important model inputs. For example, very stringent standards with very high technology costs would result in the same estimated production volumes as less stringent standards with very low technology costs. For this analysis, as in the proposal, the CAFE model begins with the first-year production volumes (i.e., MY 2017 for today’s analysis) and adjusts ensuing sales mix year by year (between cars and trucks, and between manufacturers) endogenously as part of the analysis, rather than using external forecasts of future car/truck split and future manufacturer sales volumes. This leads the model to produce different estimates of future production volumes under different standards and in response to different inputs, reflecting the expectation that regulatory standards and other external factors will, in fact, impact the market. (4) Comments on Other Analysis Fleet Data Some commenters suggest that the CAFE model should run as a full consumer choice model (and this idea is discussed in more detail in Section VI.D.1). While this sounds like a reasonable request on the surface, such an approach would place enormous new demands on the data characterized in the fleet (and preceding fleets, which may be needed to calibrate a model properly). For instance, some model concepts may depend on a bevy of product features, such as interior cargo room, artistic appeal of the design, and perceived quality of the vehicle. But product features alone may not be sufficient. Additional information about dealership channels, product awareness and advertising effectiveness, and financing terms also may be required. Such information could dramatically increase the scope of work needed to characterize the analysis fleet for future rulemakings. As described in Section VI.D.1.b)(2)(d) Using Vehicle Choice Models in Rulemaking Analysis. Accordingly, the agencies decided not to develop such a model for this rulemaking. PO 00000 Frm 00131 Fmt 4701 Sfmt 4700 24303 2. Treatment of Compliance Credit Provisions Today’s final rule involves a variety of provisions regarding ‘‘credits’’ and other compliance flexibilities. Some recently introduced regulatory provisions allow a manufacturer to earn ‘‘credits’’ that will be counted toward a vehicle’s rated CO2 emissions level, or toward a fleet’s rated average CO2 or CAFE level, without reference to required levels for these average levels of performance. Such flexibilities effectively modify emissions and fuel economy test procedures, or methods for calculating fleets’ CAFE and average CO2 levels. Such provisions are discussed below in Section VI.B.2. Other provisions (for CAFE, statutory provisions) allow manufacturers to earn credits by achieving CAFE or average CO2 levels beyond required levels; these provisions may hence more appropriately be termed ‘‘compliance credits.’’ EPCA has long provided that, by exceeding the CAFE standard applicable to a given fleet in a given model year, a manufacturer may earn corresponding ‘‘credits’’ that the same manufacturer may, within the same regulatory class, apply toward compliance in a different model year. EISA amended these provisions by providing that manufacturers may, subject to specific statutory limitations, transfer compliance credits between regulatory classes, and trade compliance credits with other manufacturers. The CAA provides EPA with broad standardsetting authority for the CO2 program, with no specific directives regarding either CO2 standards or CO2 compliance credits. EPCA also specifies that NHTSA may not consider the availability of CAFE credits (for transfer, trade, or direct application) toward compliance with new standards when establishing the standards themselves.403 Therefore, this analysis, like that presented in the NPRM, considers 2020 to be the last model year in which carried-forward or transferred credits can be applied for the CAFE program. Beginning in model year 2021, today’s ‘‘standard setting’’ analysis for NHTSA’s program is conducted assuming each fleet must comply with the CAFE standard separately in every model year. The ‘‘unconstrained’’ perspective acknowledges that these flexibilities exist as part of the program, and, while not considered by NHTSA in setting standards, are nevertheless important to consider when attempting to estimate the real impact of any alternative. Under 403 49 E:\FR\FM\30APR2.SGM U.S.C. 32902(h)(3). 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24304 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations the ‘‘unconstrained’’ perspective, credits may be earned, transferred, and applied to deficits in the CAFE program throughout the full range of model years in the analysis. The Final Environmental Impact Analysis (FEIS) accompanying today’s final rule, like the corresponding Draft EIS analysis, presents results of ‘‘unconstrained’’ modeling. Also, because the CAA provides no direction regarding consideration of any CO2 credit provisions, today’s analysis, like the NPRM analysis, includes simulation of carried-forward and transferred CO2 credits in all model years. Some commenters took issue broadly with this treatment of compliance credits. Michalek and Whitefoot wrote that ‘‘we find this requirement problematic because the automakers use these flexibilities as a common means of complying with the regulation, and ignoring them will bias the cost-benefit analysis to overestimate costs.’’ 404 Counter to the above general claim, the CAFE model does provide means to simulate manufacturers’ potential application of some compliance credits, and both the analysis of CO2 standards and the NEPA analysis of CAFE standards do make use of this aspect of the model. As discussed above, NHTSA does not have the discretion to consider the credit program—in fact, the agency is prohibited by statute from doing so— in establishing maximum feasible standards. Further, as discussed below, the agencies also continue to find it appropriate for the analysis largely to refrain from simulating two of the mechanisms allowing the use of compliance credits. The model’s approach to simulating compliance decisions accounts for the potential to earn and use CAFE credits as provided by EPCA/EISA. The model similarly accumulates and applies CO2 credits when simulating compliance with EPA’s standards. Like past versions, the current CAFE model can be used to simulate credit carry-forward (a.k.a. banking) between model years and transfers between the passenger car and light truck fleets but not credit carry-back (a.k.a. borrowing) from future model years or trading between manufacturers. Regarding the potential to carry back compliance credits, UCS commented that, although past versions of the CAFE model had ‘‘considered this flexibility in its approach to multiyear modeling,’’ NHTSA had, without explanation, ‘‘abruptly discontinued support of this method of compliance,’’ such that 404 Michalek, J. and Whitefoot, K., NHTSA–2018– 0067–11903, at 10–11. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 ‘‘manufacturers are generally incentivized to over comply, regardless of whether carrying forward a deficit to be compensated by later overcompliance would be a more costeffective method of compliance.’’ 405 Citing the potential that manufacturers could make use of carried back credits in the future, UCS also stated that ‘‘NHTSA’s decision to constrain it in the model is unreasonable and arbitrary.’’ 406 UCS effectively implies that the agencies should base standards on analysis that presumes manufacturers will take full theoretical advantage of provisions allowing credits to be borrowed. The agencies have carefully considered these comments, and while EPA’s decisions regarding CO2 standards can consider the potential to carry back compliance credits from later to earlier model years, and NHTSA’s ‘‘unconstrained’’ evaluation could also do so, past examples of failed attempts to carry back CAFE credits (e.g., a MY2014 carry back default leading to a civil penalty payment) underscore the riskiness of such ‘‘borrowing.’’ Recent evidence indicates manufacturers are disinclined to take such risks,407 and both agencies find it reasonable and prudent to refrain from attempting to simulate such ‘‘borrowing’’ in rulemaking analysis. Unlike past versions, the NPRM and current versions of CAFE model provide a basis to specify (in model inputs) CAFE credits available from model years earlier than those being explicitly simulated. For example, with this analysis representing model years 2017– 2050 explicitly, credits earned in model year 2012 are made available for use through model year 2017 (given the current five-year limit on carry-forward of credits). The banked credits are specific to both the model year and fleet in which they were earned. In addition to the above-mentioned comments, UCS also cited as ‘‘errors’’ that ‘‘the model does not accurately reflect the one-time exemption from the EPA 5-year credit life for credits earned in the MY 2010–2015 timeframe’’ and ‘‘NHTSA assumes that there will be absolutely no credit trading between manufacturers.’’ As discussed below, in the course of updating the analysis fleet from MY 405 UCS, NHTSA–2018–0067–12039, Technical Appendix, at 44. 406 UCS, op. cit., at 77. 407 Section IX, below, reviews data regarding manufacturers’ use of CAFE compliance credit mechanism during MYs 2011–2016, and shows that the use of ‘‘carry back’’ credits is, relative to the use of other compliance credit mechanisms, too small to discern. PO 00000 Frm 00132 Fmt 4701 Sfmt 4700 2016 to MY 2017, the agencies have updated and expanded the manner in which the model accounts for credits earned prior to MY 2017, including credits earned as early as MY 2009. In order to increase the realism with which the model transitions between the early model year (MYs 2017–2020) and the later years that are the subject of this action, the agencies have accounted for the potential that some manufacturers might trade some of these pre-MY 2017 credits to other manufacturers. However, as with the NPRM, the analysis refrains from simulating the potential that manufacturers might continue to trade credits during and beyond the model years covered by today’s action. The agencies remain concerned that any realistic simulation of such trading would require assumptions regarding which specific pairs of manufacturers might actually trade compliance credits, and the evidence to date makes it clear that the credit market is far from fully ‘‘open.’’ With respect to the FCA comment cited above, the agencies also remain concerned that to set standards based on an analysis that presumes the use of program flexibilities risks making the corresponding actions mandatory. Some flexibilities—credit carry-forward (banking) and transfers between fleets in particular—involve little risk, because they are internal to a manufacturer and known in advance. As discussed above, credit carry-back involves significant risk, because it amounts to borrowing against future improvements, standards, and production volume and mix—and anticipated market demand for fuel efficient vehicles often fail to materialize. Similarly, credit trading also involves significant risk, because the ability of manufacturer A to acquire credits from manufacturer B depends not just on manufacturer B actually earning the expected amount of credit, but also on manufacturer B being willing to trade with manufacturer A, and on potential interest by other manufacturers. Manufacturers’ compliance plans have already evidenced cases of compliance credit trades that were planned and subsequently aborted, reinforcing the agencies’ judgment that, like credit banking, credit trading involves too much risk to be included in an analysis that informs decisions about the stringency of future standards. Nevertheless, recognizing that some manufacturers have actually been trading credits, the agencies have, as in the NPRM, included in the sensitivity analysis a case that simulates ‘‘perfect’’ trading of compliance credits, focusing E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 on CO2 standards to illustrate the hypothetical maximum potential impact of trading. The FRIA summarizes results of this and other cases included in the sensitivity analysis. As discussed in the CAFE model documentation, the model’s default logic attempts to maximize credit carryforward—that is, to ‘‘hold on’’ to credits for as long as possible. If a manufacturer needs to cover a shortfall that occurs when insufficient opportunities exist to add technology in order to achieve compliance with a standard, the model will apply credits. Otherwise the manufacturer carries forward credits until they are about to expire, at which point it will use them before adding technology that is not considered costeffective. The model attempts to use credits that will expire within the next three years as a means to smooth out technology application over time to avoid both compliance shortfalls and high levels of over-compliance that can result in a surplus of credits. Although it remains impossible precisely to predict manufacturer’s actual earning and use of compliance credits, and this aspect of the model may benefit from future refinement as manufacturers and regulators continue to gain experience with these provisions, this approach is generally consistent with manufacturers’ observed practices. NHTSA introduced the CAFE Public Information Center to provide public access to a range of information VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 regarding the CAFE program,408 including manufacturers’ credit balances. However, there is a data lag in the information presented on the CAFE PIC that may not capture credit actions across the industry for as much as several months. Furthermore, CAFE credits that are traded between manufacturers are adjusted to preserve the gallons saved that each credit represents.409 The adjustment occurs at the time of application rather than at the time the credits are traded. This means that a manufacturer who has acquired credits through trade, but has not yet applied them, may show a credit balance that is either considerably higher or lower than the real value of the credits when they are applied. For example, a manufacturer that buys 40 million credits from Tesla may show a credit balance in excess of 40 million. However, when those credits are applied, they may be worth only 1/10 as much—making that manufacturer’s true credit balance closer to 4 million than 40 million. For the NPRM, the agencies reviewed then-recent credit balances, estimated the potential that some manufacturers could trade credits, and developed inputs that make carried-forward credits 408 CAFE Public Information Center, http:// www.nhtsa.gov/CAFE_PIC/CAFE_PIC_Home.htm (last visited June 22, 2018). 409 CO credits for EPA’s program are 2 denominated in metric tons of CO2 rather than gram/mile compliance credits and require no adjustment when traded between manufacturers or fleets. PO 00000 Frm 00133 Fmt 4701 Sfmt 4700 24305 available in each of model years 2011– 2015, after subtracting credits assumed to be traded to other manufacturers, adding credits assumed to be acquired from other manufacturers through such trades, and adjusting any traded credits (up or down) to reflect their true value for the fleet and model year into which they were traded.410 For today’s analysis, an additional model year’s data was available in mid-2019, and the agencies updated these inputs, as summarized in Table VI–12, Table VI– 13, and Table VI–14. While the CAFE model will transfer expiring credits into another fleet (e.g., moving expiring credits from the domestic car credit bank into the light truck fleet), some of these credits were moved into the initial banks to improve the efficiency of application and both to reflect better the projected shortfalls of each manufacturer’s regulated fleets and to represent observed behavior. For context, a manufacturer that produces one million vehicles in a given fleet, and experiences a shortfall of 2 mpg, would need 20 million credits, adjusted for fuel savings, to offset the shortfall completely. BILLING CODE 4910–59–P 410 The adjustments, which are based upon the CAFE standard and model year of both the party originally earning the credits and the party applying them, were implemented assuming the credits would be applied to the model year in which they were set to expire. For example, credits traded into a domestic passenger car fleet for MY 2014 were adjusted assuming they would be applied in the domestic passenger car fleet for MY 2019. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.098</GPH> Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00134 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.097</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24306 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations In addition to the inclusion of these existing credit banks, the CAFE model also updated its treatment of credits in the rulemaking analysis. EPCA requires that NHTSA set CAFE standards at maximum feasible levels for each model year without consideration of the program’s credit mechanisms. However, as recent NHTSA CAFE/EPA tailpipe CO2 emissions rulemakings have evaluated effects of standards over longer time periods, the early actions taken by manufacturers required more nuanced representation. Accordingly, the CAFE model now provides for a setting to establish a ‘‘last year to consider credits.’’ This adjustment is set at the last year for which new standards are not being considered (MY 2020 in this analysis). This allows the model to replicate the practical application of existing credits toward compliance in early years but also to examine the impact of proposed standards based solely on fuel economy improvements in all years for which new standards are being considered. Regarding the model’s simulation of manufacturers’ potential earning and application of compliance credits, UCS commented that the model ‘‘inexplicably lets credits expire’’ because ‘‘all technologies which pay for VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 themselves within the assumed payback period are applied to all manufacturers, regardless of credit status.’’ UCS also claimed that ‘‘NHTSA did not accurately reflect unique attributes of EPA’s credit bank,’’ that ‘‘credits are not traded between manufacturers,’’ and that ‘‘NHTSA does not model credit carryback for compliance.’’ 411 Relatedly, as discussed above, UCS attributes modeling outcomes to the ‘‘effective cost’’ metric used to select from among available fuel-saving technologies.412 As discussed in Section VI.B.1, the agencies expect that manufacturers are likely to improve fuel economy voluntarily insofar as doing so ‘‘pays back’’ economically within a short period (30 months), and the agencies note that periods of regulatory stability have, in fact, been marked by CAFE levels exceeding requirements. As discussed above, the agencies have excluded simulation of credit trading (except in MYs prior to those under consideration, aside from an idealized case presented in the sensitivity analysis) and likewise excluded simulation of potential ‘‘carryback’’ provisions. The agencies have excluded 411 UCS, NHTSA–2018–0067–12039, Technical Appendix, at 35–46. 412 UCS, NHTSA–2018–0067–12039, Technical Appendix, at 28–30. PO 00000 Frm 00135 Fmt 4701 Sfmt 4700 modeling these scenarios not just because of the analytical complexities involved (and rejecting, for example, the random number generator analysis suggested by UCS), but also because the agencies agree that the actual provisions regarding trading and borrowing of compliance credits create too much risk to be used in the analysis underlying consideration of standards. However, as discussed above, the agencies have revised the ‘‘metric’’ used to prioritize available options to apply fuel-saving technologies. As discussed below, the agencies have revised model inputs to include the large quantity of ‘‘legacy’’ compliance credits EPA has made available under its CO2 standards. The CAFE model has also been modified to include a similar representation of existing credit banks in EPA’s CO2 program. While the life of a CO2 credit, denominated in metric tons of CO2, has a five-year life, matching the lifespan of CAFE credits, such credits earned in the early MY 2009–2011 years of the EPA program, may be used through MY 2021.413 The CAFE model was not modified to allow 413 In the 2010 rule, EPA placed limits on credits earned in MY 2009, which expired prior to this rule. However, credits generated in MYs 2010–2011 may be carried forward, or traded, and applied to deficits generated through MY 2021. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.099</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C 24307 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 exceptions to the life-span of compliance credits, and, to reflect statutory requirements, treated them as if they may be carried forward for no more than five years, so the initial credit banks were modified to anticipate the years in which those credits might be needed. MY 2016 was simulated explicitly in the NPRM analysis to VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 prohibit the inclusion of banked credits in MY 2016 (which could be carried forward from MY 2016 to MY 2021), and thus underestimated the extent to which individual manufacturers, and the industry as a whole, could rely on these early credits to comply with EPA standards between MY 2016 and MY 2021. However, as indicated in the PO 00000 Frm 00136 Fmt 4701 Sfmt 4725 NPRM, the final rule’s model inputs updated the analysis fleet’s basis to MY 2017, such that these additional banked credits can be included. The credit banks with which the simulations in this analysis were conducted are presented in the following Tables: BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.100</GPH> 24308 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations While the CAFE model does not simulate the ability to trade credits between manufacturers, it does simulate the strategic accumulation and application of compliance credits, as well as the ability to transfer credits between fleets to improve the compliance position of a less efficient fleet by leveraging credits earned by a more efficient fleet. The model prefers to hold on to earned compliance credits within a given fleet, carrying them forward into the future to offset potential future deficits. This assumption is consistent with observed strategic manufacturer behavior dating back to 2009. From 2009 to present, no manufacturer has transferred CAFE credits into a fleet to offset a deficit in the same year in which they were earned. This has occurred with credits acquired from other manufacturers via trade but not with a manufacturer’s own credits. Therefore, the current representation of credit transfers between fleets—where the model prefers to transfer expiring, or soon-tobe-expiring credits rather than newly earned credits—is both appropriate and consistent with observed industry behavior. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 This may not be the case for CO2 standards, though it is difficult to be certain at this point. The CO2 program seeded the industry with a large quantity of early compliance credits (earned in MYs 2009–2011) 414 prior to the existence formal CO2 standards. Early credits from MYs 2010 and 2011, however, do not expire until 2021. Thus, for manufacturers looking to offset deficits, it is more sensible to exhaust credits that were generated during later model years (which are set to expire within the next five years), rather than relying on the initial bank of credits from MYs 2010 and 2011. The first model year for which earned credits outlive the initial bank is MY 2017, for which final manufacturer CO2 performance data (and hence, banked credits) has not yet been released. However, considering that under the CO2 program manufacturers simultaneously comply with passenger car and light truck fleets, to more accurately represent the CO2 credit system the CAFE model allows (and encourages) intra-year transfers between regulated fleets for the purpose of 414 In response to public comment, EPA eliminated the possible use of credits earned in MY 2009 for future model years. However, credits earned in MY 2010 and MY 2011 remain available for use. PO 00000 Frm 00137 Fmt 4701 Sfmt 4700 simulating compliance with the CO2 standards. a) Off-Cycle and A/C Efficiency Adjustments to CAFE and Average CO2 Levels In addition to more rigorous accounting of CAFE and CO2 credits, the model now also accounts for air conditioning efficiency and off-cycle adjustments. NHTSA’s program considers those adjustments in a manufacturer’s compliance calculation starting in MY 2017, and the NPRM version of the model used the adjustments claimed by each manufacturer in MY 2016 as the starting point for all future years. Because air conditioning efficiency and off-cycle adjustments are not credits in NHTSA’s program, but rather adjustments to compliance fuel economy (much like the Flexible Fuel Vehicle adjustments due to phase out in MY 2019), they may be included under either a ‘‘standard setting’’ or ‘‘unconstrained’’ analysis perspective. The manner in which the CAFE model treats the EPA and CAFE A/C efficiency and off-cycle credit programs is similar, but the model also accounts for A/C leakage (which is not part of NHTSA’s program). When determining the compliance status of a E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.101</GPH> khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C 24309 24310 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 manufacturer’s fleet (in the case of EPA’s program, PC and LT are the only fleet distinctions), the CAFE model weighs future compliance actions against the presence of existing (and expiring) CO2 credits resulting from over-compliance with earlier years’ standards, A/C efficiency credits, A/C leakage credits, and off-cycle credits. Another aspect of credit accounting, implemented in the NPRM version of the CAFE model, involved credits related to the application of off-cycle and A/C efficiency adjustments, which manufacturers earn by taking actions such as special window glazing or using reflective paints that provide fuel economy improvements in real-world operation but do not produce measurable improvements in fuel consumption on the 2-cycle test. NHTSA’s inclusion of off-cycle and A/C efficiency adjustments began in MY 2017, while EPA has collected several years’ worth of submissions from manufacturers about off-cycle and A/C efficiency technology deployment. Currently, the level of deployment can vary considerably by manufacturer, with several claiming extensive Fuel Consumption Improvement Values (FCIV) for off-cycle and A/C efficiency technologies, and others almost none. The analysis of alternatives presented here (and in the NPRM) does not attempt to project how future off-cycle and A/C efficiency technology use will evolve or speculate about the potential proliferation of FCIV proposals submitted to the agencies. Rather, this analysis uses the off-cycle credits submitted by each manufacturer for MY 2017 compliance, and, with a few exceptions, carries these forward to future years. Several of the technologies described below are associated with A/ C efficiency and off-cycle FCIVs. In particular, stop-start systems, integrated starter generators, and full hybrids are assumed to generate off-cycle adjustments when applied to vehicles to improve their fuel economy. Similarly, higher levels of aerodynamic improvements are assumed to include active grille shutters on the vehicle, which also qualify for off-cycle FCIVs. The NPRM analysis assumed that any off-cycle FCIVs that are associated with actions outside of the technologies discussed in Section VI.C (either chosen from the pre-approved ‘‘pick list,’’ or granted in response to individual manufacturer petitions) remained at the levels claimed by manufacturers in MY 2017. Any additional A/C efficiency and off-cycle adjustments that accrued as the result of explicit technology application calculated dynamically in each model year for each alternative. The NPRM version of the CAFE model also represented manufacturers’ credits for off-cycle improvements, A/C efficiency improvements, and A/C leakage reduction in terms of values applicable across all model years. Recognizing that application of these improvements thus far varies considerably among manufacturers, such that some manufacturers have opportunities to earn significantly more of the corresponding adjustments over time, the agencies have expanded the CAFE model’s representation of these credits to provide for year-by-year specification of the amounts of each type of adjustment for each manufacturer, denominated in grams CO2 per mile,415 as summarized in the following table: BILLING CODE 4910–59–P 415 For estimating their contribution to CAFE compliance, the grams CO2/mile values in Table VI–1711 are converted to gallons/mile and applied to a manufacturer’s 2-cycle CAFE performance. When calculating compliance with EPA’s CO2 program, there is no conversion necessary (as standards are also denominated in grams/mile). 416 These values are specified in the ‘‘market_ ref.xlsx’’ input file’s ‘‘Credits and Adjustments’’ worksheet. The file is available with the archive of model inputs and outputs posted at https:// www.nhtsa.gov/corporate-average-fuel-economy/ compliance-and-effects-modeling-system. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00138 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00139 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24311 ER30AP20.102</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00140 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.103</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24312 BILLING CODE 4910–59–C VerDate Sep<11>2014 23:30 Apr 30, 2020 In addition to these refinements to the estimation of the quantities of Jkt 250001 PO 00000 Frm 00141 Fmt 4701 Sfmt 4700 24313 adjustments earned over time by each manufacturer, the agencies revised the E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.104</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations CAFE model to apply estimates of the corresponding costs. For today’s analysis, the agencies applied estimates developed previously by EPA, adjusting these values to 2019 dollars. The following table summarizes inputs through model year 2030: The model currently accounts for any off-cycle adjustments associated with technologies that are included in the set of fuel-saving technologies explicitly simulated as part of this proposal (for example, start-stop systems that reduce fuel consumption during idle or active grille shutters that improve aerodynamic drag at highway speeds) and accumulates these adjustments up to the 10 g/mi cap. As a practical matter, most of the adjustments for which manufacturers are claiming off-cycle FCIV exist outside of the technology tree, so the cap is rarely reached during compliance simulation. The agencies have considered the potential to model their application explicitly. However, doing so would require data regarding which vehicle models already possess these improvements as well as the cost and expected value of applying them to other models in the future. Such data is currently too limited to support explicit modeling of these technologies and adjustments. b) Alternative Fuel Vehicles When establishing maximum feasible fuel economy standards, NHTSA is prohibited from considering the availability of alternatively fueled vehicles,417 and credit provisions related to AFVs that significantly increase their fuel economy for CAFE compliance purposes. Under the ‘‘standard setting’’ perspective, these technologies (pure battery electric vehicles and fuel cell vehicles) 418 are not available in the compliance simulation to improve fuel economy. Under the ‘‘unconstrained’’ perspective, such as is documented in the DEIS and FEIS, the CAFE model considers these technologies in the same manner as other available technologies, and may apply them if they represent costeffective compliance pathways. However, under both perspectives, the analysis continues to include dedicated AFVs that already exist in the MY 2017 fleet (and their projected future volumes). Also, because the CAA provides no direction regarding consideration of alternative fuels, the final rule’s analysis includes simulation of the potential that some manufacturers might introduce new AFVs in response to CO2 standards. To represent the compliance benefit from such a response fully, NHTSA modified the CAFE model to include the specific provisions related to AFVs under the CO2 standards. In particular, the CAFE model now carries a full representation of the production multipliers related to electric vehicles, fuel cell vehicles, plug-in hybrids, and CNG vehicles, all of which vary by year through MY 2021. EPCA also provides that CAFE levels may, subject to limitations, be adjusted upward to reflect the sale of flexible fuel vehicles (FFVs). Although these adjustments end after model year 2020, the final rule’s analysis, like the NPRM’s, includes estimated potential use through MY 2019, as summarized below: 418 Dedicated compressed natural gas (CNG) vehicles should also be excluded in this perspective but are not considered as a compliance strategy under any perspective in this analysis. 417 49 U.S.C. 32902(h). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00142 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.105</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24314 For its part, EPA has provided that manufacturers selling sufficient numbers of PHEVs, BEVs, and FCVs may, when calculating fleet average CO2 levels, ‘‘count’’ each unit of production as more than a single unit. The CAFE model accounts for these ‘‘multipliers.’’ As for the NPRM, the final rule’s analysis applies the following multipliers: For example, under EPA’s current regulation, when calculating the average CO2 level achieved by its MY 2019 passenger car fleet, a manufacturer may treat each 1,000 BEVs as 2,000 BEVs. When calculating the average level required of this fleet, the manufacturer must use the actual production volume (in this example, 1,000 units). Similarly, the manufacturer must use the actual production volume when calculating compliance credit balances. There were no natural gas vehicles in the baseline fleet, and the analysis did not apply natural gas technology due to cost effectiveness. The application of a 2.0 multiplier for natural gas vehicles for MYs 2022–2026 would have no impact on the analysis because given the state of natural gas vehicle refueling infrastructure, the cost to equip vehicles with natural gas tanks, the outlook for petroleum prices, and the outlook for battery prices, we have little basis to project more than an inconsequential response to this incentive in the foreseeable future. For the final rule’s analysis, the CAFE model can be exercised in a manner that simulates these current EPA requirements, or that simulates two alternative approaches. The first includes the above-mentioned multipliers in the calculation of average requirements, and the second also includes the multipliers in the calculation of credit balances. The central analysis reflects current regulations. The sensitivity analysis presented in the FRIA includes a case VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00143 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.107</GPH> 24315 ER30AP20.106</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24316 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations applying multipliers to the calculation of achieved and required average CO2 levels, and calculation of credit balances. khammond on DSKJM1Z7X2PROD with RULES2 c) Civil Penalties Throughout the history of the CAFE program, some manufacturers have consistently achieved fuel economy levels below applicable standards, electing instead to pay civil penalties as specified by EPCA. As in previous versions of the CAFE model, the current version allows the user to specify inputs identifying such manufacturers and to consider their compliance decisions as if they are willing to pay civil penalties for non-compliance with the CAFE program. As with the NPRM, the civil penalty rate in the current analysis is $5.50 per 1/10 of a mile per gallon, per vehicle manufactured for sale. NHTSA notes that treating a manufacturer as if it is willing to pay civil penalties does not necessarily mean that it is expected to pay penalties in reality. Doing so merely implies that the manufacturer will only apply fuel economy technology up to a point, and then stop, regardless of whether or not its corporate average fuel economy is above its standard. In practice, the agencies expect that many of these manufacturers will continue to be active in the credit market, using trades with other manufacturers to transfer credits into specific fleets that are challenged in any given year, rather than paying penalties to resolve CAFE deficits. The CAFE model calculates the amount of penalties paid by each manufacturer, but it does not simulate trades between manufacturers. In practice, some (possibly most) of the total estimated penalties may be a transfer from one OEM to another. Although EPCA, as amended in 2007 by the Energy Independence and Security Act (EISA), prescribes these specific civil penalty provisions for VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 CAFE standards, the Clean Air Act (CAA) does not contain similar provisions. Rather, the CAA’s provisions regarding noncompliance prohibit sale of a new motor vehicle that is not covered by an EPA certificate of conformity, and in order to receive such a certificate the new motor vehicle must meet EPA’s Section 202 regulations, including applicable emissions standards. Therefore, inputs regarding civil penalties—including inputs regarding manufacturers’ potential willingness to treat civil penalty payment as an economic choice—apply only to simulation of CAFE standards. On the other hand, some of the same manufacturers recently opting to pay civil penalties instead of complying with CAFE standards have also recently led adoption of lower-GWP refrigerants, and the ‘‘A/C leakage’’ credits count toward compliance only with CO2 standards, not CAFE standards. The model accounts for this difference between the programs. When considering technology applications to improve fleet fuel economy, the model will add technology up to the point at which the effective cost of the technology (which includes technology cost, consumer fuel savings, consumer welfare changes, and the cost of penalties for non-compliance with the standard) is less costly than paying civil penalties or purchasing credits. Unlike previous versions of the model, the current implementation further acknowledges that some manufacturers experience transitions between product lines where they rely heavily on credits (either carried forward from earlier model years or acquired from other manufacturers) or simply pay penalties in one or more fleets for some number of years. The model now allows the user to specify, when appropriate for the regulatory program being simulated, on a year-by- PO 00000 Frm 00144 Fmt 4701 Sfmt 4700 year basis, whether each manufacturer should be considered as willing to pay penalties for non-compliance. This provides additional flexibility, particularly in the early years of the simulation. As discussed above, this assumption is best considered as a method to allow a manufacturer to under-comply with its standard in some model years—treating the civil penalty rate and payment option as a proxy for other actions it may take that are not represented in the CAFE model (e.g., purchasing credits from another manufacturer, carry-back from future model years, or negotiated settlements with NHTSA to resolve deficits). For the NPRM, NHTSA relied on past compliance behavior and certified transactions in the credit market to designate some manufacturers as willing to pay CAFE penalties in some model years. The full set of NPRM assumptions regarding manufacturer behavior with respect to civil penalties is presented in Table VI–21, which shows all manufacturers were assumed to be willing to pay civil penalties prior to MY 2020. This was largely a reflection of either existing credit balances (which manufacturers will use to offset CAFE deficits until the credits reach their expiration dates) or inter-manufacturer trades assumed likely to happen in the near future, based on previous behavior. The manufacturers in the table whose names appear in bold all had at least one regulated fleet (of three) whose CAFE was below its standard in MY 2016. Because the NPRM analysis began with the MY 2016 fleet, and no technology could be added to vehicles that are already designed and built, all manufacturers could generate civil penalties in MY 2016. However, once a manufacturer is designated as unwilling to pay penalties, the CAFE model will attempt to add technology to the respective fleets to avoid shortfalls. E:\FR\FM\30APR2.SGM 30APR2 Several of the manufacturers in Table VI–21 that were presumed to be willing to pay civil penalties in the early years of the program have no history of paying civil penalties. However, several of those manufacturers have either bought or sold credits—or transferred credits from one fleet to another to offset a shortfall in the underperforming fleet. As the CAFE model does not simulate credit trades between manufacturers, providing this additional flexibility in the modeling avoids the outcome where the CAFE model applies more technology than needed in the context of the full set of compliance flexibilities at the industry level. By statute, NHTSA cannot consider credit flexibilities when setting standards, so most manufacturers (those without a history of civil penalty payment) are assumed to comply with their standards through fuel economy improvements for the model years being considered in this analysis. The notable exception to this assumption is Fiat Chrysler Automobiles (FCA), which could still satisfy the requirements of the program through a combination of credit application and civil penalties through MY 2025 before eventually complying exclusively through fuel economy improvements in MY 2026. As mentioned above, the CAA does not provide civil penalty provisions similar to those provisions specified in EPCA/EISA, and the above-mentioned VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 corresponding inputs apply only to simulation of compliance with CAFE standards. Some stakeholders offering comments related to the analytical treatment of civil penalties indicated that NHTSA should tend toward assuming manufacturers will take advantage of this EPCA provision as an economically attractive alternative to compliance. Other commenters implied that NHTSA should tend toward not relying on compliance flexibilities in the analysis used to determine the maximum feasible stringency of CAFE standards. For example, New York University’s Institute for Policy Integrity (IPI) offered the following comments: NHTSA assumes that most manufacturers will be unwilling to pay penalties based in part on the fact that most manufacturers have not paid penalties in recent years. The Proposed Rule cites the statutory prohibition on NHTSA considering credit trading as a reason to assume manufacturers without a history of paying penalties will comply through technology alone, whatever the cost. But this is an arbitrary assumption and is in no way dictated by the statute. NHTSA knows as much, since elsewhere in the proposed rollback, the agency explains ‘‘EPCA is very clear as to which flexibilities are not to be considered’’ and NHTSA is allowed to consider off-cycle adjustments because they are not specifically mentioned. But considering penalties are not mentioned as off-limits for NHTSA in setting the standards either. Instead, the prohibition focuses on credit trading and transferring. PO 00000 Frm 00145 Fmt 4701 Sfmt 4700 24317 The penalty safety valve has existed in EPCA for decades, and Congress clearly would have known how to add penalties to the list of trading and transferring. The fact that Congress did not bar NHTSA from considering penalties as a safety valve means that NHTSA must consider manufacturer’s efficient use of penalties as a cost minimizing compliance option. Besides, NHTSA does consider penalties for some of the manufacturers making its statutory justification even less rational.419 On the other hand, in more general comments about NHTSA’s analytical treatment of program flexibilities, FCA stated that ‘‘when flexibilities are considered while setting targets, they cease to be flexibilities and become simply additional technology mandates.’’ 420 NHTSA agrees with IPI that EPCA does not expressly prohibit NHTSA, when conducting analysis supporting determinations of the maximum feasible stringency of future CAFE standards, from including manufacturers’ potential tendency to pay civil penalties rather than complying with those standards. However, EPCA also does not require NHTSA to include this tendency in its analysis. NHTSA also notes, as does IPI, that EPCA does prohibit NHTSA from including credit trading, transferring, or the availability of credits in such 419 Institute for Policy Integrity, NHTSA–2018– 0067–12213, at 24. 420 FCA, Docket #NHTSA–2018–0067–11943, at 6. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.108</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24318 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 analysis (although NHTSA interprets this prohibition to apply only to the model years for which standards are being set). This statutory difference is logical based on the way credits and penalties function differently under EPCA. Because credits help manufacturers achieve compliance with CAFE standards, absent the statutory prohibition, credits would be relevant to the feasibility of a standard.421 Penalties, on the other hand, do not enable a manufacturer to comply with an applicable standard; penalties are for noncompliance.422 When Congress added credit trading provisions to EPCA in 2007, NHTSA anticipated that competitive considerations would make manufacturers reluctant to engage in such trades. Since that time, manufacturers actually have demonstrated otherwise, although the reliance on trading—especially between specific pairs of OEMs—appears to vary widely. At this time, NHTSA considers it most likely that manufacturers will shift away from paying civil penalties and toward compliance credit trading. Consequently, for NHTSA to include civil penalty payment in its analysis would increasingly amount to using civil penalty payment as an analytical proxy for credit trading. Having further considered the question, NHTSA’s current view is, therefore, that including civil penalty payment beyond MY 2020 would effectively subvert EPCA’s prohibition against considering credit trading. Therefore, for today’s announcement, NHTSA has modified its analysis to assume that BMW, Daimler, FCA, JLR, and Volvo would consider paying civil penalties through MY 2020, and that all manufacturers would apply as much technology as would be needed in order to avoid paying civil penalties after MY 2020. 3. Technology Effectiveness Values The next input required to simulate manufacturers’ decision-making processes for the year-by-year application of technologies to specific vehicles is estimates of how effective each technology would be at reducing fuel consumption. In the NPRM, the agencies used full-vehicle modeling and simulation to estimate the fuel economy improvements manufacturers could make to a fleet of vehicles, considering those vehicles’ technical specifications and how combinations of technologies interact. Full-vehicle modeling and simulation uses computer software and 421 See 49 U.S.C. 32911(b) (‘‘Compliance is determined after considering credits available to the manufacturer . . . . ’’). 422 See id. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 physics-based models to predict how combinations of technologies perform as a full system under defined conditions. A model is a mathematical representation of a system, and simulation is the behavior of that mathematical representation over time. In this analysis, the model is a mathematical representation of an entire vehicle,423 including its individual components such as the engine and transmission, overall vehicle characteristics such as mass and aerodynamic drag, and the environmental conditions, such as ambient temperature and barometric pressure. The agencies simulated the model’s behavior over test cycles, including the 2-cycle laboratory compliance tests (or 2-cycle tests),424 to determine how the individual components interact. 2-cycle tests are test cycles that are used to measure fuel economy and emissions for CAFE and CO2 compliance, and therefore are the relevant test cycles for determining technology effectiveness when establishing standards. In the laboratory, 2-cycle testing involves sophisticated test and measurement equipment, carefully controlled environmental conditions, and precise procedures to provide the most repeatable results possible with human drivers. Measurements using these structured procedures serve as a yardstick for fuel economy and CO2 emissions. Full-vehicle modeling and simulation was initially developed to avoid the costs of designing and testing prototype parts for every new type of technology. For example, if a truck manufacturer has a concept for a lightweight tailgate and wants to determine the fuel economy impact for the weight reduction, the manufacturer can use physics-based computer modeling to estimate the impact. The vehicle, modeled with the proposed change, can be simulated on a defined test route and under a defined test condition, such as city or highway 423 Our full vehicle model was composed of submodels, which is why the full vehicle model could also be referred to as a full system model, composed of sub-system models. 424 EPA’s compliance test cycles are used to measure the fuel economy of a vehicle. For readers unfamiliar with this process, it is like running a car on a treadmill following a program—or more specifically, two programs. The ‘‘programs’’ are the ‘‘urban cycle,’’ or Federal Test Procedure (abbreviated as ‘‘FTP’’), and the ‘‘highway cycle,’’ or Highway Fuel Economy Test (abbreviated as ‘‘HFET’’), and they have not changed substantively since 1975. Each cycle is a designated speed trace (of vehicle speed versus time) that all certified vehicles must follow during testing. The FTP is meant roughly to simulate stop and go city driving, and the HFET is meant roughly to simulate steady flowing highway driving at about 50 mph. For further details on compliance testing, see the discussion in Section VI.B.3.a)(7). PO 00000 Frm 00146 Fmt 4701 Sfmt 4700 driving in warm ambient temperature conditions, and compared against the baseline reference vehicle. Full-vehicle modeling and simulation allows the consideration and evaluation of different designs and concepts before building a single prototype. In addition, full vehicle modeling and simulation is beneficial when considering technologies that provide small incremental improvements. These improvements are difficult to measure in laboratory tests due to variations in how vehicles are driven over the test cycle by human drivers, variations in emissions measurement equipment, and variations in environmental conditions.425 Full-vehicle modeling and simulation requires detailed data describing the individual technologies and performance-related characteristics. Those specifications generally come from design specifications, laboratory measurements, and other subsystem simulations or modeling. One example of data used as an input to the full vehicle simulation are engine maps for each engine technology that define how much fuel is consumed by the engine technology across its operating range. Using full-vehicle modeling and simulation to estimate technology efficiency improvements has two primary advantages over using single or limited point estimates. An analysis using single or limited point estimates may assume that, for example, one fuel economy improving technology with an effectiveness value of 5 percent by itself and another technology with an effectiveness value of 10 percent by itself, when applied together achieve an additive improvement of 15 percent. Single point estimates generally do not provide accurate effectiveness values because they do not capture complex relationships among technologies. Technology effectiveness often differs significantly depending on the vehicle type (e.g., sedan versus pickup truck) and how the technology interacts with other technologies on the vehicle, as different technologies may provide different incremental levels of fuel economy improvement if implemented alone or in tandem with other technologies. Any oversimplification of these complex interactions leads to less accurate and often overestimated effectiveness estimates. In addition, because manufacturers often implement several fuel-saving 425 Difficulty with controlling for such variability is reflected, for example, in 40 CFR 1065.210, Work input and output sensors, which describes complicated instructions and recommendations to help control for variability in real world (nonsimulated) test instrumentation set up. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations technologies simultaneously when redesigning a vehicle, it is difficult to isolate the effect of individual technologies using laboratory measurement of production vehicles alone. Modeling and simulation offers the opportunity to isolate the effects of individual technologies by using a single or small number of baseline vehicle configurations and incrementally adding technologies to those baseline configurations. This provides a consistent reference point for the incremental effectiveness estimates for each technology and for combinations of technologies for each vehicle type. Vehicle modeling also reduces the potential for overcounting or undercounting technology effectiveness. An important feature of this analysis is that the incremental effectiveness of each technology and combinations of technologies be accurate and relative to a consistent baseline vehicle. The absolute fuel economy values of the full vehicle simulations are used only to determine incremental effectiveness and are never used directly to assign an absolute fuel economy value to any vehicle model or configuration for the rulemaking analysis. For this analysis, absolute fuel economy levels are based on the individual fuel economy values from CAFE compliance data for each vehicle in the baseline fleet. The incremental effectiveness from the full vehicle simulations performed in Autonomie, a physics-based full-vehicle modeling and simulation software developed and maintained by the U.S. Department of Energy’s Argonne National Laboratory, are applied to baseline fuel economy to determine the absolute fuel economy of applying the first technology change. For subsequent technology changes, incremental effectiveness is applied to the absolute fuel economy level of the previous technology configuration. For example, if a Ford F150 2-wheel drive crew cab and short bed in the baseline fleet has a fuel economy value of 30 mpg for CAFE compliance, 30 mpg will be considered the reference absolute fuel economy value. A similar full vehicle model in the Autonomie simulation may begin with an average fuel economy value of 32 mpg, and with incremental addition of a specific technology X its fuel economy improves to 35 mpg, a 9.3 percent improvement. In this example, the incremental fuel economy improvement (9.3 percent) from technology X would be applied to the F150’s 30 mpg absolute value. For this analysis, the agencies determined the incremental effectiveness of technologies as applied VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 to the 2,952 unique vehicle models in the analysis fleet. Although, as mentioned above, full-vehicle modeling and simulation reduces the work and time required to assess the impact of moving a vehicle from one technology state to another, it would be impractical—if not impossible—to build a unique vehicle model for every individual vehicle in the analysis fleet. Therefore, as explained further below, vehicle models are built in a way that maintains similar attributes to the analysis fleet vehicles, which ensures key components are reasonably represented. We received a wide array of comments regarding the full-vehicle modeling and simulation performed for the NPRM, but there was general agreement that full-vehicle modeling and simulation was the appropriate method to determine technology effectiveness.426 Stakeholders commented on other areas, such as full vehicle simulation tools, inputs, and assumptions, and these comments will be discussed in the following sections. For this final rule, the agencies continued to use the same full-vehicle simulation approach to estimate technology effectiveness for technology adoption in the rulemaking timeframe. The next sections will discuss the details of the explicit input specifications and assumptions used for the final rule analysis. a) Why This Rulemaking Used Autonomie Full-Vehicle Modeling and Simulation To Determine Technology Effectiveness The NPRM and final rule analysis use effectiveness estimates for technologies developed using Autonomie, a physicsbased full-vehicle modeling and simulation software developed and maintained by the U.S. Department of Energy’s Argonne National Laboratory.427 Autonomie was designed to serve as a single tool to meet requirements of automotive engineering throughout the vehicle development process, and has been under continuous improvement by Argonne for over 20 years. Autonomie is commercially available and widely used in the automotive industry by suppliers, 426 See NHTSA–2018–0067–12039; NHTSA– 2018–0067–12073. UCS and AAM both agreed that full vehicle simulation can significantly improve the estimates of technology effectiveness. 427 More information about Autonomie is available at https://www.anl.gov/technology/ project/autonomie-automotive-system-design (last accessed June 21, 2018). As mentioned in the preliminary regulatory impact analysis (PRIA) for this rule, the agencies used Autonomie version R15SP1, the same version used for the 2016 Draft TAR. PO 00000 Frm 00147 Fmt 4701 Sfmt 4700 24319 automakers, and academic researchers (who publish findings in peer reviewed academic journals).428 DOE and manufacturers have used Autonomie and its ability to simulate a large number of powertrain configurations, component technologies, and vehiclelevel controls over numerous drive cycles to support studies on fuel efficiency, cost-benefit analysis, and carbon dioxide emissions,429 and other topics. Autonomie has also been used to provide the U.S. government with data to make decisions about future research, and is used by DOE for analysis supporting budget priorities and plans for programs managed by its Vehicle Technologies Office (VTO), and to support decision making among competing vehicle technology research and development projects.430 In addition, Autonomie is the primary vehicle simulation tool used by DOE to support its U.S. DRIVE program, a government-industry partnership focused on advanced automotive and related energy infrastructure technology research and development.431 Autonomie is a MathWorks-based software environment and framework for automotive control-system design, simulation, and analysis.432 It is designed for rapid and easy integration of models with varying levels of detail (low to high fidelity), abstraction (from subsystems to systems and entire architectures), and processes (e.g., calibration, validation). By building models automatically, Autonomie allows the quick simulation of many component technologies and powertrain configurations, and, in this case, to assess the energy consumption of advanced powertrain technologies. Autonomie simulates subsystems, 428 Rousseau, A. Shidore, N. Karbowski, D. Sharer, ‘‘Autonomie Vehicle Validation Summary.’’ https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/anlautonomie-vehicle-model-validation-1509.pdf. 429 Delorme et al. 2008, Rousseau, A, Sharer, P, Pagerit, S., & Das, S. ‘‘Trade-off between Fuel Economy and Cost for Advanced Vehicle Configurations,’’ 20th International Electric Vehicle Symposium (EVS20), Monaco (April 2005); Elgowainy, A., Burnham, A., Wang, M., Molburg, J., & Rousseau, A. ‘‘Well-To-Wheels Energy Use and Greenhouse Gas Emissions of Plug-in Hybrid Electric Vehicles,’’ SAE 2009–01–1309, SAE World Congress, Detroit, April 2009. 430 U.S. DOE Benefits & Scenario Analysis publications is available at https:// www.autonomie.net/publications/fuel_economy_ report.html (last accessed September 11, 2019). 431 For more information on U.S. Drive, see https://www.energy.gov/eere/vehicles/us-drive. 432 Halbach, S. Sharer, P. Pagerit, P., Folkerts, C. & Rousseau, A. ‘‘Model Architecture, Methods, and Interfaces for Efficient Math-Based design and Simulation of Automotive Control Systems,’’ SAE 2010–01–0241, SAE World Congress, Detroit, April, 2010. E:\FR\FM\30APR2.SGM 30APR2 24320 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 systems, or entire vehicles; evaluates and analyzes fuel efficiency and performance; performs analyses and tests for virtual calibration, verification, and validation of hardware models and algorithms; supports system hardware and software requirements; links to optimization algorithms; and supplies libraries of models for propulsion architectures of conventional powertrains as well as hybrid and electric vehicles. With hundreds of pre-defined powertrain configurations along with vehicle level control strategies developed from dynamometer test data, Autonomie is a highly capable tool for analyzing advantages and drawbacks of applying different technology options within each technology family, including conventional, parallel hybrid, power-split hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEV) and fuel cell vehicles (FCVs). Autonomie also allows users to evaluate the effect of component sizing on fuel consumption for different powertrain technologies as well as to define component requirements (e.g., power, energy) to maximize fuel displacement for a specific application.433 To evaluate properly any powertrain-configuration or component-sizing influence, vehiclelevel control models are critical, especially for electric drive vehicles like hybrids and plug-in hybrids. Argonne has extensive expertise in developing vehicle-level control models based on different approaches, from global optimization to instantaneous optimization, rule-based optimization, and heuristic optimization.434 Autonomie has been developed to consider real-world vehicle metrics like performance, hardware limitations, utility, and drivability metrics (e.g., towing capability, shift busyness, frequency of engine on/off transitions), which are important to producing 433 Nelson, P., Amine, K., Rousseau, A., & Yomoto, H. (EnerDel Corp.), ‘‘Advanced Lithiumion Batteries for Plug-in Hybrid-electric Vehicles,’’ 23rd International Electric Vehicle Symposium (EVS23), Anaheim, CA, (Dec. 2007); Karbowski, D., Haliburton, C., & Rousseau, A. ‘‘Impact of Component Size on Plug-in Hybrid Vehicles Energy Consumption using Global Optimization,’’ 23rd International Electric Vehicle Symposium (EVS23), Anaheim, CA, (Dec. 2007). 434 Karbowski, D., Kwon, J., Kim, N., & Rousseau, A., ‘‘Instantaneously Optimized Controller for a Multimode Hybrid Electric Vehicle,’’ SAE paper 2010–01–0816, SAE World Congress, Detroit, April 2010; Sharer, P., Rousseau, A., Karbowski, D., & Pagerit, S. ‘‘Plug-in Hybrid Electric Vehicle Control Strategy—Comparison between EV and ChargeDepleting Options,’’ SAE paper 2008–01–0460, SAE World Congress, Detroit (April 2008); and Rousseau, A., Shidore, N., Carlson, R., & Karbowski, D. ‘‘Impact of Battery Characteristics on PHEV Fuel Economy,’’ AABC08. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 realistic estimates of fuel economy and CO2 emission rates. This increasing realism has, in turn, steadily increased confidence in the appropriateness of using Autonomie to make significant investment decisions. Autonomie has also been validated for a number of powertrain configurations and vehicle classes using Argonne’s Advanced Mobility Technology Laboratory (AMTL) (formerly Advanced Powertrain Research Facility, or APRF) vehicle test data.435 Argonne has spent several years developing, applying, and expanding the means to use distributed computing to exercise its Autonomie full-vehicle simulation tool over the scale necessary for realistic analysis to provide data for CAFE and CO2 standards rulemaking. The NPRM and PRIA detailed how Argonne used Autonomie to estimate the fuel economy impacts for roughly a million combinations of technologies and vehicle types.436 437 Argonne developed input parameters for Autonomie to represent every combination of vehicle, powertrain, and component technologies considered in this rulemaking. The sequential 435 Jeong, J., Kim, N., Stutenberg, K., Rousseau, A., ‘‘Analysis and Model Validation of the Toyota Prius Prime.’’ SAE 2019–01–0369, SAE World Congress, Detroit, April 2019; Kim, N, Jeong, J. Rousseau, A. & Lohse-Busch, H. ‘‘Control Analysis and Thermal Model Development of PHEV,’’ SAE 2015–01–1157, SAE World Congress, Detroit, April 2015; Kim, N., Rousseau, A. & Lohse-Busch, H. ‘‘Advanced Automatic Transmission Model Validation Using Dynamometer Test Data,’’ SAE 2014–01–1778, SAE World Congress, Detroit, Apr. 14; Lee, D. Rousseau, A. & Rask, E. ‘‘Development and Validation of the Ford Focus BEV Vehicle Model,’’ 2014–01–1809, SAE World Congress, Detroit, Apr. 14; Kim, N., Kim, N., Rousseau, A., & Duoba, M. ‘‘Validating Volt PHEV Model with Dynamometer Test Data using Autonomie,’’ SAE 2013–01–1458, SAE World Congress, Detroit, Apr. 13; Kim, N., Rousseau, A., & Rask, E. ‘‘Autonomie Model Validation with Test Data for 2010 Toyota Prius,’’ SAE 2012–01–1040, SAE World Congress, Detroit, Apr. 12; Karbowski, D., Rousseau, A, Pagerit, S., & Sharer, P. ‘‘Plug-in Vehicle Control Strategy—From Global Optimization to Real Time Application,’’ 22th International Electric Vehicle Symposium (EVS22), Yokohama, (October 2006). 436 As part of the Argonne simulation effort, individual technology combinations simulated in Autonomie were paired with Argonne’s BatPAC model to estimate the battery cost associated with each technology combination based on characteristics of the simulated vehicle and its level of electrification. Information regarding Argonne’s BatPAC model is available at http:// www.cse.anl.gov/batpac/. 437 Additionally, the impact of engine technologies on fuel consumption, torque, and other metrics was characterized using GT POWER simulation modeling in combination with other engine modeling that was conducted by IAV Automotive Engineering, Inc. (IAV). The engine characterization ‘‘maps’’ resulting from this analysis were used as inputs for the Autonomie full-vehicle simulation modeling. Information regarding GT Power is available at https://www.gtisoft.com/gtsuite-applications/propulsion-systems/gt-powerengine-simulation-software. PO 00000 Frm 00148 Fmt 4701 Sfmt 4700 addition of more than 50 fuel economyimproving technologies to ten vehicle types generated more than 140,000 unique technology and vehicle combinations. Running the Autonomie powertrain sizing algorithms to determine the appropriate amount of engine downsizing needed to maintain overall vehicle performance when vehicle mass reduction is applied and for certain engine technology changes (discussed further, below) increased the total number of simulations to more than one million. The result of these simulations is a useful dataset identifying the impacts of combinations of vehicle technologies on energy consumption—a dataset that can be referenced as an input to the CAFE model for assessing regulatory compliance alternatives. The following sections discuss the full-vehicle modeling and simulation inputs and data assumptions, and comments received on the NPRM analysis. The discussion is necessarily technical, but also important to understand the agencies’ decisions to modify (or not) the Autonomie analysis for the final rule. (1) Full-Vehicle Modeling, Simulation Inputs and Data Assumptions The agencies provided extensive documentation that quantitatively and qualitatively described the over 50 technologies considered as inputs to the Autonomie modeling.438 439 These inputs consisted of engine technologies, transmission technologies, powertrain electrification, light-weighting, aerodynamic improvements, and tire rolling resistance improvements.440 The PRIA provided an overview of the submodels for each technology, including the internal combustion engine model, automatic transmission model, and others.441 The Argonne NPRM model documentation expanded on these submodels in detail to show the interaction of each sub-model input and output.442 438 NHTSA–2018–0067–12299. Preliminary Regulatory Impact Analysis (July 2018). 439 NHTSA–2018–0067–0007. Islam, E., S, Moawad, A., Kim, N, Rousseau, A. ‘‘A Detailed Vehicle Simulation Process To Support CAFE Standards 04262018—Report’’ ANL Autonomie Documentation. Aug 21, 2018. NHTSA–2018–0067– 0004. ANL Autonomie Data Dictionary. Aug 21, 2018. NHTSA–2018–0067–0003. ANL Autonomie Summary of Main Component Assumptions. Aug 21, 2018. NHTSA–2018–0067–0005. ANL Autonomie Model Assumptions Summary. Aug 21, 2018. NHTSA–2018–0067–1692. ANL BatPac Model 12 55. Aug 21, 2018. 440 SAFE Rule for MY2021–2026 PRIA Chapter 6.2.3 Technology groups in Autonomie simulations and CAFE model. 441 PRIA at 189. 442 NHTSA–2018–0067–0007. Islam, E., S, Moawad, A., Kim, N, Rousseau, A. ‘‘A Detailed E:\FR\FM\30APR2.SGM 30APR2 24321 For example, as shown in Figure VI–2, the input for Autonomie’s driver model (i.e., the model used to approximate the driving behavior of a real driver) is vehicle speed, and outputs are accelerator pedal, brake pedal, and torque demand. Effectiveness inputs for the NPRM and the final rule analysis were specifically developed to consider many real world and compliance test cycle constraints, to the extent a computer model could capture them. Examples include the advanced engine knock model discussed below, in addition to other constraints like allowing cylinder deactivation to occur in ways that would not negatively impact noisevibration-harshness (NVH), and similarly optimizing the number of engine on/off events (e.g., from start/ stop 12V micro hybrid systems) to balance between effectiveness and NVH. One major input used in the effectiveness modeling that the agencies provided key specifications for in the PRIA are engine fuel maps that define how an engine equipped with specific technologies operates over a variety of engine load (torque) and engine speed conditions. The engine maps used as inputs to the Autonomie modeling portion of the analysis were developed by starting with a base map and then modifying that base map, incrementally, to model the addition of engine technologies. These engine maps, developed using the GT-Power modeling tool by IAV, were based off real-world engine designs. Simulated operation of these engines included the application of an IAV knock model, also developed from real-world engine data.443 444 Using this process, which incorporated real-world data, ensured that real-world constraints were considered for each vehicle type. Although the same type of engine map is used for all technology classes, the effectiveness varies based on the characteristics of each vehicle type. For example, a compact car with a turbocharged engine will have different fuel economy and performance values than a pickup truck with the same engine technology type. The engine map specifications are discussed further in Section VI.C.1 of this preamble and Section VI of FRIA. The agencies also provided key details about input assumptions for various vehicle specifications like transmission gear ratios, tire size, final drive ratios, and individual component weights.445 Each of these assumptions, to some extent, varied between the ten technology classes to capture appropriately real-world vehicle specifications like wheel mass or fuel tank mass. These specific input assumptions were developed based on the latest test data and current market fleet information.446 The agencies relied on default assumptions developed by the Autonomie team, based on test data and technical publication review, for other model inputs required by Autonomie, such as throttle time response and shifting strategies for different transmission technologies. The Autonomie modeling tool did not simulate vehicle attributes determined to have minimal impacts, like whether a vehicle had a sun roof or hood scoops, as those attributes would have trivial impact in the overall analysis. Because the agencies model ten different vehicle types to represent the 2,952 vehicles in the baseline fleet, improper assumptions about an advanced technology could lead to errors in estimating effectiveness. Autonomie is a sophisticated fullvehicle modeling tool that requires extensive technology characteristics based on both physical and intangible data, like proprietary software. With a few technologies, the agencies did not have publicly available data, but had received confidential business information confirming such technologies potential availability in the market during the rulemaking time frame. For such technologies, including advanced cylinder deactivation, the agencies adopted a method in the CAFE model to represent the effectiveness of the technology, and did not explicitly simulate the technologies in the Autonomie model. For this limited set of technologies, the agencies determined that effectiveness could reasonably be represented as a fixed value.447 Effectiveness values for technologies not explicitly simulated in Autonomie are discussed further in the individual technology sections of this preamble. The agencies sought comments on all effectiveness inputs and input assumptions, including the specific data used to characterize the technologies, Vehicle Simulation Process To Support CAFE Standards 04262018—Report’’ ANL Autonomie Documentation. Aug 21, 2018. 443 Engine knock in spark ignition engines occurs when combustion of some of the air/fuel mixture in the cylinder does not result from propagation of the flame front ignited by the spark plug, but one or more pockets of air/fuel mixture explodes outside of the envelope of the normal combustion front. 444 See IAV material submitted to the docket; IAV_20190430_Eng 22–26 Updated_Docket.pdf, IAV_Engine_tech_study_Sept_2016_Docket.pdf, IAV_Study for 4 Cylinder Gas Engines_Docket.pdf. 445 ANL Autonomie Model Assumptions Summary. Aug 21, 2018, NHTSA–2018–0067–0005. ANL—Summary of Main Component Performance and Assumptions NPRM. Aug 21, 2018, NHTSA– 2018–0067–0003. 446 See further details in Section VI.B.1 Analysis Fleet. 447 For final rule, 9 out of 50 plus technologies use fixed offset effectiveness values. The total effectiveness of these technologies cannot be captured on the 2-cycle test or, like ADEAC, they are a new technology where robust data that could be used as an input to the technology effectiveness modeling does not yet exist. Specifically, these nine technologies are LDB, SAX, EPS, IACC, EFR, ADEAC, DSLI, DSLIAD and TURBOAD. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00149 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.109</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 24322 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations such as data to build the technology input, data representing operating range of technologies, and data for variation among technology inputs. The agencies also sought comment on the effectiveness values used for technologies not explicitly defined in Autonomie. Meszler Engineering Services, commenting on behalf of the Natural Resources Defense Council, and ICCT questioned the accuracy of the effectiveness estimates in the Argonne database, and as an example Meszler analyzed the fuel economy impacts of a 10-speed automatic transmission relative to a baseline 8-speed automatic transmission, concluding that the widely ranging effectiveness estimates were unexpected. ICCT questioned the accuracy of the IAV engine maps that serve as an input to the Autonomie effectiveness modeling, and asked whether those could ‘‘reasonably stand as a foundation for automotive developments and technology combinations’’ discussed elsewhere in their comments. ICCT also questioned whether Autonomie realistically and validly modeled synergies between technologies, using the effectiveness values from CEGR and transmissions as an example. Meszler stated that the agencies have an obligation to validate the Autonomie estimates before using them to support the NPRM or any other rulemaking. The agencies also received comments on the specific effectiveness estimates generated by Autonomie; however, those comments will be discussed in each individual technology section, below. Despite these criticisms, Meszler stated that the critiques of the Autonomie technology database were not meant to imply that the Autonomie vehicle simulation model used to develop the database was fundamentally flawed, or that the model could not be used to derive accurate fuel economy impact estimates. Meszler noted that, as with any model, estimates derived with Autonomie are only valid for a given set of modeling parameters and if those parameters are well defined, the estimates should be accurate and reliable. Conversely, if those parameters are not well defined, the estimates would be inaccurate and unreliable. Meszler stated that the agencies must make the full set of modeling assumptions used for the Autonomie database available for review and comment. We agree with Meszler that, in general, when inputs to a model are inaccurate, output effectiveness results may be too high or too low. The technology effectiveness estimates from VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 modeling results often vary with the type of vehicle and the other technologies that are on that vehicle.448 The Autonomie output database consists of permutations of over 50 technologies for each of the ten technology classes simulated by the CAFE model. A wide range of effectiveness is expected when going from a baseline technology to an advanced technology across different technology classes because there are significant differences in how much power is required from the powertrain during 2-cycle testing across the ten vehicle types. This impacts powertrain operating conditions (e.g., engine speed and load) during 2-cycle testing. Fuel economy improving technologies have different effectiveness at each of those operating conditions so vehicles that have higher average power demands will have different effectiveness than vehicles with lower average power demands. Further, the differences in effectiveness at higher power and lower power vary by technology so the overall relationship is complex. Large-scale full-vehicle modeling and simulation account for these interactions and complexities. Before conducting any full-vehicle modeling and simulation, the agencies spent a considerable amount of time and effort developing the specific inputs used for the Autonomie analysis. The agencies believe that these technology inputs provide reasonable estimates for the light-duty vehicle technologies the agencies expect to be available in the market in the rulemaking timeframe. As discussed earlier, these inputs vary in effectiveness due to how different vehicles, like compact cars and pickup trucks, operate on the 2-cycle test and in the real world. Some technologies, such as 10-speed automatic transmissions (AT10) relative to 8-speed automatic transmissions (AT8), can and should have different effectiveness results in the analysis between two different technology classes.449 These unique synergistic effects can only be taken into account through conducting full-vehicle modeling and simulation, which the agencies did here. With regards to Meszler’s comment that the agencies have an obligation to validate the Autonomie estimates before using them to support the NPRM or any 448 The PRIA Chapter 6.2.2.1, Table 6–2 and Table 6–3 defined the characteristics of the reference technology classes that representative of the analysis fleet. 449 Separately, the agencies modified specific transmission modeling parameters for the final rule after additional review, including a thorough review of public comments, and this review is discussed in detail in Section VI.C.2. PO 00000 Frm 00150 Fmt 4701 Sfmt 4700 other rulemaking, the agencies would like to point Meszler to the description of the Argonne Autonomie team’s robust process for vehicle model validation that was contained in the PRIA.450 To summarize, the NPRM and final rule analysis leveraged extensive vehicle test data collected by Argonne National Laboratory.451 Over the past 20 years, the Argonne team has developed specific instrumentation lists and test procedures for collecting sufficient information to develop and validate full vehicle models. In addition, the agencies described the Argonne team’s efforts to validate specific component models as well, such as the advanced automatic transmission and dual clutch transmission models.452 The agencies also described the process for validating inputs used to develop the IAV engine maps,453 454 another input to the Autonomie simulations. As discussed in the PRIA, IAV’s engine model development relied on a collection of sub-models that controlled independent combustion characteristics such as heat release, combustion knock, friction, heat flow, and other combustion optimization tools. These sub-models and other 450 PRIA at 216–7. See also N. Kim, A. Rousseau, E. Rask, ‘‘Autonomie Model Validation with Test Data for 2010 Toyota Prius,’’ SAE 2012–01–1040, SAE World Congress, Detroit, Apr12. https:// www.autonomie.net/docs/5%20-%20Presentations/ Validation/SAE%202012-01-1040.pdf; Vehicle Validation Status, February 2010 https:// www.autonomie.net/docs/5%20-%20Presentations/ Validation/vehicle_validation_status.pdf; Tahoe HEV Model Development in PSAT, SAE paper 2009–01–1307, April 2009 https:// www.autonomie.net/docs/5%20-%20Presentations/ Validation/tahoe_hev.pdf; PHEV Model Validation, U.S.DOE Merit Review 2008 https:// www.autonomie.net/docs/5%20-%20Presentations/ Validation/phev_model_validation.pdf ; PHEV HyMotion Prius model validation and control improvements, 23rd International Electric Vehicle Symposium (EVS23), Dec. 2007 https:// www.autonomie.net/docs/5%20-%20Presentations/ Validation/phev_hymotion_prius.pdf; Integrating Data, Performing Quality Assurance, and Validating the Vehicle Model for the 2004 Prius Using PSAT, SAE paper 2006–01–0667, April 2006; https:// www.autonomie.net/docs/5%20-%20Presentations/ Validation/integrating_data.pdf. 451 A list of the vehicles that have been tested at the APRF can be found under http://www.anl.gov/ energy-systems/group/downloadable-dynamometerdatabase. 452 Kim, N., Rousseau, N., Lohse-Bush, H. ‘‘Advanced Automatic Transmission Model Validation Using Dynamometer Test Data,’’ SAE 2014–01–1778, SAE World Congress, Detroit, April 2014; Kim, N., Lohse-Bush, H., Rousseau, A. ‘‘Development of a model of the dual clutch transmission in Autonomie and validation with dynamometer test data,’’ International Journal of Automotive Technologies, March 2014, Volume 15, Issue 2, pp 263–71. 453 See PRIA at 251. 454 See IAV material submitted to the docket; IAV_20190430_Eng 22–26 Updated_Docket.pdf, IAV_Engine_tech_study_Sept_2016_Docket.pdf, IAV_Study for 4 Cylinder Gas Engines_Docket.pdf. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations computational fluid dynamics models were utilized to convert test data for use in the IAV engine map development. Specific combustion parameters, like from test data for the coefficient of variation for the indicated mean effective pressure (COV of IMEP), which is a common variable for combustion stability in a spark ignited engine, was used to assure final engine models were reasonable. The assumptions and inputs used in the modeling and validation of engine model results leveraged IAV’s global engine database, which included benchmarking data, engine test data, single cylinder test data and prior modeling studies, and also technical publications and information presented at conferences. The agencies referenced in the PRIA that engine maps were validated with engine dynamometer test data to the maximum extent possible.455 Because the NPRM and the final rule analysis considered some technologies not yet in production, the agencies relied on technical publications and engine modeling by IAV to develop and corroborate inputs and input assumptions where engine dynamometer test data was not available. In addition, as described earlier in this section, the full set of NPRM modeling assumptions used for the Autonomie database were available for review and comment in the docket for this rulemaking.456 The full set of modeling assumptions used for the final rule are also available in the docket.457 Both ICCT and Meszler also commented on the availability of technologies within the Autonomie database, with Meszler stating that with limited exceptions, technologies were not included in the NPRM CAFE model if they were not included in the simulation modeling that underlay the Argonne database, and accordingly if a combination of technologies was not modeled during the development of the Argonne database, that package (or 455 See PRIA at 288. khammond on DSKJM1Z7X2PROD with RULES2 456 NHTSA–2018–0067–0007. Islam, E., S, Moawad, A., Kim, N, Rousseau, A., ‘‘A Detailed Vehicle Simulation Process To Support CAFE Standards 04262018—Report’’ ANL Autonomie Documentation. Aug 21, 2018. NHTSA–2018–0067– 0004. ANL Autonomie Data Dictionary. Aug 21, 2018. NHTSA–2018–0067–0003. ANL Autonomie Summary of Main Component Assumptions. Aug 21, 2018. NHTSA–2018–0067–0005. ANL Autonomie Model Assumptions Summary. Aug 21, 2018. NHTSA–2018–0067–1692. ANL BatPac Model 12 55. Aug 21, 2018. Preliminary Regulatory Impact Analysis (July 2018). Posted July 2018 and updated August 23 and October 16, 2018. 457 The CAFE Model is available at https:// www.nhtsa.gov/corporate-average-fuel-economy/ compliance-and-effects-modeling-system with documentation and all inputs and outputs supporting today’s notice. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 24323 combination) of technologies was not available for adoption in the CAFE model. Meszler stated that these constraints limited the slate of technologies available to respond to fuel economy standards, and independently expanding the model to include additional technologies or technology combinations is not trivial. ICCT gave specific examples of key efficiency technologies that it stated Autonomie did not include, like advanced DEAC, VCR, Miller Cycle, eboost, and HCCI. ICCT argued that this was especially problematic as the agencies appeared to have available engine maps from IAV on advanced DEAC, VCR, Miller Cycle, E-boost (and from advanced DEAC, VCR, Miller Cycle, E-boost, HCCI from EPA) that Argonne or the agencies have been unable to or opted not to include in their modeling. ICCT stated that the agencies must disclose how Autonomie had been updated to incorporate ‘‘cutting edge’’ 2020–2025 automotive technologies to ensure they reflect available improvements.458 The agencies have updated the final rule analysis to include additional technologies. In the NPRM, the agencies presented the engine maps for all of the technologies that ICCT listed, except HCCI, and sought comment on the engine maps, technical assumptions and the potential use of the technologies for the final rule analysis. Based on the available technical information and the ICCT and Meszler comments, for the final rule analysis, VCR, Miller Cycle (VTG), and e-boost (VTGe with 48V BISG) technologies have been added and included in the Autonomie modeling and simulations, and advanced DEAC technology has been added using fixed point effectiveness estimates in the CAFE model analysis. The agencies disagree with ICCT’s assessment of HCCI and do not believe it will be available for wide-scale application in the rulemaking timeframe, and therefore have not included it as a technology. HCCI technology has been in the research phase for several decades, and the only production applications to date use a highly-limited version that restricts HCCI combustion to a very narrow range of engine operating conditions.459 460 461 Additional discussion of how Autonomie-modeled and non-modeled technologies are incorporated into the CAFE Model is located in Section VI.B.3.c), below. ICCT and Meszler also commented that the agencies overly limited the availability of several technologies in the NPRM analysis. In response, the agencies reconsidered the restrictions that were applied in the NPRM analysis, and agree with the commenters for several technologies and technology classes. Many technologies identified by the commenters are now in production for the MY2017 as well as MY2018 and MY2019. The agencies also think that the baseline fleet compliance data reflects adoption of many of these technologies. For the final rule analysis, the agencies have expanded the availability of several technologies. In the CAFE model, the agencies are now allowing parallel hybrids (SHEVP2) to be adopted with high compression Atkinson mode engines (HCR0 and HCR1). In addition, as mentioned above, the Autonomie full-vehicle modeling included Variable Compression Ratio engine (VCR), Miller Cycle Engine (VTG), E-boost (VTGe) technologies, and cylinder deactivation technologies (DEAC) to be applied to turbocharged engines (TURBO1). As these changes relate to the technology effectiveness modeling, the CAFE model analysis now includes effectiveness estimates based on full vehicle simulations for all of these technology combinations. We disagree with comments stating the agencies should allow every technology to be available to every vehicle class.462 Discussed earlier in this section, Autonomie models key aspects of vehicle operation that are most relevant to assessing fuel economy, vehicle performance and certain aspects of drivability (like EPA 2-cycle tests, EPA US06 cycle tests, gradability, low speed acceleration time from 0-to-60 mph, passing acceleration time from 50 to 80 mph, and number of transmission shifts). However, there are other critical aspects of vehicle functionality and operation that the agencies considered beyond those criteria, that cannot necessarily be reflected in the Autonomie modeling. For example, a pickup truck can be modeled with a 458 ICCT also made the same request of EPA’s ALPHA model, and the agencies’ response to that comment is discussed in Section VI.C.1 Engine Paths, below. 459 Mazda introduced Skyactiv-X in Europe with a mild hybrid technology to assist the engine. 460 Mazda News. ‘‘Revolutionary Mazda SkyactivX engine details confirmed as sales start,’’ May 6, 2019. https://www.mazda-press.com/eu/news/2019/ revolutionary-mazda-skyactiv-x-engine-details- confirmed-as-sales-start/. Last accessed Dec. 2, 2019. 461 Confer. K. Kirwan, J. ‘‘Ultra Efficient LightDuty Powertrain with Gasoline Low-Temperature Combustion.’’ DOE Merit Review. June 9, 2017. https://www.energy.gov/sites/prod/files/2017/06/ f34/acs094_confer_2017_o.pdf. Last accessed Dec. 2, 2019. 462 NHTSA–2018–0067–11723. NRDC Attachment2 at p. 4. PO 00000 Frm 00151 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 24324 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 continuously variable transmission (CVT) and show improvements on the 2cycle tests. However, pickup trucks are designed to provide high load towing utility.463 CVTs lack the torque levels needed to provide that towing utility, and would fail mechanically if subject to high load towing.464 The agencies provided discussions of some of these technical considerations in the PRIA, and explained why the agencies had limited technologies for certain vehicle classes, such as limiting CVTs on pickups as in the example above. These and other limitations are discussed further in the individual technology sections. The agencies also received a variety of comments that conflated aspects of the Autonomie models with technology inputs and input assumptions. For example, commenters expressed concern about the transmission gear set and final drive values used for the NPRM analysis, or more specifically, that the gear ratios were held constant across applications.465 In this case, both the inputs (gear set and final drive ratio) and input assumption (ratios held constant) were discussed by the commenters. Because these comments are actually about technology inputs to the Autonomie model, for these and similar cases, the agencies are addressing the comments in the individual technology sections which discuss the technology inputs and input assumptions that impact the effectiveness values for those technologies. For the NPRM analysis, the agencies prioritized using inputs that were based on data for identifiable technology configurations and that reflected practical real world constraints. The agencies provided detailed information on the NPRM analysis inputs and input assumptions in the NPRM Preamble, PRIA and Argonne model documentation for engine technologies, transmission technologies, powertrain electrification, light-weighting, aerodynamic improvements, tire rolling resistance improvements, and other vehicle technologies. Comments and the agencies’ assessment of comments for each technology are discussed in the individual technology sections below. Through careful consideration of the comments, the agencies have updated 463 SAE J2807. ‘‘Performance Requirements for Determining Tow-Vehicle Gross Combination Weight Rating and Trailer Weight Rating.’’ Feb. 4, 2016. 464 PRIA at p. 223 and 340. 465 NHTSA–2018–0067–11873. Comments from Roush Industries, Attachment 1, at p. 14–15. NHTSA–2018–0067–11873. Comments from CARB, at p.110. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 analytical inputs associated with several technologies, and as discussed above, have included several advanced technologies for which technical information was included in the NPRM. However, for most technologies, the agencies have determined that the technology inputs and input assumptions that were used in the NPRM analysis remain reasonable and the best available for the final rule analysis. (2) How The Agencies Defined Different Vehicle Types in Autonomie As described in the NPRM, Argonne produced full-vehicle models and ran simulations for many combinations of technologies, on many types of vehicles, but it did not simulate literally every single vehicle model/configuration in the analysis fleet because it would be impractical to assemble the requisite detailed information—much of which would likely only be provided on a confidential basis—specific to each vehicle model/configuration and because the scale of the simulation effort would correspondingly increase by orders of magnitude. Instead, Argonne simulated 10 different vehicle types, corresponding to the five ‘‘technology classes’’ generally used in CAFE analysis over the past several rulemakings, each with two performance levels and corresponding vehicle technical specifications (e.g., small car, small performance car, pickup truck, performance pickup truck, etc.). Technology classes are a means of specifying common technology input assumptions for vehicles that share similar characteristics. Because each vehicle technology class has unique characteristics, the effectiveness of technologies and combinations of technologies is different for each technology class. Conducting Autonomie simulations uniquely for each technology class provides a specific set of simulations and effectiveness data for each technology class. Like the Draft TAR analysis, there are separate technology classes for compact cars, midsize cars, small SUVs, large SUVs, and pickup trucks. However, new for the NPRM analysis and carried into this final rule analysis, each of those vehicle types has been split into ‘‘low’’ (or ‘‘standard’’) performance and a ‘‘high’’ performance versions, which represent two classes with similar body styles but different levels of performance attributes (for a total of 10 technology classes). The separate technology classes for high performance and low performance PO 00000 Frm 00152 Fmt 4701 Sfmt 4700 vehicles better account for performance diversity across the fleet. NHTSA directed Argonne to develop a vehicle assumptions database to capture vehicle attributes that would comprise the full vehicle models. For each vehicle technology class, representative vehicle attributes and characteristics were identified from publicly available information and automotive benchmarking databases like A2Mac1,466 Argonne’s Downloadable Dynamometer Database (D3),467 and EPA compliance and fuel economy data,468 EPA’s guidance on the cold start penalty on 2-cycle tests.469 The resulting vehicle assumptions database consists of over 100 different attributes like vehicle frontal area, drag coefficient, fuel tank weight, transmission housing weight, transmission clutch weight, hybrid vehicle component weights, and weights for components that comprise engines and electric machines, tire rolling resistance, transmission gear ratios and final drive ratio. Each of the 10 different vehicle types was assigned a set of these baseline attributes and characteristics, to which combinations of fuel-saving technologies were added as inputs for the Autonomie simulations. For example, the characteristics of the MY 2016 Honda Fit were considered along with a wide range of other compact cars to identify representative characteristics for the Autonomie simulations for the base compact car technology class. The simulations determined the fuel economy achieved when applying each combination of technologies to that vehicle type, given its baseline characteristics. For each vehicle technology class and for each vehicle attribute, Argonne estimated the attribute value using statistical distribution analysis of publicly available data and data obtained from the A2Mac1 benchmarking database.470 Some 466 A2Mac1: Automotive Benchmarking. (Proprietary data). Retrieved from https:// a2mac1.com. 467 Downloadable Dynamometer Database (D3). ANL Energy Systems Division. https:// www.anl.gov/es/downloadable-dynamometerdatabase. Last accessed Oct. 31, 2019. 468 Data on Cars used for Testing Fuel Economy. EPA Compliance and Fuel Economy Data. https:// www.epa.gov/compliance-and-fuel-economy-data/ data-cars-used-testing-fuel-economy. Last accessed Oct. 31, 2019. 469 EPA PD TSD at p.2–265—2–266. 470 A2Mac1 is subscription-based benchmarking service that conducts vehicle and component teardown analyses. Annually, A2Mac1 removes individual components from production vehicles such as oil pans, electric machines, engines, transmissions, among the many other components. These components are weighed and documented for E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 vehicle attributes were also based on test data and vehicle benchmarking, like the cold-start penalty for the FTP test cycle and vehicle electrical accessories load. The analysis of vehicle attributes used in the NPRM was discussed in the Argonne model documentation,471 and values for each vehicle technology class were provided with the NPRM for public review.472 The agencies did not believe it was appropriate to assign one single engine mass for each vehicle technology class in the NPRM analysis. To account for the difference in weight for different engine types, Argonne performed a regression analysis of engine peak power versus weight, based on attribute data taken from the A2Mac1 benchmarking database. For example, to account for weight of different engine sizes like 4-cylinder versus 8-cylinder, Argonne developed a relationship curve between peak power and engine weight based on the A2Mac1 benchmarking data. For the NPRM analysis, this relationship was used to estimate mass for all engine types regardless of technology type (e.g., variable valve lift and direct injection). Secondary weight reduction associated with changes in engine technology was applied by using this linear relationship between engine power and engine weight from the A2Mac1 benchmarking database. When a vehicle in the analysis fleet with an 8cylinder engine adopted a more fuel efficient 6-cylinder engine, the total vehicle weight would reflect the updated engine weight with two less cylinders based on the peak power versus engine weight relationship. The impact of engine mass reduction on effectiveness is accounted for directly in the Autonomie simulation data through the application of the above relationship. Engine mass reduction through downsizing is, therefore, appropriately not included as part of vehicle mass reduction technology that is discussed in Section VI.C.4 because doing so would result in double counting the impacts. As discussed further below, for the final rule the agencies improved upon the precision of engine weights by creating two curves to separately represent naturally aspirated engine designs and turbocharged engine designs. key specifications which is then available to their subscribers. 471 NHTSA–2018–0067–0007, at 131. Islam, E., S, Moawad, A., Kim, N, Rousseau, A., ‘‘A Detailed Vehicle Simulation Process To Support CAFE Standards 04262018—Report’’ ANL Autonomie Documentation. Aug 21, 2018. 472 NHTSA–2018–0067–0003. ANL Autonomie Summary of Main Component Assumptions. Aug 21, 2018. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 In addition, certain attributes were held at constant levels within each technology class to maintain vehicle functionality, performance and utility including noise, vibration, and harshness (NVH), safety, performance and other utilities important for customer satisfaction. For example, in addition to the vehicle performance constraints discussed in Section VI.B.3.a)(6), the analysis does not allow the frontal area of the vehicle to change, in order to maintain utility like ground clearance, head-room space, and cargo space, and a cold-start penalty is used to account for fuel economy degradation for heater performance and emissions system catalyst light-off.473 This allows us to capture the discrete improvement in technology effectiveness while maintaining vehicle attributes that are important vehicle utility, consumer acceptance and compliance with criteria emission standards, and considering these constraints similar to how manufacturers do in the real world. The agencies sought comment on the analytical approach used to determine vehicle attributes and characteristics for the Autonomie modeling. In response, the agencies received a wide variety of comments on vehicle attributes ranging from discussions of performance increase from technology adoption (e.g., if a vehicle adopting an electrified powertrain improved its time to accelerate from 0–60 mph), to comments on vehicle attributes not modeled in Autonomie, like heated seats and cargo space. Toyota and the Alliance commented that the inclusion of performance vehicle classes addressed the market reality that some consumers will purchase vehicles for their performance attributes and will accept the corresponding reduction in fuel economy. Furthermore, Toyota commented that some gain in performance is more realistic, and that ‘‘dedicating all powertrain improvements to fuel efficiency is inconsistent with market reality.’’ Toyota ‘‘supports the agencies’ inclusion of performance classes in compliance modeling where a subset of certain models is defined to have higher performance and a commensurate reduction in fuel efficiency.’’ 474 Also, in support of the addition of performance vehicle classes, the Alliance commented that ‘‘vehicle categories have been increased to 10 to better recognize the 473 The catalyst light-off is the temperature necessary to initiate the catalytic reaction and this energy is generated from engine. 474 Toyota, Attachment 1, Docket No. NHTSA– 2018–0067–12098, at p. 6. PO 00000 Frm 00153 Fmt 4701 Sfmt 4700 24325 range of 0–60 performance characteristics within each of the 5 previous categories, in recognition of the fact that many vehicles in the baseline fleet significantly exceeded the previously assumed 0–60 performance metrics. This provides better resolution of the baseline fleet and more accurate estimates of the benefits of technology.’’ 475 UCS commented that the CAFE model incorporates technology improvements to each vehicle by applying the effectiveness improvement of the average vehicle in the technology class, leading to discrete ‘‘stepped’’ effectiveness levels for technologies across the different vehicle types. UCS stated that in contrast, the OMEGA model takes into account a vehicle’s performance characteristics through response-surface modeling based on relative deviation from the class average modeled in ALPHA.476 Although differences between the ALPHA and Autonomie models are discussed in more detail below, for the NPRM vehicle simulation analysis the agencies expanded the number of vehicle classes from the five classes used in the Draft TAR to ten classes, to represent better the diversity of vehicle characteristics across the fleet. Each of these ten vehicle technology classes are empirically built from benchmarking data and other information from various sources, amounting to hundreds of vehicle characteristics data points to develop each vehicle class. The agencies expand on these vehicle classes and characteristics in Section VI.B.3.(a)(2) Vehicle Types in Autonomie and Section VI.B.3.(a)(3) How Vehicle Models are Built in Autonomie and Optimized for Simulation. The agencies believe that the real-world data used to define vehicle characteristics for each of the ten vehicle classes, in addition to the ten vehicle technology classes themselves, ensures the analysis reasonably accounts for the diversity in vehicle characteristics across the fleet. The agencies believe that UCS’s characterization of how technology improvements are applied in the analysis is a misleading oversimplification. While the analysis approach in the final rule uses a representative effectiveness value, the value is not linked solely to the vehicle technology class, as the UCS implies. The entire technology combination, or technology key, which includes the vehicle technology class, is used to 475 Alliance of Automobile Manufacturers, Attachment ‘‘Full Comment Set,’’ Docket No. NHTSA–2018–0067–12073, at p.135. 476 NHTSA–2018–0067–12039, at p.24. E:\FR\FM\30APR2.SGM 30APR2 24326 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 determine the value for the platform being considered. Within each vehicle class, the interactions between the added technology and the full vehicle system (including other technologies and substantial road load characteristics) are considered in the effectiveness values calculated for each technology during compliance modeling. As discussed under each of the technology pathways sections, the effectiveness for most technologies is reported as a range rather than a single value. The range exists because the effectiveness for each technology is adjusted based on the technologies it is coupled with and the major road load characteristics of the full vehicle system. This approach, in combination with using the baseline vehicle’s initial performance values as a starting point for performance improvement, results in a widely variable level of improvement for the system, dependent on individual vehicle platform characteristics. As a result, the application of a responsesurface approach would likely result in minimal improvement in accuracy for the Autonomie and CAFE model analysis approach. For the final rule analysis, the agencies used the same process to obtain the vehicle attributes and characteristics for the vehicle technology classes. Data was acquired from publicly available sources, Argonne D3, EPA compliance and fuel economy data, and A2mac1 benchmarking data. Accordingly, the attributes and characteristics of the modeled vehicles reflect actual vehicles that meet customer expectations and automakers’ capabilities to manufacture the vehicles. In addition, for the final rule, the agencies improved the NPRM analysis by updating some of the attribute values to account for changes in the fleet. For example, the agencies have updated vehicle electrical accessory load on the test cycle to reflect higher electrical loads associated with contemporary vehicle features. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 (3) How This Rulemaking Builds Vehicle Models for Autonomie and Optimize Them for Simulation Before any simulation is initiated in Autonomie, Argonne must ‘‘build’’ a vehicle by assigning reference technologies and initial attributes to the components of the vehicle model representing each technology class.477 The reference technologies are baseline technologies that represent the first step on each technology pathway used in the analysis. For example, a compact car is built by assigning it a baseline engine, a baseline 6-speed automatic transmission (AT6), a baseline level of aerodynamic improvement (AERO0), a baseline level of rolling resistance improvement (ROLL0), a baseline level of mass reduction technology (MR0), and corresponding attributes from the Argonne vehicle assumptions database like individual component weights.478 A baseline vehicle will have a unique starting point for the simulation and a unique set of assigned inputs and attributes, based on its technology class. The next step in the process is to run a powertrain sizing algorithm that ensures the built vehicle meets or exceeds defined performance metrics, including low-speed acceleration (i.e., time required to accelerate from 0–60 mph), high-speed passing acceleration (time required to accelerate from 50–80 mph), gradeability (e.g. the ability of the vehicle to maintain constant 65 miles per hour speed on a six percent upgrade), and towing capacity. Together, these performance criteria are widely used by industry as metrics to quantify vehicle performance attributes that consumers observe and that are important for vehicle utility and customer satisfaction. In the compact car example used above, the agencies assigned an initial specific engine design and engine power, transmission, AERO, ROLL, and MR technologies, and other attributes like vehicle weight. If the built vehicle 477 For the NPRM analysis, Chapter 8 VehicleSizing Process in the ANL Model Documentation had discussed this process in detail. Further discussion of this process is located in Chapter 8 of the ANL Model Documentation for this final rule. 478 See Section VI.A.7. PO 00000 Frm 00154 Fmt 4701 Sfmt 4700 does not meet all the performance criteria in the first iteration, then the engine power is increased to meet the performance requirement. This increase in power is from higher engine displacement, which could involve an increase in number of cylinders, leading to an increase in the engine weight. The iterative process continues to check whether the compact car with updated engine power, and corresponding updated engine weight, meets its defined performance metrics. The loop stops once all the metrics are met, and at this point, a compact car technology class vehicle model becomes ready for simulation. For further discussion of the vehicle performance metrics, see Section VI.B.3.(a). Autonomie then adopts a single fuel saving technology to the baseline vehicle model, keeping everything else the same except for that one technology and the attributes associated with it. For example, the model would apply an 8speed automatic transmission in place of the baseline 6-speed automatic transmission, which would lead to either an increase or decrease in the total weight of the vehicle based on the technology class assumptions. At this point, Autonomie confirms whether performance metrics are met for this new vehicle model through the previously discussed sizing algorithm. Once a technology has been assigned to the vehicle model and the resulting vehicle meets its performance metrics, those vehicle models will be used as inputs to the full vehicle simulations. So, in the example of the 6-speed to 8speed automatic transmission technology update, the agencies now have the initial ten vehicle models (one for each technology class), plus the ten new vehicle models with the updated 8speed automatic transmission, which adds up to 20 different vehicle models for simulation. This permutation process is conducted for each of the over 50 technologies considered, and for all ten technology classes, which results in more than one million optimized vehicle models. Figure VI–3 shows the process for building vehicles in Autonomie for simulation. E:\FR\FM\30APR2.SGM 30APR2 Some of the technologies require extra steps for optimization before the vehicle models are built for simulation; for example, the sizing and optimization process is more complex for the electrified vehicles (i.e., HEVs, PHEVs) compared to vehicles with internal combustion engines, as discussed further, below. Throughout the vehicle building process, the following items are considered for optimization: • Vehicle weight is decreased or increased in response to switching from one type of technology to another for the technologies for which the agencies consider weight, such as different engine and transmission types; • Vehicle performance is decreased or increased in response to the addition of mass reduction technologies when switching from one vehicle model to another vehicle model for the same engine; • Vehicle performance is decreased or increased in response to the addition of a new technology when switching from one vehicle model to another vehicle VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 model for the same hybrid electric machine; and • Electric vehicle battery size is decreased or increased in response to the addition of mass, aero and/or tire rolling resistance technologies when switching from one vehicle model to another vehicle model. Every time a vehicle adopts a new technology, the vehicle weight is updated to reflect the new component weight. For some technologies, the direct weight change is easy to assess. For example, in the NPRM the agencies designated weights for transmissions so, when a vehicle is updated to a higher geared transmission, the weight of the original transmission is replaced with the corresponding transmission weight (e.g., the weight of a vehicle moving from a 5-speed automatic transmission to an 8-speed automatic transmission will be updated based on the 8-speed transmission weight). For other technologies, like engine technologies, assessing the updated vehicle weight is much more complex. Discussed earlier, modeling a change in PO 00000 Frm 00155 Fmt 4701 Sfmt 4700 24327 engine technology involves both the new technology adoption and a change in power (because the reduction in vehicle weight leads to lower engine loads, and a resized engine). When a new engine technology is adopted on a vehicle the agencies account for the associated weight change to the vehicle based on the earlier discussed regression analysis of weight versus power. For the NPRM engine weight regression analysis, the agencies considered 19 different engine technologies that consisted of unique components to achieve fuel economy improvements. This regression analysis is technology agnostic by taking the approach of using engine peak power versus engine weight because it removed biases to any specific engine technology in the analysis. Although the agencies do not estimate the specific weight for each individual engine technology, such as VVT and SGDI, this process provides a reasonable estimate of the weight differences among engine technologies. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.110</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations For the final rule analysis, the agencies used the same process to assign initial weights to the original 19 engines, plus the added engines. However, the agencies improved upon precision of the weights by creating two separate curves separately to represent naturally aspirated engine designs and turbocharged engine designs.479 This update resulted in two benefits. First, small naturally aspirated 4-cylinder engines that adopted turbocharging technology reflected the increased weight of associated components like ducting, clamps, the turbocharger itself, a charged air cooler, wiring, fasteners, and a modified exhaust manifold. Second, larger cylinder count engines like naturally aspirated 8-cylinder and 6-cylinder engines that adopted turbocharging and downsized technologies would have lower weight due to having fewer engine cylinders. For example, a naturally aspirated 8cylinder engine that adopts turbocharging technology when downsized to a 6-cylinder turbocharged engine appropriately reflects the added weight of turbocharging components, and the lower weight of fewer cylinders. As with conventional vehicle models, electrified vehicle models were built from the ground up. For the NPRM analysis, Argonne used data from the A2mac1 database and vehicle test data to define different attributes like weights and power. Argonne used one 479 ANL Model Documentation for the final rule analysis, Chapter 5.2.9 Engine Weight Determination. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 electric motor specific power for each type of hybrid and electric vehicle.480 For MY2017, the U.S. market has an expanded number of available hybrid and electric vehicle models. To capture appropriately the improvements for electrified vehicles for the final rule analysis, the agencies applied the same regression analysis process that considers electric motor weight versus electric motor power for vehicle models that have adopted electric motors. Benchmarking data for hybrid and electric vehicles from the A2Mac1 database was analyzed to develop a regression curve of electric motor peak power versus electric motor weight.481 (4) How Autonomie Sizes Powertrains for Full Vehicle Simulation The agencies maintain performance neutrality of the full vehicle simulation analysis by resizing engines, electric machines, and hybrid electric vehicle battery packs at specific incremental technology steps. To address product complexity and economies of scale, engine resizing is limited to specific incremental technology changes that would typically be associated with a major vehicle or engine redesign.482 480 NHTSA–2018–0067–0005. ANL Autonomie Model Assumptions Summary. Aug 21, 2018. Non_ Vehicle_Attributes tab. Specific power for PS and P2 HEVs was set to 2750 watts/kg, plug-in HEVs were set to 375 watts/kg, and electric vehicles were set to 1400 watts/kg. 481 ANL Model Documentation for the final rule analysis, Chapter 5.2.10 Electric Machines System Weight. 482 See 83 FR 43027 (Aug. 24, 2018). PO 00000 Frm 00156 Fmt 4701 Sfmt 4700 Manufacturers have repeatedly told the agencies that the high costs for redesign and the increased manufacturing complexity that would result from resizing engines for small technology changes preclude them from doing so. It would be unreasonable and unaffordable to resize powertrains for every unique combination of technologies, and exceedingly so for every unique combination of technologies across every vehicle model due to the extreme manufacturing complexity that would be required to do so. The agencies reiterated in the NPRM that the analysis should not include engine resizing with the application of every technology or for combinations of technologies that drive small performance changes so that the analysis better reflects what is feasible for manufacturers.483 When a powertrain does need to be resized, Autonomie attempts to mimic manufacturers’ development approaches to the extent possible. Discussed earlier, the Autonomie vehicle building process is initiated by building a baseline vehicle model with a baseline engine, transmission, and other baseline vehicle technologies. This baseline vehicle model (for each technology class) is sized to meet a specific set of 483 For instance, a vehicle would not get a modestly bigger engine if the vehicle comes with floor mats, nor would the vehicle get a modestly smaller engine without floor mats. This example demonstrates small levels of mass reduction. If manufacturers resized engines for small changes, manufacturers would have dramatically more part complexity, potentially losing economies of scale. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.111</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24328 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations performance criteria, including acceleration and gradeability. The modeling also accounts for the industry practice of platform, engine, and transmission sharing to manage component complexity and the associated costs.484 At a vehicle refresh cycle, a vehicle may inherit an already resized powertrain from another vehicle within the same engine-sharing platform that adopted the powertrain in an earlier model year. In the Autonomie modeling, when a new vehicle adopts fuel saving technologies that are inherited, the engine is not resized (the properties from the baseline reference vehicle are used directly and unchanged) and there may be a small change in vehicle performance. For example, in Figure VI– 3, Vehicle 2 inherits Eng01 from Vehicle 1 while updating the transmission. Inheritance of the engine with new transmission may change performance. This example illustrates how manufacturers generally manage manufacturing complexity for engines, transmissions, and electrification technologies. Autonomie implements different powertrain sizing algorithms depending on the type of powertrain being considered because different types of powertrains contain different components that must be optimized.485 khammond on DSKJM1Z7X2PROD with RULES2 484 Ford EcoBoost Engines are shared across ten different models in MY2019. https://www.ford.com/ powertrains/ecoboost/. Last accessed Nov. 05, 2019. 485 ANL Model Documentation for the final rule Analysis, Chapter 8.3.1 Conventional-Vehicle Sizing Algorithm; Chapter 8.3.2 Split-HEV Sizing Algorithm; 8.3.4 Blended PHEV sizing Algorithm; 8.3.5 Voltec PHEV (Extended Range) Vehicle Sizing Algorithm; Chapter 8.3.6 BEV Sizing Algorithm. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 For example, the conventional powertrain resizing considers the reference power of the conventional engine (e.g., Eng01, a basic VVT engine, is rated at 108 kilowatts and this is the starting reference power for all technology classes) against the powersplit hybrid (SHEVPS) resizing algorithm that must separately optimize engine power, battery size (energy and power), and electric motor power. An engine’s reference power rating can either increase or decrease depending on the architecture, vehicle technology class, and whether it includes other advanced technologies. Performance requirements also differ depending on the type of powertrain because vehicles with different powertrain types may need to meet different criteria. For example, a plug-in hybrid electric vehicle (PHEV) powertrain that is capable of traveling a certain number of miles on its battery energy alone (referred to as all-electric range, or AER, or as performing in electric-only mode) is also sized to ensure that it can meet the performance requirements of a US06 cycle in electriconly mode. The powertrain sizing algorithm is an iterative process that attempts to optimize individual powertrain components at each step. For example, the sizing algorithm for conventional powertrains estimates required power to meet gradeability and acceleration performance and compares it to the reference engine power for the technology class. If the power required to meet gradeability and acceleration performance exceeds the reference PO 00000 Frm 00157 Fmt 4701 Sfmt 4700 24329 engine power, the engine power is updated to the new value. Similarly, if the reference engine power exceeds the gradeability and acceleration performance power, it will be decreased to the lower power rating. As the change in power requires a change design of the engine, like increasing displacement (e.g., going from a 5.2-liter to 5.6-liter engine, or vice versa) or increasing cylinder count (e.g., going from an I4 to a V6 or vice versa), the engine weight will also change. The new engine power is used to update the weight of the engine. Next, the conventional powertrain sizing algorithm enters an acceleration algorithm loop to verify low-speed acceleration performance (time it takes to go from 0 mph to 60 mph). In this step, Autonomie adjusts engine power to maintain a performance attribute for the given technology class and updates engine weight accordingly. Once the performance criteria are met, Autonomie ends the low-speed acceleration performance algorithm loop and enters a high-speed acceleration (time it takes to go from 50 mph to 80 mph) algorithm loop. Again, Autonomie might need to adjust engine power to maintain a performance attribute for the given technology, and it exits this loop once the performance criteria have been met. At this point, the sizing algorithm is complete for the conventional powertrain based on the designation for engine type, transmissions type, aero type, mass reduction technology and low rolling resistance technology. Figure VI–5 below shows the sizing algorithm for conventional powertrains. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Depending on the type of powertrain considered, the sizing algorithms may also size to meet different performance criteria in different order. The powertrain sizing algorithms for electrified vehicles are considerably more complex, and are discussed in further detail in Section VI.C.3, below. (5) How the Agencies Considered Maintaining Vehicle Attributes khammond on DSKJM1Z7X2PROD with RULES2 For this rulemaking analysis, consistent with past CAFE and CO2 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 rulemakings, the agencies have analyzed technology pathways manufacturers could use for compliance that attempt to maintain vehicle attributes, utility, and performance. Using this approach allows the agencies to assess costs and benefits of potential standards under a scenario where consumers continue to get the similar vehicle attributes and features, other than changes in fuel economy. The purpose of constraining vehicle attributes is to simplify the analysis and reduce variance in other PO 00000 Frm 00158 Fmt 4701 Sfmt 4700 attributes that consumers value across the analyzed regulatory alternatives. This allows for a more streamlined accounting of costs and benefits by not requiring the values of other vehicle attributes that trade off with fuel economy. Several examples of vehicle attributes, utility and performance that could be impacted by adoption of fuel economy improving technology include the following. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.112</GPH> 24330 Consequences for the agencies not fully considering or accounting for potential changes in vehicle attributes, utility, and performance are degradation in vehicle attributes, utility, and performance that lead to consumer acceptance issues without accounting for the corresponding costs and/or not accounting for the costs of technology designs that maintain vehicle attributes, utility, and performance. The agencies incorporated changes in the NPRM analysis and that are carried into this final rule that address deficiencies in past analyses, including the Draft TAR and Proposed Determination analyses. These changes were discussed in the NPRM and are repeated in the discussion of individual technologies in VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 this Preamble, the FRIA, and supporting documents. The following are several examples of technologies that did not maintain vehicle attributes, utility, and performance in the Draft TAR and Proposed Determination analyses. For the EPA Draft TAR and Proposed Determination analyses, HCR engine and downsized and turbocharged engine technologies effectiveness was estimated using Tier 2 certification fuel, which has a higher octane rating compared to regular octane fuel.486 487 This does not maintain functionality 486 Tier 2 fuel has an octane rating of 93. Typical regular grade fuel has an octane rating of 87 ((R+M)/ 2 octane. 487 EPA Proposed Determination at 2–209 to 2– 212. PO 00000 Frm 00159 Fmt 4701 Sfmt 4700 24331 because consumers would incur higher costs for using premium fuel in order to achieve the modeled fuel economy improvements, compared to baseline engines that were replaced, which operated on lower cost regular octane fuel. By not maintaining the fuel octane functionality and vehicle attributes, the EPA Draft TAR and Proposed Determination analyses applied higher effectiveness for these technologies than could be achieved had regular octane fuel been assumed for the HCR and downsized turbocharged engines. The Draft TAR and Proposed Determination analyses also did not account for the higher costs that would be incurred by consumers to pay for high octane fuel. These issues were addressed in the E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.113</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 24332 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations NPRM and this final rule analysis, and account for some of the effectiveness and cost differences between the Draft TAR/Proposed Determination and the NPRM/final rule.488 Another example is mass reduction technology. As background, the agencies characterize mass reduction as either primary mass reduction or secondary mass reduction. Primary mass reduction involves reducing mass of components that can be done independently of the mass of other components. For example, the mass of a hood (e.g., replacing a steel hood with an aluminum hood) or reducing the mass of a seat are examples of primary mass reduction because each can be implemented independently. When there is a significant level of primary mass reduction, other components that are designed based on the mass of primary components, may be redesigned and have lower mass. An example of secondary mass reduction is the brake system. If the mass of primary components is reduced sufficiently, the resulting lighter weight vehicle could maintain braking performance and attributes, and safety with a lighter weight brake system. Mass reduction in the brake system is secondary mass reduction because it requires primary mass reduction before it can be incorporated. For the EPA Draft TAR and Proposed Determination analyses, secondary mass reduction was applied exclusively based on cost, with no regard to whether sufficient primary mass reduction was applied concurrently. The analyses did not account for the degraded functionality of the secondary components and systems and also understated the costs for lower levels of mass reduction.489 These issues were addressed in the NPRM and this final rule analysis, and account for some of the cost differences between the Draft TAR/Proposed Determination and the NPRM/final rule. The agencies note that for some technologies it is not reasonable or practicable to match exactly the baseline vehicle’s attributes, utility, and performance. For example, when engines are resized to maintain acceleration performance, if the agencies applied a criterion that allowed no shift in performance whatsoever, there would be an extreme proliferation of unique engine displacements. Manufacturers have repeatedly and consistently told the agencies that the high costs for redesign and the increased manufacturing complexity 488 For more details, see Section VI.C.1 Engine Paths. 489 For more details, see Section VI.C.4 Mass Reduction. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 that would result from resizing engines for small technology changes preclude them from doing so. It would be unreasonable and unaffordable to resize powertrains for every unique combination of technologies, and exceedingly so for every unique combination technologies across every vehicle model due to the extreme manufacturing complexity that would be required to do so.490 For the NPRM and final rule analyses, engine resizing is limited to specific incremental technology changes that would typically be associated with a major vehicle or engine redesign to address product complexity and economies of scale considerations. The EPA Draft TAR and Proposed Determination analyses adjusted the effectiveness of every technology combination assuming performance could be held constant for every combination, and the analysis did not recognize or account for the extreme complexity nor the associated costs for that impractical assumption. The NPRM and final rule analyses account for these real-world practicalities and constraints, and doing so explains some of the effectiveness and cost differences between the Draft TAR/Proposed Determination and the NPRM/final rule. The subsections for individual technologies discuss the technology assumptions and constraints that were considered to maintain vehicle attributes, utility, and performance as closely as possible. The agencies believe that any minimal remaining differences, which may directionally either improve or degrade vehicle attributes, utility and performance are small enough to have de minimis impact on the analysis. (6) How the Agencies Considered Performance Neutrality The CAFE model examines technologies that can improve fuel economy and reduce CO2 emissions. An improvement in efficiency can be realized by improving the powertrain that propels the vehicle (e.g., replacing a 6-cylinder engine with a smaller, turbocharged 4-cylinder engine), or by reducing the vehicle’s loads or burdens (e.g., lowering aerodynamic drag, reducing vehicle mass and/or rolling resistance). Either way, these changes reduce energy consumption and create a range of choices for automobile manufacturers. At the two ends of the range, the manufacturer can choose either: (A) To design a vehicle that does same the amount of work as before but uses less fuel. 490 For more details, see Section VI.B.3.a)(6) Performance Neutrality. PO 00000 Frm 00160 Fmt 4701 Sfmt 4700 For example, a redesigned pickup truck would receive a turbocharged V6 engine in place of the outgoing V8. The pickup would offer no additional towing capacity, acceleration, larger wheels and tires, expanded infotainment packages, or customer convenience features, but would achieve a higher fuel economy rating (and correspondingly lower CO2 emissions). (B) To design a vehicle that does more work and uses the same amount of fuel as before. For example, a redesigned pickup truck would receive a turbocharged V6 engine in place of the outgoing V8, but with engine efficiency improvements that allow the same amount of fuel to do more work. The pickup would offer improved towing capacity, improved acceleration, larger wheels and tires, an expanded (heavier) infotainment package, and more convenience features, while maintaining (not improving) the fuel economy rating of the previous year’s model. In other words, automakers weigh the trade-offs between vehicle performance/ utility and fuel economy, and they choose a blend of these attributes to balance meeting fuel economy and emissions standards and suiting the demands of their customers. Historically, vehicle performance has improved over the years. The average horsepower is the highest that it has ever been; all vehicle types have improved horsepower by at least 49 percent compared to the 1975 model year, and pickup trucks have improved by 141 percent.491 Since 1978, the 0–60 acceleration time of vehicles has improved by 39–47 percent depending on vehicle type.492 Also, to gain consumer acceptance of downsized turbocharged engines, manufacturers have stated they often offer an increase in performance.493 Fuel economy has also improved, but the horsepower and acceleration trends show that not 100 percent of technological improvements have been applied to fuel savings. While future trends are uncertain, the past trends suggest vehicle performance is unlikely to decrease, as it seems reasonable to assume that customers 491 The 2018 EPA Automotive Trends Report (EPA–420–R–19–002 March 2019) https:// www.epa.gov/automotive-trends/downloadautomotive-trends-report. 492 The 2018 EPA Automotive Trends Report (EPA–420–R–19–002 March 2019) https:// www.epa.gov/automotive-trends/downloadautomotive-trends-report. 493 Alliance of Automobile Manufacturers, Attachment ‘‘Comment,’’ Docket No. EPA–HQ– OAR–2015–0827–4089, at p. 122. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations will at a minimum demand vehicles that offer the same utility as today’s fleet. For this rulemaking analysis, consistent with past CAFE and CO2 rulemakings, the agencies have analyzed technology pathways manufacturers could use for compliance that attempt to maintain vehicle attributes, utility and performance. NHTSA’s analysis in the Draft TAR used the same approach for performance neutrality as was used for the NPRM and is being carried into this final rule. This approach is described throughout this section and further in FRIA Section VI. For the Draft TAR and Proposed Determination, the EPA analyses used an approach that maintained 0–60 mph acceleration time for every technology package. However, that approach did not account for the added development, manufacturing, assembly and service parts complexity and associated costs that would be incurred by manufacturers to produce the substantial number of engine variants that would be required to achieve those CO2 improvements.494 Using the NPRM approach, which is carried into this final rule, allows the agencies to assess costs and benefits of potential standards under a scenario where consumers continue to get the same vehicle attributes and features, other than changes in fuel economy (approaching the scenario in example ‘‘A’’ above). This approach also eliminates the need to assess the value of changes in vehicle attributes and features. As discussed later in this section, while some small level of performance increase is unavoidable when conducting this type of analysis, the added technology results almost exclusively in improved fuel economy. This allows the cost of these technologies to reflect almost entirely the cost of compliance with standards with nearly neutral vehicle performance. The CAFE model maintains the initial performance and utility levels of the analysis vehicle fleet, while considering real world constraints faced by manufacturers. To maintain performance neutrality when applying fuel economy technologies, it is first necessary to characterize the performance levels of each of the nearly 3000 vehicle models in the MY 2017 baseline fleet. As discussed in Section VI.B.1.b) Assigning Vehicle Technology Classes, above, each individual vehicle model in the analysis fleet was assigned to one of ten vehicle 494 Each variant would require a unique engine displacement, requiring unique internal engine components, such as crankshaft, connecting rods and others. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 ‘‘technology classes’’—the class that is most similar to the vehicle model. The technology classes include five standard class vehicles (compact car, midsize car, small SUV, midsize SUV, pickup) plus five ‘‘performance’’ versions of these same body styles.495 Each vehicle class has a unique set of attributes and characteristics, including vehicle performance metrics, that describe the typical characteristics of the vehicles in that class. The analysis used four criteria to characterize vehicle performance attributes and utility: • Low-speed acceleration (time required to accelerate from 0–60 mph) • High-speed acceleration (time required to accelerate from 50–80 mph) • Gradeability (the ability of the vehicle to maintain constant 65 miles per hour speed on a six percent upgrade) • Towing capacity Low-speed and high-speed acceleration target times are typical of current production vehicles and range from 6 to 10 seconds depending on the vehicle class; for example, the midsize SUV performance class has a low- and high-speed acceleration target of 7 seconds.496 The gradeability criterion requires that the vehicle, given its attributes of weight, engine power, and transmission gearing, be capable of maintaining a minimum of 65 mph while going up a six percent grade. The towing criterion, which is applicable only to the pickup truck and performance pickup truck vehicle technology classes, is the same as the gradeability requirement but adds an additional payload/towing mass (3,000 lbs. for pickups, or 4,350 lbs for performance pickups) to the vehicle, essentially making the vehicle heavier. In addition, to maintain the capabilities of certain electrified vehicles in the 2017 baseline fleet, the analysis required that those vehicles be capable of achieving the accelerations and speeds of certain standard driving cycles. The agencies use the US06 ‘‘aggressive driving’’ cycle and the UDDS ‘‘city driving’’ cycle to ensure that core capabilities of BEVs and PHEVs, such as driving certain speeds and/or distances in electric-only mode, are maintained. In addition to the four criteria discussed above, the following 495 Separate technology classes were created for high performance and low performance vehicles to better account for performance diversity across the fleet. 496 Note, for all vehicle classes, the low and highspeed acceleration targets use the same value. See section VI.B.1.b)(1) Assigning Vehicle Technology Classes for a list of low-speed acceleration target by vehicle technology class. PO 00000 Frm 00161 Fmt 4701 Sfmt 4700 24333 performance criteria are applied to these electrified vehicles: • Battery electric vehicles (BEV) are sized to be capable of completing the US06 ‘‘aggressive driving’’ cycle. • Plug-in hybrid vehicles with 50 mile all-electric range (PHEV50) are sized to be capable of completing the US06 ‘‘aggressive driving’’ cycle in electric-only mode. • Plug-in hybrid vehicles with 20 mile all-electric range (PHEV20) are sized to be capable of completing the UDDS ‘‘city driving’’ cycle in electriconly (charge depleting) mode.497 Together, these performance criteria are widely used by industry as metrics to quantify vehicle performance attributes that consumers observe and that are important for vehicle utility and customer satisfaction.498 When certain fuel-saving technologies are applied that affect vehicle performance to a significant extent, such as replacing a pickup truck’s V8 engine with a turbocharged V6 engine, iterative resizing of the vehicle powertrain (engine, electric motors, and/or battery) is performed in the Autonomie simulation such that the above performance criteria is maintained. For example, if the aforementioned engine replacement caused an improvement in acceleration, the engine may be iteratively resized until vehicle acceleration performance is shifted back to the initial target time for that vehicle technology class. For the low and highspeed acceleration criteria, engine resizing iterations continued until the acceleration time was within plus or minus 0.2 seconds of the target time,499 500 which is judged to balance 497 PHEV20’s are blended-type plug-in hybrid vehicles, which are capable of completing the UDDS cycle in charge depleting mode without assistance from the engine. However, under higher loads, this charge depleting mode may use supplemental power from the engine. 498 Conlon, B., Blohm, T., Harpster, M., Holmes, A. et al., ‘‘The Next Generation ‘‘Voltec’’ Extended Range EV Propulsion System,’’ SAE Int. J. Alt. Power. 4(2):2015, doi:10.4271/2015–01–1152. Kapadia, J., Kok, D., Jennings, M., Kuang, M., et al., ‘‘Powersplit or Parallel—Selecting the Right Hybrid Architecture,’’ SAE Int. J. Alt. Power. 6(1):2017, doi:10.4271/2017–01–1154. Islam, E., A. Moawad, N. Kim, and A. Rousseau, 2018a, An Extensive Study on Vehicle Sizing, Energy Consumption and Cost of Advance Vehicle Technologies, Report No. ANL/ESD–17/17, Argonne National Laboratory, Lemont, Ill., Oct 2018. 499 For example, if a vehicle has a target 0–60 acceleration time of 6 seconds, a time within 5.8– 6.2 seconds was accepted. 500 With the exception of a few performance electrified vehicle types which, based on observations in the marketplace, use different criteria to maintain vehicle performance without battery assist. Performance PHEV20, and Performance PHEV50 resize to the performance of a conventional six-speed automatic (CONV 6AU). E:\FR\FM\30APR2.SGM Continued 30APR2 24334 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 reasonably the precision of engine resizing with the number of simulation iterations needed to achieve performance within the 0.2 second window, and the associated computer resources and time required to perform the iterative simulations. Engine resizing is explained further in Section VI.B.3.a)(4) How Autonomie Sizes Powertrains for Full Vehicle Simulation and the Argonne Model Documentation for the final rule analysis. The Autonomie simulation resizes until the least capable of the performance criteria is met, to ensure the pathways do not degrade any of the vehicle performance metrics. It is possible that as one criterion target is reached after the application of a specific technology or technology package, other criteria may be better than their target values. For example, if the engine size is decreased until the low speed acceleration target is just met, it is possible that the resulting engine size would cause high speed acceleration performance to be better than its target.501 Or, a PHEV50 may have an electric motor and battery appropriately sized to operate in all electric mode through the repeated accelerations and high speeds in the US06 driving cycle, but the resulting motor and battery size enables the PHEV50 slightly to over-perform in 0– 60 acceleration, which utilizes the power of both the electric motor and combustion engine. To address product complexity and economies of scale, engine resizing is limited to specific incremental technology changes that would typically be associated with a major vehicle or engine redesign.502 Manufacturers have repeatedly and consistently told the agencies that the high costs for redesign and the increased manufacturing complexity that would result from resizing engines for small technology changes preclude them from doing so. It would be unreasonable and unaffordable to resize powertrains for every unique combination of technologies, and exceedingly so for every unique combination technologies across every vehicle model due to the extreme manufacturing complexity that would be required to do so. Engine displacements are further described in Section VI.C.1 Engine Paths. Performance SHEVP2, engines/electric-motors were resized if the 0–60 acceleration time was worse than the target, but not resized if the acceleration time was better than the target time. 501 The Autonomie simulation databases include all of the estimated performance metrics for each combination of technology as modeled. 502 See 83 FR 43027 (Aug. 24, 2018). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 To address this issue, and consistent with past rulemakings, the NPRM simulation allowed engine resizing when mass reductions of 7.1 percent, 10.7 percent, 14.2 percent (and 20 percent for the final rule analysis) were applied to the vehicle curb weight,503 and when one powertrain architecture was replaced with another architecture during a redesign cycle.504 At its refresh cycle, a vehicle may also inherit an already resized powertrain from another vehicle within the same engine-sharing platform. The analysis did not re-size the engine in response to adding technologies that have smaller effects on vehicle performance. For instance, if a vehicle’s curb weight is reduced by 3.6 percent (MR1), causing the 0–60 mile per hour time to improve slightly, the analysis would not resize the engine. The criteria for resizing used for the analysis better reflects what is feasible for manufacturers to do.505 Automotive manufacturers have commented that the CAFE model’s consideration of the constraints faced in relation to vehicle performance and economies of scale are realistic. Industry associations and individual manufacturers widely supported the use of the performance metrics used in the NPRM analysis, the use of standard and higher performance technology classes, and the representation in the analysis of the real-world manufacturing complexity constraints and criteria for powertrain redesign. The Alliance of Automobile Manufacturers (Alliance), Ford, and Toyota stated that the inclusion of additional performance metrics such as gradeability are appropriate. Specifically in support of the gradeability performance criteria, the Alliance commented that ‘‘performance metrics related to vehicle operation in top gear are just as critical to customer acceptance as are performance metrics 503 These correspond, respectively, to reductions of 10%, 15%, 20%, and 28.2% of the vehicle glider mass. For more detail on glider mass calculation, see section VI.C.4 Mass Reduction. 504 Some engine and accessory technologies may be added to an engine without an engine architecture change. For instance, manufacturers may adapt, but not replace engine architectures to include cylinder deactivation, variable valve lift, belt-integrated starter generators, and other basic technologies. However, switching from a naturally aspirated engine to a turbo-downsized engine is an engine architecture change typically associated with a major redesign and radical change in engine displacement. 505 For instance, a vehicle would not get a modestly bigger engine if the vehicle comes with floor mats, nor would the vehicle get a modestly smaller engine without floor mats. This example demonstrates small levels of mass reduction. If manufacturers resized engines for small changes, manufacturers would have dramatically more part complexity, potentially losing economies of scale. PO 00000 Frm 00162 Fmt 4701 Sfmt 4700 such as 0–60 mph times that focus on performance in low-gear ranges.’’ 506 The Alliance also commented specifically on the relationship between gradeability and downsized engines, stating that as ‘‘engine downsizing levels increase, top-gear gradeability becomes more and more important,’’ and further that the consideration of gradeability ‘‘helps prevent the inclusion of small displacement engines that are not commercially viable and that would artificially inflate fuel savings.’’ 507 Ford and Toyota similarly commented in support of the CAFE model’s consideration of multiple performance criteria. Ford stated that this model ‘‘takes a more realistic approach to performance modeling’’ and ‘‘better replicates OEM attribute-balancing practices.’’ Ford stated furthermore that ‘‘OEMs must ensure that each individual performance measure—and not an overall average—meets its customer’s requirements,’’ and that, in contrast, previous analyses did ‘‘not align with product planning realities.’’ 508 Toyota commented in support of including gradeability as a performance metric ‘‘to avoid underpowered engines and overestimated fuel savings.’’ 509 Toyota and the Alliance commented that the inclusion of performance vehicle classes addressed the market reality that some consumers will purchase vehicles for their performance attributes and will accept the corresponding reduction in fuel economy. Furthermore, Toyota commented that most consumers consider more than just fuel economy when purchasing a vehicle, and that ‘‘dedicating all powertrain improvements to fuel efficiency is inconsistent with market reality.’’ Toyota ‘‘supports the agencies’ inclusion of performance classes in compliance modeling where a subset of certain models is defined to have higher performance and a commensurate reduction in fuel efficiency.’’ 510 Also in support of the addition of performance vehicle classes, the Alliance commented that ‘‘vehicle categories have been increased to 10 to better recognize the range of 0–60 performance 506 Alliance of Automobile Manufacturers, Attachment ‘‘Full Comment Set,’’ Docket No. NHTSA–2018–0067–12073, at 139. 507 Alliance of Automobile Manufacturers, Attachment ‘‘Full Comment Set,’’ Docket No. NHTSA–2018–0067–12073, at 135. 508 Ford, Attachment 1, Docket No. NHTSA– 2018–0067–11928, at 8. 509 Toyota, Attachment 1, Docket No. NHTSA– 2018–0067–12098, at 6. 510 Toyota, Attachment 1, Docket No. NHTSA– 2018–0067–12098, at 6. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations characteristics within each of the 5 previous categories, in recognition of the fact that many vehicles in the baseline fleet significantly exceeded the previously assumed 0–60 performance metrics. This provides better resolution of the baseline fleet and more accurate estimates of the benefits of technology.’’ 511 Toyota also commented in support of various real-world manufacturing complexity constraints employed in the analysis for powertrain redesigns. Toyota commented that model parameters such as redesign cycles and engine sharing across vehicle models place a more realistic limit on the number of engines and transmissions that a manufacturer is capable of introducing. Toyota also commented in support of the constraints that the CAFE model placed on engine resizing, stating that ‘‘there are now more realistic limits placed on the number of engines and transmissions in a powertrain portfolio which better recognizes [how] manufacturers must manage limited engineering resources and control supplier, production, and service costs. Technology sharing and inheritance between vehicle models tends to limit the rate of improvement in a manufacturer’s fleet.’’ Toyota pointed out that this is in contrast to previous analyses in which resizing was too unconstrained, which created an ‘‘unmanageable number of engine configurations within a vehicle platform’’ and spawned cases where ‘‘engine downsizing and power reduction sometimes exceeded limits beyond basic acceleration requirements needed for vehicle safety and customer satisfaction.’’ 512 The above comments from the Alliance, Ford, and Toyota support the methodologies the agencies employed to conduct a performance neutral analysis. These methodologies helped to ensure that multiple performance criteria, including gradeability, are all individually accounted for and maintained when a vehicle powertrain is resized, and that real-world manufacturing complexity constraints are factored in to the agencies’ analysis of feasible pathways manufacturers could take to achieve compliance with CAFE standards. The agencies continue to believe this is a reasonable approach for the aforementioned reasons. Environmental advocacy groups and CARB criticized the CAFE model’s 511 Alliance of Automobile Manufacturers, Attachment ‘‘Full Comment Set,’’ Docket No. NHTSA–2018–0067–12073, at 135. 512 Toyota, Attachment 1, Docket No. NHTSA– 2018–0067–12098, at 6. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 engine resizing constraints and how they affected the acceleration performance criteria. CARB, The International Council on Clean Transportation (ICCT), the Union of Concerned Scientists (UCS), and the American Council for an EnergyEfficient Economy (ACEEE) commented that the CAFE model was not performance neutral, allowing an improvement in performance which reduced the effectiveness of applied fuel-saving technologies and/or increased the cost of compliance. Specifically, ACEEE stated that there appeared to be a shortfall in the fuel economy effectiveness of technology packages, potentially resulting from the effectiveness being ‘‘consumed’’ by additional vehicle performance rather than improvement of fuel economy. Several of these same commenters conducted analyses attempting to quantify the magnitude of these changes in vehicle performance for various vehicle technology classes. CARB commented on the performance shift of several vehicle types. Analyzing the 0–60 acceleration for the medium car non-performance technology class and looking at all cases with resized engines, CARB claimed that ‘‘effectively half of the simulations resulted in improved performance.’’ 513 Focusing on electrified vehicles in that same technology class, CARB stated that ‘‘the data from the Argonne simulations shows that 76 of the 88 strong electrified packages (including P2HPV, SHEVPS, BEV, FCEV, PHEV), where Argonne purposely resized the system to maintain performance neutrality, resulted in notably faster 0 to 60 mph acceleration times and passing times.’’ Specifically regarding parallel hybrid electric vehicles (SHEVP2), CARB stated that all modeled packages resulted in improved performance.514 UCS commented that the NPRM analysis allowed too much change in vehicle performance, stating that ‘‘while some performance creep may be reasonable’’ many performance values show ‘‘an overlap between performance and nonperformance vehicles’’ within the compact car technology class.515 The agencies carefully considered these comments. For the NPRM analysis, the SHEVP2 engines/electric513 California Air Resources Board, Attachment 2, Docket No. NHTSA–2018–0067–11873, at 180. Note that the target acceleration time for medium car non-performance is in fact 9.0 seconds, as indicated in ANL documentation, but was incorrectly reported as 9.4s in NPRM table II–7 in the NPRM. 514 California Air Resources Board, Attachment 2, Docket No. NHTSA–2018–0067–11873, at 186. 515 Union of Concerned Scientists, Attachment 2, Docket No. NHTSA–2018–0067–12039, at 24. PO 00000 Frm 00163 Fmt 4701 Sfmt 4700 24335 motors were resized if the 0–60 acceleration time was worse than the target, but not resized if the acceleration time was better than the target. This approach maintained vehicle performance with a depleted battery (without electric assist) in order to maintain fully the performance and utility characteristics under all conditions, and improved performance when electric assist was available (when the battery is not depleted), such as during the 0–60 mph acceleration. The agencies found that this resulted in some parallel hybrid vehicles having improved 0–60 acceleration times. This approach was initially chosen for the NPRM because the resulting level of improved performance was consistent with observations of how industry had applied SHEVP2 technology. However, in assessing the CARB comment, the agencies balanced the NPRM approach for SHEVP2 performance with the agencies’ criteria of maintaining vehicle functionality and performance when technology is applied. Both could not be fully achieved under all conditions for the case of the SHEVP2. The agencies concluded it is reasonable to maintain performance including electric assist when SHEVP2 technology is applied to a standard (non-performance) vehicle, and therefore the analysis for the final rule allows upsizing and downsizing of the parallel hybrid powertrain (SHEVP2) using the 0.2 seconds window around the target.516 For performance vehicles, the agencies concluded that it remains reasonable to maintain vehicle performance with a depleted battery (without electric assist) in order to maintain fully the performance characteristics under all conditions, and continued to use the NPRM methodology. The refinement for the standard performance SHEVP2 resolved the electrified packages issue identified by CARB, and also addressed most of the change in performance in the overall fleet, including with compact cars as mentioned by UCS. As explained further below, the agencies assessed performance among the alternatives for the final rule analysis. That assessment showed that, with the final rule refinements, 245 out of 255 total resized vehicles (96 percent of vehicles) in the medium non-performance class (same 516 To represent marketplace trends better, the performance class of SHEVP2’s allow acceleration time below 0.2 seconds less than the target, and PHEV20’s and PHEV50’s inherit combustion engine size from the conventional powertrain they are replacing. Further discussion of resizing targets can be found in Chapter 8 of the ANL Model Documentation for the final rule analysis. E:\FR\FM\30APR2.SGM 30APR2 24336 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 class focused on by CARB), had 0–60 mph acceleration times within the plusor-minus 0.2 second window (8.8 to 9.2 seconds).517 The only vehicles outside the window were certain strong electrified vehicles which exceeded 0– 60 the acceleration target as a result of achieving other performance criteria, such as the US06 driving cycles in allelectric-mode.518 The assessment also showed that for the small car class (mentioned by UCS) the acceleration times of performance and non-performance vehicles do not go beyond each other’s targets. For example, the vehicle in the small car class with the very best 0–60 mph time and a conventional powertrain achieves an 8.38 second 0–60 mph time, which is slower than the performance small car baseline of 8 seconds. This vehicle had multiple incremental technologies applied, including for example aerodynamic improvements, and has not reached the threshold for engine resizing.519 After engine resizing, the ‘‘fastest’’ conventional small car has a 0–60 mph time of 9.9 seconds, only 0.1 seconds from the target of 10 seconds.520 CARB also commented on the improvement of ‘‘passing times,’’ or 50– 80 mph high-speed acceleration times. As stated above, an improvement in one or more of the performance criteria is an expected outcome when using the rulemaking analysis methodology that resizes powertrains such that there is no degradation in any of the performance metrics. Consistent with past rulemakings, the agencies do not believe it is appropriate for the rulemaking analysis to show pathways that degrade vehicle performance or utility for one or more of the performance criteria, as doing so would adversely impact functional capability of the vehicle and could lead to customer dissatisfaction. The agencies agree there is very small increase in passing performance for some technology combinations, and believe this is an appropriate outcome. High-speed acceleration is rarely the least-capable performance criteria. CARB, ICCT, UCS, and H–D Systems (HDS), in an attempt to identify a potential cause for changes in performance, commented that the CAFE model should have placed fewer 517 This includes 135 strong electrified vehicles. noted earlier, electrified vehicles had to be capable of successfully completing UDDS or US06 driving cycles in all-electric mode, and in some cases the resulting motor size produced improved acceleration times. 519 Discussion of engine resizing can be found in Section VI.B.3.a)(5). 520 See NPRM Autonomie simulation database for Small cars, Docket ID NHTSA–2018–0067–1855. 518 As VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 constraints on engine resizing. CARB and ICCT commented that engine resizing should have been allowed even at low levels of mass reduction. Comments from CARB, UCS, HDS, and ICCT stated that engine resizing should also have been allowed for other incremental technologies, and within their comments they conducted performance analysis of non-resized cases. CARB claimed that requiring a minimum of 7.1 percent curb weight reduction before engine resizing is a constraint that ‘‘limits the optimization of the technologies being applied.’’ 521 UCS stated that ‘‘a significant share of the benefit of a few percent reduction in mass has gone towards improved performance rather than improved fuel economy, leaving a substantial benefit of mass reduction underutilized and/or uncounted.’’ 522 ICCT also commented that ‘‘when vehicle lightweighting is deployed at up to a 7 percent mass reduction, the engine is not resized even though less power would be needed for the lighter vehicle, meaning any such vehicles inherently are higher performance.’’ 523 UCS and HDS commented on the lack of resizing for technologies other than mass reduction, with HDS stating that ‘‘the Agencies incorrectly limited the efficacy of technologies that reduce tractive load because their modeling does not re-optimize engine performance after applying these technologies.’’ 524 CARB also commented that the lack of resizing when a BISG or CISG system is added ‘‘results in a less than optimized system that does not take full advantage of the mild hybrid system.’’ Similarly, ICCT noted a case in which a Dodge RAM ‘‘did not apply engine downsizing with the BISG system on that truck, so there are also significant performance benefits that should be accounted for, meaning that for constant-performance the fuel 521 California Air Resources Board, Attachment 2, Docket No. NHTSA–2018–0067–11873, at 178. Note, a 7.1% curb weight reduction equates to the agencies’ third level of mass reduction (MR3); additional discussion of engine resizing for mass reduction can be found in Section VI.B.3.a)(4) Autonomie Sizes Powertrains for Full Vehicle Simulation] and in the ANL Model Documentation for the final rule analysis. 522 Union of Concerned Scientists, Attachment 2, Docket No. NHTSA–2018–0067–12039, at 11. 523 International Council on Clean Transportation, Attachment 3, Docket No. NHTSA–2018–0067– 11741, at I–50. 524 H–D Systems, Attachment 1, Docket No. NHTSA–2018–0067–12395, at 4. For reference, technologies that reduce tractive road load include mass reduction, aerodynamic drag reduction, and tire rolling resistance reduction. PO 00000 Frm 00164 Fmt 4701 Sfmt 4700 consumption reduction would be even greater.’’ 525 CARB further commented on the performance improvement in cases without engine resizing by stating that ‘‘94 percent of the packages modeled result in improved performance,’’ and that for these non-resized cases that were actually adopted by a vehicle in the simulation, ‘‘fewer than 20 percent maintained baseline performance with gains of 2 percent or less in acceleration time.’’ 526 Referring specifically to nonresized electrified vehicles, CARB also stated that ‘‘44,878 of the 53,818 packages, or greater than 83 percent, result in improved performance.’’ 527 CARB also commented that engine sharing across different vehicles within a platform, which in some cases may constrain resizing for a member of that platform, should not dictate that these engines must remain identical in all aspects, and that ‘‘this overly restrictive sharing of identical engines newly imposed in the CAFE Model is not consistent with today’s industry practices and results in less optimal engine sizing and causes a systematic overestimation of technology costs to meet the existing standards.’’ 528 The agencies note broadly, in response to these comments, that when conducting an analysis which balances performance neutrality against the realities faced by manufacturers, such as manufacturing complexity, economies of scale, and maintaining the full range of performance criteria, it is inevitable to observe at least some minor shift in vehicle performance. For example, if a new transmission is applied to a vehicle, the greater number of gear ratios helps the engine run in its most efficient range which improves fuel economy, but also helps the engine to run in the optimal ‘‘power band’’ which improves performance. Thus, the technology can provide both improved fuel economy and performance. Another example is applying a small amount of mass reduction that improves both fuel economy and performance by a small amount. Resizing the engine to maintain performance in these examples would require a unique engine displacement that is only slightly different than the baseline engine. While engine resizing in these incremental cases could have some small benefit to fuel economy, the 525 International Council on Clean Transportation, Attachment 3, Docket No. NHTSA–2018–0067– 11741, at I–24. 526 California Air Resources Board, Attachment 2, Docket No. NHTSA–2018–0067–11873, at 183. 527 California Air Resources Board, Attachment 2, Docket No. NHTSA–2018–0067–11873, at 187. 528 California Air Resources Board, Attachment 2, Docket No. NHTSA–2018–0067–11873, at 185. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 gains may not justify the costs of producing unique niche engines for each combination of technologies. If manufacturers were to produce marginally downsized engines to complement every small increment of mass reduction or technology, the resulting large number of engine variants that would need to be manufactured would cause a substantial increase in manufacturing complexity, and require significant changes to manufacturing and assembly plants and equipment.529 The high costs would be economically infeasible. Also, as noted in the NPRM, the 2015 NAS report stated that ‘‘[f]or small (under 5 percent [of curb weight]) changes in mass, resizing the engine may not be justified, but as the reduction in mass increases (greater than 10 percent [of curb weight]), it becomes more important for certain vehicles to resize the engine and seek secondary mass reduction opportunities.’’ 530 In consideration of both the NAS report and comments received from manufacturers, the agencies determined it would be reasonable to allow allows engine resizing upon adoption of 7.1 percent, 10.7 percent, 14.2 percent, and 20 percent curb weight reduction, but not at 3.6 percent and 5.3 percent.531 Resizing is also allowed upon changes in powertrain type or the inheritance of a powertrain from another vehicle in the same platform. The increments of these higher levels of mass reduction, or complete powertrain changes, more appropriately match the typical engine displacement increments that are available in a manufacturer’s engine portfolio. The agencies point to the comments from manufacturers, discussed further above, which support the agencies’ assertion that the CAFE model’s resizing constraints are appropriate. As discussed previously, Toyota commented that this approach better considers the constraints of engineering 529 For example, each unique engine would require unique internal components such as crankshafts, pistons, and connecting rods, as well as unique engine calibrations for each displacement. Assembly plants would need to stock and feed additional unique engines to the stations where engines are dressed and inserted into vehicles. 530 National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC—The National Academies Press. http://nap.edu/12924. 531 These curb weight reductions equate to the following levels of mass reduction as defined in the analysis: MR3, MR4, MR5 and MR6, but not MR1 and MR2; additional discussion of engine resizing for mass reduction can be found in Section VI.B.3.a)(6) Autonomie Sizes Powertrains for Full Vehicle Simulation. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 resources and manufacturing costs and results in a more realistic number of engines and transmissions.532 The Alliance also commented on the benefit of constraining engine resizing, stating that ‘‘the platform and engine sharing methodology in the model better replicates reality by making available to each manufacturer only a finite number of engine displacements, helping to prevent unrealistically ‘over-optimized’ engine sizing.’’ 533 Another comment from CARB stated that engine resizing ‘‘was only simulated for cases where those levels of mass reduction were applied, in the absence of virtually all other technology or efficiency improvements.’’ 534 The agencies do not agree that resizing should be simulated in all cases which involve small incremental technologies. In the final rule analysis, vehicles can have engines resized at four (out of six) levels of mass reduction technology, during a vehicle redesign cycle which changes powertrain architecture, and by inheritance during a vehicle refresh cycle. As discussed previously, the application of small incremental technologies such as reductions in aerodynamic drag or rolling resistance does not justify the high cost and complexity of producing additional varieties of engine sizes. Accordingly, for each curb weight reduction level of 7.1 percent or above and for each vehicle technology class, Autonomie sized a baseline engine by running a simulation of a vehicle without incremental technologies applied; then, those baseline engines were inherited by all other simulations using the same levels of curb weight reduction, which also added any variety of incremental technologies.535 For further clarification, in any case in which a vehicle adopts a 7.1 percent or more curb weight reduction, no matter what other technologies were already present or are added to the vehicle in conjunction with the mass reduction, that vehicle will receive an engine which has been appropriately sized for the newly applied mass reduction 532 Toyota, Attachment 1, Docket No. NHTSA– 2018–0067–12098, at 6. 533 Alliance of Automobile Manufacturers, Attachment ‘‘Full Comment Set,’’ Docket No. NHTSA–2018–0067–12073, at 140. 534 California Air Resources Board, Attachment 2, Docket No. NHTSA–2018–0067–11873, at 178. 535 In the Autonomie simulation database files, the simulations which establish baseline sized engines are marked ‘‘yes’’ in the ‘‘VehicleSized’’ column, and the subsequent simulations which use this engine and add other incremental technologies are marked ‘‘inherited.’’ For a list of Autonomie simulation database files, see Table VI–4 Autonomie Simulation Database Output Files in Section VI.A.7 Structure of Model Inputs and Outputs. PO 00000 Frm 00165 Fmt 4701 Sfmt 4700 24337 level.536 This can be observed in the Autonomie simulation databases by tracking the ‘‘EngineMaxPower’’ column (not the ‘‘VehicleSized’’ column). Finally, ICCT claimed that the agencies did not sufficiently report performance-related vehicle information. ICCT commented that the output files did not show data on ‘‘engine displacement, the maximum power of each engine, the maximum torque of each engine, the initial and final curb weight of each vehicle (in absolute terms), and estimated 0–60 mph acceleration.’’ ICCT claimed that because this data was not found, the agencies are ‘‘showing that they have not even attempted to analyze accurately the future year fleet for their performance’’ and that ‘‘the agencies are intentionally burying a critical assumption, whereby their future fleet has not been appropriately downsized, and it therefore has greatly increased utility and performance characteristics.’’ 537 In fact, for the NPRM, and again for this final rule, the agencies did analyze vehicle performance and have made the data available to the public. An indication of the actual engine displacement change is available by noting the displacements used in Automonie simulation database for each of the technology states. The displacements reported in Autonomie are used by the full-vehicle-simulation within the Autonomie model, and while they do not directly represent each specific vehicle’s actual engine sizes, they do fully reflect the relative change in engine size that is applied to each vehicle. It is the relative change in engine size that is relevant for the analysis. Similarly, the vehicle power and torque used by the full vehicle simulations are reported in the Autonomie simulation databases; their values and relative change across an engine resizing event can be observed. Initial and final curb weights for the analysis fleet are reported in Vehicles Report output file column titled ‘‘CW Initial’’ and ‘‘CW,’’ respectively. The time required for 0–60 mph acceleration is reported in the Autonomie simulation database files. A detailed description of the engine resizing methodology is available in the Argonne Model 536 For example, if a vehicle possesses MR2, AERO1, and ROLL1 and subsequently adopts MR3, AERO1, ROLL2, the vehicle will adopt the lower engine power level associated with MR3. As a counter example, if a vehicle possesses MR3, ROLL1, and AERO1 and subsequently adopts MR3, ROLL1, AERO2, the engine will not be resized and it will retain the power level associated with MR3. 537 International Council on Clean Transportation, Attachment 3, Docket No. NHTSA–2018–0067– 11741, at I–74. E:\FR\FM\30APR2.SGM 30APR2 24338 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 Documentation, which explains how vehicle characteristics are used to calculate powertrain size.538 These data and information that are available in the Autonomie and CAFE model documentation provide the information needed to analyze performance, and in fact, this is evidenced by the statements of numerous commenters discussed in this section. The agencies have conducted their own performance analysis, which is discussed further below, using the same data documentation mentioned here. Updates to the CAFE model have minimized performance shift over the simulated model years, and have eliminated performance differences between simulated standards. The Autonomie simulation updates, discussed previously, were included in the final rule analysis, and have resulted in average performance that is similar across the regulatory alternatives. Because the regulatory analysis compares differences in impacts among the alternatives, the agencies believe that having consistent performance across the alternatives is an important aspect of performance neutrality. If the vehicle fleet had performance gains which varied significantly depending on the alternative, performance differences would impact the comparability of the simulations. Using the NPRM CAFE model data, the agencies analyzed the sales-weighted average 0–60 performance of the entire simulated vehicle fleet for MYs 2016 and 2029, and identified that the Augural standards had 4.7 percent better 0–60 mph acceleration time compared to the NPRM preferred alternative, which had no changes in standards in MYs 2021– 2026.539 This assessment confirmed the observations of the various commenters. With the refinements that were incorporated for the final rule, similar analysis showed that the Augural standards had a negligible 0.1 percent difference in 0–60 mph acceleration 538 See Chapter 8 of the ANL Model documentation for the final rule analysis. 539 The agencies’ analysis matched all MY 2016 and MY 2029 vehicles in the NPRM Vehicles Report output file, under both the Augural standards and preferred alternative, with the appropriate 0–60 mph acceleration time from the NPRM Autonomie simulation databases. This was done by examining each vehicle’s assigned technologies, finding the Autonomie simulation with the corresponding set of technologies, and extracting that simulation’s 0– 60 mph acceleration time. This process effectively assigned a 0–60 time to every vehicle in the fleet for four scenarios: (1) MY 2016 under augural standards, (2) MY 2016 under the preferred alternative, (3) MY 2029 under augural standards, and (4) MY 2029 under the preferred alternative. For each scenario, an overall fleet-wide weighted average 0–60 time was calculated, using each vehicle’s MY2016 sales volumes as the weight. For more information, see the FRIA Section VI. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 time compared to the NPRM preferred alternative.540 The updates applied to the final rule Autonomie simulations also resulted in further minimizing the performance change across model years. As the agencies attempted to minimize this performance shift occurring ‘‘over time,’’ it was also acknowledged that a small increase would be expected and would be reasonable. This increase is attributed to the analysis recognizing the practical constraints on the number of unique engine displacements manufacturers can implement, and therefore not resizing powertrains for every individual technology and every combination of technologies when the performance impacts are small. Perfectly equal performance with 0 percent change would not be achievable while accounting for these real world resizing constraints. The performance analysis in the 2011 NAS report shared a similar view on performance changes, stating that ‘‘truly equal performance involves nearly equal values . . . within 5 percent.’’ 541 In response to comments, using NPRM CAFE model data, the agencies analyzed the sales-weighted average 0–60 performance of the entire simulated vehicle fleet, and identified that the performance increase from MYs 2016 and 2029 was 7.5 percent under Augural Standards and 3.1 percent under the NPRM preferred alternative standards. The agencies conducted a similar analysis using final rule data and found the performance increase over time from MYs 2017 to 2029 was 3.9 percent for Augural Standards and 4.0 percent for the NPRM preferred alternative standards. The agencies determined this change in performance is reasonable and note it is within the 5 percent bound in discussed by NAS in its 2011 report. This assessment shows that for the final rule analysis, performance is neutral across regulatory alternatives and across the simulated model years allowing for fair, direct comparison among the alternatives. (7) How the Agencies Simulated Vehicle Models on Test Cycles After vehicle models are built for every combination of technologies and vehicle classes represented in the analysis, Autonomie simulates their 540 This updated analysis used the FRM CAFE Model Vehicles Report output file and the FRM Autonomie simulation databases. The final rule analysis introduced an updated MY 2017 fleet as a starting point, replacing the NPRM 2016MY fleet. For more information, see the FRIA Chapter VI. 541 National Research Council. 2011. Assessment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC—The National Academies Press, at 62. http://nap.edu/12924. PO 00000 Frm 00166 Fmt 4701 Sfmt 4700 performance on test cycles to calculate the effectiveness improvement of the fuel-economy-improving technologies that have been added to the vehicle. Discussed earlier, the agencies minimize the impact of potential variation in determining effectiveness by using a series of tests and procedures specified by federal law and regulations under controlled conditions. Autonomie simulates vehicles in a very similar process as the test procedures and energy consumption calculations that manufacturers must use for CAFE and CO2 compliance.542 543 544 Argonne simulated each vehicle model on several test procedures to evaluate effectiveness. For vehicles with conventional powertrains and micro hybrids, Autonomie simulates the vehicles on EPA 2-cycle test procedures and guidelines.545 For mild and full hybrid electric vehicles and FCVs, Autonomie simulates the vehicles using the same EPA 2-cycle test procedure and guidelines, and the drive cycles are repeated until the initial and final state of charge are within a SAE J1711 tolerance. For PHEVs, Autonomie simulates vehicles in similar procedures and guidelines as SAE J1711.546 For BEVs Autonomie simulates vehicles in similar procedures and guidelines as SAE J1634.547 b) Selection of One Full-Vehicle Modeling and Simulation Tool The NPRM described tools that the agencies previously used to estimate technology effectiveness. For the analysis supporting the 2012 final rule for MYs 2017 and beyond, the agencies used technology effectiveness estimates from EPA’s lumped parameter model (LPM). The LPM was calibrated using data from vehicle simulation work performed by Ricardo Engineering.548 542 EPA, ‘‘How Vehicles are Tested.’’ https:// www.fueleconomy.gov/feg/how_tested.shtml. Last accessed Nov 14, 2019. 543 ANL model documentation for final rule Chapter 6. Test Procedures and Energy Consumption Calculations. 544 EPA Guidance Letter. ‘‘EPA Test Procedures for Electric Vehicles and Plug-in Hybrids.’’ Nov. 14, 2017. https://www.fueleconomy.gov/feg/pdfs/ EPA%20test%20procedure%20for%20EVs-PHEVs11-14-2017.pdf. Last accessed Nov. 7, 2019. 545 40 CFR part 600. 546 PHEV testing is broken into several phased based on SAE J1711. Charge-Sustaining on the City cycle, Charge-Sustaining on the HWFET cycle, Charge-Depleting on the City and HWFET cycles. 547 SAE J1634. ‘‘Battery Electric Vehicle Energy Consumption and Range Test Procedure.’’ July 12, 2017. 548 Response to Peer Review of: Ricardo Computer Simulation of Light-Duty Vehicle Technologies for Greenhouse Gas Emission Reduction in the 2020– 2025 Timeframe, EPA–420–R–11–021 (December 2011), available at https://nepis.epa.gov/Exe/ E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 The agencies also used full vehicle simulation modeling data from Autonomie vehicle simulations performed by Argonne for mild hybrid and advanced transmission effectiveness estimates.549 550 For the 2016 Draft TAR analysis, EPA and NHTSA used two different full system simulation programs for complementary but separate analyses. NHTSA used Argonne’s Autonomie tool, described in detail above, with engine map inputs developed by IAV using GT-Power in 2014, and updated in 2016.551 552 553 Argonne, in coordination with NHTSA, developed a methodology for large scale simulation using Autonomie and distributed computing, thus overcoming one of the challenges to full vehicle simulation that the NAS committee outlined in its 2015 report and implementing a recommendation that the agencies use full-vehicle simulation to improve the analysis method of estimating technology effectiveness.554 EPA used a limited number of full-vehicle simulations performed using its ALPHA model, an EPA-developed full-vehicle simulation model,555 to calibrate the LPM, used to ZyPDF.cgi/P100D5BX.PDF? Dockey=P100D5BX.PDF. 549 Joint TSD: Final Rulemaking for 2017–2025 Light-Duty Vehicle Greenhouse Emission Standards and Corporate Average Fuel Economy Standards. August 2012. EPA–420–R–12–901.3.3.1.3 Argonne National Laboratory Simulation Study p. 3–69. 550 Moawad, A. and Rousseau, A., ‘‘Impact of Electric Drive Vehicle Technologies on Fuel Efficiency,’’ Energy Systems Division, Argonne National Laboratory, ANL/ESD/12–7, August 2012. 551 GT-Power Engine Simulation Software. https://www.gtisoft.com/gt-suite-applications/ propulsion-systems/gt-power-engine-simulationsoftware/. Last accessed Oct. 10, 2019. 552 2016 Draft TAR Engine Maps by IAV Automotive Engineering using GT-Power. https:// www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/ IAV_EngineMaps_Details.xlsx. Lass accessed Oct. 10, 2019. 553 NHTSA–2018–0067–0003. ANL—Summary of Main Component Performance Assumptions NPRM. 554 See National Research Council. 2015. Cost, Effectiveness, and Deployment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press [hereinafter ‘‘2015 NAS Report’’] at p. 263, available at https:// www.nap.edu/catalog/21744/cost-effectivenessand-deployment-of-fuel-economy-technologies-forlight-duty-vehicles (last accessed June 21, 2018). See also A. Moawad, A. Rousseau, P. Balaprakash, S. Wild, ‘‘Novel Large Scale Simulation Process to Support DOT’s CAFE Modeling System,’’ International Journal of Automotive Technology (IJAT), Paper No. 220150349, Nov 2015; Pagerit, S., Sharper, P., Rousseau, A., Sun, Q. Kropinski, M. Clark, N., Torossian, J., Hellestrand, G., ‘‘Rapid Partitioning, Automatic Assembly and Multicore Simulation of Distributed Vehicle Systems.’’ ANL, General Motors, EST Embedded Systems Technology. 2015. https://www.autonomie.net/ docs/5%20-%20Presentations/VPPC2015_ppt.pdf. Last accessed Dec. 9, 2019. 555 See Lee, B., S. Lee, J. Cherry, A. Neam, J. Sanchez, and E. Nam. 2013. Development of Advanced Light-Duty Powertrain and Hybrid VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 estimate technology effectiveness. EPA also used the same modeling approach for its Proposed Determination analysis.556 In the subsequent August 2017 Request for Comment on Reconsideration of the Final Determination of the Mid-Term Evaluation of Greenhouse Gas Emissions Standards for MY 2022–2025 Light-Duty Vehicles, the agencies requested comments on whether EPA should use alternative methodologies and modeling, including the Autonomie full-vehicle simulation tool and DOT’s CAFE model, for the analysis that would accompany its revised Final Determination.557 As discussed in the NPRM, stakeholders questioned the efficacy of the combined outputs and assumptions of the LPM and ALPHA,558 especially as the tools were used to evaluate increasingly heterogeneous combinations of technologies in the vehicle fleet.559 More specifically, the Auto Alliance noted that their previous comments to the midterm evaluation, in addition to comments from individual manufacturers, highlighted multiple concerns with EPA’s ALPHA model that were unresolved, but addressed in Autonomie.560 First, the Alliance expressed concern over ALPHA modeling errors related to road load reductions, stating that an error derived from how mass and coast-down coefficients were updated when mass, tire and aero improvements were made resulted in benefits overstated by 3 percent to 11 percent for all vehicle types. Next, the Alliance repeated its concern that EPA should consider topgear gradeability as one of its performance metrics to maintain Analysis Tool. SAE Technical Paper 2013–01–0808. doi: 10.4271/2013–01–0808. 556 Proposed Determination on the Appropriateness of the Model Year 2022–2025 Light-Duty Vehicle Greenhouse Gas Emissions Standards under the Midterm Evaluation, EPA– 420–R–16–020 (November 2016), available at https://nepis.epa.gov/Exe/ZyPDF.cgi? Dockey=P100Q3DO.pdf; Final Determination on the Appropriateness of the Model Year 2022–2025 Light-Duty Vehicle Greenhouse Gas Emissions Standards under the Midterm Evaluation, EPA– 420–R–17–001 (January 2017), available at https:// nepis.epa.gov/Exe/ZyPDF.cgi?Dockey= P100QQ91.pdf. 557 82 FR 39551 (Aug. 21, 2017). 558 83 FR 43022 (‘‘At NHTSA–2016–0068–0082, p. 49, FCA provided the following comments, ‘‘FCA believes EPA is overestimating the benefits of technology. As the LPM is calibrated to those projections, so too is the LPM too optimistic.’’ FCA also shared the chart, ‘LPM vs. Actual for 8 Speed Transmissions.’ ’’). 559 83 FR 43022 (referencing Automotive News ‘‘CAFE math gets trickier as industry innovates’’ (Kulisch), March 26, 2018.). 560 EPA–HQ–OAR–2015–0827–9194, at p. 36–44. PO 00000 Frm 00167 Fmt 4701 Sfmt 4700 24339 functionality, noting that EPA had acknowledged the industry’s comments in the Proposed Determination, ‘‘but generally dismissed the auto industry concerns.’’ Additional analysis by EPA in its Response to Comments document did not allay the Alliance’s concerns,561 as the Alliance concluded that ‘‘[c]onsistent with the National Academy of Sciences recommendation from 2011, EPA should monitor gradeability to ensure minimum performance.’’ Furthermore, the Alliance stated that ALPHA vehicle technology walks provided in response to manufacturer comments on the Proposed Determination did not correctly predict cumulative effectiveness when compared to technologies in real world applications. The Alliance stated that many of the individual technologies and assumptions used by ALPHA overestimated technology effectiveness and were derived from questionable sources. As an example, the Alliance referenced an engine map used by EPA to represent the Honda L15B7 engine, where the engine map data was collected by ‘‘(1) taking a picture of an SAE document containing an image of the engine map, and then (2) ‘digitizing’ the image by ‘tracing image contours’ ’’ (citing EPA’s ALPHA documentation). The Alliance could not definitively state whether the ‘‘digitization’’ process, lack of detail in the source image, or another factor were the reasons that some regions of overestimated efficiency were observed in the engine map, but concluded that ‘‘the use of this map should be discontinued within ALPHA,’’ and ‘‘any analysis conducted with it is highly questionable.’’ Based on these concerns and others, the Alliance recommended that Autonomie be used to inform the downstream cost optimization models (i.e., the CAFE model and/or OMEGA). Global Automakers argued that NHTSA’s CAFE model, which incorporates data from Autonomie simulations, provided a more transparent and discrete step through each of the modeling scenarios.562 Global pointed out that the LPM is ‘‘of particular concern due to its simplified technology projection processes,’’ and it ‘‘propagates fundamentally flawed 561 The Alliance noted that in higher-gear-count transmissions, like 8-speed automatics, modeled by ALPHA with an expanded ratio spread to achieve fuel economy, are concerning for gradeability. Additionally, infinite engine downsizing along with expanded ratio spread transmission, in real world gradeability may cause further deteriorate as modeled in ALPHA, which leads to inflated effectiveness values for powertrains that would not meet customer demands. 562 EPA–HQ–OAR–2015–0827–9728, at 14. E:\FR\FM\30APR2.SGM 30APR2 24340 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 content into the ALPHA and OMEGA models and therefore cannot accurately assess the efficacy of fuel economy technologies.’’ Global did note that EPA ‘‘plans to abandon its reliance on LPM in favor of another modeling approach,’’ referring to the RSE,563 but stated that ‘‘EPA must provide stakeholders with adequate time to evaluate the updated modeling approach, ensure it is analytically robust, and provide meaningful feedback.’’ Global Automakers concluded that EPA’s engine mapping and tear-down analyses have played an important role in generating publicly-available information, and stated that the data should be integrated into the Autonomie model. On the other hand, other stakeholders commented that EPA’s ALPHA modeling should continue to be used, for procedural reasons like, ‘‘[i]t would appear arbitrary for EPA now, after five years of modeling based on ALPHA, to declare it can no longer use its internally developed modeling tools and must rely solely on the Autonomie model,’’ and ‘‘[t]he ALPHA model is inextricably built into the regulatory and technical process. It will require years of new analysis to replace the many ALPHA and OMEGA modeling inputs and outputs that permeate the entire rulemaking process, should EPA suddenly decide to change its models.’’ 564 Commenters also cited technical reasons to use ALPHA, like EPA’s progress benchmarking and validating the ALPHA model to over fifteen various MY 2013–2015 vehicles,565 and that technologies like the ‘‘Atkinson 2’’ engine technology were not considered in NHTSA’s compliance modeling.566 Commenters also cited that ALPHA was created to be publicly available, open-sourced, and peer-reviewed, ‘‘to allow for transparency to both automakers and public stakeholders, without hidden and proprietary aspects that are present in commercial modeling products.’’ 567 The agencies described in the NPRM that after having reviewed comments about whether EPA should use 563 See Moskalik, A., Bolon, K., Newman, K., and Cherry, J. ‘‘Representing GHG Reduction Technologies in the Future Fleet with Full Vehicle Simulation,’’ SAE Technical Paper 2018–01–1273, 2018, doi:10.4271/2018–01–1273. Since 2018, EPA has employed vehicle-class-specific response surface equations automatically generated from a large number of ALPHA runs to more readily apply large-scale simulation results, which eliminated the need for manual calibration of effectiveness values between ALPHA and the LPM. 564 EPA–HQ–OAR–2015–9826, at 39–40. 565 EPA–HQ–OAR–2015–9826, at 40. 566 EPA–HQ–OAR–2015–9197, at 28. 567 EPA–HQ–OAR–2015–9826, at 38. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 alternative methodologies and modeling, and after having considered the matter fully, the agencies determined it was reasonable and appropriate to use Autonomie for fullvehicle simulation.568 The agencies stated that nothing in Section 202(a) of the Clean Air Act (CAA) mandated that EPA use any specific model or set of models for analysis of potential CO2 standards for light duty vehicles. The agencies also distinguished the models and the inputs used to populate them; specifically, comments presented as criticisms of the models, such as ‘‘Atkinson 2’’ engine technology not considered in the compliance modeling, actually concerned model inputs.569 With regards to modeling technology effectiveness, the agencies concluded that, although the CAFE model requires no specific approach to developing effectiveness inputs, the National Academy of Sciences recommended, and stakeholders have commented, that full-vehicle simulation provides the best balance between realism and practicality. As stated above, Argonne has spent several years developing, applying, and expanding means to use distributed computing to exercise its Autonomie full-vehicle simulation tool at the scale necessary for realistic analysis of technologies that could be used to comply with CAFE and CO2 standards, and this scalability and related flexibility (in terms of expanding the set of technologies to be simulated) makes Autonomie well-suited for developing inputs to the CAFE model. In response to the NPRM, the Auto Alliance commented that NHTSA’s modeling and analysis tools are superior to EPA’s, noting that NHTSA’s tools have had a significant lead in their development.570 The Alliance pointed out that Autonomie was developed from the beginning to address the complex task of combining two power sources in a hybrid powertrain, while EPA’s ALPHA model had not been validated or used to simulate hybrid powertrains. While both models are physics-based forward looking vehicle simulators, the Alliance commented that Autonomie is fully documented with available training, while ALPHA ‘‘has not been documented with any instructions making it difficult for users outside of EPA to run and interpret the model.’’ The Alliance also mentioned specific improvements in the Autonomie simulations since the Draft TAR, including expanded performance classes to better consider vehicle 568 83 FR 43001. FR 43002. 570 NHTSA–2018–0067–12073. 569 83 PO 00000 Frm 00168 Fmt 4701 Sfmt 4700 performance characteristics, the inclusion of gradeability as a performance metric, as recommended by the NAS, the inclusion of new fuel economy technologies, and the removal of unproven technologies. The Alliance, Global Automakers, and other automakers writing separately all stated that the agencies should use one simulation and modeling tool for analysis.571 572 The Alliance stated that since both the Autonomie and ALPHA modeling systems answer essentially the same questions, using both systems leads to inconsistencies and conflicts, and is inefficient and counterproductive. The agencies agree with the Alliance that the fully developed and validated Autonomie model fulfills the agencies’ analytical needs for full-vehicle modeling and simulation. The agencies also agree that it is counterintuitive to have two separate models conducting the same work. Some commenters stated that broadly, EPA was required to conduct its own technical analysis and rely on its own models to do so.573 Those comments are addressed in Section IV. Regarding the merits of EPA’s models, and based on previous inputs and assumptions used to populate those models, ICCT commented that ‘‘[b]ased on the ICCT’s global analysis of vehicle regulations, the EPA’s physics-based ALPHA modeling offers the most sophisticated and thorough modeling of the applicable technologies that has ever been conducted.’’ ICCT listed several reasons for this, including that the EPA modeling is based on systematic modeling of technologies and their synergies; it was built and improved upon by extensive modeling by and with Ricardo (an engineering consulting firm); it incorporated National Academies input at multiple stages; it has included many peer reviews at many stages of the modeling and the associated technical reports published by engineers in many technical journal articles and conference proceedings; and EPA’s Draft TAR analysis, which used ALPHA, used state-of-the-art engine maps based on benchmarked high-efficiency engines. ICCT concluded 571 NHTSA–2018–0067–12073; NHTSA–2018– 0067–12032. Comments of the Association of Global Automakers, Inc. on the Safer Affordable FuelEfficient Vehicles Rule Docket ID Numbers: NHTSA–2018–0067 and EPA–HQ–OAR–2018–0283 October 26, 2018. 572 NHTSA–2018–0067–11943. FCA Comments on The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021–2026 Passenger Cars and Light Trucks Notice of Proposed Rulemaking. 573 NHTSA–2018–0067–12000; NHTSA–2018– 0067–12039. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations that ‘‘[d]espite these rigorous advances in vehicle simulation modeling, it appears that the agencies have inexplicably abandoned this approach, expressly disregarding the EPA benchmarked engines, ALPHA modeling, and all its enhancements since the last rulemaking.’’ The hallmarks ICCT lists regarding the ALPHA modeling are equally applicable to Autonomie.574 Autonomie is also based on systematic modeling of technologies and their synergies when combined as packages. The U.S. Department of Energy created Autonomie, and over the past two decades, helped to develop and mature the processes and inputs used to represent real-world vehicles using continuous feedback from the tool’s worldwide user base of vehicle manufacturers, suppliers, government agencies, and other organizations. Moreover, using Autonomie brings the agencies closer to the NAS Committee’s stated goal of ‘‘full system simulation modeling for every important technology pathway and for every vehicle class.’’ 575 While the NAS Committee originally thought that full vehicle simulation modeling would not be feasible for the thousands of vehicles in the analysis fleets because the technologies present on the vehicles might differ from the configurations used in the simulation modeling,576 Argonne has developed a process to simulate explicitly every important technology pathway for every vehicle class. Moreover, although separate from the Autonomie model itself, the Autonomie modeling for this rulemaking incorporated other NAS committee recommendations regarding full vehicle simulation inputs and input assumptions, including using enginemodel-generated maps derived from a validated baseline map in which all parameters except the new technology of interest are held constant.577 As discussed further below and in VI.C.1 Engine Paths, this is one reason why the IAV maps were used instead of the EPA maps, and the agencies instead referenced EPA’s engine maps to corroborate the Autonomie effectiveness results. The IAV maps are enginemodel-generated maps derived from a validated baseline map in which all parameters except the new technology of interest are held constant. While EPA’s engine maps benchmarking specific vehicles’ engines incorporate 574 See Theo LeSieg, Ten Apples Up On Top! (1961), at 4–32. 575 2015 NAS Report at 358. 576 2015 NAS Report at 359. 577 NAS Recommendation 2.1. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 multiple technologies, for example including improvements in engine friction and reduction in accessory parasitic loads, comparisons presented in Section VI.C.1 showed that engine maps developed by IAV, while not exactly the same, are representative of EPA’s engine benchmarking data. In addition, both ALPHA and Autonomie have been used to support analyses that have been published in technical journal articles and conference proceedings, but those analyses differ fundamentally because of the nature of the tools. ALPHA was developed as a tool to be used by EPA’s in-house experts.578 As EPA stated in the ALPHA model peer review,579 ‘‘ALPHA is not intended to be a commercial product or supported for wide external usage as a development tool.’’ 580 Accordingly, EPA experts have published several peer-reviewed journal articles using ALPHA and have presented the results of those papers at conference proceedings.581 To explore ICCT’s comments on the importance of peer review further, it is important to take the actual substantive content of the ALPHA peer review into account.582 One reviewer raised significant questions over the availability of ALPHA documentation, stating ‘‘[t]here is an overall lack of detail on key technical features that are new in the model,’’ and ‘‘[w]e were not able to find any information on how the model handles component weight changes.’’ Reviewers also raised questions related to model readiness, stating ‘‘[a]ccording to the documentation review, ALPHA’s stop/ start modeling appears to be very simplistic.’’ Moreover, when running ALPHA simulations, the reviewer noted the results ‘‘strongly suggest that the model has errors in the underlying equations or coding with respect to all of the load reductions.’’ Also, one reviewer said the following of ALPHA: ‘‘A specific simulation runtime is 578 ALPHA Peer Review, at 4–1. comments intimate that ALPHA has been peer reviewed at many stages of the modeling; although EPA has published several peer-reviewed technical papers, the ALPHA model itself has been subject to one peer review. See Peer Review of ALPHA Full Vehicle Simulation Model, available at https://nepis.epa.gov/Exe/ZyPdf.cgi? Dockey=P100PUKT.pdf. 580 ALPHA Peer Review, at 4–2. 581 See, e.g., Dekraker, P., Kargul, J., Moskalik, A., Newman, K. et al., ‘‘Fleet-Level Modeling of Real World Factors Influencing Greenhouse Gas Emission Simulation in ALPHA,’’ SAE Int. J. Fuels Lubr. 10(1):2017, doi:10.4271/2017–01–0899. 582 EPA. ‘‘Peer Review of ALPHA Full Vehicle Simulation Model.’’ EPA–420–R–16–013. October 2016. https://nepis.epa.gov/Exe/ZyPdf.cgi? Dockey=P100PUKT.pdf. Last accessed Nov 18, 2019. 579 ICCT’s PO 00000 Frm 00169 Fmt 4701 Sfmt 4700 24341 significantly high, more than 10 mins. without providing any indication to the user progress made so far. A fairly more complicated model such as Autonomie available even with enhanced capabilities is significantly faster today.’’ 583 The peer reviewer’s assessment of Autonomie as a more complicated model with enhanced capabilities is not surprising, given Autonomie’s history of development. Autonomie is a commercial tool with more than 275 worldwide organizational users, including vehicle manufacturers, suppliers, government agencies, and nonprofit organizations having licensed and used Autonomie. Both Autonomie’s creators and user base unaffiliated with Argonne have published over 100 papers, including peer-reviewed papers in journals, related to Autonomie validation and other studies.584 585 One could even argue that the tool has been continuously peer reviewed by these thousands of experts over the past two decades. In fact, in responding to a peer review comment on the ALPHA model’s underlying equations and coding with respect to road load reductions, EPA noted that Autonomie had been used as a reference system simulation tool to validate ALPHA model results.586 Outside of formal peer-reviewed studies, Autonomie has been used by organizations like ICCT to support policy documents, position briefs, and white papers assessing the potential of future efficiency technologies to meet potential regulatory requirements,587 583 Peer Review of ALPHA Full Vehicle Simulation Model, at C–4, available at https:// nepis.epa.gov/Exe/ZyPdf.cgi? Dockey=P100PUKT.pdf. 584 At least 15 peer-reviewed papers authored by ANL experts have been referenced throughout this Section, and others can be found at SAE International’s website, https://www.sae.org/, using the search bar for ‘‘Autonomie.’’ 585 See, e.g., Haupt, T., Henley, G., Card, A., Mazzola, M. et al., ‘‘Near Automatic Translation of Autonomie-Based Power Train Architectures for Multi-Physics Simulations Using High Performance Computing,’’ SAE Int. J. Commer. Veh. 10(2):483– 488, 2017, https://doi.org/10.4271/2017-01-0267; Samadani, E., Lo, J., Fowler, M., Fraser, R. et al., ‘‘Impact of Temperature on the A123 Li-Ion Battery Performance and Hybrid Electric Vehicle Range,’’ SAE Technical Paper 2013–01–1521, 2013, https:// doi.org/10.4271/2013-01-1521. 586 Peer Review of ALPHA Full Vehicle Simulation Model, at 4–14 and 4–15, available at https://nepis.epa.gov/Exe/ZyPdf.cgi?Dockey= P100PUKT.pdf. 587 See, e.g., Oscar Delgado and Nic Lutsey, Advanced Tractor-Trailer Efficiency Technology Potential in the 2020–2030 Timeframe (April 2015), available at https://theicct.org/sites/default/files/ publications/ICCT_ATTEST_20150420.pdf; Ben Sharpe, Cost-Effectiveness of Engine Technologies for a Potential Heavy-Duty Vehicle Fuel Efficiency Regulation in India (June 2015), available at https:// E:\FR\FM\30APR2.SGM Continued 30APR2 24342 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 just as the agencies did in this rulemaking. Similarly to ICCT, UCS stated that in contrast to Autonomie, ALPHA had been thoroughly peer-reviewed and is constantly being updated to reflect the latest technology developments based on work performed by the National Vehicle and Fuel Emissions Laboratory.588 UCS also stated that because EPA has direct control over the model and its interface to OMEGA, EPA can better ensure that the inputs into OMEGA reflect the most up-to-date data, unlike the Autonomie work, which effectively has to be ‘‘locked in’’ before it can be deployed in the CAFE model. UCS also stated that ALPHA is based on the GEM model (used to simulate compliance with heavy-duty vehicle regulations) which was been updated with feedback from heavy-duty vehicle manufacturers and suppliers, and in fact, ‘‘NHTSA has such confidence in the GEM model that they accept its simulation-based results as compliance with the heavy-duty fuel economy regulations.’’ Again, the agencies believe that it is important to note that Autonomie not only meets, but also exceeds, UCS’ listed metrics. Autonomie’s models, sub-models, and controls are constantly being updated to reflect the latest technology developments based on work performed by Argonne National Laboratory’s Advanced Mobility Technology Laboratory (AMTL) (formerly Advanced Powertrain Research Facility, or ARPF).589 590 The theicct.org/sites/default/files/publications/ICCT_ position-brief_HDVenginetech-India_jun2015.pdf; Ben Sharpe and Oscar Delgado, Engines and tires as technology areas for efficiency improvements for trucks and buses in India (working paper published March 2016), available at https://theicct.org/sites/ default/files/publications/ICCT_HDV-engines-tires_ India_20160314.pdf. 588 NHTSA–2018–0067–12039 (UCS). 589 See NPRM PRIA. The agencies cited a succinctly-summarized presentation of Autonomie vehicle validation procedures based on AMTL test data in the NPRM ANL modeling documentation and PRIA docket for stakeholders to review at NHTSA–2018–0067–1972 and NHTSA–2018–0067– 0007. 590 Jeong, J., Kim, N., Stutenberg, K., Rousseau, A., ‘‘Analysis and Model Validation of the Toyota Prius Prime,’’ SAE 2019–01–0369, SAE World Congress, Detroit, April 2019; Kim, N, Jeong, J., Rousseau, A. & Lohse-Busch, H. ‘‘Control Analysis and Thermal Model Development of PHEV,’’ SAE 2015–01–1157, SAE World Congress, Detroit, April 15; Kim, N., Rousseau, A. & Lohse-Busch, H. ‘‘Advanced Automatic Transmission Model Validation Using Dynamometer Test Data,’’ SAE 2014–01–1778, SAE World Congress, Detroit, Apr. 14.; Lee, D. Rousseau, A. & Rask, E. ‘‘Development and Validation of the Ford Focus BEV Vehicle Model,’’ 2014–01–1809, SAE World Congress, Detroit, Apr. 14; Kim, N., Kim, N., Rousseau, A., & Duoba, M. ‘‘Validating Volt PHEV Model with Dynamometer Test Data using Autonomie,’’ SAE 2013–01–1458, SAE World Congress, Detroit, Apr. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 Autonomie validation has included nine validation studies with accompanying reports for software, six validation studies and reports for powertrains, nine validation studies and reports for advanced components, ten validation studies and reports for advanced controls, and overall model validation using test data from over 50 vehicles.591 In fact, using Autonomie, which has validated data based on test data from over 50 vehicles, alleviates other stakeholder concerns about the level of model validation in past analyses. For example, Global Automakers expressed concerns about whether the effectiveness values used in past EPA analysis, generated from ALPHA fullvehicle model simulations, were properly validated, stating that ‘‘[a]lthough EPA claims that the LPM was calibrated based on thorough testing and modeling with the ALPHA model, the materials provided with the Proposed and Final Determination only cover 18 percent of the projected vehicle fleet with regards to specific combinations of powertrain technology presented by EPA in the MY 2025 OMEGA pathway. It is unclear how EPA calibrated the LPM for the remaining 82 percent of the projected vehicles. EPA’s failure to publicly share the data for such a large percentage of vehicles raises questions about the quality of data.’’ 592 While simple modeled parameters like single dimensional linear systems, such as engine dynamometer torque measurements can be validated through other models,593 full vehicle systems are complex multidimensional non-linear systems that need to be developed with multiple data sets, and validated with other fully independent data sets. Autonomie’s models and sub-models have undergone extensive validation that has proven the 13.; Kim, N., Rousseau, A., & Rask, E. ‘‘Autonomie Model Validation with Test Data for 2010 Toyota Prius,’’ SAE 2012–01–1040, SAE World Congress, Detroit, Apr. 12; Karbowski, D., Rousseau, A, Pagerit, S., & Sharer, P. ‘‘Plug-in Vehicle Control Strategy—From Global Optimization to Real Time Application,’’ 22th International Electric Vehicle Symposium (EVS22), Yokohama, (October 2006). 591 Rousseau, A. Moawad, A. Kim, Namdoo. ‘‘Vehicle System Simulation to Support NHTSA CAFE standards for the Draft Tar.’’ https:// www.nhtsa.gov/sites/nhtsa.dot.gov/files/anl-nhtsaworkshop-vehicle-system-simulation.pdf. Last accessed Nov 20, 2019. 592 Docket ID EPA–HQ–OAR–2015–0827–9728. Global later repeated that ‘‘only 18% of all vehicle data used as inputs to the ALPHA modeling was made available in the EPA’s public sources. Additional data had to be specifically requested subsequent to the publication of the Draft TAR and Proposed Determination. This lack of publicly available data highlights transparency concerns, which Global Automakers has raised on several previous occasions.’’ 593 Section 89.307 Dynamometer calibration. PO 00000 Frm 00170 Fmt 4701 Sfmt 4700 models’ agreement with empirical data and the principles of physics. In addition, the agencies disagree with UCS’ comment that EPA’s direct control over its effectiveness modeling and interface to OMEGA results in a more up-to-date analysis. Argonne’s participation in developing inputs for the rulemaking analysis allowed the agencies access to vehicle benchmarking data from more vehicles than if the agencies were limited by their own resources, and access to the Argonne staff’s extensive experience based on direct coordination with vehicle manufacturers, suppliers, and researchers that all actively use Autonomie for their own work. In addition to Autonomie’s continuous updates to incorporate the latest fueleconomy-improving technologies, discussed throughout this section, the data supplied to and generated by Autonomie for use in the CAFE model was continuously updated during the analysis process. This is just one part of the iterative quality assurance (QA) and quality check (QC) process that the agencies developed when Argonne’s large-scale simulation modeling based in Autonomie was first used for the Draft TAR. In addition to Argonne’s team constantly updating Autonomie, Argonne’s use of high performance computing (HPC) allowed for constant update of the analysis during the rulemaking process. Argonne’s HPC platform allows a full set of simulations—over 750,000 modeled vehicles that incorporate over 50 different fuel-economy-improving technologies—to be simulated in one week. Subsets of the simulations can be re-run should issues come up during QA/QC in a day or less. Tools like the internet and high performance computers have allowed the agencies to evaluate technology effectiveness with up-to-date inputs without the proximity of the computers and the people running them working as a detriment the analysis. Finally, GEM, ALPHA, and Autonomie were all developed in the MATLAB computational environment as forward-looking physics-based vehicle models. Just as ALPHA has roots in GEM, created in 2010 to accompany the agencies’ heavy-duty vehicle CO2 emissions and fuel consumption standards, Autonomie has its origins in the software PSAT, developed over 20 years ago. While this information is useful, as implied by UCS’ comment, the origin of the software was less important than the capabilities the software could provide for today’s analysis. NHTSA’s acceptance of GEM E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 results for compliance with heavy-duty fuel economy regulations had no bearing on the decision to use Autonomie to assess the effectiveness of light-duty fuel economy and CO2 improving technologies. GEM was developed to serve as the compliance model for heavy-duty vehicles,594 and GEM serves that limited scope very well. UCS did comment that full vehicle simulation could significantly improve the estimates of technology effectiveness, but thought it critical that the process be as open and transparent as possible. UCS pointed to ALPHA results published in peer-reviewed journals as an example of how transparency has provided the ALPHA modeling effort with significant and valuable feedback, and contrasted what they characterized as Autonomie’s ‘‘black box’’ approach, which they stated ‘‘does not lend itself to similar dialog, nor does it make it easy to assess the validity of the results.’’ Specifically, UCS stated that it is ‘‘impossible to verify, replicate, or alter the work done by Autonomie due to the expensive nature of the tools used and lack of open source or peer-reviewed output.’’ In contrast, UCS stated that EPA’s ALPHA model has been thoroughly peer reviewed, and is readily ‘‘downloadable, editable, and accessible to anyone with a MATLAB license.’’ The agencies responses on the merits of how ALPHA and Autonomie were peer-reviewed are discussed above. Regarding UCS’ comment that it is impossible to verify, replicate, or alter the work done by Autonomie, the agencies disagree. All inputs, assumptions, model documentation— including of component models and individual control algorithms—and outputs for the NPRM Autonomie modeling were submitted to the docket for review.595 Commenters were able to 594 Newman, K., Dekraker, P., Zhang, H., Sanchez, J. et al., ‘‘Development of Greenhouse Gas Emissions Model (GEM) for Heavy- and MediumDuty Vehicle Compliance,’’ SAE Int. J. Commer. Veh. 8(2):2015, doi:10.4271/2015–01–2771. 595 NHTSA–2018–0067–1855. ANL Autonomie Compact Car Vehicle Class Results. Aug 21, 2018. NHTSA–2018–0067–1856. ANL Autonomie Performance Compact Car Vehicle Class Results. Aug 21, 2018. NHTSA–2018–0067–1494. ANL Autonomie Midsize Car Vehicle Class Results. Aug 21, 2018. NHTSA–2018–0067–1487. ANL Autonomie Performance Pick-Up Truck Vehicle Class Results. Aug 21, 2018. NHTSA–2018–0067– 1663. ANL Autonomie Performance Midsize Car Vehicle Class Results. Aug 21, 2018. NHTSA–2018– 0067–1486. ANL Autonomie Small SUV Vehicle Class Results. Aug 21, 2018 NHTSA–2018–0067– 1662. ANL Autonomie Performance Midsize SUV Vehicle Class Results. Aug 21, 2018. NHTSA–2018– 0067–1661. ANL Autonomie Pickup Truck Vehicle Class Results. Aug 21, 2018. NHTSA–2018–0067– 1485. ANL Autonomie Small Performance SUV VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 provide a robust analysis of Autonomie’s technology effectiveness inputs, input assumptions, and outputs, as shown by their comments on specific vehicle technology effectiveness assumptions, discussed throughout this section and in the individual technology sections below. The agencies also disagree with UCS’ assessment of Autonomie as ‘‘expensive.’’ While Autonomie is a commercial product, the biggest financial barrier to entry for both ALPHA and Autonomie is the same: A MathWorks license.596 597 Regardless, Argonne has made the version of Autonomie used for this final rule analysis available upon request, including the individual runs used to generate each technology effectiveness estimate.598 Next, ICCT supplanted its statement that the agencies ‘‘inexplicably’’ abandoned ALPHA, commenting that the agencies’ explanation and justification for relying on Autonomie rather than ALPHA failed to discuss ALPHA in detail, and the agencies did not compare and contrast the two models. ICCT continued, ‘‘the EPA cannot select its modeling tool arbitrarily, yet it appeared that the EPA has whimsically shifted from an extremely well-vetted, up-to-date, industry-grade modeling tool to a lessvetted, academic-grade framework with outdated inputs without even attempt to scrutinize the change.’’ ICCT also stated that the agencies are legally obligated to acknowledge and explain when they change position, and ‘‘cannot simply ignore that EPA previously concluded Vehicle Class Results. Aug 21, 2018 NHTSA–2018– 0067–1492. ANL Autonomie Midsize SUV Vehicle Class Results. Aug. 21, 2018. NHTSA–2018–0067– 0005. ANL Autonomie Model Assumptions Summary. Aug 21, 2018. NHTSA–2018–0067–0003. ANL Autonomie Summary of Main Component Assumptions. Aug 21, 2018. NHTSA–2018–0067– 0007. Islam, E. S, Moawad, A., Kim, N, Rousseau, A. ‘‘A Detailed Vehicle Simulation Process To Support CAFE Standards 04262018—Report’’ ANL Autonomie Documentation. Aug 21, 2018. NHTSA– 2018–0067–0004. ANL Autonomie Data Dictionary. Aug 21, 2018. NHTSA–2018–0067–1692. ANL BatPac Model 12 55. Aug 21, 2018. NHTSA–2018– 0067–12299. Preliminary Regulatory Impact Analysis (July 2018). Posted July 2018 and updated August 23 and October 16, 2018. 596 Autonomie. Frequently Asked Questions. ‘‘Which version of matlab can I use?’’ https:// www.autonomie.net/faq.html#faq2. Last accessed Nov. 19, 2019. 597 EPA ALPHA v2.2 Technology Walk Samples. ‘‘Running this version of ALPHA requires Matlab/ Simulink with StateFlow 2016b.’’ https:// www.epa.gov/regulations-emissions-vehicles-andengines/advanced-light-duty-powertrain-andhybrid-analysis-alpha. 598 Argonne Nationally Laboratory. Autonomie License Information. https://www.autonomie.net/ asp/LicenseRequest.aspx. Last accessed Nov, 18, 2019. PO 00000 Frm 00171 Fmt 4701 Sfmt 4700 24343 that the ALPHA modeling accurately projected real-world effects of technologies and technology packages.’’ The agencies disagree that a more indepth discussion of ALPHA was required in the NPRM. In acknowledging the transition to using Autonomie for effectiveness modeling and the CAFE model for analysis of regulatory alternatives,599 the agencies described several analytical needs that using a single analysis from the CAFE model—with inputs from the Autonomie tool—addressed. These included that Autonomie produced realistic estimates of fuel economy levels and CO2 emission rates through consideration of real-world constraints, such as the estimation and consideration of performance, utility, and drivability metrics (e.g., towing capability, shift busyness, frequency of engine on/off transitions).600 That EPA previously concluded the ALPHA modeling accurately projected realworld effects of technologies and technology packages has no bearing on Autonomie’s ability to fulfill the analytical needs that the agencies articulated in the NPRM, including that Autonomie also accurately projects realworld effects of technologies and technology packages. The agencies also disagree with ICCT’s characterization of ALPHA as ‘‘an extremely well-vetted, up-to-date, industry-grade modeling tool’’ and Autonomie as a ‘‘less-vetted, academicgrade framework with outdated inputs.’’ Again, Autonomie has been used by government agencies, vehicle manufacturers (and by agencies and manufacturers together in the collaborative government-industry partnership U.S. DRIVE program), suppliers, and other organizations because of its ability to simulate many powertrain configurations, component technologies, and vehicle-level controls over numerous drive cycles. Characterizing ALPHA as an ‘‘industrygrade modeling tool’’ contravenes EPA’s own description of its tool—an in-house vehicle simulation model used by EPA, not intended to be a commercial product.601 599 83 FR 43000 (Aug. 24, 2018). FR 43001 (Aug. 24, 2018). 601 See, e.g., Overview of ALPHA Model, https:// www.epa.gov/regulations-emissions-vehicles-andengines/advanced-light-duty-powertrain-andhybrid-analysis-alpha; ALPHA Effectiveness Modeling: Current and Future Light-Duty Vehicle & Powertrain Technologies (Jan. 20, 2016), available at https://www.epa.gov/sites/production/files/201610/documents/alpha-model-sae-govt-ind-mtg-201601-20.pdf (‘‘ALPHA is not a commercial product (e.g. there are no user manuals, tech support hotlines, graphical user interfaces, or full libraries 600 83 E:\FR\FM\30APR2.SGM Continued 30APR2 24344 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 That characterization also contravenes documentation from the automotive industry indicating that manufacturers consider ALPHA to generate overly optimistic effectiveness values, to be unrepresentative of real-world constraints, and a difficult tool to use.602 603 The Alliance commented to the MTE reconsideration that ‘‘[p]revious comments from the Alliance and individual manufacturers to the MTE docket have highlighted multiple concerns with EPA’s ALPHA model. Many of these concerns remain unresolved.’’ 604 Furthermore, the Alliance commented that ALPHA ‘‘has not been documented with any instructions making it difficult for users outside of EPA to run and interpret the model.’’ 605 Global Automakers further stated that the ‘‘lack of publicly available data [related to inputs used in the ALPHA modeling] highlights transparency concerns, which Global Automakers has raised on several previous occasions.’’ 606 In fact, both the Alliance of Automobile Manufacturers and Global Automakers, the two trade organizations that represent the automotive industry, concluded that Autonomie should be used to generate effectiveness inputs for the CAFE model.607 of components).’’). See also Peer Review of ALPHA Full Vehicle Simulation Model, available at https:// nepis.epa.gov/Exe/ZyPdf.cgi?Dockey= P100PUKT.pdf. While ALPHA peer reviewers found the model to be a ‘‘fairly simple transparent model . . . [t]he model execution requires an expert MatLab/Simulink user since no user-friendly interface currently exists.’’ Indeed, EPA noted in response to this comment that ‘‘[a]s with any internal tool, EPA does not have the need for a ‘‘user-friendly interface’’ like one that would normally accompany a commercial product which is available for purchase and fully supported for wide external usage.’’ 602 See EPA–HQ–OAR–2015–0827–10125, at 7. As part of their assessment that known technologies could not meet the original MY 2022–2025 standards, Toyota noted that the ALPHA conversion of Toyota’s MY 2015 to MY 2025 performance ‘‘appears to yield overly optimistic results because the powertrain efficiency curves represent best-case targets and not the average vehicle, the imposed performance constraints are unmarketable, and the generated credits are out of sync with product cadence and design cycles.’’ See also NHTSA– 2018–0067–12431, at 7. More recently, Toyota stated in their comments to the NPRM that ‘‘Toyota’s position [on the efficacy of the OMEGA and LPM models] has been clearly represented by comments previously submitted by the Alliance of Automobile Manufacturers, Global Automakers, and Novation Analytics. Those comments identify the LPM and OMEGA models as sources of inaccuracy in EPA technology evaluations and provide suggested improvements. Neither model is transparent, intuitive, or user friendly.’’ 603 EPA–HQ–OAR–2015–0827–9194. 604 EPA–HQ–OAR–2015–0827–9194, at 33. 605 EPA–HQ–OAR–2015–0827–9194. 606 EPA–HQ–OAR–2015–0827–9728. 607 EPA–HQ–OAR–2015–0827–9163 at 5. (‘‘EPA should abandon the lumped-parameter model and VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 In addition, Autonomie contains upto-date sub-models to represent the latest electrification and advanced transmission and advanced engine technologies. As summarized by the Alliance, ‘‘Autonomie was developed from the start to address the complex task of combining 2 power sources in a hybrid powertrain.’’ 608 Autonomie has continuously improved over the years by adopting new technologies into its modeling framework. Even a small sampling of SAE papers shows how Autonomie has been validated to simulate the latest fuel-economyimproving technologies like hybrid vehicles and PHEVs.609 Moreover, Autonomie effectively considers other real-world constraints faced by the automotive industry. Vehicle manufacturers and suppliers spend significant time and effort to ensure technologies are incorporated into vehicles in ways that will balance consumer acceptance for attributes such as driving quality,610 noise-vibrationharshness (NVH), and meeting other regulatory mandates, like EPA’s and CARB’s On-Board Diagnostics (OBD) requirements,611 and EPA’s and CARB’s criteria exhaust emissions standards.612 The implementation of new fuel economy improving technologies have at times raised consumer acceptance issues.613 As discussed earlier, there are instead use NHTSA’s Autonomie and Volpe models to support the Revised Final Determination.’’). See also EPA–HQ–OAR–2015–0827–9728 at 15 (stating the EPA’s engine mapping and tear down analyses ‘‘should be integrated into the Autonomie model, which then feeds into the Volpe modeling process.’’); EPA–HQ–OAR–2015–0827–9194 at 33. 608 Alliance, Docket ID NHTSA–2018–0067– 12073 at 135. 609 Jeong, J., Kim, N., Stutenberg, K., Rousseau, A., ‘‘Analysis and Model Validation of the Toyota Prius Prime,’’ SAE 2019–01–0369, SAE World Congress, Detroit, April 2019; Kim, N, Jeong, J. Rousseau, A. & Lohse-Busch, H. ‘‘Control Analysis and Thermal Model Development of PHEV,’’ SAE 2015–01–1157. 610 An example of a design requirement is accommodating the ‘‘lag’’ in torque delivery due to the spooling of a turbine in a turbocharged downsized engine. This affects real-world vehicle performance, as well as the vehicle’s ability to shift during normal driving and test cycles. 611 EPA adopted and incorporated by reference current OBD regulations by the California ARB, effective for MY 2017, that cover all vehicles except those in the heavier fraction of the heavy-duty vehicle class. 612 Tier 3 emission standards for light-duty vehicles were proposed in March 2013 78 FR 29815 (May 21, 2013) and signed into law on March 3, 2014 79 FR 23413 (June 27, 2014). The Tier 3 standards—closely aligned with California LEV III standards—are phased-in over the period from MY2017 through MY2025. The regulation also tightens sulfur limits for gasoline. 613 Atiyeh, C. ‘‘What you need to know about Ford’s PowerShift Transmission Problems’’ Car and Driver. July 11, 2019. https:// www.caranddriver.com/news/a27438193/fordpowershift-transmission-problems/. PO 00000 Frm 00172 Fmt 4701 Sfmt 4700 diminishing returns for modeling every vehicle attribute and tradeoff, as each takes time and incurs cost; however, Autonomie sub-models are designed to account for a number of the key attributes and tradeoffs, so the resulting effectiveness estimates reflect these real world constraints. Furthermore, aside from the fact that Autonomie represents the structural state-of-the-art in full-vehicle modeling and simulation, Autonomie can be populated with any inputs that could be populated in the ALPHA model.614 The agencies chose to use specific inputs for this rulemaking because, as discussed further in Sections VI.C below, they best represent the technologies that manufacturers could incorporate in the rulemaking timeframe, in a way that balanced important concerns like consumer acceptance. Some other examples of how Autonomie inputs have been updated with the latest vehicle technology data specifically for this analysis include test data incorporated from both Argonne and NHTSA-sponsored vehicle benchmarking, including an updated automatic transmission skip-shifting feature,615 additional application of cylinder deactivation for turbocharged downsized engines, and as discussed above, new modeling and simulation that includes variable compression ratio and Miller Cycle engines. Finally, ICCT commented that the agencies must conduct a systematic comparison of the Autonomie modeling system and ALPHA modeling in several respects, including the differences in technical inputs and resulting efficiency estimates, to explain how the choice of model altered the regulatory technology penetration and compliance cost estimations, and the differences in modeling methodologies, including regarding the relative level of experience of the teams conducting the effectiveness modeling, to demonstrate that the choice to use Autonomie was not ‘‘due to convenience and easier access by the NHTSA research team, rather than for any technical improvement.’’ ICCT stated that without performing this comparison, ‘‘it otherwise appears that the agencies switched from a better-vetted model and system of inputs with more recent input data to a less-vetted model and system of inputs as a way to bury many dozens of changes without transparency or expert assessment (as illustrated in the 614 For example, Autonomie used the HCR1 and HCR2 engine maps used as inputs to ALHPA in the Draft TAR and Proposed Determination. 615 NHTSA Benchmarking, ‘‘Laboratory Testing of a 2017 Ford F–150 3.5 V6 EcoBoost with a 10-speed transmission.’’ DOT HS 812 520. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations above errors and invalidated data on individual technologies).’’ Each issue is discussed below in turn. First, regarding technical inputs, technology pathways, and resulting outputs, ICCT stated that the agencies must compare (1) whether the models have been routinely strengthened by incorporating cutting edge 2020–2025 automotive technologies to ensure they reflect the available improvements; (2) every efficiency technology in the 2016 Draft TAR and original EPA TSD and Proposed and Final Determination analysis against the NPRM; (3) all the major technology package pathways (i.e., all combinations with high uptake in the Adopted and Augural 2025 standards) in the current NPRM versus the 2016 Draft TAR and the 2016 TSD and original Final Determination analysis; (4) each of the major 2025 technology package synergies; (5) the modeling work of EPA’s, Ricardo’s, and Argonne’s 2014–2018 model year engine benchmarking and modeling of top engine and transmission models; and ‘‘defend why they appear to have chosen to dismiss the superior and better vetted technology modeling approach.’’ ICCT stated that the agencies must make these comparisons because, ‘‘[o]therwise, it seems obvious that the agencies have subjectively decided to use the modeling that increases the modeled cost, providing further evidence of a high degree of bias without an objective accounting of the methodological differences and the sensitivity of the results to their new decision.’’ Moreover, ICCT stated that ‘‘[b]ecause ALPHA is the dominant, preferred, and better-vetted modeling and was used in the original Proposed and Final Determination, the agencies are responsible for assessing and describing how the use of the ALPHA modeling would result in a different regulatory result for their analysis of the 2017–2025 adopted [CO2] and Augural CAFE standards.’’ The agencies do not believe that it is necessary to conduct a retrospective comparison of ALPHA/LPM and Autonomie effectiveness for every technology in the Draft TAR and Proposed Determination to the NPRM and final rule analyses, between the two models for technologies and packages used in the NPRM and final rule analysis, or to explain where and why Autonomie provided different results from ALPHA and the LPM, to assess and describe how the use of the ALPHA modeling would result in a different regulatory result of CAFE and CO2 standards, per ICCT’s request. While it is anticipated that different values will VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 be produced using different tools in an analysis, it is not appropriate to select the tool for use based on preferred results. The selection of an analysis tool should be based on an evaluation of the tool’s capabilities and appropriateness for the analysis task. The analysis tool should support the full extent of the analysis and support the level of input and output resolution required. To compare the output of the two models for the purpose of selecting a tool for the analysis would likely be biased and disingenuous to the purpose of the analysis. In this case, Autonomie was selected for this analysis for the reasons discussed throughout this section, and accordingly the agencies believe that it was reasonable to consider effectiveness estimates developed with Autonomie. That said, comparison of how the tools behave is discussed here to further support the agencies’ decision process. To demonstrate, in addition to everything discussed previously in this section, differences in how each model handles powertrain systems modeling with specific examples are discussed below as a reference, and differences between the agencies’ approaches to effectiveness modeling for specific technologies is discussed in Section VI.C where appropriate. While the improved approach to estimating technology effectiveness estimates certainly impacted the regulatory technology penetration, compliance cost estimates, and ‘‘major 2025 technology packages and synergies,’’ how technologies are applied in the compliance modeling and the associated costs of the technologies is equally as important to consider when examining factors that might impact the regulatory analysis; that consideration goes beyond the scope of simply considering which full vehicle simulation model better performs the functions required of this analysis. The agencies have discussed updates to the technologies considered in the Autonomie modeling throughout this section, in addition to Autonomie’s models and sub-models that control advanced technologies like hybrid and electrified powertrains. Autonomie’s explicit models, sub-models, and controls for hybrid and electric vehicles have been continuously validated over the past several years,616 as Autonomie 616 Karbowski, D., Kwon, J., Kim, N., & Rousseau, A., ‘‘Instantaneously Optimized Controller for a Multimode Hybrid Electric Vehicle,’’ SAE paper 2010–01–0816, SAE World Congress, Detroit, April 2010; Sharer, P., Rousseau, A., Karbowski, D., & Pagerit, S. ‘‘Plug-in Hybrid Electric Vehicle Control Strategy—Comparison between EV and ChargeDepleting Options,’’ SAE paper 2008–01–0460, SAE World Congress, Detroit (April 2008); and PO 00000 Frm 00173 Fmt 4701 Sfmt 4700 24345 was developed from the beginning to address the complex task of combining two power sources in a hybrid powertrain. Also regarding the inputs to both models, as highlighted in Section VI.C.3.a), and discussed above, inputs and assumptions for the ALPHA modeling used for the EPA Draft TAR and Proposed Determination analysis were projected from benchmarking testing. While it is straightforward to measure engine fuel consumption and create an engine fuel map, it is extremely challenging to identify the specific technologies and levels of technologies present on a benchmarking engine. Attributing changes in the overall engine fuel consumption to the individual engine technologies that make up the complete engine involves significant uncertainty. The fixed-point model approach used by the ALPHA model does not develop an effectiveness function and assigns a single value to a technology. The single value is derived from benchmark testing, which often does not isolate the effect of a single technology from the effects of other technologies on the tested vehicle. To isolate a single technology’s effect for use in fixed point modeling properly, the agencies would need to benchmark multiple versions of a single vehicle, carefully controlling changes to the vehicles’ fuel efficiency technologies. This process would need to be repeated for a large portion of the vehicle fleet and would require significant funding and thousands of lab hours to complete. Without this level of data, fixed-point effectiveness estimates tend to be too high, as they are unable to account for synergetic effects of multiple technologies. Specifically, when EPA benchmarks vehicles like the 2018 Toyota Camry, the resulting fuel map captures the benefits of many Rousseau, A., Shidore, N., Carlson, R., & Karbowski, D. ‘‘Impact of Battery Characteristics on PHEV Fuel Economy,’’ AABC08; Jeong, J., Kim, N., Stutenberg, K., Rousseau, A., ‘‘Analysis and Model Validation of the Toyota Prius Prime,’’ SAE 2019–01–0369, SAE World Congress, Detroit, April 2019; Kim, N, Jeong, J. Rousseau, A. & Lohse-Busch, H. ‘‘Control Analysis and Thermal Model Development of PHEV,’’ SAE 2015–01–1157, SAE World Congress, Detroit, April 15; Lee, D. Rousseau, A. & Rask, E. ‘‘Development and Validation of the Ford Focus BEV Vehicle Model,’’ 2014–01–1809, SAE World Congress, Detroit, Apr. 14; Kim, N., Kim, N., Rousseau, A., & Duoba, M. ‘‘Validating Volt PHEV Model with Dynamometer Test Data using Autonomie,’’ SAE 2013–01–1458, SAE World Congress, Detroit, Apr. 13.; Kim, N., Rousseau, A., & Rask, E. ‘‘Autonomie Model Validation with Test Data for 2010 Toyota Prius,’’ SAE 2012–01–1040, SAE World Congress, Detroit, Apr. 12; Karbowski, D., Rousseau, A, Pagerit, S., & Sharer, P. ‘‘Plug-in Vehicle Control Strategy—From Global Optimization to Real Time Application,’’ 22th International Electric Vehicle Symposium (EVS22), Yokohama, (October 2006). E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations technologies associated with that engine. This data can be helpful when developing controls and validating component operations in modeling, but it is inaccurate to conclude that fuel consumption is directly related to individual engine technologies, such as lubrication and friction reduction, and geometric improvements in efficiency. Contrasted, the NPRM and final rule Autonomie analyses selected specific base engine maps and applied technologies incrementally, both individually and in known combinations, to better isolate the impacts of the technologies. As discussed above, this also implemented NAS Recommendation 2.1, to use engine-model-generated maps in the full vehicle simulations derived from a validated baseline map in which all parameters except the new technology of interest are held constant.617 While the different methods are valid for different purposes, the method selected for the analysis presented today was more useful for measuring the incremental effectiveness increments as opposed to the absolute values of technology effectiveness, e.g., that could be measured by benchmarking a technology package. To provide an example of another difference in behavior between the simulation tools, a comparison between ALPHA and Autonomie transmissions shifting behavior was conducted. The comparison highlighted the differences in how each simulation tool approaches transmission shift logic. The ALPHA simulation tool used ALPHAShift. ALPHAShift is an optimization algorithm that uses numerous vehicle characteristics to find a best shifting strategy. The primary inputs for the algorithm includes the fuel consumption (or cost) map for the vehicle engine.618 Although a public version of ALPHA is available for evaluation, the ALPHAShift algorithm used by the tool is hard coded with fixed values.619 620 This is an issue, because despite peer reviewed documentation on how to tune the algorithm,621 no documentation of how the algorithm logic works is available for review. This is confounding for the use of the software, particularly when the observed behavior of the model departs from expected behavior. Figure VI–6 below shows simulated gear shift (left) versus actual gear shift (right), demonstrating an unexpected shift to neutral before shifting to the requested gear. By contrast, and discussed further in VI.C.2 Transmission Paths, Autonomie uses a fully documented algorithm to develop a best shifting strategy for each unique vehicle configuration. The algorithm develops shifting strategies unique to each individual vehicle based on gear ratio, final drive ratio, engine BSFC and other vehicle characteristics. This is one example of model behavior, in addition to the availability of more transparency on this behavior for greater stakeholder review, that led the agencies to determine it was reasonable and appropriate to use Autonomie for this analysis. Regarding the technical expertise of the team conducting the effectiveness modeling, ICCT commented: the Ricardo, IAV, and Argonne Autonomie teams that underpin the modeling of EPA and NHTSA, respectively, including how much related research they have done for auto industry clients over the past ten years. We mention this because we strongly suspect that Ricardo, upon which EPA built its ALPHA model, has done at least an order of magnitude (in number of projects, personhours, and budget) more work with and for the automotive industry than the IAV and Autonomie teams have in direct work for 620 ALPHA v2.2 Technology Walk Samples. EPA. January 2017. https://www.epa.gov/sites/ production/files/2017-01/alpha-20170112.zip. Last Accessed March 9, 2020. 621 Newman, K., Kargul, J., and Barba, D., ‘‘Development and Testing of an Automatic Transmission Shift Schedule Algorithm for Vehicle Simulation,’’ SAE Int. J. Engines 8(3):2015, doi:10.4271/2015–01–1142. 622 ALPHA v2.2 Technology Walk Samples. Jan. 12, 2017. https://www.epa.gov/sites/production/ files/2017-01/alpha-20170112.zip. Last accessed Dec 9, 2019. khammond on DSKJM1Z7X2PROD with RULES2 [T]he agencies should also disclose how much commercial business is conducted by 617 2015 NAS Report at p. 82. K., Kargul, J., and Barba, D., ‘‘Development and Testing of an Automatic Transmission Shift Schedule Algorithm for Vehicle Simulation,’’ SAE Int. J. Engines 8(3):2015, doi:10.4271/2015–01–1142. 619 Aymeric, R. Islam, E. S. ‘‘Analysis of EPA’s ALPHA Shift Model—ALPHAShift.’’ ANL. March 9, 2020. 618 Newman, VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00174 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.114</GPH> 24346 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations automotive industry clients. A conventional government procurement effort that competitively vets potential research expert teams would presumably have selected for such automotive industry credentials and experience, yet it appears that the agencies are wholly deferring to Autonomie’s less rigorous research-grade modeling framework and data due to convenience and easier access by the NHTSA research team, rather than for any technical improvement, and this is to the detriment of showing clear understanding of real-world automotive engineering developments (as demonstrated by many erroneous technology combination results throughout these comments). First, NHTSA follows Federal Acquisition Regulation (FAR) to award contracts and Interagency Agreements (IAAs),623 and any awarded contracts and IAAs must follow the FAR requirements. Importantly, FAR 3.101–1 includes key aspects of conduct and ethics that NHTSA must follow in awarding a contract or IAA: khammond on DSKJM1Z7X2PROD with RULES2 Government business shall be conducted in a manner above reproach and, except as authorized by statute or regulation, with complete impartiality and with preferential treatment for none. Transactions relating to the expenditure of public funds require the highest degree of public trust and an impeccable standard of conduct. The general rule is to avoid strictly any conflict of interest or even the appearance of a conflict of interest in Government-contractor relationships. While many Federal laws and regulations place restrictions on the actions of Government personnel, their official conduct must, in addition, be such that they would have no reluctance to make a full public disclosure of their actions.624 While some factors are more relevant than others in considering whether to award a contract or enter into an IAA, the amount of work that an organization has performed, characterized by projects, person-hours, and budget, is only one of a multitude of factors that is considered (if it is even considered at all—an agency might not request this information and an organization might decline to provide it because of contractual clauses or to protect commercial business interests) when assessing whether an organization meets the agency’s needs for a specific task. Other factors, such as the federal budget, also set boundaries for the scope of work that can be performed under any competitive government procurement effort. As discussed throughout this section, the team at Argonne National Laboratory behind Autonomie has developed and refined a state-of-the-art tool that is used by the automotive 623 Federal Acquisition Regulation (FAR). https:// www.acquisition.gov/. 624 FAR 3.101–1. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 industry, government agencies, and research or other nongovernmental institutions around the world. The tool has been and continues to be validated to production vehicles, and updated to include models, sub-models, and controls representing the state-of-the-art in fuel economy improving technology. To the extent that ICCT believes that ‘‘research done for auto industry clients,’’ ‘‘work with and for the automotive industry,’’ and ‘‘automotive industry credentials and experience,’’ are metrics upon which to base this type of important decision, the agencies point ICCT to the statements from the automotive industry, above, recommending Autonomie be used for technology effectiveness modeling. ICCT concluded that ‘‘[w]hile the agencies are in their process of conducting a proper vetting of their NPRM’s foundational Autonomie-based modeling, we recommend that they rely on what appears to be the superior and better vetted technology modeling approach with more thorough and stateof-the-art advanced powertrain systems modeling and engine maps from the EPA ALPHA modeling.’’ The agencies properly vetted the Autonomie modeling and decided that Autonomie represented a reasonable and appropriate tool to provide technology effectiveness estimates for this rulemaking. To the extent that commenters’ concerns were more about the effectiveness results than the tools used to model technology effectiveness, modeling updates detailed in the Section VI.B.3.c), below, address those comments. While some commenters may still be dissatisfied with Autonomie’s technology effectiveness estimates, the agencies believe that the refinement of inputs and input assumptions, and associated explanation of why those refinements are appropriate and reasonable, have appropriately addressed comments on these issues. Importantly, none of these refinements have led either agency to reconsider using Autonomie for this rulemaking analysis. Additional discussion of the agencies’ decision to rely on one set of modeling tools for this rulemaking is located in Section VI.A of this preamble. c) Technology Effectiveness Values Implementation in the CAFE Model While the Autonomie model produces a large amount of information about each simulation run—for a single technology combination, in a single technology class—the CAFE model only uses two elements of that information: Battery costs and fuel consumption on the city and highway cycles. The PO 00000 Frm 00175 Fmt 4701 Sfmt 4700 24347 agencies combine the fuel economy information from the two cycles to produce a composite fuel economy for each vehicle, on each fuel. Plug-in hybrids, being the only dual-fuel vehicles in the Autonomie simulation, require efficiency estimates of operation on both gasoline and electricity—as well as an estimate of the utility factor, or the number of miles driven on each fuel. The fuel economy information for each technology combination, for each technology class, is converted into a single number for use in the CAFE model. As described in greater detail below, each Autonomie simulation record represents a unique combination of technologies, and the agencies create a technology ‘‘key’’ or technology state vector that describes all the technology content associated with a record. The 2cycle fuel economy of each combination is converted into fuel consumption (gallons per mile) and then normalized relative to the starting point for the simulations. In each technology class, the combination with the lowest technology content is the VVT (only) engine, with a 5-speed transmission, no electrification, and no body-level improvements (mass reduction, aerodynamic improvements, or low rolling resistance tires). This is the reference point (for each technology class) for all the effectiveness estimates in the CAFE model. The improvement factors that the model uses are a given combination’s fuel consumption improvement relative to the reference vehicle in its technology class. For the majority of the technologies analyzed within the CAFE Model, the fuel economy improvements were derived from the database of Autonomie’s detailed full-vehicle modeling and simulation results. In addition to the technologies found in the Autonomie simulation database, the CAFE modeling system also incorporated a handful of technologies that were required for CAFE modeling, but were not explicitly simulated in Autonomie. The total effectiveness of these technologies either could not be captured on the 2-cycle test, or there was no robust data that could be used as an input to the full-vehicle modeling and simulation, like with emerging technologies such as advanced cylinder deactivation (ADEAC). These additional technologies are discussed further in Sections VI.B.3 Technology Effectiveness and individual technologies sections. For calculating fuel economy improvements attributable to these additional technologies, the model used defined fuel consumption improvement factors that are constant E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24348 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations across all technology combinations in the database and scale multiplicatively when applied together. The Autonomiesimulated and additional technologies were then externally combined, forming a single dataset of simulation results (referred to as the vehicle simulation database, or simply, database), which may then be utilized by the CAFE modeling system. To incorporate the results of the combined database of Autonomiesimulated and additional technologies, while still preserving the basic structure of the CAFE Model’s technology subsystem, it was necessary to translate the points in this database into corresponding locations defined by the technology pathways. By recognizing that most of the pathways are unrelated, and are only logically linked to designate the direction in which technologies are allowed to progress, it is possible to condense the paths into a smaller number of groups based on the specific technology. In addition, to allow for technologies present on the Basic Engine and Dynamic Road Load (DLR—i.e., MASS, AERO, and ROLL) paths to be evaluated and applied in any given combination, a unique group was established for each of these technologies. As such, the following technology groups are defined within the modeling system: Engine cam configuration (CONFIG), VVT engine technology (VVT), VVL engine technology (VVL), SGDI engine technology (SGDI), DEAC engine technology (DEAC), non-basic engine technologies (ADVENG), transmission technologies (TRANS), electrification and hybridization (ELEC), low rolling resistance tires (ROLL), aerodynamic improvements (AERO), mass reduction levels (MR), EFR engine technology (EFR), electric accessory improvement technologies (ELECACC), LDB technology (LDB), and SAX technology (SAX). The combination of technologies along each of these groups forms a unique technology state vector and defines a unique technology combination that corresponds to a single point in the database for each technology class evaluated within the modeling system. As an example, a technology state vector describing a vehicle with a SOHC engine, variable valve timing (only), a 6speed automatic transmission, a beltintegrated starter generator, rolling resistance (level 1), aerodynamic improvements (level 2), mass reduction (level 1), electric power steering, and low drag brakes, would be specified as ‘‘SOHC; VVT; AT6; BISG; ROLL10; VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 AERO20; MR1; EPS; LDB.’’ 625 By assigning each unique technology combination a state vector such as the one in the example, the CAFE Model can then assign each vehicle in the analysis fleet an initial state that corresponds to a point in the database. Once a vehicle is assigned (or mapped) to an appropriate technology state vector (from one of approximately three million unique combinations, which are defined in the vehicle simulation database as CONFIG; VVT; VVL; SGDI; DEAC; ADVENG; TRANS; ELEC; ROLL; AERO; MR; EFR; ELECACC; LDB; SAX), adding a new technology to the vehicle simply represents progress from a previous state vector to a new state vector. The previous state vector simply refers to the technologies that are currently in use on a vehicle. The new state vector, however, is computed within the modeling system by adding a new technology to the combination of technologies represented by the previous state vector, while simultaneously removing any other technologies that are superseded by the newly added one. For example, consider the vehicle with the state vector described as: SOHC; VVT; AT6; BISG; ROLL10; AERO20; MR1; EPS; LDB. Assume the system is evaluating PHEV20 as a candidate technology for application on this vehicle. The new state vector for this vehicle is computed by removing SOHC, VVT, AT6, and BISG technologies from the previous state vector,626 while also adding PHEV20, resulting in the following: PHEV20; ROLL10; AERO20; MR1; EPS; LDB. From here, it is relatively simple to obtain a fuel economy improvement factor for any new combination of technologies and apply that factor to the fuel economy of a vehicle in the analysis fleet. The formula for calculating a vehicle’s fuel economy after application of each successive technology represented within the database is defined, simply put, as the difference between the fuel economy improvement factor associated with the technology state vector before application of a candidate technology, and after the application of a candidate 625 In the example technology state vector, the series of semicolons between VVT and AT6 correspond to the engine technologies which are not included as part of the combination, while the gap between MR1 and EPS corresponds to EFR and the omitted technology after LDB is SAX. The extra semicolons for omitted technologies are preserved in this example for clarity and emphasis, and will not be included in future examples. 626 For more discussion of how the CAFE Model handles technology supersession, see Section VI.A.7. PO 00000 Frm 00176 Fmt 4701 Sfmt 4700 technology.627 This is applied to the original compliance fuel economy value for a discrete vehicle in the MY 2017 analysis fleet, as discussed previously in Section VI.B.3 Technology Effectiveness. The fuel economy improvement factor is defined in a way that captures the incremental improvement of moving between points in the database, where each point is defined uniquely as a combination of up to 15 distinct technologies describing, as mentioned above, the engine’s cam configuration, multiple distinct combinations of engine technologies, transmission, electrification type, and various vehicle body level technologies. Unlike the preceding versions of the modeling system, the current version of the CAFE Model relies entirely on the vehicle simulation database for calculating fuel economy improvements resulting from all technologies available to the system. The fuel economy improvements are derived from the factors defined for each unique technology combination or state vector. Each time the improvement factor for a new state vector is added to a vehicle’s existing fuel economy, the factor associated with the old technology combination is entirely removed. In that sense, application of technologies obtained from the Autonomie database is ‘‘self-correcting’’ within the model. As such, special-case adjustments defined by the previous version of the model are not applicable to this one. Meszler Engineering Services, commenting on behalf of Natural Resources Defense Council, commented that ‘‘[w]ith very limited exception, technology is not included in the NPRM CAFE model if it was not included in the simulation modeling that underlies the Argonne database,’’ citing the ‘‘addon’’ technologies and technologies with fixed effectiveness values.628 Meszler continued, ‘‘[t]his same limitation controls the coupling of technologies, and by extension the definition of the CAFE model technology pathways. If a combination of technologies were not modeled during the development of the Argonne database, that package (or combination) of technologies is not available for adoption in the CAFE model. Both of these design constraints serve to limit the slate of technologies available to respond to fuel economy 627 For more discussion of how the CAFE Model calculates a vehicle’s fuel economy where the vehicle switches from one type of fuel to another, for example, from gasoline operation to diesel operation or from gasoline operation to plug-in hybrid/electric vehicle operation, see Section VI.A CAFE Model. 628 NHTSA–2018–0067–11723, at 4–5. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 standards. The slate of available technologies is basically constrained to those included in NHTSA’s research activity. If a technology or technology combination was not in the NHTSA research planning process, it is not available in the model.’’ Finally, Meszler stated that ‘‘because of the constrained model architecture and the reliance on the Argonne database for impact estimates, independently expanding the model to include additional technologies or technology combinations is not trivial.’’ We agree that expanding the database to include new technologies is not trivial. However, it is possible. The set of available technologies is part of the model code, and the code is made public upon each release of the model. Many commenters made modifications to the model code, conducted additional tests of their own, and presented their results to the agencies in the form of public comments before the end of the public comment period. A user could add the new technology, identify the associated engineering restrictions that determine combinations for which that technology should not be considered, and add the relevant rows (representing possible technology combinations that include the new technology) in the database (which exists locally on every computer that runs the model). An enterprising user could also take an existing technology along a given path and replace the efficiency values with new values—presumably from their own full vehicle simulations for each technology combination that contains the technology in question. Given the length of time and computing power required to simulate vehicle fuel economy on the test cycle for every possible combination that could be considered by the CAFE model, using a pre-defined database that represents a large ensemble of simulated technology combinations is preferable to the alternative of fully integrating a vehicle simulation model that would be required to run in real-time during the compliance simulation to evaluate the effectiveness of every combination considered (not just applied) by the model. 4. Technology Costs In the proposal, the agencies estimated present and future costs for fuel-saving technologies, taking into consideration the type of vehicle, or type of engine if technology costs vary by application. These cost estimates are based on three main inputs. First, the agencies estimated direct manufacturing costs (DMCs), or the component and labor costs of producing and assembling VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 the physical parts and systems, with estimated costs assuming high volume production. DMCs generally do not include the indirect costs of tools, capital equipment, financing costs, engineering, sales, administrative support or return on investment. Second, the agencies accounted for these indirect costs via a scalar markup of direct manufacturing costs (the retail price equivalent, or RPE). Finally, costs for technologies may change over time as industry streamlines design and manufacturing processes. The agencies therefore estimated potential cost improvements with learning effects (LE). The retail cost of equipment in any future year is estimated to be equal to the product of the DMC, RPE, and LE. Considering the retail cost of equipment, instead of merely direct manufacturing costs, is important to account for the real-world price effects of a technology, as well as market realities. Absent a government mandate, motor vehicle manufacturers will not undertake expensive development and production efforts to implement technologies without realistic prospects of consumers being willing to pay enough for such technology to allow for the manufacturers to recover their investment. a) Direct Manufacturing Costs Direct manufacturing costs (DMCs) are the component costs of the physical parts and systems that make up a complete vehicle. The analysis used agency-sponsored tear-down studies of vehicles and parts to estimate the DMCs of individual technologies, in addition to independent tear-down studies, other publications, and confidential business information. In the simplest cases, the agency-sponsored studies produced results that confirmed third-party industry estimates, and aligned with confidential information provided by manufacturers and suppliers. In cases with a large difference between the teardown study results and credible independent sources, study assumptions were scrutinized, and sometimes the analysis was revised or updated accordingly. Due to the variety of technologies and their applications, and the cost and time required to conduct detailed tear-down analyses, the agencies did not sponsor teardown studies for every technology. In addition, many fuel-saving technologies were considered that are pre-production, or sold in very small pilot volumes. For those technologies, a tear-down study could not be conducted to assess costs because the product is not yet in the marketplace for evaluation. In these cases, the agencies PO 00000 Frm 00177 Fmt 4701 Sfmt 4700 24349 relied upon third-party estimates and confidential information from suppliers and manufacturers were relied upon; however, there are some common pitfalls with relying on confidential business information to estimate costs. The agencies and the source may have had incongruent or incompatible definitions of ‘‘baseline.’’ The source may have provided DMCs at a date many years in the future, and assumed very high production volumes, important caveats to consider for agency analysis. In addition, a source, under no contractual obligation to the agencies, may provide incomplete and/or misleading information. In other cases, intellectual property considerations and strategic business partnerships may have contributed to a manufacturer’s cost information and could be difficult to account for in the model as not all manufacturer’s may have access to proprietary technologies at stated costs. The agencies carefully evaluated new information in light of these common pitfalls, especially regarding emerging technologies. Specifically, the analysis used thirdparty, forward-looking information for advanced cylinder deactivation and variable compression ratio engines. While these cost estimates may be preliminary (as is the case with many emerging technologies prior to commercialization), the agencies consider them to be reasonable estimates of the likely costs of these technologies. While costs for fuel-saving technologies reflect the best estimates available today, technology cost estimates will likely change in the future as technologies are deployed and as production is expanded. For emerging technologies, the best information available at the time of the analysis was utilized, and cost assumptions will continue to be updated for any future analysis. Below, discussion of each category of technologies (e.g., engines, transmissions, electrification) summarizes comments on corresponding direct cost estimates, and reviews estimates the agencies have applied for today’s analysis. Indirect Costs As discussed above, direct costs represent the cost associated with acquiring raw materials, fabricating parts, and assembling vehicles with the various technologies manufacturers are expected to use to meet future CAFE and CO2 standards. They include materials, labor, and variable energy costs required to produce and assemble the vehicle. However, they do not E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations include overhead costs required to develop and produce the vehicle, costs incurred by manufacturers or dealers to sell vehicles, or the profit manufacturers and dealers make from their investments. All of these items contribute to the price consumers ultimately pay for the vehicle. These components of retail prices are illustrated in Table VI–23 below. In addition to direct manufacturing costs, the agencies estimated and considered indirect manufacturing costs. To estimate indirect costs, direct manufacturing costs are multiplied by a factor to represent the average price for fuel-saving technologies at retail. In the Draft TAR and preceding CAFE and safety rulemaking analyses, NHTSA relied on a factor, referred to as the retail price equivalent (RPE), to account for indirect manufacturing costs. The RPE accounts for indirect costs like engineering, sales, and administrative support, as well as other overhead costs, business expenses, warranty costs, and return on capital considerations. In the Draft TAR (and subsequent Determination) as well as the 2012 rulemaking analysis, EPA applied an ‘‘Indirect Cost Multiplier’’ (ICM) approach that it first applied in the 2010 rulemaking regarding standards for MYs 2012–2016, which also accounted for indirect manufacturing costs, albeit in a different way than the RPE approach. Some commenters recommended the agencies rely on the ICM approach for the current rulemaking, citing EPA’s prior peer review and use of this approach.629 Others supported the agencies’ reliance on the RPE approach, citing the National Research Council’s observations in 2015 that the ICM approach lacks an empirical basis.630 The agencies have carefully considered these comments, and conclude that while the ICM approach has conceptual merit, its application requires a range of specific estimates, and data to support such estimates is scant and, in some cases, nonexistent. The agencies have, therefore, applied the RPE approach for this final rule, as in the NPRM analysis and other rulemaking analyses. The following sections discuss both approaches in detail to explain why the RPE approach was chosen for this final rule. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 629 See, e.g., ICCT, NHTSA–2018–0067–11741, Attachment 3, at I–83. See also CFA, NHTSA–2018– 0067–12005, Attachment B, at p.189. 630 See, e.g., Alliance, NHTSA–2018–0067–12073, at 143. See also National Research Council, ‘‘Cost, Effectiveness, and Deployment of Fuel Economy Technologies for Light-Duty Vehicles,’’ 2015, available at https://www.nap.edu/catalog/21744/ cost-effectiveness-and-deployment-of-fuel-economytechnologies-for-lightduty-vehicles (‘‘. . . the empirical basis for such multipliers is still lacking, and, since their application depends on expert judgment, it is not possible for to determine whether the Agencies’ ICMs are accurate or not’’). PO 00000 Frm 00178 Fmt 4701 Sfmt 4700 (1) Retail Price Equivalent Historically, the method most commonly used to estimate indirect costs of producing a motor vehicle has been the retail price equivalent (RPE). The RPE markup factor is based on an examination of historical financial data contained in 10–K reports filed by manufacturers with the Securities and Exchange Commission (SEC). It represents the ratio between the retail price of motor vehicles and the direct costs of all activities that manufacturers engage in, including the design, development, manufacturing, assembly, E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.115</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24350 24351 and sales of new vehicles, refreshed vehicle designs, and modifications to meet safety or fuel economy standards. Figure VI–7 indicates that for more than three decades, the retail price of motor vehicles has been, on average, roughly 50 percent above the direct cost expenditures of manufacturers. This ratio has been remarkably consistent, averaging roughly 1.5 with minor variations from year to year over this period. At no point has the RPE markup exceeded 1.6 or fallen below 1.4.631 During this time frame, the average annual increase in real direct costs was 2.5 percent, and the average annual increase in real indirect costs was also 2.5 percent. Figure VI–7 illustrates the historical relationship between retail prices and direct manufacturing costs.632 An RPE of 1.5 does not imply that manufacturers automatically mark up each vehicle by exactly 50 percent. Rather, it means that, over time, the competitive marketplace has resulted in pricing structures that average out to this relationship across the entire industry. Prices for any individual model may be marked up at a higher or lower rate depending on market demand. The consumer who buys a popular vehicle may, in effect, subsidize the installation of a new technology in a less marketable vehicle. But, on average, over time and across the vehicle fleet, the retail price paid by consumers has risen by about $1.50 for each dollar of direct costs incurred by manufacturers. It is also important to note that direct costs associated with any specific technology will change over time as some combination of learning and resource price changes occurs. Resource costs, such as the price of steel, can fluctuate over time and can experience real long-term trends in either direction, depending on supply and demand. However, the normal learning process generally reduces direct production costs as manufacturers refine production techniques and seek out less costly parts and materials for increasing production volumes. By contrast, this learning process does not generally influence indirect costs. The implied RPE for any given technology would thus be expected to grow over time as direct costs decline relative to indirect costs. The RPE for any given year is based on direct costs of technologies at different stages in their learning cycles, and which may have different implied RPEs than they did in previous years. The RPE averages 1.5 across the lifetime of technologies of all ages, with a lower average in earlier years of a technology’s life, and, because of learning effects on direct costs, a higher average in later years. The RPE has been used in all NHTSA safety and most previous CAFE rulemakings to estimate costs. The National Academy of Sciences recommends RPEs of 1.5 for suppliers and 2.0 for in-house production be used to estimate total costs. The Alliance of Automobile Manufacturers also advocates these values as appropriate markup factors for estimating costs of technology changes. An RPE of 2.0 has also been adopted by a coalition of environmental and research groups (NESCCAF, ICCT, Southwest Research Institute, and TIAX–LLC) in a report on reducing heavy truck emissions, and 2.0 is recommended by the U.S. Department of Energy for estimating the cost of hybrid-electric and automotive fuel cell costs ((see Vyas et al. (2000) in Table VI–24, below). Table VI–24 below lists other estimates of the RPE. Note that all RPE estimates vary between 1.4 and 2.0, with most in the 1.4 to 1.7 range. 631 Based on data from 1972–1997 and 2007. Data were not available for intervening years, but results for 2007 seem to indicate no significant change in the historical trend. 632 Rogozhin, A., Gallaher, M., & McManus, W., 2009, Automobile Industry Retail Price Equivalent and Indirect Cost Multipliers. Report by RTI International to Office of Transportation Air Quality. U.S. Environmental Protection Agency, RTI Project Number 0211577.002.004, February, Research Triangle Park, N.C. Spinney, B.C., Faigin, B., Bowie, N., & St. Kratzke, 1999, Advanced Air Bag Systems Cost, Weight, and Lead Time analysis Summary Report, Contract NO. DTNH22–96–0– 12003, Task Orders—001, 003, and 005. Washington, DC, U.S. Department of Transportation. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00179 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.116</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations The RPE hasthus enjoyed widespread use and acceptance by a variety of governmental, academic, and industry organizations. The RPE has been the most commonly used basis for indirect cost markups in regulatory analyses. However, as noted above, the RPE is an aggregate measure across all technologies applied by manufacturers and is not technology specific. A more detailed examination of these technologies is possible through an alternative measure, the indirect cost multiplier, which was developed to focus more specifically on technologies used to meet CAFE and CO2 standards. khammond on DSKJM1Z7X2PROD with RULES2 (2) Indirect Cost Multiplier A second approach to accounting for indirect costs is the indirect cost multiplier (ICM). ICMs specifically evaluate the components of indirect costs likely to be affected by vehicle modifications associated with environmental regulation. EPA 633 Duleep, K.G. ‘‘2008 Analysis of Technology Cost and Retail Price.’’ Presentation to Committee on Assessment of Technologies for Improving Light Duty Vehicle Fuel Economy, January 25, Detroit, MI.; Jack Faucett Associates, September 4, 1985. Update of EPA’s Motor Vehicle Emission Control Equipment Retail Price Equivalent (RPE) Calculation Formula. Chevy Chase, MD—Jack Faucett Associates; McKinsey & Company, October 2003. Preface to the Auto Sector Cases. New Horizons—Multinational Company Investment in Developing Economies, San Francisco, CA.; NRC (National Research Council), 2002. Effectiveness and Impact of Corporate Average Fuel Economy Standards, Washington, DC—The National Academies Press; NRC, 2011. Assessment of Fuel Economy Technologies for Light Duty Vehicles. Washington, DC—The National Academies Press; Sierra Research, Inc., November 21, 2007, Study of Industry-Average Mark-Up Factors used to Estimate Changes in Retail Price Equivalent (RPE) for Automotive Fuel Economy and Emissions Control Systems, Sacramento, CA—Sierra Research, Inc.; Vyas, A. Santini, D., & Cuenca, R. 2000. Comparison of Indirect Cost Multipliers for Vehicle Manufacturing. Center for Transportation Research, Argonne National Laboratory, April. Argonne, Ill. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 developed the ICM concept to enable the application of markups more specific to each technology. For example, the indirect cost implications of using tires with better rolling resistance would not be the same as those for developing an entire new hybrid vehicle technology, which would require far more R&D, capital investment, and management oversight. With more than 80 different technologies available to incrementally achieve fuel economy improvements,634 a wide range of indirect cost effects might be expected. ICMs attempt to isolate only those indirect costs that would have to change to develop a specific technology. Thus, for example, if a company were to hire additional staff to sell vehicles equipped with fuel economy improving technology, or to search the technology requirements of new CO2 or CAFE standards, the cost of these staff would be included in ICMs. However, if these functions were accomplished by existing staff, they would not be included. For example, if an executive who normally devoted 10 percent of his time to fuel economy standards compliance were to devote 50 percent of his time in response to new more stringent requirements, his salary would not be included in ICMs because he would be paid the same salary regardless of whether he devoted his time to addressing CAFE requirements, developing new performance technologies, or improving the company’s market share. ICMs thus do not account for the diverted resources required for manufacturers to meet these standards, but rather for the net change in costs manufacturers might experience 634 There are roughly 40 different basic unique technologies, but variations among these technologies roughly double the possible number of different technology applications. PO 00000 Frm 00180 Fmt 4701 Sfmt 4700 because of hiring additional personal or acquiring additional assets or services. For past rulemakings EPA developed both short-term and long-term ICMs. Long-term ICMs are lower than shortterm ICMs. This decline reflects the belief that many indirect costs will decline over time. For example, research is initially required to develop a new technology and apply it throughout the vehicle fleet, but a lower level of research will be required to improve, maintain, or adapt that new technology to subsequent vehicle designs. While the RPE was derived from data in financial statements (reflecting realworld operating and financial results), no similar data sources were available to estimate ICMs. ICMs are based on the RPE, broken into its components, as shown in Table VI–25. Adjustment factors were then developed for those components, based on the complexity and time frame of low-, medium-, and high-complexity technologies. The adjustment factors were developed from two panels of engineers with background in the automobile industry. Initially, a group of engineers met and developed an estimate of ICMs for three different technologies. This ‘‘consensus’’ panel examined one low complexity technology, one medium complexity technology, and one high complexity technology, with the initial intent of using these technologies to represent ICM factors for all technologies falling in those categories. At a later date, a second panel was convened to examine three more technologies (one low, one medium, and one high complexity), using a modified Delphi approach to estimate indirect cost effects. The results from the second panel identified the same pattern as those of the original report—the indirect cost multipliers increase with the E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.117</GPH> 24352 complexity of the technology and decrease over time. The values derived in process are higher than those in the RPE/IC Report by values ranging from 0.09 (that is, the multiplier increased from 1.20 to 1.29) to 0.19 (the multiplier increased from 1.45 to 1.64). This variation may be due to differences in the technologies used in each panel. The results are shown in Figure VI–8, together with the historical average RPE. In subsequent CAFE and CO2 analyses for MYs 2011, as well as for the 2012– 2016 rulemaking, a simple average of the two resulting ICMs in the low and medium technology complexity categories was applied to direct costs for all unexamined technologies in each specific category. For high complexity technologies, the lower consensus-based estimate was used for high complexity technologies currently being produced, while the higher modified Delphi-based estimate was used for more advanced technologies, such as plug-in hybrid or electric vehicles, which had little or no current market penetration. Note that ICMs originally did not include profit or ‘‘return on capital,’’ a fundamental difference from the RPE. However, prior to the 2012–2016 CAFE analysis, ICMs were modified to include provision for return on capital. revisited technologies evaluated by EPA staff and reconsidered their method of application. The agencies were concerned that averaging consensus and modified Delphi ICMs might not be the most accurate way to develop an estimate for the larger group of unexamined technologies. Specifically, there was concern that some technologies might not be representative of the larger groups they were chosen to represent. Further, the agencies were concerned that the values developed under the consensus method were not subject to the same analytical discipline as those developed from the modified Delphi method. As a result, the agencies relied primarily on the modified Delphibased technologies to establish their revised distributions. Thus, for the MY 2017–2025 analysis, the agencies used the following basis for estimating ICMs: • All low complexity technologies were estimated to equal the ICM of the modified Delphi-based low technologypassive aerodynamic improvements. • All medium complexity technologies were estimated to equal the ICM of the modified Delphi-based medium technology-engine turbo downsizing. • Strong hybrids and non-battery plug-in hybrid electric vehicles (PHEVs) were estimated to equal the ICM of the high complexity consensus-based high technology-hybrid electric vehicle. • PHEVs with battery packs and full electric vehicles were estimated to equal the ICM of the high complexity modified Delphi-based high technologyplug-in hybrid electric vehicle. In addition to shifting the proxy basis for each technology group, the agencies reexamined each technology’s complexity designation in light of the examined technologies that would serve as the basis for each group. The resulting designations together with the associated proxy technologies are shown in Table VI–25. (3) Application of ICMs in the 2017– 2025 Analysis For the model year 2017–2025 rulemaking analysis, NHTSA and EPA khammond on DSKJM1Z7X2PROD with RULES2 24353 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00181 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.118</GPH> Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 Many basic technologies noted in Table VI–25 have variations sharing the same complexity designation and ICM estimate. Table VI–26 lists each technology used in the CAFE model VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 together with their ICM category and the year through which the short-term ICM would be applied. Note that the number behind each ICM category designation refers to the source of the ICM estimate, PO 00000 Frm 00182 Fmt 4701 Sfmt 4700 with 1 indicating the consensus panel and 2 indicating the modified Delphi panel. BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.119</GPH> 24354 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00183 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24355 ER30AP20.120</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00184 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.121</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24356 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00185 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24357 ER30AP20.122</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 BILLING CODE 4910–59–C An additional adjustment was made to ICMs to account for the fact that they were derived from the RPE analysis for a specific year (2007). The agencies believed it would be more appropriate to base ICMs on the expected long-term average RPE rather than that of one specific year. To account for this, ICMs were normalized to an average RPE multiplier level of 1.5. Table VI–27 lists values of ICMs by technology category used in the previous MY 2017–2025 rulemaking. As noted previously, the Low 1 and Medium 1 categories, which were derived using the initial consensus panel, are not used. Short-term values applied to CAFE technologies thus range from 1.24 for Low complexity technologies, 1.39 for Medium complexity technologies, 1.56 for High1 complexity technologies, and 1.77 for High2 complexity technologies. When long-term ICMs are applied in the year following that noted in the far-right column of Table VI–27, these values will drop to 1.19 for Low, 1.29 for Medium, 1.35 for High1 and 1.50 for High2 complexity technologies. Note that ICMs for warranty costs are listed separately in Table VI–27. This was done because warranty costs are treated differently than other indirect costs. In some previous analyses (prior to MY 2017–2025), learning was applied directly to total costs. However, the agencies believe learning curves are more appropriately applied only to direct costs, with indirect costs established up front based on the ICM and held constant while direct costs are reduced by learning. Warranties are an exception to this because warranty costs involve future replacement of defective parts, and the cost of these parts would reflect the effect of learning. Warranty costs were thus treated as being subject to learning along with direct costs.635 The effect of learning on direct costs, together with the eventual substitution of lower long-term ICMs, causes the effective markup from ICMs to differ from the initial ICM on a yearly basis. An example of how this occurs is provided in Table VI–28.636 This table, which was originally developed for the MY 2017–2025 analysis, traces the effect of learning on direct costs and its implications for both total costs and the ICM-based markup. Direct costs are assigned a value (proportion) of 1 to facilitate analysis on the same basis as ICMs (in an ICM markup factor, the proportion of direct costs is represented by 1 while the proportion of indirect costs is represented by the fraction of 1 to the right of the decimal.) Table VI– 28 examines the effects of these factors on turbocharged downsized engines, one of the more prevalent CAFE technologies. 635 Note that warranty costs also involve labor costs for installation. This is typically done at dealerships, and it is unlikely labor costs would be subject to learning curves that affect motor vehicle parts or assembly costs. However, the portion of these costs that is due to labor versus that due to parts is unknown, so for this analysis, learning is applied to the full warranty cost. 636 Table VI–22 illustrates the learning process from the base year consistent with the direct cost estimate obtained by the agencies. It is a mature technology well into the flat portion of the learning curve. Note that costs were actually applied in this rulemaking example beginning with MY 2017. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00186 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.124</GPH> Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations ER30AP20.123</GPH> 24358 The second column of Table VI–28 lists the learning schedule applied to turbocharged downsized engines. Turbocharged downsized engines are a mature technology, so the learning schedule captures the relatively flat portion of the learning curve occurring after larger decreases have already reduced direct costs. The cost basis for turbocharged downsized engines in the analysis was effective in 2012, so this is the base year for this calculation when direct costs are set to 1. The third column shows the progressive decline in direct costs as the learning schedule in column 2 is applied to direct costs. Column 4 contains the value of all indirect costs except warranty. Turbocharged downsized engines are a medium-complexity technology, so this value is taken from the Medium2 row of Table VI–27. The initial value in 2012 is the short-term value, which is used through 2018. During this time, these indirect costs are not affected by learning, and they remain constant. Beginning in 2019, the long-term ICM from Table VI–27 is applied. The fifth column contains warranty costs. As previously mentioned, these costs are considered to be affected by VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 learning like direct costs, so they decline steadily until the long-term ICM is applied in 2019, at which point they drop noticeably before continuing their gradual decline. In the sixth column, direct and indirect costs are totaled. Results indicate a decline in total costs of roughly 30 percent during this 14year period. The last column shows the effective ICM-based markup, which is derived by dividing total costs by direct costs. Over this period, the ICM-based markup rose from the initial short-term ICM level of 1.39 to 1.45 in 2018. It then declined to 1.35 in 2019 when the longterm ICM was applied to the 2019 direct cost. Over the remaining years, it gradually rises back up to 1.41 as learning continues to degrade direct costs. There are thus two somewhat offsetting processes affecting total costs derived from ICMs. The first is the learning curve, which reduces direct costs, which raises the effective ICMbased markup. As noted previously, learning reflects learned efficiencies in assembly methods as well as reduced parts and materials costs. The second is the application of a long-term ICM, which reduces the effective ICM-based PO 00000 Frm 00187 Fmt 4701 Sfmt 4700 24359 markup. This represents the reduced burden needed to maintain new technologies once they are fully developed. In this case, the two processes largely offset one another and produce an average real ICM over this 14-year period that roughly equals the original short-term ICM. Figure VI–9 illustrates this process for each of the 4 technologies used to represent the universe of fuel economy and CO2 improving technologies. As with the turbocharged engines, aerodynamic improvements and mild hybrid vehicles show a gradual increase in the effective ICM-based markup through the point where the long-term ICM is applied. At that time, the ICMbased markup makes an abrupt decline before beginning a gradual rise. The decline due to application of long-term ICMs is particularly pronounced in the case of the mild hybrid—even more so than for the advanced hybrid. The advanced hybrid ICM behaves somewhat differently because it is shown through its developing stages when more radical learning is applied, but only every few years. This produces a significant step-up in ICM levels concurrent with each learning E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.125</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations application, followed by a sharp decline when the long-term ICM is applied. After that, it begins a gradual rise as more moderate learning is applied to reflect its shift to a mature technology. Note that as with the turbocharged downsized engine example above, for the aerodynamic improvements and mild hybrid technologies, the offsetting processes of learning and long-term ICMs result in an average ICM over the full time frame that is roughly equal to the initial short-term ICM. However, the advanced hybrid ICM rose to a level significantly higher than the initial ICM. This is a direct function of the rapid learning schedule applied in the early years to this developing technology. Brand new technologies might thus be expected to have effective lifetime ICM markups exceeding their initial ICMs, while more mature technologies are more likely to experience ICMs over their remaining life span that more closely approximate their initial ICMs. ICMs for these 4 technologies would drive the indirect cost markup rate for the analysis. However, the effect on total costs is also a function of the relative incidence of each of the 50+ technologies shown in Table VI–26 which are assumed to have ICMs similar to one of these 4 technologies. The net effect on costs of these ICMs is also influenced by the learning curve appropriate to each technology, creating numerous different and unique ICM paths. The average ICM applied by the model is also a function of each technology’s direct cost and because ICMs are applied to direct costs, the measured indirect cost is proportionately higher for any given ICM when direct costs are higher. The average ICM applied to the fleet for any given model year is calculated as follows: where: D = direct cost of each technology A = application rate for each technology ICM = average ICM applied to each technology and n = 1, 2 . . . . 88 The CAFE model predicts technology application rates assuming manufacturers will apply technologies VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00188 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.127</GPH> Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations ER30AP20.126</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24360 24361 to meet standards in a logical fashion based on estimated costs and benefits. The application rates will thus be different for each model year and for each alternative scenario examined. For the MY 2017–2025 FRIA, to illustrate the effects of ICMs on total technology costs, NHTSA calculated the weighted average ICM across all technologies for the preferred alternative.637 This was done separately for each vehicle type and then aggregated based on predicted sales of each vehicle type used in the model. Results are shown in Table VI– 29. The ICM-based markups in Table VI– 29 were derived in a manner consistent with the way the RPE is measured, that is, they reflect combined influences of direct cost learning and changes in indirect cost requirements weighted by both the incidence of each technology’s adaptation and the relative direct cost of each technology. The results indicate generally higher ICMs for passenger cars than for light trucks. This is a function of the technologies estimated to be adopted for each respective vehicle type, especially in later years when hybrids and electric vehicles become more prevalent in the passenger car fleet. The influence of these advanced vehicles is driven primarily by their direct costs, which greatly outweigh the costs of other technologies. This results in the application of much more weight to their higher ICMs. This is most notable in MYs 2024 and 2025 for passenger cars, when electric vehicles begin to enter the fleet. The average ICM increased 0.013 in 2024 primarily because of these vehicles. It immediately dropped 0.017 in 2025 because both an additional application of steep (20 percent) learning is applied to the direct cost of these vehicles (which reduces their relative weight), and the long-term ICM becomes effective in that year (which decreases the absolute ICM factor). Both influences occur one year after these vehicles begin to enter the fleet because of CAFE requirements. ICMs also change over time, again, reflecting the different mix of technologies present during earlier years but that are often replaced with more expensive technologies in later years. Across all model years, the wideranging application of diverse technologies required to meet CAFE and CO2 standards produced an average ICM-based markup (or RPE equivalent) of approximately 1.34, applying only 67 percent of the indirect costs found in the RPE and implying total costs 11 percent below those predicted by the RPE-based calculation. As noted above, the RPE and ICM assign different markups over direct manufacturing costs, and thus imply different total cost estimates for CAFE and CO2 technologies. While there is a level of uncertainty associated with both markups, this uncertainty stems from different issues. The RPE is derived from financial statements and is thus grounded in historical data. Although compilation of this data is subject to some level of interpretation, the two independent researchers who derived RPE estimates from these financial reports each reached essentially identical conclusions, placing the RPE at roughly 1.5. All other estimates of the RPE fall between 1.4 and 2.0, and most are between 1.4 and 1.7. There is thus a reasonable level of consistency among researchers that RPEs are 1.4 or greater. In addition, the RPE is a measure of the cumulative effects of all operations manufacturers undertake in the course of producing their vehicles, and is thus not specific to individual technologies, nor of CAFE or CO2 technologies in particular. Because this provides only a single aggregate measure, using the RPE multiplier results in the application of a common incremental markup to all technologies. This assures the aggregate cost effect across all technologies is consistent with empirical data, but it does not allow for indirect cost discrimination among different technologies or over time. Because it is applied across all changes, this implies the markup for some technologies is likely to be understated, and for others it is likely to be overstated. By contrast, the ICM process derives markups specific to several CAFE and CO2 technologies, but these markups 637 For each alternative, this rulemaking examined numerous scenarios based on different assumptions, and these assumptions could influence the relative frequency of selection of different technologies, which in turn could affect the average ICM. The scenario examined here assumed a 3 percent discount rate, a 1-year payback period, real world application of expected civil VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 (4) Uncertainty PO 00000 Frm 00189 Fmt 4701 Sfmt 4700 penalties, and reflects expected voluntary overcompliance by manufacturers. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.128</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations have no basis in empirical data. They are based on informed judgment of a panel of engineers with auto industry experience regarding cost effects of a small sample (roughly 8 percent) of the 50+ technologies applied to achieve compliance with CAFE and CO2 standards. Uncertainty regarding ICMs is thus based both on the accuracy of the initial assessments of the panel on the examined technologies and on the assumption that these 4 technologies are representative of the remaining technologies that were not examined. Both agencies attempted to categorize these technologies in the most representative way possible. However, while this represented the best judgment of EPA and NHTSA’s engineering staffs at that time, the actual effect on indirect costs remains uncertain for most technologies. As with RPEs, this means that even if ICMs were accurate for the specific technologies examined, indirect cost will be understated for some technologies and overstated for others. There was considerable uncertainty demonstrated in the ICM panel’s assessments, as illustrated by the range of estimates among the 14 modified Delphi panel members surrounding the central values reported by the panel. These ranges are shown in Table VI–30 and Figure VI–10, Figure VI–11, and Figure VI–12 below. For the low complexity technology, passive aerodynamic improvements, panel responses ranged from a low of basically no indirect costs (1.001 short term and 1.0 long term), to a high of roughly a 40 percent markup (1.434 and 1.421). For the medium complexity technology, turbo charged and downsized engines, responses ranged from a low estimate implying almost no indirect cost (1.018 and 1.011), to a high estimate implying that indirect costs for this technology would roughly equal the average RPE (1.5) for all technologies (1.527 and 1.445). For the high complexity technology, plug-in hybrid electric vehicles, responses ranged from a low estimate that these vehicles would require significantly less indirect cost than the average RPE (1.367 and 1.121) to a high estimate implying they would require more indirect costs than the average RPE (2.153 and 1.691). There was considerable diversity of opinion among the panel members.638 This is apparent in Figure VI–10, Figure VI–11, and Figure VI–12, which show the 14 panel members’ final estimates for short-term ICMs as scatter plots. 638 Sample confidence intervals, which mitigate the effect of outlying opinions, indicate a less extreme but still significant range of ICMs. Applying mean ICMs helps mitigate these potential differences, but there is clearly a significant level of uncertainty regarding indirect costs. A t- distribution is used to estimate confidence intervals because of the small sample size (14 panel members). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00190 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.129</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24362 ER30AP20.131</GPH> 24363 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00191 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.130</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Although these results were based on modified Delphi panel techniques, it is apparent the goal of the Delphi process, an eventual consensus or convergence of opinion among panel experts, was not achieved. Given this lack of consensus and the divergence of ICM-based results from the only available empirical measure (the RPE), there is considerable uncertainty that current ICM estimates provide a realistic basis of estimating indirect costs. ICMs have not been validated through a direct accounting of actual indirect costs for individual technologies, and they produce results that conflict with the only available empirical evidence of indirect cost markups. Further, they are intended to represent indirect costs specifically associated with the most comprehensive redesign effort ever undertaken by the auto industry, with virtually every make/model requiring ground-up design modifications to comply. This includes entirely new vehicle design concepts, extensive material substitution, and complete drivetrain redesigns, all of which require significant research efforts and assembly plant redesign. Under these circumstances, one might expect indirect costs to equal or possibly increase above the historical average, but not to decrease, as implied by estimated ICMs. For regulations, such as the CAFE and CO2 emission standards under consideration, that drive changes to nearly every vehicle system, the overall average indirect costs should align with the RPE value. Applying RPE to the cost for each technology assures that alignment. In the 2015 NAS study, the Committee stated a conceptual VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 agreement with the ICM method because ICM takes into account design challenges and the activities required to implement each technology. However, although endorsing ICMs as a concept, the NAS Committee stated ‘‘the empirical basis for such multipliers is still lacking, and, since their application depends on expert judgment, it is not possible to determine whether the Agencies’ ICMs are accurate or not.’’ 639 NAS also stated ‘‘the specific values for the ICMs are critical because they may affect the overall estimates of costs and benefits for the overall standards and the cost effectiveness of the individual technologies.’’ 640 The Committee encouraged continued research into ICMs given the lack of empirical data for them to evaluate ICMs used by the agencies in past analyses. On balance, and considering the relative merits of both approaches for realistically estimating indirect costs, the agencies consider the RPE method to be a more reliable basis for estimating indirect costs. (5) Using RPE To Evaluate Indirect Costs in This Analysis To ensure overall indirect costs in the analysis align with the historical RPE value, the primary analysis has been developed based on applying the RPE value of 1.5 to each technology. As noted previously, the RPE is the ratio of aggregate retail prices to aggregate direct 639 National Research Council of the National Academies (2015). Cost, Effectiveness, and Deployment of Fuel Economy Technologies for Light-Duty Vehicles. https://www.nap.edu/ resource/21744/deps_166210.pdf. 640 Ibid. PO 00000 Frm 00192 Fmt 4701 Sfmt 4700 manufacturing costs. The ratio already reflects the mixture of learned costs of technologies at various stages of maturity. Therefore, the RPE is applied directly to the learned direct cost for each technology in each year. This was previously done in the MY 2017–2025 FRIA for the preferred alternative for that rulemaking, used in the above analysis of average ICMs. Results are shown in Table VI–31. Recognizing there is uncertainty in any estimate of indirect costs, a sensitivity analyses of indirect costs has also been conducted by applying a lower RPE value as a proxy for the ICM approach. This value was derived from a direct comparison of incremental technology costs determined in the MY 2017–2025 FRIA.641 This analysis is summarized in Table VI–31 below. From this table, total costs were estimated to be roughly 18 percent lower using ICMs compared to the RPE. As previously mentioned, there are two different reasons for these differences. The first is the direct effect of applying a higher retail markup. The second is an indirect effect resulting from the influence these differing markups have on the order of the selection of technologies in the CAFE model, which can change as different direct cost levels interact with altered retail markups, shifting their relative overall effectiveness. The relative effects of ICMs may vary somewhat by scenario, but in this case, the application of ICMs produces total 641 See Table 5–9a in Final Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2017–MY 2025 Passenger Cars and Light Trucks. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.132</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24364 technology cost estimates roughly 18 percent lower than those that would result from applying a single RPE factor to all technologies, or, conversely, the RPE produces estimates that averaged 21 percent higher than the ICM. Under the CAFE model construct, which will apply an alternate RPE to the same base technology profile to represent ICMs, this implies an RPE equivalent of 1.24 would produce similar net impacts [1.5/ (1 + x) = 1.21, x = 0.24]. This value is applied for the ICM proxy estimate. Additional values were also examined over a range of 1.1–2.0. The results, as well as the reference case using the 1.5 RPE, are summarized in Table VI–32. Several responders submitted comments on the issue of indirect costs. The International Council on Clean Transportation (ICCT) stated that ‘‘The agencies abandoned their previouslyused indirect cost multiplier method for estimating total costs, which was vetted with peer review, and more complexly handled differing technologies with different supply chain and manufacturing aspects. The agencies have, at this point, opted to use a simplistic retail price equivalent method, which crudely assumes all technologies have a 50 percent markup from the direct manufacturing technology cost. We recommend the agencies revert back to the previously- used and better substantiated ICM approach.’’ 642 A private commenter, Thomas Stephens, noted that ‘‘In Section II. Technical Foundation for NPRM Analysis, under 1. Data Sources and Processes for Developing Individual Technology Assumptions, the agencies state that indirect costs are estimated using a Retail Price Equivalent (RPE) factor. Concerns with RPE factors and the difficulty of accounting for differences in indirect costs of different technologies when using this approach were identified by the EPA (Rogozhin et al., Using indirect cost multipliers to estimate the total cost of adding new technology in the automobile industry, International Journal of Production Economics 124, 360–368, 2010), which suggested using indirect cost (IC) multipliers instead of RPE factors. The EPA developed and updated IC multipliers for relevant vehicle technologies with automotive industry input and review. The agencies should consider using these IC multipliers to estimate indirect manufacturing costs instead of RPE factors.’’ 643 By contrast, the Alliance of Automobile Manufacturers (The Alliance) ‘‘supports the use of retail VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 642 NHTSA–2018–0067–11741. PO 00000 Frm 00193 Fmt 4701 Sfmt 4700 643 NHTSA–2018–0067–12067. E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.134</GPH> 24365 ER30AP20.133</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24366 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations price equivalents in the compliance cost modeling to estimate the indirect costs associated with the additional added technology required to meet a given future standard. The alternative indirect cost multiplier (‘‘ICM’’) approach is not sufficiently developed for use in rulemaking. As noted by the National Research Council, the indirect cost multipliers previously developed by EPA have not been validated with empirical data.644 Furthermore, in reference to the memorandum documenting the development of ICMs previously used by EPA, Exponent Failure Analysis Associates found that, Past Toyota Comments on AtkinsonCycle Benefits Have Addressed Only Those Derived From Variable Valve Timing In response to these comments the agencies continue to find the RPE approach preferable to the ICM approach, at least at this stage in the development ICM estimates, for the reasons discussed both above and previously in the NPRM. The agencies note that the concerns are not with the concept of ICMs, but rather with the judgment-based values suggested for use as ICMs, which have not been validated, and which conflict with the empirically derived RPE value. The agencies will continue to monitor any developments in ICM methodologies as part of future rulemakings. c) Stranded Capital Costs Past analyses accounted for costs associated with stranded capital when fuel economy standards caused a technology to be replaced before its costs were fully amortized. The idea behind stranded capital is that manufacturers amortize research, development, and tooling expenses over many years, especially for engines and transmissions. The traditional production life-cycles for transmissions and engines have been a decade or longer. If a manufacturer launches or updates a product with fuel-saving technology, and then later replaces that technology with an unrelated or different fuel-saving technology before khammond on DSKJM1Z7X2PROD with RULES2 644 Cost, Effectiveness, and Development of Fuel Economy Technologies for Light-Duty Vehicles, pages 248–49, National research Council, the National Academies Press (2015). VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 the equipment and research and development investments have been fully paid off, there will be unrecouped, or stranded, capital costs. Quantifying stranded capital costs accounts for such lost investments. In the Draft TAR and NPRM analyses, only a few technologies for a few manufacturers were projected to have stranded capital costs. As more technologies are included in this analysis, and as the CAFE model has been expanded to account for platform and engine sharing and updated with redesign and refresh cycles, accounting for stranded capital has become increasingly complex. Separately, manufacturers may be shifting their investment strategies in ways that may alter how stranded capital calculations were traditionally considered. For example, some suppliers sell similar transmissions to multiple manufacturers. Such arrangements allow manufacturers to share in capital expenditures, or amortize expenses more quickly. Manufacturers share parts on vehicles around the globe, achieving greater scale and greatly affecting tooling strategies and costs. Given these trends in the industry and their uncertain effect on capital amortization, and given the difficulty of handling this uncertainty in the CAFE model, this analysis does not account for stranded capital. The agencies’ analysis continues to rely on the CAFE model’s explicit year-by-year accounting for estimated refresh and redesign cycles, and shared vehicle platforms and engines, to moderate the cadence of technology adoption and thereby limit the implied occurrence of stranded capital and the need to account for it explicitly. The agencies will monitor these trends to assess the role of stranded capital moving forward. d) Cost Learning Manufacturers make improvements to production processes over time, which often result in lower costs. ‘‘Cost learning’’ reflects the effect of experience and volume on the cost of production, which generally results in better utilization of resources, leading to higher and more efficient production. As manufacturers gain experience through production, they refine production techniques, raw material and component sources, and assembly PO 00000 Frm 00194 Fmt 4701 Sfmt 4700 methods to maximize efficiency and reduce production costs. Typically, a representation of this cost learning, or learning curves, reflect initial learning rates that are relatively high, followed by slower learning as additional improvements are made and production efficiency peaks. This eventually produces an asymptotic shape to the learning curve, as small percent decreases are applied to gradually declining cost levels. These learning curve estimates are applied to various technologies that are used to meet CAFE standards. For the NPRM and this final rule, the agencies estimated cost learning by considering methods established by T.P. Wright 645 and later expanded upon by J.R. Crawford. Wright, examining aircraft production, found that every doubling of cumulative production of airplanes resulted in decreasing labor hours at a fixed percentage. This fixed percentage is commonly referred to as the progress rate or progress ratio, where a lower rate implies faster learning as cumulative production increases. J.R. Crawford expanded upon Wright’s learning curve theory to develop a single unit cost model,646 that estimates the cost of the nth unit produced given the following information is known: (1) Cost to produce the first unit; (2) cumulative production of n units; and (3) the progress ratio. As pictured in Figure VI–13, Wright’s learning curve shows the first unit is produced at a cost of $1,000. Initially cost per unit falls rapidly for each successive unit produced. However, as production continues, cost falls more gradually at a decreasing rate. For each doubling of cumulative production at any level, cost per unit declines 20 percent, so that 80 percent of cost is retained. The CAFE model uses the basic approach by Wright, where cost reduction is estimated by applying a fixed percentage to the projected cumulative production of a given fuel economy technology. 645 Wright, T.P., Factors Affecting the Cost of Airplanes. Journal of Aeronautical Sciences, Vol. 3 (1936), pp. 124–125. Available at http:// www.uvm.edu/pdodds/research/papers/others/ 1936/wright1936a.pdf. 646 Crawford, J.R., Learning Curve, Ship Curve, Ratios, Related Data, Burbank, California-Lockheed Aircraft Corporation (1944). E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations (1) Time Versus Volume-Based Learning For the 2012 joint CAFE/CO2 rulemaking, the agencies developed learning curves as a function of vehicle model year.647 Although the concept of this methodology is derived from Wright’s cumulative production volume-based learning curve, its application for CAFE and CO2 technologies was more of a function of time. More than a dozen learning curve schedules were developed, varying between fast and slow learning, and assigned to each technology corresponding to its level of complexity and maturity. The schedules were applied to the base year of direct manufacturing cost and incorporate a percentage of cost reduction by model year declining at a decreasing rate 647 CAFE 2012 Final Rule, NHTSA DOT, 77 FR 62624. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 through the technology’s production life. Some newer technologies experience 20 percent cost reductions for introductory model years, while mature or less complex technologies experience 0–3 percent cost reductions over a few years. In their 2015 report to Congress, the National Academy of Sciences (NAS) recommended the agencies should ‘‘continue to conduct and review empirical evidence for the cost reductions that occur in the automobile industry with volume, especially for large-volume technologies that will be relied on to meet the CAFE/GHG standards.’’ 648 In response, the agencies have incorporated statically projected cumulative volume production data of fuel economy improving technologies, representing an improvement over the previously used time-based method. Dynamic projections of cumulative production are not feasible with current CAFE model capabilities, so one set of projected cumulative production data for most vehicle technologies was developed for the purpose of determining cost impact. For many technologies produced and/or sold in the U.S., historical cumulative production data was obtained to establish a starting point for learning schedules. Groups of similar technologies or technologies of similar complexity may share identical learning schedules. The slope of the learning curve, which determines the rate at which cost 648 Cost, Effectiveness, and Deployment of Fuel Economy Technologies for Light-Duty Vehicles, National Research Council of the National Academies (2015), available at https:// www.nap.edu/resource/21744/deps_166210.pdf. PO 00000 Frm 00195 Fmt 4701 Sfmt 4700 reductions occur, has been estimated using research from an extensive literature review and automotive cost tear-down reports (see below). The slope of the learning curve is derived from the progress ratio of manufacturing automotive and other mobile source technologies. (2) Deriving the Progress Ratio Used in This Analysis Learning curves vary among different types of manufactured products. Progress ratios can range from 70 to 100 percent, where 100 percent indicates no learning can be achieved.649 Learning effects tend to be greatest in operations where workers often touch the product, while effects are less substantial in operations consisting of more automated processes. As automotive manufacturing plant processes become increasingly automated, a progress ratio towards the higher end would seem more suitable. The agencies incorporated findings from automotive cost-teardown studies with EPA’s literature review of learningrelated studies to estimate a progress ratio used to determine learning schedules of fuel economy improving technologies. EPA’s literature review examined and summarized 20 studies related to learning in manufacturing industries and mobile source manufacturing.650 649 Martin, J., ‘‘What is a Learning Curve?’’ Management and Accounting Web, University of South Florida, available at: https://www.maaw.info/ LearningCurveSummary.htm. 650 Cost Reduction through Learning in Manufacturing Industries and in the Manufacture of Mobile Sources, United States Environmental Protection Agency (2015). Prepared by ICF International and available at https://19january2017 E:\FR\FM\30APR2.SGM Continued 30APR2 ER30AP20.135</GPH> khammond on DSKJM1Z7X2PROD with RULES2 The analysis accounts for learning effects with model year-based cost learning forecasts for each technology that reduce direct manufacturing costs over time. The agencies evaluated the historical use of technologies, and reviewed industry forecasts to estimate future volumes for the purpose of developing the model year-based technology cost learning curves. The following section discusses the agencies’ development of model yearbased cost learning forecasts, including how the approach has evolved from the 2012 rulemaking for MY 2017–2025 vehicles, and how the progress ratios were developed for different technologies considered in the analysis. Finally, the agencies discuss how these learning effects are applied in the CAFE Model. 24367 The studies focused on many industries, including motor vehicles, ships, aviation, semiconductors, and environmental energy. Based on several criteria, EPA selected five studies providing quantitative analysis from the mobile source sector (progress ratio estimates from each study are summarized in Table VI–33, below). Further, those studies expand on Wright’s Learning Curve function by using cumulative output as a predictor variable, and unit cost as the response variable. As a result, EPA determined a best estimate of 84 percent as the progress ratio in mobile source industries. However, of those five studies, EPA at the time placed less weight on the Epple et al. (1991) study, because of a disruption in learning due to incomplete knowledge transfer from the first shift to introduction of a second shift at a North American truck plant. While learning may have decelerated immediately after adding a second shift, the agencies note that unit costs continued to fall as the organization gained experience operating with both shifts. The agencies now recognize that disruptions are an essential part of the learning process and should not, in and of themselves, be discredited. For this reason, the analysis uses a re-estimated average progress ratio of 85 percent from those five studies (equally-weighted). In addition to EPA’s literature review, this progress ratio estimate was informed based on NHTSA’s findings from automotive cost-teardown studies. NHTSA routinely performs evaluations of costs of previously issued Federal Motor Vehicle Safety Standards (FMVSS) for new motor vehicles and equipment. NHTSA’s engages contractors to perform detailed engineering ‘‘tear-down’’ analyses for representative samples of vehicles, to estimate how much specific FMVSS add to the weight and retail price of a vehicle. As part of the effort, cost and production volume are examined for automotive safety technologies. In particular, the agency estimated costs from multiple cost tear-down studies for technologies with actual production data from the Cost and weight added by the Federal Motor Vehicle Safety Standards for MY 1968–2012 passenger cars and LTVs (2017).656 NHTSA chose five vehicle safety technologies with sufficient data to estimate progress ratios of each, because these technologies are large-volume technologies and are used by almost all vehicle manufacturers. Table VI–34 below includes these five technologies and yields an average progress rate of 92 percent: snapshot.epa.gov/sites/production/files/2016-11/ documents/420r16018.pdf. 651 Argote, L., Epple, D., Rao, R. D., & Murphy, K., The acquisition and depreciation of knowledge in a manufacturing organization—Turnover and plant productivity, Working paper, Graduate School of Industrial Administration, Carnegie Mellon University (1997). 652 Benkard, C. L., Learning and Forgetting—The Dynamics of Aircraft Production, The American Economic Review, Vol. 90(4), pp. 1034–54 (2000). 653 Epple, D., Argote, L., & Devadas, R., Organizational Learning Curves—A Method for Investigating Intra-Plant Transfer of Knowledge Acquired through Learning by Doing, Organization Science, Vol. 2(1), pp. 58–70 (1991). 654 Epple, D., Argote, L., & Murphy, K., An Empirical Investigation of the Microstructure of Knowledge Acquisition and Transfer through Learning by Doing, Operations Research, Vol. 44(1), pp. 77–86 (1996). 655 Levitt, S. D., List, J. A., & Syverson, C., Toward an Understanding of Learning by Doing—Evidence from an Automobile Assembly Plant, Journal of Political Economy, Vol. 121 (4), pp. 643–81 (2013). 656 Simons, J. F., Cost and weight added by the Federal Motor Vehicle Safety Standards for MY 1968–2012 Passenger Cars and LTVs (Report No. DOT HS 812 354). Washington, DC—National Highway Traffic Safety Administration (November 2017), at pp. 30–33. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00196 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.137</GPH> Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations ER30AP20.136</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24368 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations For a final progress ratio used in the CAFE model, the five progress rates from EPA’s literature review and five progress rates from NHTSA’s evaluation of automotive safety technologies results were averaged. This resulted in an average progress rate of approximately 89 percent. Equal weight was placed on progress ratios from all 10 sources. More specifically, equal weight was placed on the Epple et al. (1991) study, because disruptions have more recently been recognized as an essential part in the learning process, especially in an effort to increase the rate of output. Further discussion of how the progress ratios were derived for this analysis is located in FRIA Section 9. ICCT commented that the choice to use safety technology as a model for fuel efficiency led to lower learning rates in the NPRM analysis compared to prior analyses.657 ICCT stated that safety technologies were chosen for the NPRM because they are used by almost every manufacturer, in contrast to fuel efficiency technologies, where not every manufacturer will use them, particularly when they are first introduced. ICCT stated that to show the impact of changing learning rates, the agencies should run a sensitivity analysis using the learning rates in the TAR, as well as EPA’s learning rates in its Final Determination. ICCT concluded that ‘‘[w]ithout doing so and without conducting a peer review of the change in approach, it appears clear the agencies have decided to switch to a new costing method that affects all future costs, but without any significant research justification, vetting, or review.’’ The agencies’ selection of a progress rate of 0.89 is based on an average of findings across research and literature reviews conducted by NHTSA and EPA. The EPA cited rates were derived from five studies selected from a sample of 20 transportation modal learning studies that were examined by an EPA contractor, ICF International.658 One of these 5 studies (Benkard (2000) examines learning in the commercial aircraft industry, which the author notes has many unique features that influence marginal costs. It also has the lowest progress rate. The agencies note that EPA regulates all mobile sources, and while the inclusion of non-passenger vehicle studies in their report was 657 NHTSA–2018–0067–11741. 658 Cost Reduction through Learning in Manufacturing Industries and in the Manufacture of Mobile Sources. United States Environmental Protection Agency. Prepared by ICF International and available at: https://19january 2017snapshot.epa.gov/sites/production/files/201611/documents/420r16018.pdf. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 justified, it may have biased the estimate of learning attributable to the motor vehicle industry. Notably, nearly all of the other studies included in the ICF International study found progress rates higher than the 0.84 rate selected by the authors at that time. In reviewing the ICF study, NHTSA found many other studies not included in the report, including many specific to the motor vehicle and environmental technology industries. Over 90 percent of those studies indicated higher progress ratios than ICF recommended.659 The agencies’ current approach includes a broader and more representative sample of these studies rather than the narrow sample selected by ICF. The agencies do not agree that safety technologies are adopted by all manufacturers at an early stage. Most safety technologies are initially offered as options or standard equipment on only a small segment of the vehicle fleet, typically luxury vehicles. After a number of years, these technologies may be adopted on less expensive vehicles, and eventually they will become required equipment on all vehicles, but the production process is gradual, as it is with fuel efficiency technologies. FMVSS are necessarily established as performance standards—and automakers are free to develop or choose from existing technologies to achieve such performance requirements—much like automakers can develop or choose from a number of established fuel efficiency technologies to achieve fuel economy requirements. Further, the derivation of progress ratios is based on the concept of a doubling of cumulative production, not time. Therefore, even if production continues at a different pace, it should not disqualify non-fuel efficiency studies. Moreover, the derivation of the progress ratio used in the TAR and Final Determination document were not confined to fuel efficiency technologies. In fact, as noted above, they even included at least one entirely unrelated study of the aircraft industry. Finally, the agencies note that the previous learning schedules used in the TAR and EPA’s Final Determination were only developed through 2025, whereas this final rule projects learning 659 See, for example, progress ratios of multiple technologies referenced in The Carbon Productivity Challenge: Curbing Climate Change and Sustaining Economic Growth, McKinsey Climate Change Special Initiative, McKinsey Global Institute, June 2008 (quoting from UC Berkeley Energy Resource Group, Navigant Consulting) and Technology Innovation for Climate Mitigation and its Relation to Government Policies, Edward S. Rubin, Carnegie Mellon University, Presentation to the UNFCCC Workshop on Climate Change Mitigation, Bonn, Germany, June 19, 2004. PO 00000 Frm 00197 Fmt 4701 Sfmt 4700 24369 through 2050. The previous learning schedules are thus not directly compatible with the analysis conducted in this Final Rule, making a sensitivity analysis problematic. (3) Obtaining Appropriate Baseline Years for Direct Manufacturing Costs To Create Learning Curves Direct manufacturing costs for each fuel economy improving technology were obtained from various sources, as discussed above. To establish a consistent basis for direct manufacturing costs in the rulemaking analysis, each technology cost is adjusted to MY 2018 dollars. For each technology, the DMC is associated with a specific model year, and sometimes a specific production volume, or cumulative production volume. The base model year is established as the MY in which direct manufacturing costs were assessed (with learning factor of 1.00). With the aforementioned data on cumulative production volume for each technology and the assumption of a 0.89 progress ratio for all automotive technologies, the agencies can solve for an implied cost for the first unit produced. For some technologies, the agencies used modestly different progress ratios to match detailed cost projections if available from another source (for instance, batteries for plugin hybrids and battery electric vehicles). This approach produced reasonable estimates for technologies already in production, and some additional steps were required to set appropriate learning rates for technologies not yet in production. Specifically, for technologies not yet in production in MY 2017 (the baseline analysis fleet), the cumulative production volume in MY 2017 is zero, because manufacturers have not yet produced the technologies. For pre-production cost estimates in the NPRM, the agencies often relied on confidential business information sources to predict future costs. Many sources for pre-production cost estimates include significant learning effects, often providing cost estimates assuming high volume production, and often for a timeframe late in the first production generation or early in the second generation of the technology. Rapid doubling and re-doubling of a low cumulative volume base with Wright’s learning curves can provide unrealistic cost estimates. In addition, direct manufacturing cost projections can vary depending on the initial production volume assumed. Accordingly, the agencies carefully examined direct costs with learning, and made adjustments to the starting point for those technologies on the learning curve to better align E:\FR\FM\30APR2.SGM 30APR2 24370 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations with the assumptions used for the initial direct cost estimate. khammond on DSKJM1Z7X2PROD with RULES2 (4) Cost Learning as Applied in the CAFE Model For the NPRM analysis, the agencies updated the manner in which learning effects apply to costs. In the Draft TAR analysis, the agencies had applied learning curves only to the incremental direct manufacturing costs or costs over the previous technology on the technology tree. In practice, two things were observed: (1) If the incremental direct manufacturing costs were positive, technologies could not become less expensive than their predecessors on the technology tree, and (2) absolute costs over baseline technology depended on the learning curves of root technologies on the technology tree. For the NPRM and final rule analysis, the agencies applied learning effects to the incremental cost over the null technology state on the applicable technology tree. After this step, the agencies calculated year-by-year incremental costs over preceding technologies on the tech tree to create the CAFE model inputs. As discussed below, for the final rule, the agencies revised the CAFE model to replace incremental cost estimates with absolute estimates, each specified relative to the null technology state on the applicable technology tree. This change facilitated quality assurance and is expected to make cost inputs more transparently relatable to detailed model output. Likewise, this change made it easier to apply learning curves in the course of developing inputs to the CAFE model. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 The agencies grouped certain technologies, such as advanced engines, advanced transmissions, and nonbattery electric components and assigned them to the same learning schedule. While these grouped technologies differ in operating characteristics and design, the agencies chose to group them based on their complexity, technology integration, and economies of scale across manufacturers. The low volume of certain advanced technologies, such as hybrid and electric technologies, poses a significant issue for suppliers and prevents them from producing components needed for advanced transmissions and other technologies at more efficient high scale production. The technology groupings were carried over from the NPRM analysis for the final rule analysis.660 Like the NPRM, this final rule analysis uses the same groupings that considers market availability, complexity of technology integration, and production volume of the technologies that can be implemented by manufacturers and suppliers. For example, technologies like ADEAC and VCR are grouped together; these technologies were not in production or were only in limited introduction in MY 2017, and are planned to be introduced in limited production by a few manufacturers. The details of these technologies are discussed in Section VI.C. In addition, for the final rule, as discussed in Section VI.A.4 Compliance Simulation, the agencies expanded 660 See PO 00000 PRIA Chapter 6 for technology groupings. Frm 00198 Fmt 4701 Sfmt 4700 model inputs to extend the explicit simulation of technology application through MY 2050, in response to comments on the NPRM. Accordingly, the agencies updated the learning curves for each technology group to cover MYs through 2050. For MYs 2017–2032, the agencies expect incremental improvements in all technologies, particularly in electrification technologies because of increased production volumes, labor efficiency, improved manufacturing methods, specialization, network building, and other factors. While these and other factors contribute to continual cost learning, the agencies believe that many fuel economy improving technologies considered in this rule will approach a flat learning level by the early 2030s. Specifically, older and less complex internal combustion engine technologies and transmissions will reach a flat learning curve sooner when compared to electrification technologies, which have more opportunity for improvement. For batteries and non-battery electrification components, the agencies estimated a steeper learning curve that will gradually flatten after MY 2040. For a more detailed discussion of the electrification learning curves used for the final rule analysis, see Section VI.C.3.e) Electrification Costs. The following Table VI–35 and Table VI–36 show the learning curve schedules for CAFE model technologies for MYs 2017–2033 and MYs 2034–2050. BILLING CODE 4910–59–P E:\FR\FM\30APR2.SGM 30APR2 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00199 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24371 ER30AP20.138</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations VerDate Sep<11>2014 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00200 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.139</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24372 VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00201 Fmt 4701 Sfmt 4725 E:\FR\FM\30APR2.SGM 30APR2 24373 ER30AP20.140</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations BILLING CODE 4910–59–C VerDate Sep<11>2014 23:30 Apr 30, 2020 Each technology in the CAFE Model is assigned a learning schedule Jkt 250001 PO 00000 Frm 00202 Fmt 4701 Sfmt 4700 developed from the methodology explained previously. For example, the E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.141</GPH> khammond on DSKJM1Z7X2PROD with RULES2 24374 24375 following chart shows learning rates for several technologies applicable to midsize sedans, demonstrating that while the agencies estimate that such learning effects have already been almost entirely realized for engine turbocharging (a technology that has been in production for many years), the agencies estimate that significant opportunities to reduce the cost of the greatest levels of mass reduction (e.g., MR5) remain, and even greater opportunities remain to reduce the cost of batteries for HEVs, PHEVs, BEVs. In fact, for certain advanced technologies, the agencies determined that the results predicted by the standard learning curves progress ratio was not realistic, based on unusual market price and production relationships. For these technologies, the agencies developed specific learning estimates that may diverge from the 0.89 progress rate. As shown in Figure VI–14, these technologies include: Turbocharging and downsizing level 1 (TURBO1), variable turbo geometry electric (VTGE), aerodynamic drag reduction by 15 percent (AERO15), mass reduction level 5 (MR5), 20 percent improvement in low-rolling resistance tire technology over the baseline, and battery integrated starter/generator (BISG). (5) Potential Future Approaches to Considering Cost Learning in the CAFE Model reduction achieved through experience producing a given technology should depend on the actual accumulated experience (i.e., volume) producing that technology, such dynamic implementation in the CAFE model is thus far infeasible. Insufficient data have been available regarding manufacturers’ historical application of specific technology. Further, insofar as the agencies’ estimates of underlying direct manufacturing costs already make some assumptions about volume and scale, insufficient information is currently available to determine how to dynamically adjust these underlying costs. It should be noted that if learning responds dynamically to volume, and volume responds dynamically to learning, an internally consistent model solution would likely require iteration of the CAFE model to seek a stable solution within the model’s representation of multiyear planning. As discussed below, the CAFE model now supports iteration to balance vehicle As discussed above, cost inputs to the CAFE model incorporate estimates of volume-based learning. As an alternative approach, the agencies have considered modifications to the CAFE model that would calculate degrees of volume-based learning dynamically, responding to the model’s application of affected technologies. While it is intuitive that the degree of cost VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 PO 00000 Frm 00203 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 ER30AP20.142</GPH> khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations 24376 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 cost and fuel economy changes with corresponding changes in sales volumes, but, this iteration is not yet implemented in a manner that would necessarily support the balance of learning effects on a multiyear basis. The agencies invited comment on the issue, seeking data and methods that would provide the basis for a practicable approach to doing so. Having reviewed comments on cost learning effects, the agencies conclude it remains infeasible to calculate degrees of volume-based learning in a manner that responds dynamically to modeled technology application. The agencies will continue to examine this issue for future development. e) Cost Accounting The CAFE model applied for the NPRM analysis used an incremental approach to specifying technology cost estimates, such that the cost for any given technology was specified as an incremental value, relative to the technology immediately preceding on the relevant technology pathway. For example, the cost of a 7-speed transmission was specified as an amount beyond the cost of a 6-speed transmission. This approach necessitated careful dynamic accounting for the progressive application of the technology as the model worked on a step-by-step basis to ‘‘build’’ a technology solution. As discussed in the corresponding model documentation, the model included complex logic to ‘‘back out’’ some of these costs carefully when, for example, replacing a conventional powertrain with a hybridelectric system.661 To facilitate specification of detailed model inputs and review of detailed model outputs, today’s CAFE model replaces incremental cost inputs with absolute cost inputs, such that the estimated cost of each technology is specified relative to a common reference point for the relevant technology pathway. For example, the cost of the above-mentioned 7-speed transmission is specified relative to a 4-speed transmission, as is the cost of every other transmission technology. This change in the structure of cost inputs does not, by itself, change model results, but it does make the connection between these inputs and corresponding outputs more transparent. Model documentation accompanying today’s analysis presents details of the updated structure for model cost inputs. 661 The CAFE Model is available at https:// www.nhtsa.gov/corporate-average-fuel-economy/ compliance-and-effects-modeling-system with documentation and all inputs and outputs supporting today’s notice. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 5. Other Inputs to the Agencies’ Analysis CAFE Model input files described above defining the analysis fleet and the fuel-saving technologies to be included in the analysis span more than a million records, but deal with a relatively discrete range of subjects (e.g., what vehicles are in the fleet, what are the key characteristics of those vehicles, what fuel-saving technologies are expected to be available, and how might adding those technologies impact vehicles’ fuel economy levels and costs). The CAFE Model makes use of a considerably wider range of other types of inputs, and most of these are contained in other model input files. The nature and function of many of these inputs remains unchanged relative to the model and input files applied for the analysis documented in the proposal that preceded today’s notice. The CAFE Model documentation accompanying today’s notice lists and describes all model inputs, and explains how inputs are used by the model. Many commenters addressed not only the model’s function and design, but also specific inputs. Most input values are discussed either above (e.g., the preceding subsection addresses specific inputs regarding technology costs) or below, in subsections discussing specific economic, energy, safety, and environmental factors. The remainder of this subsection provides an overview of the scope of different model input files. The overview is organized based on CAFE Model file types, as in the model documentation. a) Market Data File The ‘‘Market Data’’ file contains the detailed description—discussed above— of the vehicle models and model configurations each manufacturer produces for sale in the U.S. The file also contains a range of other inputs that, though not specific to individual vehicle models, may be specific to individual manufacturers. The file contains a set of specific worksheets, as follows: ‘‘Manufacturers’’ worksheet: Lists specific manufacturers, indicates whether manufacturers are expected to prefer paying CAFE fines to applying technologies that would not be costeffective, indicates what ‘‘payback period’’ defines buyers’ willingness to pay for fuel economy improvements, enumerates CAFE and CO2 credits banked from model years prior to those represented explicitly, and indicates how sales ‘‘multipliers’’ are to be applied when simulating compliance with CO2 standards. PO 00000 Frm 00204 Fmt 4701 Sfmt 4700 ‘‘Credits and Adjustments’’ worksheet: Enumerates estimates— specific to each manufacturer and fleet—of expected CO2 and CAFE adjustments reflecting improved AC efficiency, reduced AC refrigerant leakage, improvements to ‘‘off cycle’’ efficiency, and production of flexible fuel vehicles (FFVs). The model applies AC refrigerant leakage adjustments only to CO2 levels, and applies FFV adjustments only to CAFE levels. ‘‘Vehicles’’ worksheet: Lists vehicle models and model configurations each manufacturer produces for sale in the U.S.; identifies shared vehicle platforms; indicates which engine and transmission is present in each vehicle model configuration; specifies each vehicle model configuration’s fuel economy level, production volume, and average price; specifies several engineering characteristics (e.g., curb weight, footprint, and fuel tank volume); assigns each vehicle model configuration to a regulatory class, technology class, engine class, and safety class; specifies schedules on which specific vehicle models are expected to be redesigned and freshened; specifies how much U.S. labor is involved in producing each vehicle model/configuration; and indicates whether specific technologies are already present on specific vehicle model configurations, or, due to engineering or product planning considerations, should be skipped. ‘‘Engines’’ worksheet: Identifies specific engines used by each manufacturer and for each engine, lists a unique code (referenced by the engine code specified for each vehicle model configuration and identifies the fuel(s) with which the engine is compatible, the valvetrain design (e.g., DOHC), the engine’s displacement, cylinder configuration and count, and the engine’s aspiration type (e.g., naturally aspirated, turbocharged). The worksheet also indicates whether specific technologies are already present on specific engines, or, due to engineering or product planning considerations, should be skipped. ‘‘Transmissions’’ worksheet: Similar to the Engines worksheet, identifies specific transmissions used by each manufacturer and for each transmission, lists a unique code (referenced by the transmission code specified for each vehicle model configuration and identifies the type (e.g., automatic or CVT) and number of forward gears. Also indicates whether specific technologies are already present or, due to engineering or product planning considerations, should be skipped. E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations b) Technologies File The Technologies file identifies about six dozen technologies to be included in the analysis, indicates when and how widely each technology can be applied to specific types of vehicles, provides most of the inputs involved in estimating what costs will be incurred, and provides some of the inputs involved in estimating impacts on vehicle fuel consumption and weight. The file contains the following types of worksheets: ‘‘Parameters’’ worksheet: Not to be confused with the ‘‘Parameters’’ file discussed below, this worksheet in the Technologies file indicates, for each technology class, the share of the vehicle’s curb weight represented by the ‘‘glider’’ (the vehicle without the powertrain). ‘‘Technologies’’ worksheet: For each named technology, specifies the share of the entire fleet to which the technology may be additionally applied in each model year. Technology Class worksheets: In a separate worksheet for each of the 10 technology classes discussed above (and an additional 2—not used for this analysis—for heavy-duty pickup trucks and vans), identifies whether and how soon the technology is expected to be available for wide commercialization, specifies the percentage of miles a vehicle is expected to travel on a secondary fuel (if applicable, as for plug-in hybrid electric vehicles), indicates a vehicle’s expected electric power and all-electric range (if applicable), specifies expected impacts on vehicle weight, specifies estimates of costs in each model year (and factors by which electric battery costs are expected to be reduced in each model year), specifies any estimates of maintenance and repair cost impacts, and specifies any estimates of consumers’ willingness to pay for the technology. Engine Type worksheets: In a separate worksheet for each of 28 initial engine types identified by cylinder count, number of cylinder banks, and configuration (DOHC, unless identified as OHV or SOHC), specifies estimates of costs in each model year, as well as any estimates of impacts on maintenance and repair costs. khammond on DSKJM1Z7X2PROD with RULES2 c) Parameters File The ‘‘Parameters’’ file contains inputs spanning a range of considerations, such as economic and labor utilization impacts, vehicle fleet characteristics, fuel prices, scrappage and safety model coefficients, fuel properties, and emission rates. The file contains a set of specific worksheets, as follows: VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 Economic Values worksheet: Specifies a variety of inputs, including social and consumer discount rates to be applied, the ‘‘base year’’ to which to discount social benefits and costs (i.e., the reference years for present value analysis), discount rates to be applied to the social cost of CO2 emissions, the elasticity of highway travel with respect to per-mile fuel costs (also referred to as the rebound effect), the gap between test (for certification) and on-road (aka real world) fuel economy, the fixed amount of time involved in each refuel event, the share of the tank refueled during an average refueling event, the value of travel time (in dollars per hour per vehicle), the estimated average number of miles between mid-trip EV recharging events (separately for 200 and 300-mile EVs), the rate (in miles of capacity per hour of charging) at which EV batteries are recharged during such events, the values (in dollars per vehicle-mile) of congestion and noise costs, costs of vehicle ownership and operation (e.g., sales tax), economic costs of oil imports, estimates of future macroeconomic measures (e.g., GDP), and rates of growth in overall highway travel (separately for low, reference, and high oil prices). Vehicle Age Data worksheet: Specifies nominal average survival rates and annual mileage accumulation for cars, vans and SUVs, and pickup trucks. These inputs are used only for displaying estimates of avoided fuel savings and CO2 emissions while the model is operating. Calculations reported in model output files reflect, among other things, application of the scrappage model. Fuel Prices worksheet: Separately for gasoline, E85, diesel, electricity, hydrogen, and CNG, specifies historical and estimated future fuel prices (and average rates of taxation). Includes values reflecting low, reference, and high estimates of oil prices. Scrappage Model Values worksheet: Specifies coefficients applied by the scrappage model, which the CAFE Model uses to estimate rates at which vehicles will be scrapped (removed from service) during the period covered by the analysis. Historic Fleet Data worksheet: For model years not simulated explicitly (here, model years through 2016), and separately for cars, vans and SUVs, and pickup trucks, specifies the initial size (i.e., number new vehicles produced for sale in the U.S.) of the fleet, the number still in service in the indicated calendar year (here, 2016), the relative shares of different fuel types, and the average fuel economy achieved by vehicles with different fuel types, and the averages of PO 00000 Frm 00205 Fmt 4701 Sfmt 4700 24377 horsepower, curb weight, fuel capacity, and price (when new). Safety Values worksheet: Specifies coefficients used to estimate the extent to which changes in vehicle mass impact highway safety. Also specifies statistical value of highway fatalities, the share of incremental risk (of any additional driving) internalized by drivers, rates relating the cost of damages from non-fatal losses to the cost of fatalities, and rates relating the occurrence of non-fatal injuries to the occurrence of fatalities. Fatality Rates worksheet: Separately for each model year from 1975–2050, and separately for each vehicle age (through 39 years) specifies the estimated nominal number of fatalities incurred per billion miles of travel by which to offset fatalities. Credit Trading Values worksheet: Specifies whether various provisions related to compliance credits are to be simulated (currently limited to credit carry-forward and transfers), and specifies the maximum number of years credits may be carried forward to future model years. Also specifies statutory (for CAFE only) limits on the quantity of credit that may be transferred between fleets, and specifies amounts of lifetime mileage accumulation to be assumed when adjusting the value of transferred credits. Also accommodates a setting indicating the maximum number of model years to consider when using expiring credits. Employment Values worksheet: Specifies the estimated average revenue OEMs and suppliers earn per employee, the retail price equivalent factor applied in developing technology costs, the average quantity of annual labor (in hours) per employee, a multiplier to apply to U.S. final assembly labor utilization in order to obtain estimated direct automotive manufacturing labor, and a multiplier to be applied to all labor hours. Fuel Properties worksheet: Separately for gasoline, E85, diesel, electricity, hydrogen, and CNG, specifies energy density, mass density, carbon content, and tailpipe SO2 emissions (grams per unit of energy). Fuel Import Assumptions worksheet: Separately for gasoline, E85, diesel, electricity, hydrogen, and CNG, specifies the extent to which (a) changes in fuel consumption lead to changes in net imports of finished fuel, (b) changes in fuel consumption lead to changes in domestic refining output, (c) changes in domestic refining output lead to changes in domestic crude oil production, and (d) changes in domestic refining output lead to changes in net imports of crude oil. E:\FR\FM\30APR2.SGM 30APR2 24378 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 Emissions Health Impacts worksheet: Separately for NOX, SO2 and PM2.5 emissions, separately for upstream and vehicular emissions, and for each of calendar years 2016, 2020, 2025, and 2030, specifies estimates of various health impacts, such as premature deaths, acute bronchitis, and respiratory hospital admissions. Carbon Dioxide Emission Costs worksheet: For each calendar year through 2080, specifies low, average, and high estimates of the social cost of CO2 emissions, in dollars per metric ton. Accommodates analogous estimates for CH4 and N2O. Criteria Pollutant Emission Costs worksheet: Separately for NOX, SO2 and PM2.5 emissions, separately for upstream and vehicular emissions, and for each of calendar years 2016, 2020, 2025, and 2030, specifies social costs on a per-ton basis. Upstream Emissions (UE) worksheets: Separately for gasoline, E85, diesel, electricity, hydrogen, and CNG, and separately for calendar years 2017, 2020, 2025, 2030, 2035, 2040, 2045, and 2050, and separately for various upstream processes (e.g., petroleum refining), specifies emission factors (in grams per million BTU) for each included criteria pollutant (e.g., NOX) and toxic air contaminant (e.g., benzene). Tailpipe Emissions (TE) worksheets: Separately for gasoline and diesel, for each of model years 1975–2050, for each vehicle vintage through age 39, specifies vehicle tailpipe emission factors (in grams per mile) for CO, VOC, NOX, PM2.5, CH4, N2O, acetaldehyde, acrolein, benzene, butadiene, formaldehyde, and diesel PM10. d) Scenarios File The CAFE Model represents each regulatory alternative as a discrete scenario, identifying the first-listed scenario as the baseline relative to which impacts are to be calculated. Each scenario is described in a worksheet in the Scenarios input file, with standards and related provisions specified separately for each regulatory class (passenger car or light truck) and each model year. Inputs specify the standards’ functional forms and defining coefficients in each model year. Multiplicative factors and additive offsets are used to convert fuel economy targets to CO2 targets, the two being directly mathematically related by a linear transformation. Additional inputs specify minimum CAFE standards for domestic passenger car fleets, determine whether upstream emissions from electricity and hydrogen are to be included in CO2 compliance calculations, specify the governing rates VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 for CAFE civil penalties, specify estimates of the value of CAFE and CO2 credits (for CAFE Model operating modes applying these values), specify how flexible fuel vehicles (FFVs) and PHEVs are to be accounted for in CAFE compliance calculations, specific caps on adjustments reflecting improvements to off-cycle and AC efficiency and emissions, specify any estimated amounts of average Federal tax credits earned by HEVs, PHEVs, BEVs, and FCVs. The worksheets also accommodate some other inputs, such those as involved in analyzing standards for heavy-duty pickups and vans, not used in today’s analysis. e) ‘‘Run Time’’ Settings In addition to inputs contained in the above-mentioned files, the CAFE Model makes use of some settings selected when operating the model. These include which standards (CAFE or CO2) are to be evaluated; what model years the analysis is to span; when technology application is to begin; what ‘‘effective cost’’ mode is to be used when selecting among technologies; whether use of compliance credits is to be simulated and, if so, until what model year; whether dynamic economic models are to be exercised and, if so, how many sales model iterations are to be undertaken and using what price elasticity; whether low, average, or high estimates are to be applied for fuel prices, the social cost of carbon, and fatality rates; by how much to scale benefits to consumers; and whether to report an implicit opportunity cost. f) Simulation Inputs As mentioned above, the CAFE Model makes use of databases of estimates of fuel consumption impacts and, as applicable, battery costs for different combinations of fuel saving technologies. For today’s analysis, the agencies developed these databases using a large set of full vehicle and accompanying battery cost model simulations developed by Argonne National Laboratory. To be used as files provided separately from the model and loaded every time the model is executed, these databases are prohibitively large, spanning more than a million records and more than half a gigabyte. To conserve space and speed model operation, the agencies have integrated the databases into the CAFE Model executable file. When the model is run, however, the databases are extracted and placed in an accessible location on the user’s disk drive. The databases, each of which is in the form of a simple (if large) text file, are as follows: PO 00000 Frm 00206 Fmt 4701 Sfmt 4700 ‘‘FE1_Adjustments.csv:’’ This is the main database of fuel consumption estimates. Each record contains such estimates for a specific indexed (using a multidimensional ‘‘key’’) combination of technologies for each of the technology classes in the Market Data and Technologies files. Each estimate is specified as a percentage of the ‘‘base’’ technology combination for the indicated technology class. ‘‘FE2_Adjustments.csv:’’ Specific to PHEVs, this is a database of fuel consumption estimates applicable to operation on electricity, specified in the same manner as those in the main database. ‘‘Battery_Costs.csv:’’ Specific to technology combinations involving vehicle electrification (including 12V stop-start systems), this is a database of estimates of corresponding base costs (before learning effects) for batteries in these systems. g) On Road Fuel Economy and CO2 Emissions Gap Rather than rely on the compliance values of fuel economy for either historical vehicles or vehicles that go through the full compliance simulation, the model applies an ‘‘on-road gap’’ to represent the expected difference between fuel economy on the laboratory test cycle and fuel economy under realworld operation. In other words, all of the reported physical impacts analysis (including emissions impacts) are based on actual real world fuel consumption and emissions, not on values based on 2-cycle fuel economy ratings and CO2 emission rates, nor on regulatory incentives such as sales multipliers that treat a single vehicle as two vehicles, or that set aside emissions resulting from generation of electricity to power electric vehicles. This was a topic of interest in the recent peer review of the CAFE model. While the model currently allows the user to specify an on-road gap that varies by fuel type (gasoline, E85, diesel, electricity, hydrogen, and CNG), it does not vary over time, by vehicle age, or by technology combination. It is possible that the ‘‘gap’’ between laboratory fuel economy and real-world fuel economy has changed over time, that fuel economy changes as a vehicle ages, or that specific combinations of fuel-saving technologies have a larger discrepancy between laboratory and real-world fuel economy than others. For today’s analysis, and considering data EPA collects from manufacturers regarding vehicles’ fuel economy and CO2 as tested for both fuel economy and emissions compliance and for vehicle fuel economy and emissions labeling E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 (labeling making use of procedures spanning a wider range of real-world vehicle operating conditions), the agencies have determined that the future gap is, at this time, best estimated using the same values applied for the analysis documented in the NPRM. The agencies will continue to assess such test data and any other available data regarding real-world fuel economy and emissions and, as warranted, will revise methods and inputs representing the gap between laboratory and real-world fuel economy and CO2 emissions in future rulemakings. The sensitivity analysis summarized in the FRIA accompanying the final rule includes cases representing narrower and wider gaps. C. The Model Applies Technologies Based on a Least-Cost Technology Pathway to Compliance, Given the Framework Above The CAFE model, discussed in detail above, is designed to simulate compliance with a given set of CAFE or tailpipe CO2 emissions standards for each manufacturer that sells vehicles in the United States. For the final rule analysis, the model began with a representation of the MY 2017 vehicle model offerings for each manufacturer that included the specific engines and transmissions on each model variant, observed sales volumes, and all fuel economy improving technology that is already present on those vehicles. From there the model added technology, in response to the standards being considered, in a way that minimized the cost of compliance and reflected many real-world constraints faced by automobile manufacturers. The model addressed fleet year-by-year compliance, taking into consideration vehicle refresh and redesign schedules and shared platforms, engines, and transmissions among vehicles. The agencies evaluated a wide array of technologies manufacturers could use to improve the fuel economy of new vehicles, in both the immediate future and during the timeframe of this rulemaking, to meet the fuel economy and CO2 standards. The agencies evaluated costs for these technologies, and looked at how costs may change over time. The agencies also considered how fuel-saving technologies may be used on many types of vehicles (ranging from small cars to trucks) and how the technologies may perform in improving fuel economy and CO2 emissions in combination with other technologies. With cost and effectiveness estimates for technologies, the agencies forecast how manufacturers may respond to potential standards and can estimate the VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 associated costs and benefits related to technology and equipment changes. This assists the assessment of technological feasibility and is a building block for the consideration of economic practicability of the standards. The agencies described in the NPRM that the characterization of current and anticipated fuel-saving technologies relied on portions of the analysis presented in the Draft TAR, in addition to new information that had been gathered and developed since conducting that analysis, and the significant, substantive input that was received during the Draft TAR comment period.662 The Draft TAR considered many technologies previously assessed in the 2012 final rule; 663 in some cases, manufacturers have nearly universally adopted a technology in today’s new vehicle fleet (for example, electric power steering), but in other cases, manufacturers only occasionally use a technology in today’s new vehicle fleet (like turbocharged engines). For a few technologies considered in the 2012 rulemaking, manufacturers began implementing the technologies but have since largely pivoted to other technologies due to consumer acceptance issues (for instance, drivability and performance feel issues associated with some dual clutch transmissions without a torque converter) or limited commercial success. In some cases, EPA and NHTSA presented different analytical approaches in the Draft TAR. However, for the NPRM and final rule analysis, the agencies harmonized their analytical approach to use one set of effectiveness values (developed with one tool), one set of cost assumptions, and one set of assumptions about the limitations of some technologies. To develop these assumptions, the agencies evaluated many sources of data, in addition to many stakeholder comments received on the Draft TAR. The preferred approach was to harmonize on sources and methodologies that were datadriven and reproducible for independent verification, produced using tools utilized by OEMs, suppliers, and academic institutions, and using tools that could support both CAFE and CO2 analysis. As the agencies noted in the NPRM, a single set of assumptions also facilitated and focused public comment by reducing burden on stakeholders who sought to review all of the supporting documentation surrounding the analysis. 662 83 663 77 PO 00000 FR 43021–22 (Aug. 24, 2018). FR 62624 (Oct. 15, 2012). Frm 00207 Fmt 4701 Sfmt 4700 24379 The agencies also identified a preference to use values developed from careful review of commercialized technologies; however, in some cases for technologies that are new, and are not yet for sale in any vehicle, the analysis relied on information from other sources, including CBI and third-party research reports and publications. The agencies strived to keep the technology analysis as current as possible in light of the ongoing technology development and implementation in the automotive industry. Additional emerging technologies added for the final rule analysis are described in further detail, below. The agencies’ process to develop effectiveness assumptions is described in detail in Section VI.B.3 Technology Effectiveness, and summarized here. The NPRM and final rule analysis modeled combinations of more than 50 fuel economy-improving technologies across 10 vehicle types (an increase from five vehicle types in NHTSA’s Draft TAR analysis). Only 10 vehicle technology classes were used because large portions of the production volume in the analysis fleet have similar specifications, especially in highly competitive segments. For instance, many mid-sized sedans, small SUVs, and large SUVs coalesce around similar specifications, respectively. Baseline simulations have been aligned around these modal specifications. Parametrically combining these technologies generated more than 100,000 unique combinations per vehicle class. Multiplying the unique technology combinations by the 10 technology classes resulted in the simulation of more than one million individual full-vehicle system models. Modeling was also conducted to determine appropriate levels of engine downsizing required to maintain baseline vehicle performance when advanced mass reduction technology or advanced engine technology were applied. Performance neutrality is discussed in detail in VI.B.3. Some baseline vehicle assumptions used in the simulation modeling were updated since the Draft TAR based on public comments, and further assessment of the NPRM and final rule analysis fleets. The agencies updated assumptions about curb weight, as well as technology properties like baseline rolling resistance, aerodynamic drag coefficients, and frontal areas. Many of the assumptions are aligned with published research from the Department of Energy and other independent E:\FR\FM\30APR2.SGM 30APR2 24380 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 sources.664 Additional transmission technologies and more levels of aerodynamic technologies than NHTSA presented in the Draft TAR analysis were also added for the analysis. Having additional technologies in the model allowed the agencies to assign baselines and estimate fuel-savings opportunities with more precision. To develop technology cost assumptions, the agencies estimated present and future costs for fuel-saving technologies, taking into consideration the type of vehicle, or type of engine if technology costs vary by application. Since the 2012 final rule, many cost assessments, including tear down studies, were funded and completed, and presented as part of the Draft TAR analysis. These studies evaluated transmissions, engines, hybrid technologies, and mass reduction.665 The NPRM and final rule analyses use the 2016 Draft TAR’s cost estimates for many technologies. In addition to those studies, the analysis also leveraged research reports from other organizations to assess costs.666 Consistent with past analyses, this analysis used BatPaC to provide estimates for future battery costs for hybrids, plug-in hybrids, and electric vehicles, taking into account the different battery design characteristics and taking into account the size of the battery for different applications.667 The 664 See, e.g., Islam, E., A. Moawad, N. Kim, and A. Rousseau, 2018a, An Extensive Study on Vehicle Sizing, Energy Consumption and Cost of Advance Vehicle Technologies, Report No. ANL/ESD–17/17, Argonne National Laboratory, Lemont, Ill., Oct 2018. https://www.autonomie.net/pdfs/ANL_ BaSce_FY17_Report_10042018.pdf. Last accessed March 18, 2020; Pannone, G. ‘‘Technical Analysis of Vehicle Load Reduction Potential for Advanced Clean Cars,’’ April 29, 2015. Available at https:// www.arb.ca.gov/research/apr/past/13-313.pdf. Last accessed December 28, 2019. 665 FEV prepared several cost analysis studies for EPA on subjects ranging from advanced 8-speed transmissions to belt alternator starter, or Start/Stop systems. NHTSA also contracted with Electricore and EDAG on teardown studies evaluating mass reduction. The 2015 NAS report on fuel economy technologies for light-duty vehicles also evaluated the agencies’ technology costs developed based on these teardown studies, and the technology costs used in this proposal were updated accordingly. 666 For example, the agencies relied on reports from the Department of Energy’s Office of Energy Efficiency & Renewable Energy’s Vehicle Technologies Office. More information on that office is available at https://www.energy.gov/eere/ vehicles/vehicle-technologies-office. Other agency reports that were relied on for technology or other information are referenced throughout the NPRM and accompanying PRIA, and this final rule and the accompanying FRIA. 667 For instance, battery electric vehicles with high levels of mass reduction may use a smaller battery than a comparable vehicle with less mass reduction technology and still deliver the same range on a charge. See, e.g., Ward, J. & Gohlke, D. & Nealer, Rachael. (2017). The Importance of Powertrain Downsizing in a Benefit–Cost Analysis of Vehicle Lightweighting. JOM. 69. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 agencies also updated technology costs for the NPRM to 2016 dollars, because, as in many cases, technology costs were estimated several years ago, and since then have further updated technology costs to 2018 dollars for the final rule. Cost and effectiveness values were estimated for each technology included in the analysis. As mentioned above, more than 50 technologies were considered in the NPRM and final rule analyses, and the agencies evaluated many combinations of these technologies in many applications. In the NPRM, the agencies identified overarching potential issues in assessing technology effectiveness and cost, including: • Baseline vehicle technology level assessed as too low, or too high. Compliance information was extensively reviewed and supplemented with available literature on the vehicle models considered in the analysis fleet. Manufacturers could also review the baseline technology assignments for their vehicles, and the analysis incorporates feedback received from manufacturers. • Technology costs too low or too high. Tear down cost studies, CBI, literature, and the 2015 NAS study information were referenced to estimate technology costs. In cases where one technology appeared to exceed all other technologies on cost and effectiveness, information was acquired from additional sources to confirm or reject assumptions. Cost assumptions for emerging technologies were reassessed in cases where new information became available. • Technology effectiveness too high or too low in combination with other vehicle technologies. Technology effectiveness was evaluated using the Autonomie full-vehicle simulation modeling, taking into account the impact of other technologies on the vehicle and the vehicle type. Inputs and modeling for the analysis took into account laboratory test data for production and some pre-production technologies, technical publications, manufacturer and supplier CBI, and simulation modeling of specific technologies. Evaluating recently introduced production products to inform the technology effectiveness models of emerging technologies was preferred; however, some technologies that are not yet in production were considered using CBI. Simulation modeling used carefully chosen baseline configurations to provide a consistent, reasonable reference point for the incremental effectiveness estimates. • Vehicle performance not considered or applied in an infeasible manner. PO 00000 Frm 00208 Fmt 4701 Sfmt 4700 Performance criteria, including low speed acceleration (0–60 mph time), high speed acceleration (50–80 mph time), towing, and gradeability (six percent grade at 65 mph) were also considered. In the simulation modeling, resizing was applied to achieve the same performance level as the baseline for the least capable performance criteria but only with significant design changes. The analysis struck a balance by employing a frequency of engine downsizing that took product complexity and economies of scale into account. • Availability of technologies for production application too soon or too late. A number of technologies were evaluated that are not yet in production. CBI was gathered on the maturity and timing of these technologies and the cadence at which manufacturers could adopt these technologies. • Product complexity and design cadence constraints too low or too high. Product platforms, refresh and redesign cycles, shared engines, and shared transmissions were also considered in the analysis. Product complexity and the cadence of product launches were matched to historical values for each manufacturer. • Customer acceptance under estimated or over estimated. Resale prices for hybrid vehicles, electric vehicles, and internal combustion engine vehicles were evaluated to assess consumer willingness to pay for those technologies. The analysis accounts for the differential in the cost for those technologies and the amount consumers have actually paid for those technologies. Separately, new dualclutch transmissions and manual transmissions were applied to vehicles already equipped with these transmission architectures. The agencies sought comments on all assumptions for fuel economy technology costs, effectiveness, availability, and applicability to vehicles in the fleet. Several commenters compared the technology effectiveness and cost estimates from prior rulemaking actions to the NPRM, some commenting that the NPRM analysis represented a better balance of input from all stakeholders regarding the potential costs and benefits of future fuel economy improving technologies,668 and some commenting that the NPRM analysis represented a step back from the Draft TAR and EPA’s Proposed Determination in terms of both the analysis itself and the resulting conclusions about the level of technology required to meet the 668 See, E:\FR\FM\30APR2.SGM e.g., NHTSA–2018–0067–11928. 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations augural standards.669 Specifically, while some commenters stated that the Draft TAR and subsequent EPA midterm review documents had recently concluded that augural standards were achievable with very low levels of electrification based on currently available information on technology effectiveness and cost,670 other commenters reiterated that conventional gasoline powertrains alone were insufficient to achieve post-2021 model year targets.671 Generally, the automotive industry supported the agencies’ NPRM analysis over previous analyses. In addition to the automotive industry’s support of the agencies’ use of one modeling tool for analysis, discussed in Section IV, above, the industry also commented in support of specific technology effectiveness, cost, and adoption assumptions used in the updated analysis. The Alliance commented in support of the NPRM modeling approach, and referenced important technologyspecific features of the modeling process, including ‘‘The acknowledgement and application of real-world limitations on technology application including a limit on the number of engine displacements available to any one manufacturer, application of shared platforms, engines, and transmissions, and the reality that improvements and redesigns of components are not only extended across vehicles but sometimes constrained in implementation opportunity to common vehicle redesign cycles; recognition of the need for manufacturers to follow ‘‘technology’’ pathways that retain capital and implementation expertise, such as specializing in one type of engine or transmission instead of following an unconstrained optimization that would cause manufacturers to leap to unrelated technologies and show overly optimistic costs and benefits; the application of specific instead of generic technology descriptions that allow for the abovementioned real-world constraints; [and] the need to accommodate for intellectual property rights in that not all technologies will be available to all manufacturers.’’ 672 More specifically, the Alliance commented that the analysis appropriately restricted the application of some technologies, like the application of low rolling resistance tires on performance vehicles, and e.g., NHTSA–2018–0067–11873. 670 See, e.g., NHTSA–2018–0067–11969. 671 See, e.g., NHTSA–2018–0067–12150. 672 NHTSA–2018–0067–12073, at 9. limited aerodynamic improvements for trucks and minivans.673 Similarly, the Alliance commented in support of the decision to exclude HCR2 technology from the analysis, citing previous comments stating that ‘‘the inexplicably high benefits ascribed to this theoretical combination of technologies has not been validated by physical testing.’’ Ford commented more broadly that ‘‘[t]he previous analyses performed by the Agencies too often selected technology benefits from the high-end of the forecasted range, and cost from the lower-end, in part because deference was given to supplier or other thirdparty claims over manufacturers’ estimates.’’ 674 Ford noted that, ‘‘[m]anufacturer estimates, while viewed as conservative by some, are informed by years of experience integrating new technologies into vehicle systems in a manner that avoids compromising other important attributes (NVH, utility, safety, etc.),’’ continuing that ‘‘[t]he need to preserve these attributes often limits the actualized benefit of a new technology, an effect insufficiently considered in projections from most non-OEM sources.’’ Ford concluded, as mentioned above, that the NPRM analysis better balanced these considerations. Toyota commented that the discrepancy between the automotive industry and prior regulatory assessments stemmed from ‘‘agency modeling relying on overly optimistic assumptions about technology cost effectiveness and deployment rates.’’ 675 Toyota pointed to a prior analysis that projected compliance for Toyota’s MY 2025 lineup using the ALPHA model as an example of how ‘‘the agency’s analysis failed to account for customer requirements (cost, power, weightadding options, etc.) that erode optimal fuel economy, and normal business considerations that govern the pace of technology deployment.’’ In contrast, Toyota stated that the ‘‘[m]odeled technology cost, effectiveness, and compliance pathways in the proposed rulemaking rely on more recent data as well as more realistic assumptions about the level of technology already on the road today, the pace of technology deployment, and trade-offs between vehicle efficiency and customer requirements.’’ Honda, in its feedback on the models used in the standard setting process, commented that ‘‘the current version of the CAFE model is reasonably accurate in terms of technology efficiency, cost, 669 See, VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 24381 and overall compliance considerations, and reflects a notable improvement over previous agency modeling efforts conducted over the past few years.’’ 676 FCA commented in recognition of the CAFE model improvements over the Draft TAR version, but noted they ‘‘continue to believe that the cost and benefits used as inputs to the model are overly optimistic.’’ 677 FCA used its updated Jeep Wrangler Unlimited and Ram 1500 pickup models as examples of vehicles that ‘‘provide real life examples of the costs and benefits that can be achieved with fuel and weight saving technology;’’ however, ‘‘after all of the real world concerns such as emissions, drivability, OBD, and fuels are considered, the benefits observed remain less than those derived by the Autonomie model and used as inputs to the Volpe model.’’ Conversely, environmental groups, consumer groups, and some States and localities commented that the Draft TAR and subsequent EPA analyses were more representative of the current state of vehicle technologies. These groups all generally commented, in different terms, that the NPRM analysis technology effectiveness was understated and technology costs were overstated, and additional constraints the agencies placed on the analysis, like excluding technologies already in production or constraining technology pathways, also helped lead to that result.678 ICCT commented that the agencies ‘‘ignored their own rigorous 2015–2017 technological assessment, and have adopted a series of invalid and unsupportable decisions which artificially constrain the availability and dramatically under-estimate levels of effectiveness of many different fuel economy improvement and GHGreduction technologies and unreasonably increase modeled compliance costs.’’ 679 ICCT also commented that the agencies ignored, suppressed, dismissed, or restricted the use of work done to update technologies and technology cost and effectiveness assessments since the 2012 final rule for MYs 2017–2025. ICCT stated that the ‘‘invalid high cost result [of the modeled augural standards in 2025] was created by the agencies by making many dozens of unsupported changes in the technology effectiveness and availability inputs, the technology cost inputs, and the technology package constraints.’’ 676 NHTSA–2018–0067–11818. 677 NHTSA–2018–0067–11943. 673 NHTSA–2018–0067–12073, at 134. 674 NHTSA–2018–0067–11928. 675 NHTSA–2018–0067–12150. PO 00000 Frm 00209 Fmt 4701 Sfmt 4700 678 NHTSA–2018–0067–11873; NHTSA–2018– 0067–11984. 679 NHTSA–2018–0067–11741 full comments. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24382 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations ICCT stated that ‘‘the agencies failed to capture the latest available information and, as a result, their assessment incorrectly and artificially overstates technology costs.’’ CARB commented that the agencies did not present sufficient new evidence to change previous technical findings, specifically in regards to conventional vehicle technologies.680 CARB stated that instead of relying on new information, as had been asserted as justification for the proposal, the analysis was based on older data that did not reflect current technology. Accordingly, CARB pointed out that previous analysis by the agencies projected far less need for electrification than what was required in the proposal, stating that the underlying cause is a reduction in the assumed cumulative improvements for what advanced gasoline technology is able to achieve. A coalition of States and Cities similarly commented that ‘‘[t]he Agencies’ conclusions regarding the technology necessary to meet the 2025 standards and the cost of that technology run counter to the evidence before the agency, diverge from prior factual findings without explanation and without transparency as to the source of data relied on, and are unsupported by any reasoned analysis. Such analysis bears many hallmarks of an arbitrary and capricious action.’’ 681 Roush Industries, commenting on behalf of CARB, commented that ‘‘the 2018 PRIA projected average costs for technology implementation to achieve the existing standards to be significantly overstated and in conflict with the 2016 Draft TAR cost estimates generated by the Agencies only two years earlier.’’ 682 Roush commented that the Draft TAR analyses of cost and incremental fuel economy improvement necessary to achieve the augural standards was consistent with Roush’s own estimates and other published data. Similarly, H–D Systems (HDS), commenting on behalf of the California DOJ, commented that ‘‘the estimates in the 2016 TAR on technology cost and effectiveness still represent the correct estimates based on the latest available data.’’ 683 HDS, in its analysis of the costs of technologies to meet different potential standards between the Draft TAR and the NPRM, noted that ‘‘costs for most conventional (i.e., non-electric) drivetrain technologies were similar in 680 NHTSA–2018–0067–11873. 681 NHTSA–2018–0067–11735 (citing State Farm, 463 U.S. at 43; Fox Television, 556 U.S. at 515; Humane Soc. of U.S. v. Locke, 626 F.3d 1040, 1049 (9th Cir. 2010)). 682 NHTSA–2018–0067–11984. 683 NHTSA–2018–0067–11985. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 both reports in that costs were within +5% of the average of the costs from the two reports. The only exception was the cost estimate for the High CR second generation Atkinson cycle or HCR2 engine which was estimated to be much more expensive. Due to differences in nomenclature, transmission technology costs could not be directly compared but were similar at the highest efficiency level. In contrast, cost of hybrid technology was estimated to be much higher in the PRIA and were 200 to 250% higher for strong hybrids. Costs of drag reduction, rolling resistance reduction and auxiliary system technologies were also quite similar but the cost of mass reduction was substantially higher in the PRIA by a factor of 2 to 3. Costs of engine friction reduction appear not to be included in the cost computation for the PRIA although the technology appears to be integrated into some of the engine technology packages analyzed in the PRIA to estimate effectiveness.’’ CFA commented that ‘‘[t]he overarching discussion of technology developments that introduces the NHTSA analysis is fundamentally flawed and infects the entire proposal,’’ taking issue with the NPRM statement that ‘‘some options considered in the original order for the National Program ha[d] not worked out as EPA/NHTSA anticipated.’’ 684 CFA commented that the agencies failed to note that some technology options have performed better than anticipated, and ‘‘the fact that some technologies have done better than expected is a basis for increasing the standards, not in the context of a mid-term review that was supposed to tweak the long-term program.’’ NCAT commented that the ‘‘inflation of projected technology costs does not appear to be attributable primarily to the projected cost of any given technology, but rather to modeling constraints on the application of such technologies to vehicles. Many of these constraints appear to be arbitrary and NHTSA’s departure from prior analyses in these respects is not adequately supported.’’ 685 Environmental groups and States also commented that the agencies either should reincorporate all the Draft TAR or the EPA Proposed and Final Determination analyses’ technologies, technology effectiveness values, and technology costs into the analysis, and/ or compare the final rule analysis with those prior analyses to show how the updated assumptions changed the results from those prior analyses. For example, ICCT commented that ‘‘[f]or the agencies to conduct a credible regulatory assessment they must remove all the technology availability constraints, re-incorporate and make available the full portfolio of technology options as was available in EPA’s analysis for the original 2017 Final Determination, and include at least 15 g/mile CO2 for off-cycle credits by 2025, to credibly reflect the real-world technology developments in the auto industry.’’ 686 ICCT also stated that ‘‘[t]he agencies need to identify each and every technology cost input used in their modeling, and provide a clear engineering and evidence based justification for why that cost differs from the costs employed in the extremely well documented and well justified Draft TAR and in EPA’s 2016 TSD and 2017 Final Determination, taking into account the above discussion of significant new evidence developed since those prior estimates were made. Absent such disclosure and justification, the default assumption needs to be that the prior costs estimated based on the most recent data are more appropriate than the estimates used for the proposal.’’ In addition, groups of commenters were equally split on the ability of technologies to meet different compliance targets. For example, the Alliance commented that ‘‘the only technologies that have demonstrated the improvements necessary to meet the MY 2025 standards are strong hybrids, plugin electric vehicles, and fuel cell electric vehicles. The Agencies’ analysis for this Proposed Rule predict the need for significant growth in sales of electrified vehicles, a finding consistent with thirdparty analyses.’’ 687 In contrast, UCS commented that electrified powertrains ‘‘are not especially relevant for the MY 2022–2025 regulations.’’ 688 The agencies are aware that the prior analyses concluded that compliance with the augural standards could largely be met through advances in gasoline vehicle technologies, and with only very low levels of strong hybrids and electric vehicles. As the agencies stated in the NPRM, consistent with both agencies’ statutes, the proposal was entirely de novo, based on an entirely new analysis reflecting the best and most up-to-date information available to the agencies at the time of this rulemaking.689 As discussed in Section IV, Section VI.B, and further below, the NPRM and final rule analyses reflect updates to 686 NHTSA–2018–0067–11741 full comments. at 15. 688 NHTSA–2018–0067–UCS at 23. 689 83 FR 42897. 687 NHTSA–2018–0067-Alliance 684 NHTSA–2018–0067–12005. 685 NHTSA–2018–0067–11969. PO 00000 Frm 00210 Fmt 4701 Sfmt 4700 E:\FR\FM\30APR2.SGM 30APR2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 technology effectiveness estimates, technology costs, and the methodology for applying technologies to vehicles that the agencies believed better represent the state of technology and the associated costs compared to prior analyses, that result in pathways to compliance that look both similar and different to those in prior analyses. That said, several of the effectiveness and cost values used in the NPRM and final rule analysis were directly carried over from the 2012 rule for MYs 2017– 2025, Draft TAR, and EPA Midterm Evaluation analyses.690 Several others were carried over from the 2015 NAS report,691 which the agencies heavily relied upon in past analyses even if specific cost or effectiveness values were not used. Different technology effectiveness estimates, cost estimates, or adoption constraints were employed where the agencies had information, from technical reports, manufacturers, or other stakeholders, indicating that a technology could or could not be feasibly adopted in the rulemaking timeframe, or a technology could or could not be adopted in the way that the agencies had previously modeled it. Notably, most differences in pathways to compliance are attributable to only a few significant differences between this rulemaking analysis and prior rulemaking analyses. For example, as discussed in Section VI.B.3 Technology Effectiveness and Modeling and Section VI.C.1 Engine Paths, in the EPA Draft TAR and Proposed Determination analyses, effectiveness of HCR engine technologies and downsized turbocharged engine technologies were estimated using Tier 2 certification fuel. Tier 2 certified fuel has a higher octane rating compared to regular octane fuel.692 693 694 As summarized by EPA in the PD TSD, ‘‘EPA’s estimate of effectiveness for gasoline-fueled engines and engine technologies was based on Tier 2 Indolene fuel although protection for operation in-use on Tier 3 gasoline (87 AKI E10) was included in the analysis of engine technologies considered both within the Draft TAR and Proposed Determination. Additionally, in the technology assessment for this Proposed Determination, EPA has considered the required engine sizing and associated effectiveness adjustments when 690 See, e.g., PRIA at 449, 451, 452, 453, 458. 691 See, e.g., PRIA at 358–360. 692 Draft TAR at 5–228. 693 Tier 2 fuel has an octane rating of 93. Typical regular grade fuel has an octane rating of 87 ((R+M)/ 2 octane. 694 EPA Proposed Determination TSD at 2–209 to 2–212. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 performance neutrality is maintained on 87AKI gasoline typical of real-world use.’’ 695 NHTSA’s effectiveness analysis for the Draft TAR used some engine maps also developed using premium octane gasoline. However, at the time NHTSA stated the agency would ensure all future engine model development will be performed with regular grade octane gasoline.696 Commenters like Ford stated the effectiveness estimates for turbo downsized engine packages were too high, in part because of the use of high octane fuel. However they also commented in appreciation of NHTSA’s acknowledgement that any subsequent analysis would be based on fuel at an appropriate octane level, as they stated the impact of the change needed to be reflected in future analyses.697 Engine specifications used to create the engine maps for the NPRM and the final rule analysis were developed using Tier 3 fuel to assure the engines were capable of operating on real world regular octane (87 pump octane = (R+M/ 2)). The process was similar to what manufacturers must do to ensure engines have acceptable noise, vibration, harshness, drivability, performance, and will not fail prematurely when operated on regular octane fuel. This eliminated the need for any adjustments that were applied in the 2016 Draft TAR and PD TSD to account for Tier 2 to Tier 3 fuel properties. This accounts for some of the effectiveness and cost differences for engine technologies between the Draft TAR/Proposed Determination and the NPRM/final rule. For more details, see Section VI.C.1 Engine Paths. The agencies believe ICCT’s and other commenters’ assertions that the engine maps should reflect Tier 2 fuel and not be updated for Tier 3 fuel would ignore these important considerations, and would provide engine maps that could not achieve the fuel economy improvements unless operated on high octane fuel. Therefore, the agencies determined that engine maps developed for the Draft TAR and EPA Proposed Determination that were based on Tier 2 fuel should not be used for the NPRM and final rule analyses for these technical reasons. As another related example, the agencies described that prior analyses had relied heavily on the availability of the HCR2 (or ATK2) ‘‘future’’ Atkinson Cycle engine as a cost-effective pathway 695 EPA Proposed Determination TSD at 2–210. TAR at 5–504, 5–512. 697 Ford Motor Company Response to the Draft TAR September 26, 2016 NHTSA–2016–0068–0048, at 4. 696 Draft PO 00000 Frm 00211 Fmt 4701 Sfmt 4700 24383 to compliance for stringent alternatives, but many engine experts questioned its technical feasibility and near-term commercial practicability.698 The agencies explained that EPA staff began theoretical development of this conceptual engine with a best-in-class 2.0L Atkinson cycle engine and then increased the efficiency of the engine map further, through the theoretical application of additional technologies in combination, including cylinder deactivation, engine friction reduction, and cooled exhaust gas recirculation. While the potential of such an engine is interesting, nevertheless the engine remains entirely speculative. No production HCR2/ATK2 engine, as outlined in the EPA SAE paper,699 has ever been commercially produced. Furthermore, the engine map has not been validated with hardware, bench data, or even on a prototype level (as no such engine exists to test to validate the engine map). Vehicle manufacturers also commented on EPA’s effectiveness assumptions and estimates of HCR2/ ATK2 model’s future penetration levels in the Draft TAR, stating ‘‘[t]he effectiveness values for the ‘futured’ ATK2 package—projected at 40% penetration in 2025MY and includes cooled exhaust gas recirculation (CEGR) and cylinder deactivation (DEAC)—are too high, primarily due to overtlyoptimistic efficiencies in the base engine map, insufficient accounting of CEGR and DEAC integration losses, and no accounting of the impact of 91RON Tier 3 test fuel,’’ and that ‘‘44% fleetwide penetration of ATK2 in 2025MY is unrealistic given the limited number of powertrain refresh cycles available before 2025MY. In addition, it is unreasonable to assume that OEMs already heavily invested in different high-efficiency powertrain pathways (e.g., turbo-downsizing) would be able to commit the immense resources needed to reach these high ATK2 penetration levels in such a short time.’’ 700 Accordingly, the agencies decided to not include HCR2 technology in the NPRM and final rule analysis. The engine model was not used because no observable physical demonstration of the speculative technology combination model has yet been created. Further, 698 83 FR 43038. C. and Dekraker, P., ‘‘Potential Fuel Economy Improvements from the Implementation of cEGR and CDA on an Atkinson Cycle Engine,’’ SAE Technical Paper 2017–01–1016, 2017. Available at https://doi.org/10.4271/2017-01-1016. 700 Ford Motor Company Response to the Draft TAR September 26, 2016 NHTSA–2016–0068–0048, at 4. 699 Schenk, E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24384 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations many questions remain about the model’s practicability as specified, especially in high load, low engine speed operating conditions. The HCR2 model combines multiple technologies to provide cumulative estimate of benefits without consideration the practical interaction of technologies. This approach runs contrary to the modeling approach attempted in the NPRM and final rule analysis. The approach the agencies tried to follow restricted models to adding discrete advanced technologies. This approach allowed an accounting of synergetic effects, identified incremental benefits, and increased the precision of cost estimates. As another example, further discussed in Section VI.B.1 Analysis Fleet, the agencies had traditionally taken different approaches to assigning baseline road load reduction technology assignments. For analyzing baseline levels of mass reduction in an analysis fleet, NHTSA had developed for the Draft TAR a regression model to summarize a vehicle’s weight savings using a relative performance approach and accounting for vehicle content, using cost curves developed from teardown studies of a MY 2011 Honda Accord and MY 2014 Chevrolet Silverado pickup truck. EPA developed its own methodology that classified vehicles based on weight reductions from a MY 2008 vehicle, compared to the MY 2014 version of the same vehicle, using a cost curve from a teardown study of a MY 2010 Toyota Venza. In the EPA’s mass reduction technology costing approach, a cost reduction was applied when mass reduction 1 technology was applied to a system at mass reduction 0 technology level. NHTSA’s approach, used in the NPRM and final rule analysis, set baseline mass reduction assignments so costs of implementing mass reduction technologies are fully applied as vehicle platforms move along the mass reduction technology path. The agencies also included additional advanced powertrain technologies and other vehicle-level technologies in the technology pathways between the Draft TAR and NPRM, and between the NPRM and final rule. However, manufacturers and suppliers have repeatedly told the agencies that there are diminishing returns to increasing the complexity of advanced gasoline engines, including in the amount of fuel efficiency benefit that they can provide. For example, Toyota commented, in response to the EPA SAE paper benchmarking the 2018 Camry with the 2.5L Atkinson-cycle engine and ‘‘futuring’’ midsize exemplar vehicles VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 based on the generated engine map,701 that although EPA’s addition of cylinder deactivation to the hypothetical 2025 exemplar vehicle is technically possible and would provide some fuel economy and CO2 benefit, the primary function of cylinder deactivation is to reduce engine pumping losses which the Atkinson cycle and EGR already accomplish on the 2018 Camry.702 Toyota concluded, ‘‘The overlapping and redundant measures to reduce engine pumping losses would add costs with diminishing efficiency returns.’’ Similarly, BorgWarner commented that they ‘‘do not expect that variable compression ratio (VCR) or homogeneous charge compression ignition (HCCI) will see broad application in the short term, if ever. While each of these technologies can offer marginal efficiency gains at some engine speed-load conditions, the use of down-sized boosted engines with 8–10 speed transmissions makes it possible to run engines at near optimum conditions and effectively minimizes gains from VCR or HCCI. VCR mechanisms result in additional mass, cost and complexity, and true HCCI has yet to be demonstrated in a production vehicle. The agencies do not believe that OEMs will judge these technologies to be cost effective.’’ 703 So, while previous analyses may have shown pathways to compliance with increasingly complex advanced gasoline engines, the NPRM and final rule analyses more appropriately reflect that the most complex gasoline engine technologies will account for a smaller share of manufacturers’ products during the rulemaking timeframe. However, despite this fact, the NPRM and final rule analysis include more advanced powertrain technologies than previous analyses, in part to account for important considerations like intellectual property and the fact that some manufacturers have already started down the path of incorporating a certain advanced engine technology in their product portfolio, and that abrupt switching to another advanced engine technology would result in unrealistic stranding of capital costs. In addition, greater precision in how cumulative technologies applied to engines, as estimated through the Autonomie effectiveness modeling, appropriately reflects the diminishing returns to efficiency benefits that those advanced 701 Kargul, J., Stuhldreher, M., Barba, D., Schenk, C. et al., ‘‘Benchmarking a 2018 Toyota Camry 2.5Liter Atkinson Cycle Engine with Cooled-EGR,’’ SAE Technical Paper 2019–01–0249, 2019, doi:10.4271/2019–01–0249. 702 NHTSA–2018–0067–12431, at 8. 703 NHTSA–2018–0067–11895. PO 00000 Frm 00212 Fmt 4701 Sfmt 4700 engines can provide. Moreover, as identified by a wide range of commenters, battery costs are projected to fall in the rulemaking timeframe to a point where, in the compliance modeling, it becomes more cost effective to add electrification technologies to vehicles than to apply other advanced gasoline engine technologies. Finally, the agencies declined to incorporate some information and data for the NPRM or final rule central analysis for reasons discussed in the following sections. In general, the data produced by agencies or submitted by commenters failed to isolate effectiveness impacts of individual technologies (or in some cases a combination of two or several technologies). The data included effects from additional unaccounted and undocumented technologies. Because the effectiveness improvement measured or claimed resulted from more than just the reported sources, the actual effectiveness of the technology or technologies is obfuscated and easily under or over predicted. Using effectiveness values generated in this manner carries a high risk of double counting effectiveness and undercounting costs. In many cases, this problem exists where data or information is based on laboratory testing or on-road testing of production vehicles or components including engines and transmissions. Production vehicles and components usually include multiple technology improvements from one redesign to the next, and rarely incorporate just a single technology change. Furthermore, technology improvements on production vehicles in some cases cannot be readily observed, such as the level of mechanical friction in an engine, and isolation and identification of the improvement attributable to each technology would be impractical given the costs and time required to do so. That said, in some cases, where possible to do so, the agencies used the data or information from production vehicles to corroborate information from the Autonomie simulations. However, the agencies declined to apply that data or information directly in the analysis if the effectiveness improvement attributable to a particular technology could not be isolated. The agencies made these updates from prior analyses not, as some commenters have suggested, to ‘‘artificially overstate technology costs,’’ 704 or to ‘‘ignore the knowledge and expertise of the EPA engineering 704 NHTSA–2018–0067–11741 E:\FR\FM\30APR2.SGM 30APR2 at 7. khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations and compliance staff,’’ 705 ‘‘so that the model in many instances selects more expensive, less fuel efficient technology while excluding less expensive and more efficient alternatives,’’ 706 but because the updates reflected the agencies’ reasonable assessment of the current state of vehicle technologies and their costs, and the state of future vehicle technologies and costs in the rulemaking timeframe. Separate from the decision to update assumptions used for the NPRM analysis from prior analyses, the agencies did refine some technology effectiveness and cost assumptions from the NPRM to this final rule analysis. In addition to being appropriate for technical reasons, this should address some commenters’ overarching concerns about understated technology effectiveness and overstated technology costs. For example, several commenters noted that the costs of BISG/CISG systems were higher for small Cars/ SUVs and medium cars than for medium SUVs and pickup trucks, which the Alliance and FCA described as ‘‘implausible’’ and ‘‘misaligned with industry understanding,’’ and which ICCT described as ‘‘contrary to basic engineering logic, which holds that a system which would be smaller and have lower energy and power requirements would be less expensive, not more.’’ 707 The agencies agree, and have made changes to address this issue, as described in Section VI.C.3.a) Electrification. After considering comments, the agencies also added several engine technologies and technology combinations for the final rule analysis. These included a basic high compression ratio Atkinson cycle engine, a variable compression ratio engine, a variable turbo geometry engine, and a variable turbo geometry with electric assist engine (VTGe). The NPRM discussed and provided engine maps for each of these technologies. The agencies also added new technology combinations including diesel engines with cylinder deactivation, turbocharged engines with advanced cylinder deactivation, diesel engines paired with manual transmissions, and diesel engines paired with 12-volt startstop technology. Transmission revisions included updating the effectiveness of 6-speed automatic transmissions, applying updated shift logic for 10speed automatic transmissions, and increasing the gear span for efficient 10speed automatic transmissions. Mass 705 NHTSA–2018–0067–11741 at I–23. 706 NHTSA–2018–0067–12123. 707 NHTSA–2018–0067–11741. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 reduction technology was expanded to include up to 20 percent curb weight reduction, compared with up to 10 percent for the NPRM. These changes, and the comments upon which they were based, are described in further detail in the following sections. 1. Engine Paths The internal combustion (IC) engine is a heat engine that converts chemical energy in a fuel into mechanical energy. Chemical energy of the fuel is first converted to thermal energy by means of combustion or oxidation with air inside the engine. This thermal energy raises the temperature and pressure of the gases within the engine, and the highpressure gas then expands against the internal mechanisms of the engine. This expansion is converted by the mechanical linkages of the engine to a rotating crankshaft, which is the output of the engine. The crankshaft, in turn, is connected to a transmission to transmit the rotating mechanical energy to the desired final use, particularly the propulsion of vehicles. IC engines can be categorized in a number of different ways depending upon which technologies are designed into the engine: By type of ignition (e.g., spark ignition or compression ignition), by engine cycle (e.g., Otto cycle or Atkinson cycle), by valve actuation (e.g., overhead valve (OHV), single overhead camshaft (SOHC), or dual overhead camshaft (DOHC)), by basic design (e.g., reciprocating or rotary), by configuration and number of cylinders (e.g., inline four-cylinder (I4) or Vshaped six-cylinder (V6)), by air intake (e.g., forced induction (turbo or super charging) or naturally aspirated), by method of fuel delivery (e.g., port injection or direction injection), by fuel type (e.g., gasoline or diesel), by application (e.g., passenger car or light truck),or by type of cooling (e.g., aircooled or water-cooled). For each combination of technologies among the various categories, there is a theoretical maximum efficiency for all engines within that set. There are various metrics that can be used to compare engine efficiency, and the four metrics the agencies use or discuss in this preamble are: • Brake specific fuel consumption (BSFC), which is the mass of fuel consumed per unit of work output (amount of fuel used to produce power); • Brake thermal efficiency (BTE), which is the total fuel energy released per unit of work output (percentage of fuel used to produce power); • Fuel consumption (gallons per mile), which looks at the gallons of fuel PO 00000 Frm 00213 Fmt 4701 Sfmt 4700 24385 consumed per unit of work output (mile travelled); and • Fuel economy (in MPG), which is the amount of work output (miles travelled) per unit (gallon) of fuel consumed. When comparing the efficiency of IC engines, it is important to identify the metric(s) used and the test cycle for the measurement because results vary widely when engines operate over different test cycles. Two-cycle fuel economy tests used to certify vehicles’ compliance with the CAFE standards tend to overestimate the average fuel economy motorists will typically achieve during on-road operation.708 In the NPRM and for this final rule analysis, the agencies considered technology effectiveness for the 2-cycle test procedures and AC and off-cycle test procedures to evaluate how technologies could be applied for manufacturers to comply with standards. The agencies also considered real world operation beyond these test procedures when considering IC engine technologies in order to assure the technologies were configured and specified in a manner that could be used in real world vehicle applications. a) Fuel Octane As mentioned in other sections of the Preamble, the agencies go to great lengths to ensure engine technologies considered for potential compliance pathways are feasible for real-world implementation and effectiveness. An important facet of this evaluation are both the fuels that are used for efficiency testing and also the fuels that consumers may purchase in the marketplace. In the NPRM, the agencies included a general overview of fuel octane (stability) level, including levels currently available, and the potential impact of fuel octane on engines developed for the U.S. market.709 The agencies described that a typical, overarching goal of optimal sparkignited engine design and operation is to maximize the greatest amount of energy from the fuel available, without manifesting detrimental impacts to the engine over expected operating conditions. Design factors, such as compression ratio, intake and exhaust value control specifications, and combustion chamber and piston characteristics, among others, are all impacted by the octane of the fuel consumers are anticipated to use.710 708 77 FR 62988. at 253. 710 In addition, PRIA Chapter 6 contains a brief discussion of fuel properties, octane levels used for 709 PRIA E:\FR\FM\30APR2.SGM Continued 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 24386 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations The agencies also discussed potential challenges associated with octane levels available currently, and how those octane levels may play a role in potential vehicle fuel efficiency improvements. Vehicle manufacturers typically develop their engines and engine control system calibrations based on the fuel available to consumers. In many cases, manufacturers may recommend a fuel grade for best performance and to prevent potential damage. In some cases, manufacturers may require a specific fuel grade for both best performance, to achieve advertised power ratings, and/or to prevent potential engine damage. Consumers, though, may or may not choose to follow the manufacturer’s recommendation or requirement for a specific fuel grade for their vehicle. As such, vehicle manufacturers often choose to employ engine control strategies for scenarios where the consumer uses a lower than recommended, or required, fuel octane level, as a way to mitigate potential engine damage over the life of a vehicle. These strategies limit the extent to which some efficiency improving engine technologies can be implemented, such as increased compression ratio and intake system and combustion chamber designs that increase burn rates and rate of incylinder pressure rise. If the minimum octane level available in the market were higher (especially the current suboctane regular grade in the mountain states), vehicle manufacturers might not feel compelled to design vehicles suboptimally to accommodate such blends. When knock (also referred to as detonation) is encountered during engine operation, at the most basic level, non-turbocharged engines can adjust the timing of the spark that ignites the fuel, as well as the amounts of fuel injected at each intake stroke (‘‘fueling’’). In turbocharged applications, knocking is typically controlled by adjusting boost levels along with spark timing and/or the amount of fuel injected. Past rulemakings discussed other techniques that may be employed to allow higher compression ratios, including optimizing spark timing, and adding of cooled exhaust gas recirculation (EGR). Regardless of the type of spark-ignition engine or technology employed, efforts to reduce or prevent knock with the lower-octane fuels that are available in the market result in the loss of potential power output, creating a ‘‘knockengine simulation and in real-world testing, and how octane levels can impact performance under these test conditions. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 limited’’ constraint on performance and efficiency. The agencies noted that despite limits imposed by available fuel grades, manufacturers continue to make progress in extracting more power and efficiency from spark-ignited engines. Production engines are safely operating with regular 87 AKI fuel with compression ratios and boost levels once viewed as only possible with premium fuel. According to the Department of Energy, the average gasoline octane level has remained fundamentally flat starting in the early 1980’s and decreased slightly starting in the early 2000s. During this time, however, the average compression ratio for the U.S. fleet has increased from 8.4 to 10.52, a more than 20 percent increase. As explained by the Department of Energy, ‘‘[t]here is some concern that in the future, auto manufacturers will reach the limit of technological increases in compression ratios without further increases in the octane of the fuel.’’ 711 As such, manufacturers are still limited by the fuel grades available to consumers and the need to safeguard the durability of their products for all of the available fuels; thus, the potential improvement in the design of spark-ignition engines continues to be overshadowed by the fuel grades available to consumers. EPA and NHTSA also described ongoing research and positions from automakers and advocacy groups on fuel octane levels, including comments received during past agency rulemakings and on the 2016 Draft TAR regarding the potential for increasing octane levels in the U.S. market. The agencies described arguments for adjusting to octane levels, including making today’s premium grade the base grade of fuel available, which could enable low cost design changes to improve fuel economy and reduce tailpipe CO2 emissions. Challenges associated with this approach include the increased cost to consumers who drive vehicles designed for current regular octane grade fuel, who would not benefit from the use of the higher cost higher-octane fuel. The costs of such a transition to higher-octane fuel would be high and persist well into the future, since unless current regular octane fuel were unavailable in the North American market, manufacturers would be effectively unable to redesign 711 Fact of the Week, Fact #940: August 29, 2016 Diverging Trends of Engine Compression Ratio and Gasoline Octane Rating, U.S. Department of Energy, https://www.energy.gov/eere/vehicles/fact-940august-29-2016-diverging-trends-enginecompression-ratio-and-gasoline-octane (last visited Mar. 21, 2018). PO 00000 Frm 00214 Fmt 4701 Sfmt 4700 their engines to operate on higheroctane fuel. In addition, the full benefits of such a transition would not be realized until vehicles with such redesigned engines were produced for a sufficient number of model years largely to replace the current on-road vehicle fleet. The transition to net positive benefits would take many years. The agencies also described input received from renewable fuel industry stakeholders and from the automotive industry supporting high-octane gasoline fuel blends to enable fuel economy and CO2 improving technologies such as higher compression ratio engines. Stakeholders suggested that mid-level (e.g., E30) highoctane ethanol blends should be considered and that EPA should consider requiring that mid-level blends be made available at service stations. Stakeholders supporting higher-octane blends suggested that higher-octane gasoline could provide auto manufacturers with more flexibility to meet more stringent standards by enabling opportunities for use of lower tailpipe CO2 emitting technologies (e.g., higher compression ratio engines, improved turbocharging, optimized engine combustion). The agencies sought additional comment in the NPRM on various aspects of current fuel octane levels and how fuel octane could play a role in the future. More specifically, the agencies sought comment on how increasing fuel octane levels could have an impact on product offerings and engine technologies, as well as what improvements to fuel economy and tailpipe CO2 emissions could result from higher-octane fuels. The agencies sought comment on an ideal octane level for mass-market consumption, and whether there were downsides with increasing the available octane levels and, potentially, eliminating loweroctane fuel blends. EPA also requested comment on whether and how EPA could require the production and use of higher-octane gasoline consistent with Title II of the Clean Air Act. The agencies received numerous, wide-ranging comments in response to the NPRM discussion, and some direct responses to the agencies’ requests for comments. The commenters included fuel producers, individual vehicle manufactures, environmental groups, vehicle suppliers, fuel advocacy groups, and agricultural organizations, among others. Commenters provided a broad range of comments ranging from explication of the many challenges to increasing available octane levels, to claims of the substantial efficiency E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations increases that could be easily obtained by requiring higher-octane levels. Several ethanol industry stakeholders commented in support of requiring higher-octane fuels using mid-level ethanol blends. The High-Octane, Low Carbon (HOLC) Alliance commented that it believes ‘‘NHTSA and EPA have a critical opportunity to cost-effectively ensure progress in fuel efficiency and CO2 emissions standards. Scientific experts agree that high-octane, lowcarbon fuel can yield greater fuel economy and emissions benefits when paired with internal combustion engines (ICEs). But, to realize such benefits, automobile manufacturers require approval sooner rather than later to such fuels. Alternatively, automobile manufacturers will be limited in their ability to maximize the environmental performance of their vehicles until nonliquid fuel engines become more readily available. In finalizing the Proposed Rule, the HOLC Alliance strongly urges EPA and NHTSA to establish a pathway forward toward incentivizing the production and adoption of higheroctane, lower carbon fuels. By doing so, EPA and NHTSA can continue to incrementally increase CO2 and fuel economy standards, respectively.’’ 712 Renewable Fuels Associations (RFA) commented that ‘‘it strongly believes vehicles and fuels must be considered together as integrated systems. As EPA has recognized in the past, a ‘systems approach enables emission reductions that are both technologically feasible and cost effective beyond what would be possible looking at vehicle and fuel standards in isolation.’ Because ethanolbased high-octane low-carbon fuel blends would enable cost-effective gains in fuel economy and carbon dioxide reductions, the agencies should take steps to support [high-octane lowcarbon] fuels in the final SAFE rule.’’ 713 RFA cited several studies indicating benefits are available from raising the floor of fuel octane levels currently available, and, particularly, ‘‘[t]he results from the studies reviewed generally support a main conclusion that splash blending ethanol is a highly effective means of raising the octane rating of gasoline and enabling low-cost efficiencies and reduced emissions in modern spark-ignition engines.’’ 714 In addition, National Corn Growers Association stated that, ‘‘[w]ithout a change in fuel, automakers are reaching the limits on the efficiency gains that 712 HOLC Alliance, Detailed Comments, EPA– HQ–OAR–2018–0283–4196. 713 RFA, Detailed Comments, EPA–HQ–OAR– 2018–0283–4409. 714 RFA, Detailed Comments, EPA–HQ–OAR– 2018–0283–4409. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 can be achieved with technology changes.’’ 715 The National Corn Growers Association, in conjunction with associated corn growing and agricultural groups, pointedly stated the EPA should, ‘‘[s]et a minimum fuel octane level of 98 RON and phase out low octane fuels as new optimized vehicles enter the market in MY 2023,’’ and concluded that approving a ‘‘midlevel ethanol blend vehicle certification fuel would enable automakers to expedite design and testing of optimized vehicles for use with this new fuel.’’ 716 The 25x25 Alliance commented that ‘‘to meet the dual goals of greater fuel efficiency and reduced GHG emissions, the utilization of higher compression spark ignition internal combustion engines will be essential. Increasing engine compression improves thermal efficiency. However, as compression increases, higher-octane fuels will be needed to prevent engine knock. Automakers and advocacy groups have expressed support for increases to fuel octane levels for the US market. Ethanol with its octane rating of 113 offers engine knock resistance at a lower cost than any other octane booster in gasoline. In addition, ethanol’s lower direct and life-cycle GHG emissions as compared to gasoline are well documented. For this reason, a fuel produced from a mixture of ethanol and gasoline and used in conjunction with advanced high compression engines presents itself as a technology pathway capable of complying with new CAFE/ GHG standards.’’ They continue, ‘‘HOLC supporters recognize numerous barriers and other associated regulatory hurdles must be resolved before HOLC ethanol fuels are adopted at large scale. . . 25x25 believes it is imperative that the vehicle and fuel be treated as a comprehensive system. To date CAFE/ GHG standards have largely focused on vehicle engine technology. Advanced engine vehicles perform best in concert with fuels of suitable properties and composition to optimally enable and power them.’’ 717 The American Coalition for Ethanol (ACE) commented that ‘‘high-octane blends comprised of 25 to 30 percent ethanol would help bring down the cost for consumers compared to the 715 National Corn Growers Association, https:// www.ncga.com/file/1621/NCGA%20Comments 20Docket%20No.%20EPA-HQ-OAR-2018-0283 %20and%20NHTSA-2018-0067.pdf. 716 National Corn Growers Association, https:// www.ncga.com/file/1621/ NCGA%20Comments%20Docket%20No.%20EPAHQ-OAR-2018-0283%20and%20NHTSA-20180067.pdf. 717 25x25 Alliance, Detailed Comments, EPA– HQ–OAR–2018–0283–4210. PO 00000 Frm 00215 Fmt 4701 Sfmt 4700 24387 premium-priced octane level advocated by oil refiners. Ethanol has a blending octane rating of nearly 113 and trades at a steep discount to gasoline. In many wholesale markets today, ethanol costs at least 60 cents per gallon less than gasoline. Ethanol delivers the highest octane at the lowest cost, allowing automakers to benefit by continuing to develop high-compression engine technologies and other product offerings to achieve efficiency improvements and reduced emissions. The ideal way to transition from today’s legacy fleet to new vehicles with advanced engine technologies designed to run optimally on a high-octane fuel is to utilize FFVs as bridge vehicles that can provide immediate demand for mid-level ethanol blends.’’ 718 Growth Energy commented that with a mid-level ethanol blend, automakers not only get higher-octane that they can use to optimize engines and gain further fuel efficiency, they will also see a fuel that has demonstrably lower carbon dioxide emissions.719 The Illinois Corn Growers’ Association et al., commented that ‘‘NHTSA and EPA must adapt the existing regulatory structure to reflect the specific characteristics of mid-level blend fuels. Working together, the ethanol industry, automakers, EPA and NHTSA can bring about, during the period covered by the SAFE program, a new generation of high efficiency internal combustion engines optimized to take advantage of this new fuel’s unique properties.’’ 720 Ethanol industry commenters provided comment on several EPA actions they believe would be necessary to support higher-octane mid-level fuel blends: • Set a minimum fuel octane level and phase out low-octane fuels as new optimized vehicles enter the market; • Approve a high-octane, mid-level ethanol blend vehicle certification fuel; • Correct the fuel economy formula by updating the R-Factor to be at or nearly ‘‘1’’ to reflect documented operation of modern engine technology; • Extend a RVP waiver of 1 psi to all gasoline containing at least 10 percent ethanol; • Adopt the Argonne National Laboratory GREET model to determine updated lifecycle carbon emissions for ethanol; 718 ACE, Detailed Comments, EPA–HQ–OAR– 2018–0283–4033. 719 Growth Energy, Detailed Comments, EPA– HQ–OAR–2010–0799- 9540–A2. 720 Comment removed because it contains copyrighted data, Illinois Corn Growers Association, et al., https://www.regulations.gov/ document?D=EPA-HQ-OAR-2018-0283-4198. E:\FR\FM\30APR2.SGM 30APR2 24388 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations khammond on DSKJM1Z7X2PROD with RULES2 • Establish meaningful credits to automakers to incentivize transition to higher-octane fuel vehicles and continue to support flex-fuel vehicles; and • Provide equal treatment to vehicle technologies that reduce carbon emissions. The Clean Fuels Development Coalition, et al. suggested that, ‘‘the ‘ideal octane level’ to optimize LDV performance, fuel efficiency, and reduce harmful emissions and consumer costs is 98–100 RON produced with E30+ ‘clean octane.’ ’’ 721 Concurrently, the HOLC Alliance and ACE, among others, also supported that 98 to 100 RON would be ideal octane levels for the nation.722 BorgWarner, a supplier to major automobile manufacturers, commented that ‘‘[f]uel octane is a limiting factor in the selection of compression ratio for all spark-ignition engines and the amount of boost for turbocharged engines. Higher-octane is particularly effective for using higher compression ratios with boosted engines,’’ and stated that ‘‘[t]here is substantial merit to raising the minimum octane required because current fuel pricing penalizes consumers for using higher-octane fuel. A base octane of 95 RON would be consistent with Europe. This would allow consistent development of engines for the broader US–EU market. Prior to the introduction of ethanol into gasoline, the base blend for regular fuel was typically 92 RON. Addition of 10% ethanol to this base blend gave 95 RON regular, so the base blend would be reformulated to retain the 92 RON at a lower cost. Returning to the previous base blend would be cost effective to the consumer.’’ 723 Auto manufacturers also provided comment on the topic of higher-octane fuels. The Alliance of Automobile Manufacturers (the Auto Alliance) commented that it ‘‘has long advocated for the availability of cost-effective, higher-octane fuel. The Alliance also believes the Agencies should require a transition to a higher minimum-octane gasoline (minimum 95–98 RON). There are several ways to produce higheroctane grade gasoline, such as expanding the ethanol availability, but the Alliance does not promote any sole or particular pathway.’’ 724 The Alliance 721 Clean Fuels Development Coalition, et al., Detailed Comments, NHTSA–2018–0067–11988. 722 HOLC Alliance, Detailed Comments, EPA– HQ–OAR–2018–0283–4196; ACE, Detailed Comments, EPA–HQ–OAR–2018–0283–4033. 723 BorgWarner, Detailed Comments, EPA–HQ– OAR–2018–0283–4174. 724 Auto Alliance, Detailed Comments, NHTSA– 2018–0067–12073. VerDate Sep<11>2014 23:30 Apr 30, 2020 Jkt 250001 reiterated its position regarding fuel octane levels where, ‘‘[t]he Alliance has long supported two goals regarding the octane (anti-knock) properties of gasoline: (1) The availability of cost effective higher-octane fuels, greater than 95 Research Octane Number (RON) and (2) the immediate elimination of subgrade fuel less than 87 anti-knock index (AKI).’’ The Alliance also noted that ‘‘[t]he higher-octane fuel that is available today is sold as a premium grade. To support future engine technologies, the approach taken with today’s premium fuel option would not be expected to provide an attractive value proposition to the customer; therefore, a new higher minimumoctane gasoline, 95–98 RON, is needed to achieve anticipated performance.’’ Ford Motor Company agreed with the Auto Alliance’s collective comments on fuel octane level and added specific support to raising minimum octane levels, stating that ‘‘Ford concurs with those comments and supports increasing the marketplace octane rating in the U.S. to a minimum of 95 Research Octane Number (RON).’’ Ford also generally supported the agencies’ fuel octane discussion in terms of impacts to vehicle performance, where ‘‘[h]igher octane gasoline enables opportunities for the use of key energy-efficient technologies, including: Higher compression ratio engines, lighter and smaller engines, improved turbocharging, optimized engine combustion phasing/timing, and low temperature combustion strategies. All of these technologies paired with higher-octane gasoline permit smaller engines to meet the demands of the consumer while at the same time providing higher overall efficiencies.’’ 725 Volkswagen commented ‘‘[t]here may be several potential ways to achieve a high-octane fuel that may be more costly to the vehicle than others. Achieving an E10 high-octane fuel may mean a different hardware set than on E20 or E30 high-octane fuel. Elimination of sub-grades of market fuel (less than 87AKI) quickly is very important. If current 87 AKI and 85 AKI fuels remain in the market for backward compatibility (such as if an E30 were chosen as the high-octane fuel of the future), a robust method at the fuel dispensing station and incorporated into the fueling station equipment to prevent mis-fueling is necessary. However, an E10 high-octane pathway might have far fewer compatibility problems and might 725 Ford, Detailed Comments, EPA–HQ–OAR– 2018–0283–5691. PO 00000 Frm 00216 Fmt 4701 Sfmt 4700 bring extra fuel economy to the drivers of those current vehicles.’’ 726 The agencies also received comments from the petroleum industry regarding higher-octane fuels. API commented that ‘‘[g]iven the multiple engine technology pathways available to the automakers for achieving future fuel economy and CO2 emissions targets, the challenge of determining future market fuel gasoline octane number needs is complex and not yet settled. API believes that the octane number issue should be part of a comprehensive transport policy that addresses both vehicles and fuels as a system. API and its members are engaged in collaborations with the automakers and other stakeholders to better understand future fuel requirements for emerging powertrain technologies.’’ API also commented ‘‘the future for gasoline octane number will be driven by the stringency of regulations that set future fuel economy and CO2 requirements, the collective responses of the automakers to those regulations, consumer preferences regarding vehicles and fuels, and fuel supply economics. EPA’s authority to regulate gasoline octane number is doubtful. Therefore, EPA should not attempt to regulate gasoline octane number at this time.’’ 727 In terms of challenges associated with potential high-octane fuel deployment, the American Fuel & Petrochemical Manufacturers (AFPM) commented that, ‘‘[a]side from a lack of legal authority, EPA faces numerous technical, logistical, and legal challenges and uncertainties in requiring the use of higher-octane fuels. Any such requirement would need a separate rulemaking dedicated to such a purpose with an extensive technical record in support, including test data on vehicles designed for the higher-octane fuel and on the existing fleet with and without higher-octane.’’ 728 AFPM also commented that it does not support the potential regulatory requirement for the production or use of higher octane gasoline as a compliance option. AFPM commented that EPA lacks the authority to require the use of higher octane fuels under CAA § 211(c)(1)(A). AFPM further commented ‘‘[t]he only vehicles legally permitted to use more than 15 percent ethanol blends are flex-fuel vehicles, which are currently certified to utilize both E10 and E85. Without an alternative certification for an auto 726 Volkswagen, Detailed Comments, NHTSA– 2017–0069–0583. 727 API, Detailed Comments, EPA–HQ–OAR– 2018–0283–5458. 728 AFPM, Detailed Comments, EPA–HQ–OAR– 2018–0283–5698. E:\FR\FM\30APR2.SGM 30APR2 khammond on DSKJM1Z7X2PROD with RULES2 Federal Register / Vol. 85, No. 84 / Thursday, April 30, 2020 / Rules and Regulations manufacturer to build an E30 certified vehicle, which would require extensive testing and certification procedures as well as sufficient market availability of the certification fuel, it would be inappropriate for the Administration to consider such vehicles as a viable option in the 2022–2026 compliance period.’’ Gasoline retailers also commented regarding higher-octane fuels. NACS and SIGMA commented that they support examining the use of such fuels as a potential path towards future emissions reductions and that it will be important that the agencies appropriately consider and address a variety of related issues, including: 1. How to allow and handle the expanded sales of higher-octane fuels, which may include fuels that currently face barriers to sale, such as E15; 2. Streamlining the registration and regulation of higher-level blends of ethanol; 3. Addressing misfueling liability concerns of retailers; 4. Streamlining federal labeling requirements and ensuring federal preemption of state requirements; and 5. Addressing any other regulatory and legislative challenges associated with the use of higher-octane fuels.729 NATSO commented that ‘‘the Agencies should under no circumstances consider ‘requiring that mid-level [ethanol] blends be made available at service stations’ ’’ and went on to say that ‘‘retailers would need to be assured that they will not be held responsible for customers that misfuel . . . Federal dispenser labeling requirements would have to be streamlined and state requirements would have to be preempted. . . Auto manufacturers would have to warrant all new higher-octane vehicles up to at least E15 depending upon vehicles’ capabilities, and would have to affirmatively state which cars in the existing fleet can run on E15 and ensure that the cars are warrantied or retroactively warrantied as such.’’ 730 UCS commented that ‘‘[a]n orderly transition to high-octane fuel would take several years to complete. It will take time for the necessary regulations to be finalized, for vehicles optimized for high-octane gasoline to c