Corporate Average Fuel Economy Standards for Model Years 2024-2026 Passenger Cars and Light Trucks, 49602-49883 [2021-17496]

Download as PDF 49602 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules DEPARTMENT OF TRANSPORTATION National Highway Traffic Safety Administration 49 CFR Parts 531, 533, 536, and 537 [NHTSA–2021–0053] RIN 2127–AM34 Corporate Average Fuel Economy Standards for Model Years 2024–2026 Passenger Cars and Light Trucks National Highway Traffic Safety Administration (NHTSA), Department of Transportation (DOT). ACTION: Notice of proposed rulemaking. AGENCY: NHTSA, on behalf of the Department of Transportation, is proposing revised fuel economy standards for passenger cars and light trucks for model years 2024–2026. On January 20, 2021, President Biden signed an Executive order (E.O.) entitled, ‘‘Protecting Public Health and the Environment and Restoring Science To Tackle the Climate Crisis.’’ In it, the President directed that ‘‘The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021– 2026 Passenger Cars and Light Trucks’’ (hereafter, ‘‘the 2020 final rule’’) be immediately reviewed for consistency with our Nation’s abiding commitment to empower our workers and communities; promote and protect our public health and the environment; and conserve our national treasures and monuments, places that secure our national memory. President Biden further directed that the 2020 final rule be reviewed at once and that (in this case) the Secretary of Transportation consider ‘‘suspending, revising, or rescinding’’ it, via a new proposal, by July 2021. Because of the President’s direction in the E.O., NHTSA reexamined the 2020 final rule under its authority to set corporate average fuel economy (CAFE) standards. In doing so, NHTSA tentatively concluded that the fuel economy standards set in 2020 should be revised so that they increase at a rate of 8 percent year over year for each model year from 2024 through 2026, for both passenger cars and light trucks. This responds to the agency’s statutory mandate to improve energy lotter on DSK11XQN23PROD with PROPOSALS2 SUMMARY: VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 conservation. This proposal also makes certain minor changes to fuel economy reporting requirements. DATES: Comments: Comments are requested on or before October 26, 2021. In compliance with the Paperwork Reduction Act, NHTSA is also seeking comment on a revision to an existing information collection. For additional information, see the Paperwork Reduction Act Section under Section IX, below. All comments relating to the information collection requirements should be submitted to NHTSA and to the Office of Management and Budget (OMB) at the address listed in the ADDRESSES section on or before October 26, 2021. See the SUPPLEMENTARY INFORMATION section on ‘‘Public Participation,’’ below, for more information about written comments. Public Hearings: NHTSA will hold one virtual public hearing during the public comment period. The agency will announce the specific date and web address for the hearing in a supplemental Federal Register notification. The agency will accept oral and written comments on the rulemaking documents and will also accept comments on the Supplemental Environmental Impact Statement (SEIS) at this hearing. The hearing will start at 9 a.m. Eastern standard time and continue until everyone has had a chance to speak. See the SUPPLEMENTARY INFORMATION section on ‘‘Public Participation,’’ below, for more information about the public hearing. ADDRESSES: You may send comments, identified by Docket No. NHTSA–2021– 0053, by any of the following methods: • Federal eRulemaking Portal: https:// www.regulations.gov. Follow the instructions for submitting comments. • Fax: (202) 493–2251. • Mail: Docket Management Facility, M–30, U.S. Department of Transportation, West Building, Ground Floor, Rm. W12–140, 1200 New Jersey Avenue SE, Washington, DC 20590. • Hand Delivery: Docket Management Facility, M–30, U.S. Department of Transportation, West Building, Ground Floor, Rm. W12–140, 1200 New Jersey Avenue SE, Washington, DC 20590, between 9 a.m. and 4 p.m. Eastern Time, Monday through Friday, except Federal holidays. PO 00000 Frm 00002 Fmt 4701 Sfmt 4702 Comments on the proposed information collection requirements should be submitted to: Office of Management and Budget at www.reginfo.gov/public/do/PRAMain. To find this particular information collection, select ‘‘Currently under Review—Open for Public Comment’’ or use the search function. NHTSA requests that comments sent to the OMB also be sent to the NHTSA rulemaking docket identified in the heading of this document. Instructions: All submissions received must include the agency name and docket number or Regulatory Information Number (RIN) for this rulemaking. All comments received will be posted without change to https:// www.regulations.gov, including any personal information provided. For detailed instructions on sending comments and additional information on the rulemaking process, see the ‘‘Public Participation’’ heading of the SUPPLEMENTARY INFORMATION section of this document. Docket: For access to the dockets or to read background documents or comments received, please visit https:// www.regulations.gov, and/or Docket Management Facility, M–30, U.S. Department of Transportation, West Building, Ground Floor, Rm. W12–140, 1200 New Jersey Avenue SE, Washington, DC 20590. The Docket Management Facility is open between 9 a.m. and 4 p.m. Eastern Time, Monday through Friday, except Federal holidays. FOR FURTHER INFORMATION CONTACT: Rebecca Schade, NHTSA Office of Chief Counsel, National Highway Traffic Safety Administration, 1200 New Jersey Avenue SE, Washington, DC 20590; email: rebecca.schade@dot.gov. SUPPLEMENTARY INFORMATION: Does this action apply to me? This action affects companies that manufacture or sell new passenger automobiles (passenger cars) and nonpassenger automobiles (light trucks) as defined under NHTSA’s CAFE regulations.1 Regulated categories and entities include: 1 ‘‘Passenger car’’ and ‘‘light truck’’ are defined in 49 CFR part 523. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules NAICS Codes A Category Industry ....................................................................................... 335111 336112 811111 811112 811198 423110 335312 336312 336399 811198 Industry ....................................................................................... Industry ....................................................................................... A North 49603 Examples of potentially regulated entities Motor Vehicle Manufacturers. Commercial Importers of Vehicles and Vehicle Components. Alternative Fuel Vehicle Converters. American Industry Classification System (NAICS). 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. I. Executive Summary NHTSA, on behalf of the Department of Transportation, is proposing to amend standards regulating corporate average fuel economy (CAFE) for passenger cars and light trucks for model years (MYs) 2024–2026. This proposal responds to NHTSA’s statutory obligation to set maximum feasible CAFE standards to improve energy conservation, and to President Biden’s directive in Executive Order 13990 of January 20, 2021 that ‘‘The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021– 2026 Passenger Cars and Light Trucks’’, 2020 final rule or 2020 CAFE rule (85 FR 24174 (April 30, 2020)), be immediately reviewed for consistency with our Nation’s abiding commitment to promote and protect our public health and the environment, among other things. NHTSA undertook that review immediately, and this proposal is the result of that process. The proposed amended CAFE standards would increase in stringency from MY 2023 levels by 8 percent per year, for both passenger cars and light trucks over MYs 2024–2026. NHTSA tentatively concludes that this level is maximum feasible for these model years, as discussed in more detail in Section VI, and seeks comment on that conclusion. The proposal considers a range of regulatory alternatives, consistent with NHTSA’s obligations under the National Environmental Policy Act (NEPA) and Executive Order 12866. While E.O. 13990 directed the review of CAFE standards for MYs 2021–2026, statutory lead time requirements mean that the soonest model year that can currently be amended in the CAFE program is MY 2024. The proposed standards would remain vehicle footprint-based, like the CAFE standards in effect since MY 2011. Recognizing that many readers think about CAFE standards in terms of the miles per gallon (mpg) values that the standards are projected to eventually require, NHTSA currently projects that the proposed standards would require, on an average industry fleet-wide basis, roughly 48 mpg in MY 2026. NHTSA notes both that real-world fuel economy is generally 20–30 percent lower than the estimated required CAFE level stated above, and also that the actual CAFE standards are the footprint target curves for passenger cars and light trucks, meaning that ultimate fleet-wide levels will vary depending on the mix of vehicles that industry produces for sale in those model years. Table I–1 shows the incremental differences in stringency levels for passenger cars and light trucks, by regulatory alternative, in the model years subject to regulation. VerDate Sep<11>2014 Model Year Alternative 0 (Baseline/No Action) 2024 2025 2026 - 2024 2025 2026 - 2024 2025 2026 - 21:48 Sep 02, 2021 Passene:er cars 3.9 4.9 5.9 Li!!ht trucks 3.5 4.2 5.1 Total 3.7 4.5 5.3 - Jkt 253001 PO 00000 Alternative 1 Frm 00003 Fmt 4701 Sfmt 4725 Alternative 2 Alternative 3 3.3 6.8 10.8 4.3 9.2 14.7 2.2 4.7 7.6 3.0 6.4 10.4 2.6 5.5 8.7 3.5 7.5 11.9 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.000</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table 1-1-Incremental Stringency Levels (mpg above Baseline) for Passenger Cars and Light Trucks, by Regulatory Alternative 49604 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 This proposal is significantly different from the conclusion that NHTSA reached in the 2020 final rule, but this is because important facts have changed, and because NHTSA has reconsidered how to balance the relevant statutory considerations in light of those facts. NHTSA tentatively concludes that significantly more stringent standards are maximum feasible. Contrary to the 2020 final rule, NHTSA recognizes that the need of the United States to conserve energy must include serious consideration of the energy security risks of continuing to consume oil, which more stringent fuel economy standards can reduce. Reducing our Nation’s climate impacts can also benefit our national security. Additionally, at least part of the automobile industry appears increasingly convinced that improving fuel economy and reducing greenhouse gas (GHG) emissions is a growth market for them, and that the market rewards investment in advanced technology. Nearly all auto manufacturers have announced forthcoming new higher fuel-economy and electric vehicle models, and five major manufacturers voluntarily bound themselves to stricter GHG requirements than set forth by NHTSA and the Environmental Protection Agency (EPA) in 2020 through contractual agreements with the State of California, which will result in their achieving fuel economy levels well above the standards set forth in the 2020 final rule. These companies are sophisticated, for-profit enterprises. If they are taking these steps, NHTSA can be more confident than the agency was in 2020 that the market is getting ready to make the leap to significantly higher VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 fuel economy. The California Framework and the clear planning by industry to migrate toward more advanced fuel economy technologies are evidence of the practicability of more stringent standards. Moreover, more stringent CAFE standards will help to encourage industry to continue improving the fuel economy of all vehicles, rather than simply producing a few electric vehicles, such that all Americans can benefit from higher fuel economy and save money on fuel. NHTSA cannot consider the fuel economy of dedicated alternative fuel vehicles like battery electric vehicles when determining maximum feasible standards, but the fact that industry increasingly appears to believe that there is a market for these vehicles is broader evidence of market (and consumer) interest in fuel economy, which is relevant to NHTSA’s determination of whether more stringent standards would be economically practicable. For all of these reasons, NHTSA tentatively concludes that standards that increase at 8 percent per year are maximum feasible. This proposal is also different from the 2020 final rule in that it is issued by NHTSA alone, and EPA has issued a separate proposal. The primary reason for this is the difference in statutory authority—EPA does not have the same lead time requirements as NHTSA and is thus able to amend MY 2023 in addition to MYs 2024–2026. An important consequence of this is that EPA’s proposed rate of stringency increase, after taking a big leap in MY 2023, looks slower than NHTSA’s over the same time period. NHTSA emphasizes, however, that the proposed PO 00000 Frm 00004 Fmt 4701 Sfmt 4702 standards are what NHTSA believes best fulfills our statutory directive of energy conservation, and in the context of the EPA standards, the analysis we have done is tackling the core question of whether compliance with both standards should be achievable with the same vehicle fleet, after manufacturers fully understand the requirements from both proposals. The differences in what the two agencies’ standards require become smaller each year, until alignment is achieved. While NHTSA recognizes that the last several CAFE standard rulemakings have been issued jointly with EPA, and that issuing separate proposals represents a change in approach, the agencies worked together to avoid inconsistencies and to create proposals that would continue to allow manufacturers to build a single fleet of vehicles to meet both agencies’ proposed standards. Additionally, and importantly, NHTSA has also considered and accounted for California’s Zero Emission Vehicle (ZEV) program (and its adoption by a number of other states) in developing the baseline for this proposal, and has accounted for the aforementioned ‘‘Framework Agreements’’ between California and BMW, Ford, Honda, Volkswagen of America (VWA), and Volvo, which are national-level GHG standards to which these companies committed for several model years. A number of other improvements and updates have been made to the analysis since the 2020 final rule. Table I–2 summarizes these, and they are discussed in much more detail below and in the documents accompanying this preamble. BILLING CODE 4910–59–P E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49605 Table 1-2-Key Analytical Updates from 2020 Final Rule BILLING CODE 4910–59–C NHTSA estimates that this proposal could reduce average undiscounted fuel outlays over the lifetimes of MY 2029 vehicles by about $1,280, while increasing the average cost of those vehicles by about $960 over the baseline described above. With the social cost of VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 carbon (SCC) discounted at 2.5 percent and other benefits and costs discounted at 3 percent, for the three affected model years NHTSA finds $65.8 billion in benefits attributable to the proposed standards and $37.4 billion in proposed costs so that present net benefits could PO 00000 Frm 00005 Fmt 4701 Sfmt 4702 be $28.4 billion.2 Applied to the entire fleet for MYs 1981–2029, NHTSA estimates $120 billion in costs and $121 2 As discussed in Section III.G.2.b), NHTSA has discounted the SCC at 2.5% when other benefits and costs are discounted at 3% but seeks comment on this approach. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.001</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Key Updates In all regulatory alternatives, account for the Zero Emission Vehicle (ZEV) mandates applicable in California and the States that have adopted them. In all regulatory alternatives, account for some vehicle manufacturers' (BMW, Ford, Honda, VWA, and Volvo) voluntary commitments to the State of California to continued annual nation-wide reductions of vehicle greenhouse gas emissions through model year (MY) 2026, with greater rates of electrification than would have been required under the 2020 final rule. In all regulatory alternatives, account for manufacturers' responses to both CAFE (alternatives) and baseline carbon dioxide standards jointly (rather than only separately). Procedures to ensure that modeled technology application and production volumes are the same across all regulatory alternatives in the earliest model years. Procedures to focus application of the Energy Policy and Conservation Act's (EPCA) "standard setting constraints" (i.e., regarding the consideration of compliance credits and additional dedicated alternative fueled vehicles) more precisely to only those model years for which NHTSA is proposing or finalizing new standards. More accurate accounting for compliance treatment of flex-fuel vehicles (FFVs) and plug-in hybrid electric vehicles (PHEVs). Include CAFE civil penalties in the "effective cost" metric used when simulating manufacturers' potential application of fuel-saving technologies. COVID adjustment to vehicle miles traveled (VMT) model inputs (per Federal Highway Administration estimate of 2020 national VMT). Embed Federal Highway Administration's VMT model in CAFE Model (dynamic model). Criteria pollutant health effects reported separately for refining and electricity generation. New procedures to estimate the impacts and corresponding monetized damages of highway vehicle crashes that do not result in fatalities, now based on historical data and future trend models that reflect the impacts of advanced crash avoidance technologies. Social cost of carbon and damage costs for methane and nitrous oxide (interim guidance February 19, 2021). Fuel and electricity prices using Enern:v Information Administration's Annual Enern:v Outlook 2021. Analysis fleet updated to MY 2020. Updated large scale simulation using Argonne National Laboratory's Autonomie model. Inclusion of 400- and 500-mile battery electric vehicles (BEVs). Updated battery and battery management unit size and costs using BatPaC version 4.0 (October 2020). Updated hybrid electric vehicles, PHEV, and BEV electric machine and battery sizing. Inclusion of high compression ratio (HCR) engines with cylinder deactivation. Expanded turbo-downsizing to include reducing low-powered 4-cylinder naturally aspirated engines to 3-cylinder turbocharged engines. Updated 10-speed automatic transmission efficiency characteristics based on benchmarking data from Southwest Research Institute. Updated cold start offset assumptions using MY 2020 compliance data. Updated mass regression analysis values for engines and electric motors. More accurate accounting for off-cvcle incremental costs relative to MY 2020 baseline fleet. Updated fuel cell vehicle technology inputs. 49606 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 billion in benefits attributable to the proposed standards, such that the present value of aggregate net benefits to society could be $1 billion. Like any analysis of this magnitude attempting to forecast future effects of current policies, significant uncertainty exists about many key inputs. Changes in the price of fuel or in the social cost of carbon could dramatically change benefits, for example, and readers should expect that the eventual final rule will reflect any updates made to those (and many other) values that occur between now and then. It is also worth stressing that NHTSA’s statutory authority requires that its standards be maximum feasible, taking into account four statutory factors. While NHTSA’s estimates of costs and benefits are important considerations, it is the maximum feasible analysis that controls the setting of CAFE standards. Like many other types of regulations, CAFE standards apply only to new vehicles. The costs attributable to new CAFE standards are thus ‘‘front-loaded,’’ because they result primarily from the application of fuel-saving technology to new vehicles. On the other hand, the impact of new CAFE standards on fuel consumption and greenhouse gases— VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 and the associated benefits to society— occur over an extended time, as drivers buy, use, and eventually scrap these new vehicles. By accounting for many model years and extending well into the future (2050), our analysis accounts for these differing patterns in impacts, benefits, and costs. Our analysis also accounts for the potential that, by changing new vehicle prices and fuel economy levels, CAFE standards could indirectly impact the operation of vehicles produced before or after the model years (2024–2026) for which we are proposing new CAFE standards. This means that some of the proposal’s impacts and corresponding benefits and costs are actually attributable to indirect impacts on vehicles produced before and after model years 2024–2026. The bulk of our analysis considers a ‘‘model year’’ (MY) perspective that considers the lifetime impacts attributable to all vehicles produced prior to model year 2030, accounting for the operation of these vehicles over their entire useful lives (with some model year 2029 vehicles estimated to be in service as late as 2068). This approach emphasizes the role of model years 2024–2026, while accounting for the potential that it may take PO 00000 Frm 00006 Fmt 4701 Sfmt 4702 manufacturers a few additional years to produce fleets fully responsive to the proposed MY 2026 standards, and for the potential that the proposal could induce some changes in the operation of vehicles produced prior to MY 2024. Our analysis also considers a ‘‘calendar year’’ (CY) perspective that includes the annual impacts attributable to all vehicles estimated to be in service in each calendar year for which our analysis includes a representation of the entire registered light-duty fleet. For this NPRM, this calendar year perspective covers each of calendar years 2021– 2050, with differential impacts accruing as early as model year 2023. Compared to the ‘‘model year’’ perspective, this calendar year perspective emphasizes model years of vehicles produced in the longer term, beyond those model years for which standards are currently being proposed. Table I–3 summarizes estimates of selected physical impacts viewed from each of these two perspectives, as well as corresponding estimates of the present values of cumulative benefits, costs, and net benefits. BILLING CODE 4910–59–P E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49607 Table 1-3 - Selected Cumulative Impacts - Model and Calendar Year Perspectives I Alt.1 MYs 1981-2029 CYs 2023-2050 MYs 1981-2029 CYs 2023-2050 MYs 1981-2029 CYs 2023-2050 Alt. 2 A voided Gasoline Consumption (b. _gal 1 I 30 I 105 Additional Electricitv Consumption (TWh) I 90 I 395 CO2 Emissions (mmt) I 295 I 1 055 Benefits ($b 3% Discount Rate) I 83 I 267 Costs ($b, 3% Discount Rate) I 66 I 186 Net Benefits ($b, 3% Discount Rate) I 16 I 81 MYs 1981-2029 CYs 2023-2050 MYs 1981-2029 CYs 2023-2050 MYs 1981-2029 CYs 2023-2050 Benefits ($b 7% Discount Rate) I 52 I 145 Costs ($b, 7% Discount Rate) I 49 I 109 Net Benefits ($b. 7% Discount Rate) I 2 I 36 MYs 1981-2029 CYs 2023-2050 MYs 1981-2029 CYs 2023-2050 MYs 1981-2029 CYs 2023-2050 Finally, for purposes of comparing the benefits and costs of new CAFE standards to the benefits and costs of other Federal regulations, policies, and programs, we have computed ‘‘annualized’’ benefits and costs. These are the annual averages of the cumulative benefits and costs over the I Alt. 3 50 205 I I 75 290 275 1,150 I I 395 1,690 465 1,845 I I 665 2 615 121 434 I I 173 607 121 334 I I 176 475 0 100 I I -3 132 76 236 I I 108 332 91 199 I I 133 286 -15 37 I I -25 46 covered model or calendar years, after expressing these in present value terms. Table 1-4-Estimated Costs, Benefits, and Net Benefits Across MYs 1981-2029 (billions of dollars), Total Fleet for Alternative 1 EP03SE21.003</GPH> Annualized 3% Discount Rate 7% Discount Rate 2.61 3.58 3.24 3.75 0.63 0.17 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00007 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.002</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Costs Benefits Net Benefits Totals 3% Discount Rate 7% Discount Rate 66.5 49.3 82.6 51.6 16.1 2.3 49608 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 1-5-Estimated Costs, Benefits, and Net Benefits Across MYs 1981-2029 (billions of dollars), Total Fleet for Alternative 2 Totals 3% Discount Rate 7% Discount Rate 121.1 121.4 0.3 Costs Benefits Net Benefits Annualized 3% Discount Rate 7% Discount Rate 90.7 75.6 -15.1 4.75 4.76 0.01 6.59 5.49 -1.10 Table 1-6-Estimated Costs, Benefits, and Net Benefits Across MYs 1981-2029 (billions of dollars), Total Fleet for Alternative 3 Costs Benefits Net Benefits Totals 3% Discount Rate 7% Discount Rate 176.3 132.8 172.9 107.6 -3.4 -25.2 Annualized 3% Discount Rate 7% Discount Rate 6.91 9.65 6.78 7.82 -0.13 -1.83 Table 1-7 - Estimated Costs, Benefits, and Net Benefits Across Calendar Years 2021-2050 (billions of dollars), Total Fleet for Alternative 1 Totals 3% Discount Rate 7% Discount Rate 185.7 266.6 81.0 Costs Benefits Net Benefits Annualized 3% Discount Rate 7% Discount Rate 108.9 145.2 36.4 9.47 13.60 4.13 8.77 11.70 2.93 lotter on DSK11XQN23PROD with PROPOSALS2 Costs Benefits Net Benefits 474.8 606.5 131.7 As discussed in detail below, the monetized estimated costs and benefits VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Annualized 3% Discount Rate 7% Discount Rate 285.8 331.7 45.9 24.22 30.94 6.72 of this proposal are relevant and important to the agency’s tentative PO 00000 Frm 00008 Fmt 4701 Sfmt 4702 23.03 26.73 3.70 conclusion, but they are not the whole of the conclusion. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.007</GPH> Totals 3% Discount Rate 7% Discount Rate EP03SE21.006</GPH> Table 1-9- Estimated Costs, Benefits, and Net Benefits Across Calendar Years 2021-2050 (billions of dollars), Total Fleet for Alternative 3 EP03SE21.005</GPH> Annualized 3% Discount Rate 7% Discount Rate 17.02 16.03 22.12 19.02 5.10 2.99 EP03SE21.004</GPH> Costs Benefits Net Benefits Totals 3% Discount Rate 7% Discount Rate 333.6 198.9 433.6 236.0 100.0 37.1 EP03SE21.008</GPH> Table 1-8- Estimated Costs, Benefits, and Net Benefits Across Calendar Years 2021-2050 (billions of dollars), Total Fleet for Alternative 2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Additionally, although NHTSA is prohibited from considering the availability of certain flexibilities in making our determination about the levels of CAFE standards that would be maximum feasible, manufacturers have a variety of flexibilities available to them to reduce their compliance burden. Table I–10 through Table I–13 below summarizes available compliance 49609 flexibilities. NHTSA seeks comment on whether to retain non-statutory flexibilities for the final rule. Table 1-10- Statutory Flexibilities for Over-compliance with Standards Regulatory Item NHTSA Current Pro2ram Denominated in tenths of a mo!! Authority 49 U.S.C. 32903(a) 49U.S.C. 32903(a)(2) 49U.S.C. 32903(a)( 1) Credit Earning Credit "Carry-forward" Credit "Carryback" (AKA "deficit carry-forward")* 5 MYs into the future 3 MYs into the past Up to 2 mpg per fleet; transferred credits may not be used to meet minimum domestic passenger car standard (MDPCS) Unlimited quantity; traded credits may not be Credit Trade* 49 U.S.C. 32903(f) used to meet MDPCS *NHTSA did not expressly model credit carryback, and credit trades were only modeled for credits that existed at the beginning of the modeling simulation. All other credits in this table were modeled. Credit Transfer 49 U.S.C. 32903(g) Table 1-11- Current and Proposed Flexibilities that Address Gaps in Compliance Test Procedures Regulatory Item Air conditioning efficiency Off-cycle NHTSA Current and Proposed Proeram Authority 49 U.S.C. 32904 Allows manufacturers to earn "fuel consumption improvement values" (FCIVs) equivalent to EPA credits starting in MY 2017 49 U.S.C. 32904 Allows manufacturers to earn "fuel consumption improvement values" (FCIVs) equivalent to EPA credits starting in MY 2017 For MY 2020 and beyond, NHTSA proposes to implement CAFE provisions equivalent to the EPA proposed chan~es Table 1-12 - Incentives that Encourage Application of Technologies NHTSA Proposed Proeram Full-size pickup Allows manufacturers to earn FCIVs equivalent to EPA credits 49 U.S.C. for MYs 2017-2021 trucks with HEV or overperforming 32904 NHTSA proposes to reinstate incentives for strong hybrid OR target* oververforminf! tarf!et bv 20% for MYs 2022-2025 *These credits were not modeled for the NPRM analysis. EP03SE21.010</GPH> EP03SE21.011</GPH> Authority VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00009 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.009</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Regulatory Item 49610 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 1-13-Incentives that Encourage Alternative Fuel Vehicles Dual-fueled vehicles 49U.S.C. 32905(b), (d), and (e); 32906(a) BILLING CODE 4910–59–C lotter on DSK11XQN23PROD with PROPOSALS2 NHTSA recognizes that the lead time for this proposal is shorter than past rulemakings have provided, and that the economy and the country are in the process of recovering from a global pandemic and the resulting economic distress. At the same time, NHTSA also recognizes that at least parts of the industry are nonetheless stepping up their product offerings and releasing more and more high fuel-economy vehicle models, and many companies did not deviate significantly from product plans established in response to the standards set forth in the 2012 final rule (77 FR 62624, Oct. 15, 2012) and confirmed by EPA in its January 2017 Final Determination. With these considerations in mind, NHTSA is proposing to amend the CAFE standards for MYs 2024–2026. NHTSA, like any other Federal agency, is afforded an opportunity to reconsider prior views and, when warranted, to adopt new positions. Indeed, as a matter of good governance, agencies should revisit their positions when appropriate, especially to ensure that their actions and regulations reflect legally sound interpretations of the agency’s authority and remain consistent with the agency’s views and practices. As a matter of law, ‘‘an Agency is entitled to change its interpretation of a statute.’’ 3 Nonetheless, ‘‘[w]hen an Agency adopts a materially changed interpretation of a statute, it must in addition provide a ‘reasoned analysis’ supporting its decision to revise its interpretation.’’ 4 The analysis presented in this preamble 3 Phoenix Hydro Corp. v. FERC, 775 F.2d 1187, 1191 (D.C. Cir. 1985). 4 Alabama Educ. Ass’n v. Chao, 455 F.3d 386, 392 (D.C. Cir. 2006) (quoting Motor Vehicle Mfrs. Ass’n of U.S., Inc. v. State Farm Mut. Auto. Ins. Co., 463 U.S. 29, 57 (1983)); see also 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.’’) (citations omitted). VerDate Sep<11>2014 21:48 Sep 02, 2021 NHTSA Current Pro2ram Fuel economy calculated assuming gallon of liquid or gallon equivalent gaseous alt fuel = 0 .15 gallons of gasoline; for EVs petroleum equivalencv factor Fuel economy calculated using 50% operation on alt fuel and 50% on gasoline through MY 2019. Starting with MY 2020, NHTSA uses the Society of Automotive Engineers (SAE) defined "Utility Factor" methodology to account for actual potential use, and "Ffactor" for FFV; NHTSA will continue to incorporate the 0 .15 incentive factor Authority 49U.S.C. 32905(a) and (c) Jkt 253001 and in the accompanying Technical Support Document (TSD), Preliminary Regulatory Impact Analysis (PRIA), Supplemental Environmental Impact Statement (SEIS), CAFE Model documentation, and extensive rulemaking docket fully supports the proposed decision and revised balancing of the statutory factors for MYs 2024–2026 standards. NHTSA seeks comment on the entirety of the rulemaking record. II. Introduction In this notice of proposed rulemaking (NPRM), NHTSA is proposing to revise CAFE standards for model years (MYs) 2024–2026. On January 20, 2021, the President signed Executive Order (E.O.) 13990, ‘‘Protecting Public Health and the Environment and Restoring Science To Tackle the Climate Crisis.’’ 5 In it, the President directed that ‘‘The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021– 2026 Passenger Cars and Light Trucks’’ (hereafter, ‘‘the 2020 final rule’’), 85 FR 24174 (April 30, 2020), must be immediately reviewed for consistency with our Nation’s abiding commitment to empower our workers and communities; promote and protect our public health and the environment; and conserve our national treasures and monuments, places that secure our national memory. E.O. 13990 states expressly that the Administration prioritizes listening to the science, improving public health and protecting the environment, reducing greenhouse gas emissions, and improving environmental justice while creating well-paying union jobs. The E.O. thus directs that the 2020 final rule be reviewed at once and that (in this case) the Secretary of Transportation consider 5 86 PO 00000 FR 7037 (Jan. 25, 2021). Frm 00010 Fmt 4701 Sfmt 4702 ‘‘suspending, revising, or rescinding’’ it, via an NPRM, by July 2021.6 Section 32902(g)(1) of Title 49, United States Code allows the Secretary (by delegation to NHTSA) to prescribe regulations amending an average fuel economy standard prescribed under 49 U.S.C. 32902(a), like those prescribed in the 2020 final rule, if the amended standard meets the requirements of 32902(a). The Secretary’s authority to set fuel economy standards is delegated to NHTSA at 49 CFR 1.95(a); therefore, in this NPRM, NHTSA proposes revised fuel economy standards for MYs 2024– 2026. Section 32902(g)(2) states that when the amendment makes an average fuel economy standard more stringent, it must be prescribed at least 18 months before the beginning of the model year to which the amendment applies. NHTSA generally calculates the 18month lead time requirement as April of the calendar year prior to the start of the model year. Thus, 18 months before MY 2023 would be April 2021, because MY 2023 begins in September 2022. Because of this lead time requirement, NHTSA is not proposing to amend the CAFE standards for MYs 2021–2023, even though the 2020 final rule also covered those model years. For purposes of the CAFE program, the 2020 final rule’s standards for MYs 2021–2023 will remain in effect. For the MYs for which there is statutory lead time to amend the standards, however, NHTSA is proposing amendments to the currently applicable fuel economy standards. Although only one year has passed since the 2020 final rule, the agency believes it is reasonable and appropriate to revisit the CAFE standards for MYs 2024–2026. In particular, the agency has further considered the serious adverse effects on energy conservation that the standards finalized in 2020 would cause 6 Id., E:\FR\FM\03SEP2.SGM Sec. 2(a)(ii). 03SEP2 EP03SE21.012</GPH> Regulatory Item Dedicated alternative fuel vehicle Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 as compared to the proposed standards. The need of the U.S. to conserve energy is greater than understood in the 2020 final rule. In addition, standards that are more stringent than those that were finalized in 2020 appear economically practicable. Nearly all auto manufacturers have announced forthcoming new advanced technology vehicle models with higher fuel economy, making strong public commitments that mirror those of the Administration. Five major manufacturers voluntarily bound themselves to stricter national-level GHG requirements as part of the California Framework agreement. Meanwhile, certain facts on the ground remain similar to what was before NHTSA in the prior analysis—gas prices still remain relatively low in the U.S., for example, and while light-duty vehicle sales fell sharply in MY 2020, the vehicles that did sell tended to be, on average, larger, heavier, and more powerful, all factors that increase fuel consumption. However, the renewed focus on addressing energy conservation and the industry’s apparent ability to meet more stringent standards show that VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 a rebalancing of the EPCA factors, and the proposal of more stringent standards, is appropriate for model years 2024–2026. The following sections introduce the proposal in more detail. A. What is NHTSA proposing? NHTSA is proposing to set CAFE standards for passenger cars and light trucks manufactured for sale in the United States in MYs 2024–2026. Passenger cars are generally sedans, station wagons, and two-wheel drive crossovers and sport utility vehicles (CUVs and SUVs), while light trucks are generally four-wheel drive vehicles, larger/heavier two-wheel drive sport utility vehicles, pickups, minivans, and passenger/cargo vans.7 The proposed standards would increase at 8 percent per year for both cars and trucks, and are represented by regulatory Alternative 2 in the agency’s analysis. The proposed standards would be defined by a mathematical equation that represents a constrained linear function relating vehicle footprint to fuel 7 ‘‘Passenger car’’ and ‘‘light truck’’ are defined at 49 CFR part 523. PO 00000 Frm 00011 Fmt 4701 Sfmt 4702 49611 economy targets for both cars and trucks; vehicle footprint is roughly measured as the rectangle that is made by the four points where the vehicle’s tires touch the ground. Generally, passenger cars will have more stringent targets than light trucks regardless of footprint, and smaller vehicles will have more stringent targets than larger vehicles. No individual vehicle or vehicle model need meet its target exactly, but a manufacturer’s compliance is determined by how its average fleet fuel economy compares to the average fuel economy of the targets of the vehicles it manufactures. The proposed target curves 8 for passenger cars and light trucks are as follows; curves for MYs 2020–2023 are included in Figure II–1 and Figure II–2 for context. BILLING CODE 4910–59–P 8 NHTSA underscores that the equations and coefficients defining the curves are what the agency is proposing, and not the mpg numbers that the agency currently estimates could result from manufacturers complying with the curves. Because the estimated mpg numbers are an effect of the proposed curves, they are presented in the following section. E:\FR\FM\03SEP2.SGM 03SEP2 49612 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 60 ~ '' ---------. , ' ' ', ' >---<',, 55 ························.. e" ._,, 50 ij» ~45 =o ··.... ----------- ' ', ' ··... ...,, ', ···•...·•.. ',,,, ---------------··•.·•. .. ',...... , ________________________________________ _ ··•... ! 40 .... .... , ··········•························································································ 35 30 25 35 40 45 50 55 60 Footprint (sf) 65 70 75 80 ,........... 2020 -------2021 ---- 2022 -2023 ....... 2024 ----2025 - -2026 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00012 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.013</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure II-I -Passenger Car Fuel Economy, Proposed Target Curves 49613 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 60 55 ~----------... r --- ... ,...._ •-•'---'-••·•<.<cc'""'·-••<-•.<·••-·•••<>~~~:~:~' 35 30 25 35 40 50 45 55 60 Footprint (sf) 65 70 75 80 2020 -------2021 ---- 2022 -2023 ....... 2024 ----2025 - -2026 Figure 11-2-Light Truck Fuel Economy, Proposed Target Curves BILLING CODE 4910–59–C NHTSA is also proposing to amend the minimum domestic passenger car CAFE standards for MYs 2024–2026. The provision at 49 U.S.C. 32902(b)(4) requires NHTSA to project the minimum standard when it promulgates passenger car standards for a model year, so it is appropriate to revisit the minimum standards at this time. NHTSA is proposing to retain the 1.9 percent offset used in the 2020 final rule, such that the minimum domestic passenger car standard would be as shown in Table II–1. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 2025 2026 44.4 mpg 48.2 mpg 52.4 mpg B. What does NHTSA estimate the effects of proposing this would be? As for past CAFE rulemakings, NHTSA has used the CAFE Model to estimate the effects of proposed CAFE standards, and of other regulatory alternatives under consideration. Some inputs to the CAFE Model are derived from other models, such as Argonne National Laboratory’s ‘‘Autonomie’’ PO 00000 Frm 00013 Fmt 4701 Sfmt 4702 vehicle simulation tool and Argonne’s Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) fuel-cycle emissions analysis model, the U.S. Energy Information Administration’s (EIA’s) National Energy Modeling System (NEMS), and EPA’s Motor Vehicle Emission Simulator (MOVES) vehicle emissions model. Especially given the scope of the E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.015</GPH> The next section describes some of the effects that NHTSA estimates would follow from this proposal, including how the curves shown above translate to estimated average mile per gallon requirements for the industry. 2024 EP03SE21.014</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table 11-1- Proposed Minimum Domestic Passenger Car Standards 49614 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules NHTSA’s analysis (through model years 2050, with driving of model year 2029 vehicles accounted for through calendar year 2068), these inputs involve a multitude of uncertainties. For example, a set of inputs with significant uncertainty could include future population and economic growth, future gasoline and electricity prices, future petroleum market characteristics (e.g., imports and exports), future battery costs, manufacturers’ future responses to standards and fuel prices, buyers’ future responses to changes in vehicle prices and fuel economy levels, and future emission rates for ‘‘upstream’’ processes (e.g., refining, finished fuel transportation, electricity generation). Considering that all of this is uncertain from a 2021 vantage point, NHTSA underscores that all results of this analysis are, in turn, uncertain, and simply represent the agency’s best estimates based on the information currently before us. NHTSA estimates that this proposal would increase the eventual 9 average of manufacturers’ CAFE requirements to about 48 mpg by 2026 rather than, under the No-Action Alternative (i.e., the baseline standards issued in 2020), about 40 mpg. For passenger cars, the average in 2026 is estimated to reach about 58 mpg, and for light trucks, about 42. This compares with 47 mpg and 34 mpg for cars and trucks, respectively, under the No-Action Alternative. Table 11-2 - Estimated Average of CAFE Levels (mpg) Required Under Proposal Fleet 2024 2025 2026 2027 2028 2029 Passenger Cars Light Trucks Overall Fleet 49 35 41 53 38 44 58 42 48 58 42 48 58 42 48 58 42 48 Because manufacturers do not comply exactly with each standard in each model year, but rather focus their compliance efforts when and where it is most cost-effective to do so, ‘‘estimated achieved’’ fuel economy levels differ somewhat from ‘‘estimated required’’ levels for each fleet, for each year. NHTSA estimates that the industrywide average fuel economy achieved in MY 2029 could increase from about 44 mpg under the No-Action Alternative to about 49 mpg under the proposal. Table 11-3 - Estimated Average of CAFE Levels (mpg) Achieved Under Proposal Fleet 2024 2025 2026 2027 2028 2029 Passenger Cars Light Trucks Overall Fleet 54 37 43 57 38 45 60 40 48 61 41 48 61 41 49 61 41 49 As discussed above, NHTSA’s analysis—unlike its previous CAFE analyses—estimates manufacturers’ potential responses to the combined effect of CAFE standards and separate CO2 standards (including agreements some manufacturers have reached with California), ZEV mandates, and fuel prices. Together, the aforementioned regulatory programs are more binding than any single program considered in isolation, and this analysis, like past analyses, shows some estimated overcompliance with the proposed CAFE standards, albeit by much less than what was shown in the NPRM that preceded the 2020 final rule, and any overcompliance is highly manufacturerdependent. Expressed as equivalent required and achieved average CO2 levels (using 8887 grams of CO2 per gallon of gasoline vehicle certification fuel), the above CAFE levels appear as shown in Table II–4 and Table II–5. 2025 2026 2027 2028 2029 Passenger Cars Light Trucks Overall Fleet 181 253 219 166 233 201 153 214 185 153 214 185 153 214 185 153 214 184 9 Here, ‘‘eventual’’ means by MY 2029, after most of the fleet will have been redesigned under the MY 2026 standards. NHTSA allows the CAFE Model to VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 continue working out compliance solutions for the regulated model years for three model years after the last regulated model year, in recognition of the PO 00000 Frm 00014 Fmt 4701 Sfmt 4725 fact that manufacturers do not comply perfectly with CAFE standards in each model year. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.018</GPH> 2024 EP03SE21.017</GPH> Fleet EP03SE21.016</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table 11-4- Estimated Average of CAFE Levels Required Under Proposal (as Equivalent Gram per Mile CO2 Levels) Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49615 Table 11-5- Estimated Average of CAFE Levels Achieved Under Proposal (as Equivalent Gram per Mile CO2 Levels) Fleet 2024 2025 2026 2027 2028 2029 Passenger Cars Light Trucks Overall Fleet 165 243 206 156 234 197 149 221 187 147 218 184 145 216 182 145 215 181 NHTSA estimates that over the lives of vehicles produced prior to MY 2030, the proposal would save about 50 billion gallons of gasoline and increase electricity consumption (as the percentage of electric vehicles increases Average requirements and achieved CAFE levels would ultimately depend on manufacturers’ and consumers’ responses to standards, technology developments, economic conditions, fuel prices, and other factors. over time) by about 275 terawatts (TWh), compared to levels of gasoline and electricity consumption NHTSA projects would occur under the baseline standards (i.e., the No-Action Alternative). Table 11-6- Estimated Changes in Energy Consumption vs. No-Action Alternative Energy Source Change in Consumption Gasoline Electricity baseline), Alternative 1, Alternative 2 (the proposal), and Alternative 3. BILLING CODE 4910–59–P EP03SE21.020</GPH> consumption by the U.S. light-duty vehicle fleet evolves as shown in Figure II–3 and Figure II–4, each of which shows projections for the No-Action Alternative (Alternative 0, i.e., the VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00015 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.019</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 NHTSA’s analysis also estimates total annual consumption of fuel by the entire on-road fleet from calendar year 2020 through calendar year 2050. On this basis, gasoline and electricity -50 billion gallons +275 TWh 49616 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 140 .Q.Qoo ...•...... ,.... Oo . ;..... ooo Oo .......•. ,_. · ... Oo ..•.........,............?, Oo. ·.· ····· ooo ··. 0 20l5 2020 ···············~-.~--~.~.~ ·2040 . 2045 2025 2030 2035 Q Alt O ....... Alt 1 ·-AlL2 -+-Alt. 3 2050 2055 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00016 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.021</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure 11-3-Estimated Annual Gasoline Consumption by Light-Duty On-Road Fleet 49617 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 300 0 2015 2035 2040 2045 o Alt:O ....... Altl ·-A1L2 -+-Alt.3 2025 2020 2030 2050 2055 Figure 11-4- Estimated Electricity Consumption by Light-Duty On-Road Fleet Accounting for emissions from both vehicles and upstream energy sector processes (e.g., petroleum refining and electricity generation), NHTSA estimates that the proposal would reduce greenhouse gas emissions by about 465 million metric tons of carbon dioxide (CO2), about 500 thousand metric tons of methane (CH4), and about 12 thousand tons of nitrous oxide (N2O). Table 11-7 -Estimated Changes in Greenhouse Gas Emissions (Metric Tons) vs. No-Action Alternative Greenhouse Gas Change in Emissions Carbon Dioxide (CO2) Methane (CRi) Nitrous Oxide (N2O) Figure II–5, which accounts for both emissions from both vehicles and upstream processes. EP03SE21.023</GPH> calendar year 2050. Also accounting for both vehicles and upstream processes, NHTSA estimates that CO2 emissions could evolve over time as shown in VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00017 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.022</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 As for fuel consumption, NHTSA’s analysis also estimates annual emissions attributable to the entire on-road fleet from calendar year 2020 through -465 million tons -500 thousand tons -12 thousand tons 49618 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 1,600 l,400 ..Q.oo ·······90.o.o .... 0 0 ·······•··........... .. · Ooo ,.. • · ·•.Oo ········· ...,,..•..... ·. . o.o.• 0·• Oo ............•..... Oo ............... 200 0 2015 2020 2025 203-0 2035 2040 2045 2050 2055 O Ak O ·······Alt.1 -AIL2 -+---Alt . 3 Figure 11-5-Estimated Annual CO2 Emissions Attributable to Light-Duty On-Road Fleet VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 ‘‘unconstrained’’ analysis, which does not set aside these potential manufacturer actions. The SEIS presents much more information regarding projected GHG emissions, as well as model-based estimates of corresponding impacts on several measures of global climate change. Also accounting for vehicular and upstream emissions, NHTSA has estimated annual emissions of most criteria pollutants (i.e., pollutants for which EPA has issued National Ambient Air Quality Standards). PO 00000 Frm 00018 Fmt 4701 Sfmt 4702 NHTSA estimates that under each regulatory alternative, annual emissions of carbon monoxide (CO), volatile organic compounds (VOC), nitrogen oxide (NOX), and fine particulate matter (PM2.5) attributable to the light-duty onroad fleet will decline dramatically between 2020 and 2050, and that emissions in any given year could be very nearly the same under each regulatory alternative. For example, Figure II–6 shows NHTSA’s estimate of future NOX emissions under each alternative. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.024</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Estimated emissions of methane and nitrous oxides follow similar trends. As discussed in the TSD, PRIA, and this NPRM, NHTSA has performed two types of supporting analysis. This NPRM and PRIA focus on the ‘‘standard setting’’ analysis, which sets aside the potential that manufacturers could respond to standards by using compliance credits or introducing new alternative fuel vehicle (including BEVs) models during the ‘‘decision years’’ (for this NPRM, 2024, 2025, and 2026). The accompanying SEIS focuses on an 49619 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 1,200,000 1,000,000 200,000 0 2015 2020 2030 2035 2040 204'5 b Alt O ....... AltJ -···-.Alt. 2• -+-Alt 3 202..5 2050 2055 BILLING CODE 4910–59–C lotter on DSK11XQN23PROD with PROPOSALS2 On the other hand, as discussed in the PRIA and SEIS, NHTSA projects that annual SO2 emissions attributable to the light-duty on-road fleet could increase modestly under the action alternatives, because, as discussed above, NHTSA projects that each of the action alternatives could lead to greater use of electricity (for PHEVs and BEVs). The adoption of actions—such as actions prompted by President Biden’s Executive order directing agencies to develop a Federal Clean Electricity and Vehicle Procurement Strategy—to reduce electricity generation emission rates beyond projections underlying NHTSA’s analysis (discussed in the TSD) could dramatically reduce SO2 emissions under all regulatory alternatives considered here.10 10 https://www.whitehouse.gov/briefing-room/ presidential-actions/2021/01/27/executive-orderon-tackling-the-climate-crisis-at-home-and-abroad/, accessed June 17, 2021. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 For the ‘‘standard setting’’ analysis, the PRIA accompanying this NPRM provides additional detail regarding projected criteria pollutant emissions and health effects, as well as the inclusion of these impacts in this benefit-cost analysis. For the ‘‘unconstrained’’ or ‘‘EIS’’ type of analysis, the SEIS accompanying this NPRM presents much more information regarding projected criteria pollutant emissions, as well as model-based estimates of corresponding impacts on several measures of urban air quality and public health. As mentioned above, these estimates of criteria pollutant emissions are based on a complex analysis involving interacting simulation techniques and a myriad of input estimates and assumptions. Especially extending well past 2040, the analysis involves a multitude of uncertainties. Therefore, actual criteria pollutant emissions could ultimately be different from NHTSA’s current estimates. To illustrate the effectiveness of the technology added in response to this proposal, Table II–8 presents NHTSA’s estimates for increased vehicle cost and lifetime fuel expenditures if we assumed the behavioral response to the lower cost of driving were zero.11 These numbers are presented in lieu of NHTSA’s primary estimate of lifetime fuel savings, which would give an incomplete picture of technological effectiveness because the analysis accounts for consumers’ behavioral response to the lower cost-per-mile of driving a more fuel-efficient vehicle. 11 While this comparison illustrates the effectiveness of the technology added in response to this proposal, it does not represent a full consumer welfare analysis, which would account for drivers’ likely response to the lower cost-per- mile of driving, as well as a variety of other benefits and costs they will experience. The agency’s complete analysis of the proposal’s likely impacts on passenger car and light truck buyers appears in the PRIA, Appendix I, Table A–23–1. PO 00000 Frm 00019 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.025</GPH> Figure 11-6 - Estimated Annual NOx Emissions Attributable to Light-Duty On-Road Fleet 49620 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 11-8-Estimated Impact on Average MY 2029 Vehicle Costs vs. No-Action Alternative12 r, ~ Dollar Value -.- Price Increase $960 Lifetime Fuel Savings With the SCC discounted at 2.5% and other benefits and costs discounted at 3%, NHTSA estimates that costs and benefits could be approximately $120 billion and $121 billion, respectively, such that the present value of aggregate $1,280 net benefits to society could be somewhat less than $1 billion. With the social cost of carbon (SCC) discounted at 3% and other benefits and costs discounted at 7%, NHTSA estimates approximately $90 billion in costs and $76 billion in benefits could be attributable to vehicles produced prior to MY 2030 over the course of their lives, such that the present value of aggregate net costs to society could be approximately $15 billion.13 7% Discount Rate (3%forSCC) $121b $121b <$lb $76b $91b -$15b Benefits Costs Net Benefits lotter on DSK11XQN23PROD with PROPOSALS2 Model results can be viewed many different ways, and NHTSA’s rulemaking considers both ‘‘model year’’ and ‘‘calendar year’’ perspectives. The ‘‘model year’’ perspective, above, considers vehicles projected to be produced in some range of model years, and accounts for impacts, benefits, and costs attributable to these vehicles from the present (from the model year’s perspective, 2020) until they are projected to be scrapped. The bulk of NHTSA’s analysis considers vehicles produced prior to model year 2030, accounting for the estimated indirect impacts new standards could have on the remaining operation of vehicles already in service. This perspective 12 Assumes no rebound effect. interprets the 2021 IWG draft guidance as indicating that a 2.5% discount rate for the SCC is consistent with discounting near-term benefits and costs of the proposal at the OMB-recommended 13 NHTSA VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 emphasizes impacts on those model years nearest to those (2024–2026) for which NHTSA is proposing new standards. NHTSA’s analysis also presents some results focused only on model years 2024–2026, setting aside the estimated indirect impacts on earlier model years, and the impacts estimated to occur during model years 2027–2029, as some manufacturers and products ‘‘catch up’’ to the standards. Another way to present the benefits and costs of the proposal is the ‘‘calendar year’’ perspective shown in Table II–10, which is similar to how EPA presents benefits and costs in its proposal for GHG standards for MYs 2023–2026. The calendar year perspective considers all vehicles projected to be in service in each of some range of future calendar years. NHTSA’s presentation of results from this perspective considers calendar years 2020–2050, because the model’s representation of the full on-road fleet extends through 2050. Unlike the model year perspective, this perspective includes vehicles projected produced during model years 2030–2050. This perspective emphasizes longer-term impacts that could accrue if standards were to continue without change. Table II–10 shows costs and benefits for MYs 2023–2026 while Table II–9 shows costs and benefits through MY 2029. consumption discount rate of 3%. For the OMBrecommended discount rate of 7%, NHTSA concluded that a 3% discount rate for the SCC was reasonable given that the IWG draft guidance suggested that the appropriate discount rate for the SCC was likely lower than 3%. NHTSA refers readers specifically to pp. 16–17 of that guidance, available at https://www.whitehouse.gov/wpcontent/uploads/2021/02/TechnicalSupport Document_SocialCostofCarbonMethaneNitrous Oxide.pdf?source=email. PO 00000 Frm 00020 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.026</GPH> 3% Discount Rate (2.5% for SCC) EP03SE21.027</GPH> Table 11-9-Present Value of Estimated Benefits and Costs vs. No-Action Alternative for MYs through 2029 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49621 Table 11-10 - Estimates of Benefits and Costs of the Preferred Alternative for Model Years 2023 through 2026, 3% Discount Rate Benefit Cost MY Net Benefits Present Values $5.6 $8.9 $10.7 $12.2 $37.4 lotter on DSK11XQN23PROD with PROPOSALS2 Sum Though based on the exact same model results, these two perspectives provide considerably different views of estimated costs and benefits. Because technology costs account for a large share of overall estimated costs, and are also projected to decline over time (as manufacturers gain more experience with new technologies), costs tend to be ‘‘front loaded’’—occurring early in a vehicle’s life and tending to be higher in earlier model years than in later model years. Conversely, because social benefits of standards occur as vehicles are driven, and because both fuel prices and the social cost of CO2 emissions are projected to increase in the future, benefits tend to be ‘‘back loaded.’’ As a result, estimates of future fuel savings, CO2 reductions, and net social benefits are higher under the calendar year perspective than under the model year perspective. On the other hand, with longer-term impacts playing a greater role, the calendar year perspective is more subject to uncertainties regarding, for example, future technology costs and fuel prices. Even though NHTSA and EPA estimate benefits, costs, and net benefits using similar methodologies and achieve similar results, different approaches to accounting may give the false appearance of significant divergences. Table II–10 above presents NHTSA’s results using comparable accounting to EPA’s preamble Table 5. EPA also presents cost and benefit information in its RIA over calendar years 2021 through 2050. The numbers most comparable to those presented in EPA’s RIA are those NHTSA developed to complete its Supplemental Environmental Impact Statement (SEIS) using an identical accounting approach. This is because the statutory limitations constraining NHTSA’s standard setting analysis, such as those in 49 U.S.C. 32902(h) prohibiting consideration of full vehicle electrification during the rulemaking timeframe, or consideration VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 $3.5 $13.6 $21.2 $27.5 $65.8 of the trading or transferring of overcompliance credits, do not similarly apply to its EIS analysis.14 NHTSA’s EIS analysis estimates $312 billion in costs, $443 billion in benefits, and $132 billion in net benefits using a 3% discount rate over calendar years 2021 through 2050.15 NHTSA describes its cost and benefit accounting approach in Section V of this preamble. C. Why does NHTSA tentatively believe the proposal would be maximum feasible, and how and why is this tentative conclusion different from the 2020 final rule? NHTSA’s tentative conclusion, after consideration of the factors described below and information in the administrative record for this action, is that 8 percent increases in stringency for MYs 2024–2026 (Alternative 2 of this analysis) are maximum feasible. The Department of Transportation is deeply committed to working aggressively to improve energy conservation and reduce security risks associated with energy use, and higher standards appear increasingly likely to be economically practicable given almost-daily announcements by major automakers about forthcoming new high-fueleconomy vehicle models, as described in more detail below. Despite only one year having passed since the 2020 final rule, enough has changed in the U.S. and the world that revisiting the CAFE standards for MYs 2024–2026, and raising their stringency considerably, is both appropriate and reasonable. The 2020 final rule set CAFE standards that increased at 1.5 percent 14 As the EIS analysis contains information that NHTSA is statutorily prevented from considering, the agency does not rely on this analysis in regulatory decision-making. 15 See PRIA Chapter 6.5 for more information regarding NHTSA’s estimates of annual benefits and costs using NHTSA’s standard setting analysis. See Tables B–7–25 through B–7–30 in Appendix II of the PRIA for a more detailed breakdown of NHTSA’s EIS analysis. PO 00000 Frm 00021 Fmt 4701 Sfmt 4702 -$2.1 $4.7 $10.5 $15.3 $28.4 per year for cars and trucks for MYs 2021–2026, in large part because it prioritized industry concerns and reducing vehicle purchase costs to consumers and manufacturers. This proposed rule acknowledges the priority of energy conservation, consistent with NHTSA’s statutory authority. Moreover, NHTSA is also legally required to consider the environmental implications of this action under NEPA, and while the 2020 final rule did undertake a NEPA analysis, it did not prioritize the environmental considerations aspects of the statutory need of the U.S. to conserve energy. NHTSA recognizes that the amount of lead time available before MY 2024 is less than what was provided in the 2012 rule. As will be discussed further in Section VI, NHTSA believes that the evidence suggests that the proposed standards are still economically practicable. We note further that while this proposal is different from the 2020 final rule (and also from the 2012 final rule), NHTSA, like any other Federal agency, is afforded an opportunity to reconsider prior views and, when warranted, to adopt new positions. Indeed, as a matter of good governance, agencies should revisit their positions when appropriate, especially to ensure that their actions and regulations reflect legally sound interpretations of the agency’s authority and remain consistent with the agency’s views and practices. As a matter of law, ‘‘an Agency is entitled to change its interpretation of a statute.’’ 16 Nonetheless, ‘‘[w]hen an Agency adopts a materially changed interpretation of a statute, it must in addition provide a ‘reasoned analysis’ supporting its decision to revise its interpretation.’’ 17 16 Phoenix Hydro Corp. v. FERC, 775 F.2d 1187, 1191 (D.C. Cir. 1985). 17 Alabama Educ. Ass’n v. Chao, 455 F.3d 386, 392 (D.C. Cir. 2006) (quoting Motor Vehicle Mfrs. Ass’n of U.S., Inc. v. State Farm Mut. Auto. Ins. Co., E:\FR\FM\03SEP2.SGM Continued 03SEP2 EP03SE21.028</GPH> 2023 2024 2025 2026 49622 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 This preamble and the accompanying TSD and PRIA all provide extensive detail on the agency’s updated analysis, and Section VI contains the agency’s explanation of how the agency has considered that analysis and other relevant information in tentatively determining that the proposed CAFE standards are maximum feasible for MYs 2024–2026 passenger cars and light trucks. D. How is this proposal consistent with EPA’s proposal and with California’s programs? The NHTSA and EPA proposals remain coordinated despite being issued as separate regulatory actions. Because NHTSA and EPA are regulating the exact same vehicles and manufacturer will use the same technologies to meet both sets of standards, NHTSA and EPA coordinated during the development of each agency’s independent proposal to revise the standards set forth in the 2020 final rule. The NHTSA-proposed CAFE and EPA-proposed CO2 standards for MY 2026 represent roughly equivalent levels of stringency and may serve as a coordinated starting point for subsequent standards. While the proposed CAFE and CO2 standards for MYs 2024–2025 are different, this is largely due to the difference in the ‘‘start year’’ for the revised regulations—EPA is proposing to revise standards for MY 2023, while EPCA’s lead time requirements, which do not apply to EPA, prevent NHTSA from proposing revised standards until MY 2024. In order to set standards for MY 2023, EPA intends to issue its final rule by December 31, 2021, whereas NHTSA has until April 2022 to finalize standards for MY 2024. The difference in timing makes separate rulemaking actions reasonable and prudent. The specific differences in what the two agencies’ standards require become smaller each year, until alignment is achieved. The agencies still have coordinated closely to minimize inconsistency between the programs and will continue to do so through the final rule stage. While NHTSA’s and EPA’s programs differ in certain other respects, like programmatic flexibilities, those differences are not new in this proposal. Some parts of the programs are harmonized, and others differ, often as a result of statute. Since NHTSA and EPA began regulating together under President Obama, differences in 463 U.S. 29, 57 (1983)); see also 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.’’) (citations omitted). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 programmatic flexibilities have meant that manufacturers have had (and will have) to plan their compliance strategies considering both the CAFE standards and the GHG standards and assure that they are in compliance with both, while still building a single fleet of vehicles to accomplish that goal. NHTSA is proposing CAFE standards that increase at 8 percent per year over MYs 2024– 2026 because that is what NHTSA has tentatively concluded is maximum feasible in those model years, under the EPCA factors, and is confident that industry would still be able to build a single fleet of vehicles to meet both the NHTSA and EPA standards. Auto manufacturers are extremely sophisticated companies, well-able to manage complex compliance strategies that account for multiple regulatory programs concurrently. If different agencies’ standards are more binding for some companies in certain years, this does not mean that manufacturers must build multiple fleets of vehicles, simply that they will have to be more strategic about how they build their fleet. NHTSA has also considered and accounted for California’s ZEV mandate (and its adoption by a number of other states) in developing the baseline for this proposal, and has also accounted for the Framework Agreements between California, BMW, Ford, Honda, VWA, and Volvo. NHTSA believes that it is reasonable to include ZEV in the baseline for this proposal regardless of whether California receives a waiver of preemption under the Clean Air Act (CAA) because, according to California, industry overcompliance with the ZEV mandate has been extensive, which indicates that whether or not a waiver exists, many companies intend to produce ZEVs in volumes comparable to what a ZEV mandate would require. Because no decision has yet been made on a CAA waiver for California, and because modeling a sub-national fleet is not currently an analytical option for NHTSA, NHTSA has not expressly accounted for California GHG standards in the analysis for this proposal, although we seek comment on whether and how to account for them in the final rule. Chapter 6 of the accompanying PRIA shows the estimated effects of all of these programs simultaneously. III. Technical Foundation for NPRM Analysis A. Why does NHTSA conduct this analysis? NHTSA is proposing to establish revised CAFE standards for passenger cars and light trucks produced for model years (MYs) 2024–2026. PO 00000 Frm 00022 Fmt 4701 Sfmt 4702 NHTSA’s review of the existing standards is consistent with Executive Order 13990, Protecting Public Health and the Environment and Restoring Science to Tackle the Climate Crisis, signed on January 20, 2021, directing the review of the 2020 final rule that established CAFE standards for MYs 2021–2026 and the consideration of whether to suspend, revise, or rescind that action by July 2021.18 NHTSA establishes CAFE standards under the Energy Policy and Conservation Act, as amended, and this proposal is undertaken pursuant to that authority. This proposal would require CAFE stringency for both passenger cars and light trucks to increase at a rate of 8 percent per year annually from MY 2024 through MY 2026. NHTSA estimates that over the useful lives of vehicles produced prior to MY 2030, the proposal would save about 50 billion gallons of gasoline and increase electricity consumption by about 275 TWh. Accounting for emissions from both vehicles and upstream energy sector processes (e.g., petroleum refining and electricity generation), NHTSA estimates that the proposal would reduce greenhouse gas emissions by about 465 million metric tons of carbon dioxide (CO2), about 500 thousand tons metric tons of methane (CH4), and about 12 thousand tons of nitrous oxide (N2O). When NHTSA promulgates new regulations, it generally presents an analysis that estimates the impacts of such regulations, and the impacts of other regulatory alternatives. These analyses derive from statutes such as the Administrative Procedure Act (APA) and National Environmental Policy Act (NEPA), from Executive orders (such as Executive Order 12866 and 13653), and from other administrative guidance (e.g., Office of Management Budget Circular A–4). For CAFE, the Energy Policy and Conservation Act (EPCA), as amended by the Energy Independence and Security Act (EISA), contains a variety of provisions that require NHTSA to consider certain compliance elements in certain ways and avoid considering other things, in determining maximum feasible CAFE standards. Collectively, capturing all of these requirements and guidance elements analytically means that, at least for CAFE, NHTSA presents an analysis that spans a meaningful range of regulatory alternatives, that quantifies a range of technological, economic, and environmental impacts, and that does so in a manner that accounts for EPCA’s express requirements for the CAFE program 18 86 E:\FR\FM\03SEP2.SGM FR 7037 (Jan. 25, 2021). 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules (e.g., passenger cars and light trucks are regulated separately, and the standard for each fleet must be set at the maximum feasible level in each model year). NHTSA’s decision regarding the proposed standards is thus supported by extensive analysis of potential impacts of the regulatory alternatives under consideration. Along with this preamble, a Technical Support Document (TSD), a Preliminary Regulatory Impact Analysis (PRIA), and a Supplemental Environmental Impact Statement (SEIS), together provide an extensive and detailed enumeration of related methods, estimates, assumptions, and results. NHTSA’s analysis has been constructed specifically to reflect various aspects of governing law applicable to CAFE standards and has been expanded and improved in response to comments received to the prior rulemaking and based on additional work conducted over the last year. Further improvements may be made based on comments received to this proposal, the 2021 NAS Report,19 and other additional work generally previewed in these rulemaking documents. The lotter on DSK11XQN23PROD with PROPOSALS2 19 National Academies of Sciences, Engineering, and Medicine (NASEM), 2021. Assessment of Technologies for Improving Fuel Economy of LightDuty Vehicles—2025–2035, Washington, DC: The National Academies Press (hereafter, ‘‘2021 NAS Report’’). Available at https:// www.nationalacademies.org/our-work/assessmentof-technologies-for-improving-fuel-economy-oflight-duty-vehicles-phase-3 and for hard-copy review at DOT headquarters. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 analysis for this proposal aided NHTSA in implementing its statutory obligations, including the weighing of various considerations, by reasonably informing decision-makers about the estimated effects of choosing different regulatory alternatives. NHTSA’s 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). NHTSA uses the CAFE Compliance and Effects Modeling System (usually shortened to the ‘‘CAFE Model’’) to PO 00000 Frm 00023 Fmt 4701 Sfmt 4702 49623 estimate manufacturers’ potential responses to new CAFE and CO2 standards and to estimate various 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. The 2016 rulemaking regarding heavy-duty pickup and van fuel consumption and CO2 emissions also used the CAFE Model for analysis (81 FR 73478, October 25, 2016). 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. In a highly-summarized form, Figure III–1 shows the basic categories of CAFE Model procedures and the sequential flow between different stages of the modeling. The diagram does not present specific model inputs or outputs, as well as many specific procedures and model interactions. The model documentation accompanying this preamble presents these details, and Chapter 1 of the TSD contains a more detailed version of this flow diagram for readers who are interested. BILLING CODE 4910–59–P E:\FR\FM\03SEP2.SGM 03SEP2 49624 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Simulate Year-Inf-Year- Com!Di.mc:e Apply Technclrogy - Use Compiaoce Credits - If Applicable, Pay fines - '\,. )'' Simulate On-Road fleet - - Estimate New Vehicle Sales Estimated Used Vehicle Scrappage - Estimate Annual Travel (VMT} 'II,_ _i., calculate Phvsical lmlliKt!i - - Enef'g\!' Use Emissions and Health Impacts Crash-Related Fatalities and Injuries v~ ., annll~ - - Compliance Costs - Energy Costs Envin:mmental Damages Crash-Related losses Other Monetized Impacts - Figure 111-1 - CAFE Model Procedures and Logical Flow More specifically, 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). Additionally, and importantly, 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 the impacts of manufacturers working to meet those standards, which become the basis for comparing between different potential stringencies. A regulatory scenario, meanwhile, involves specification of the form, or shape, of VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 the standards (e.g., flat standards, or linear or logistic attribute-based standards), scope of passenger car and truck regulatory classes, and stringency of the CAFE standards for each model year to be analyzed. For example, a regulatory scenario may define CAFE standards that increase in stringency by 8 percent per year for 3 consecutive years. 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 20 initial forecast of the vehicle 20 Because the CAFE Model is publicly available, anyone can develop their own initial forecast (or other inputs) for the model to use. The DOT- PO 00000 Frm 00024 Fmt 4701 Sfmt 4702 models offered for sale during the simulation period. The compliance simulation then attempts to bring each manufacturer into compliance with the standards 21 defined by the regulatory scenario contained within an input file developed by the user. 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 developed market data file that contains the forecast used for this proposal is available on NHTSA’s website. 21 With appropriate inputs, the model can also be used to estimate impacts of manufacturers’ potential responses to new CO2 standards and to California’s ZEV program. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.029</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 BILLING CODE 4910–59–C Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 scrapped, and estimating the monetary value of these effects. Estimating impacts also involves consideration of consumer responses—e.g., the impact of vehicle fuel economy, operating costs, and vehicle price on consumer demand for passenger cars and light trucks. Both basic analytical elements involve the application of many analytical inputs. Many of these inputs are developed outside of the model and not by the model. For example, the model applies fuel prices; it does not estimate fuel prices. NHTSA also uses EPA’s MOVES model to estimate ‘‘tailpipe’’ (a.k.a. ‘‘vehicle’’ or ‘‘downstream’’) emission factors for criteria pollutants,22 and uses four Department of Energy (DOE) and DOE-sponsored models to develop inputs to the CAFE Model, including three developed and maintained by DOE’s Argonne National Laboratory. The agency uses the DOE Energy Information Administration’s (EIA’s) National Energy Modeling System (NEMS) to estimate fuel prices,23 and uses Argonne’s Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model to estimate emissions rates from fuel production and distribution processes.24 DOT also sponsored DOE/Argonne to 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.25 26 The TSD and PRIA describe details of the agency’s use of these models. In 22 See https://www.epa.gov/moves. This proposal uses version MOVES3, available at https:// www.epa.gov/moves/latest-version-motor-vehicleemission-simulator-moves. 23 See https://www.eia.gov/outlooks/aeo/info_ nems_archive.php. This proposal uses fuel prices estimated using the Annual Energy Outlook (AEO) 2021 version of NEMS (see https://www.eia.gov/ outlooks/aeo/pdf/02%20AEO2021%20 Petroleum.pdf). 24 Information regarding GREET is available at https://greet.es.anl.gov/index.php. This NPRM uses the 2020 version of GREET. 25 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 https://www.anl.gov/ cse/batpac-model-software. 26 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. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 addition, as discussed in the SEIS accompanying this NPRM, DOT relied on a range of climate models to estimate impacts on climate, air quality, and public health. The SEIS discusses and describes the use of these models. To prepare for analysis supporting this proposal, DOT has refined and expanded the CAFE Model through ongoing development. Examples of such changes, some informed by past external comments, made since early 2020 include: • Inclusion of 400- and 500-mile BEVs; • Inclusion of high compression ratio (HCR) engines with cylinder deactivation; • Accounting for manufacturers’ responses to both CAFE and CO2 standards jointly (rather than only separately) • Accounting for the ZEV mandates applicable in California and the ‘‘Section 177’’ states; • Accounting for some vehicle manufacturers’ (BMW, Ford, Honda, VW, and Volvo) voluntary agreement with the State of California to continued annual national-level reductions of vehicle greenhouse gas emissions through MY 2026, with greater rates of electrification than would have been required under the 2020 Federal final rule; 27 Æ Inclusion of CAFE civil penalties in the ‘‘effective cost’’ metric used when simulating manufacturers’ potential application of fuel-saving technologies; Æ Refined procedures to estimate health effects and corresponding monetized damages attributable to criteria pollutant emissions; Æ New procedures to estimate the impacts and corresponding monetized damages of highway vehicle crashes that do not result in fatalities; Æ Procedures to ensure that modeled technology application and production volumes are the same across all regulatory alternatives in the earliest model years; and Æ Procedures to more precisely focus application of EPCA’s ‘‘standard setting constraints’’ (i.e., regarding the consideration of compliance credits and additional dedicated alternative fueled vehicles) to only those model years for which NHTSA is proposing or finalizing new standards. These changes reflect DOT’s longstanding commitment to ongoing refinement of its approach to estimating 27 For more information on the Framework Agreements for Clean Cars, including the specific agreements signed by individual manufacturers, see https://ww2.arb.ca.gov/news/frameworkagreements-clean-cars. PO 00000 Frm 00025 Fmt 4701 Sfmt 4702 49625 the potential impacts of new CAFE standards. NHTSA underscores that this analysis exercises the CAFE Model in a manner that explicitly accounts for the fact that in producing a single fleet of vehicles for sale in the United States, manufacturers face the combination of CAFE standards, EPA CO2 standards, and ZEV mandates, and for five manufacturers, the voluntary agreement with California to more stringent CO2 reduction requirements (also applicable to these manufacturers’ total production for the U.S. market) through model year 2026. These regulations and contracts have important structural and other differences that affect the strategy a manufacturer could use to comply with each of the above. 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. 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: The provision at 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.28 The CAFE Model 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: The provision at 49 U.S.C. 32902 requires the Secretary of Transportation to set CAFE standards separately for passenger cars and light trucks. The CAFE Model accounts separately for passenger cars and light trucks when it analyzes CAFE or CO2 standards, including differentiated standards and compliance. 28 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, meet 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. E:\FR\FM\03SEP2.SGM 03SEP2 49626 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 Attribute-Based Standards: The provision at 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. The CAFE Model accounts for such functions and vehicle attributes explicitly. Separately Defined Standards for Each Model Year: The provision at 49 U.S.C. 32902 requires the Secretary to set CAFE standards (separately for passenger cars and light trucks 29) at the maximum feasible levels in each model year. The CAFE Model represents each model year explicitly, and accounts for the production relationships between model years.30 Separate Compliance for Domestic and Imported Passenger Car Fleets: The provision at 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. The CAFE Model accounts explicitly for this requirement when simulating manufacturers’ potential responses to CAFE standards, and combines any given manufacturer’s domestic and imported cars into a single fleet when simulating that manufacturer’s potential response to CO2 standards (because EPA does not have separate standards for domestic and imported passenger cars). Minimum CAFE Standards for Domestic Passenger Car Fleets: The provision at 49 U.S.C. 32902 requires that domestic passenger car fleets meet a minimum standard, which is calculated as 92 percent of the industrywide average level required under the applicable attribute-based CAFE standard, as projected by the Secretary 29 49 U.S.C. chapter 329 uses the term ‘‘nonpassenger automobiles,’’ while NHTSA uses the term ‘‘light trucks’’ in its CAFE regulations. The terms’ meanings are identical. 30 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. The CAFE Model is designed to account for these real-world factors. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 at the time the standard is promulgated. The CAFE Model accounts explicitly for this requirement for CAFE standards and sets this requirement aside for CO2 standards. Civil Penalties for Noncompliance: The provision at 49 U.S.C. 32912 (and implementing regulations) 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 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 fuelsaving 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. 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 prohibits consideration of the fuel economy of dedicated alternative fuel vehicle (AFV) models when NHTSA determines what levels of CAFE standards are maximum feasible. The CAFE Model therefore has an option to be run in a manner that excludes the additional application of dedicated AFV technologies in model years for which maximum feasible standards are under 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. The CAFE Model does account for dual- and alternative fuel vehicles when simulating manufacturers’ potential responses to CO2 standards. For natural gas vehicles, both dedicated and dualfueled, EPA has a multiplier of 2.0 for model years 2022–2026.31 31 While EPA is proposing changes to this and other flexibility provisions in its separate NPRM, PO 00000 Frm 00026 Fmt 4701 Sfmt 4702 ZEV Mandates: The CAFE Model can simulate manufacturers’ compliance with ZEV mandates applicable in California and ‘‘Section 177’’ 32 states. The approach involves identifying specific vehicle model/configurations that could be replaced with PHEVs or BEVs, and immediately making these changes in each model year, before beginning to consider the potential that other technologies could be applied toward compliance with CAFE or CO2 standards. Creation and Use of Compliance Credits: The provision at 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, credit use is also subject to 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.33 The provision at 49 for purposes of this NPRM, the CAFE Model only reflects the current EPA regulatory flexibilities. 32 The term ‘‘Section 177’’ states refers to states which have elected to adopt California’s standards in lieu of Federal requirements, as allowed under Section 177 of the CAA. 33 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 currently elected not to 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’’). NHTSA believes 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 technology improvements, it is harder to assume with confidence that manufacturers will rely on E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 U.S.C. 32902 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 for a given model year under consideration for standard setting. For modeling CO2 standards, the CAFE Model does not limit transfers. Insofar as the CAFE Model can be exercised in a manner that simulates trading of CO2 compliance credits, such simulations treat trading as unlimited.34 Statutory Basis for Stringency: The provision at 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 United States to conserve energy, and the impact of other motor vehicle standards of the Government. EPCA/ EISA authorizes the Secretary to interpret these factors, and as the Department’s interpretation has evolved, NHTSA has continued to expand and refine its qualitative and quantitative analysis to account for these statutory factors. For example, one of the ways that economic practicability considerations are incorporated into the analysis is through the technology effectiveness determinations: The Autonomie simulations reflect the agency’s judgment that it would not be economically practicable for a manufacturer to ‘‘split’’ an engine future technology investments 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 NHTSA recognizes that carry-back and trading are used more frequently when standards increase in stringency more rapidly. Given the uncertainty just discussed, and given also the fact that the agency has yet to resolve some of the analytical challenges associated with simulating use of these flexibilities, the agency considers borrowing and trading to involve sufficient risk that it is prudent to support this proposal 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 agency does not believe that the difference would be so great that it would change the policy outcome. Furthermore, a manufacturer employing a trading strategy would presumably do so because it represents a lower-cost compliance option. Thus, the estimates derived from this modeling approach are likely to be conservative in this respect, with real-world compliance costs possibly being lower. 34 To avoid making judgments 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. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 shared among many vehicle model/ configurations into myriad versions each optimized to a single vehicle model/configuration. National Environmental Policy Act: In addition, NEPA requires the Secretary to issue an EIS that documents the estimated impacts of regulatory alternatives under consideration. The SEIS accompanying this NPRM 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 SEIS, of impacts on the global climate, on tropospheric air quality, and on human health. Other Aspects of Compliance: 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 this analysis. For example, EPA has defined procedures for calculating average CO2 levels, and has revised procedures for calculating CAFE levels, to reflect manufacturers’ application of ‘‘offcycle’’ technologies that increase fuel economy (and reduce CO2 emissions). Although too little information is available to account for these provisions explicitly in the same way that the agency has accounted for other technologies, the CAFE Model does include and makes use of inputs reflecting the agency’s 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, the CAFE Model accounts for EPA ‘‘multipliers’’ for certain alternative fueled vehicles, based on 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. Besides the updates to the model described above, 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. Each successive PO 00000 Frm 00027 Fmt 4701 Sfmt 4702 49627 CAFE analysis seeks to update assumptions to reflect better the current state of the world and the best current estimates of future conditions. A number of assumptions have been updated since the 2020 final rule for this proposal. While NHTSA would have made these updates as a matter of course, we note that that the COVID–19 pandemic has been profoundly disruptive, including in ways directly material to major analytical inputs such as fuel prices, gross domestic product (GDP), vehicle production and sales, and highway travel. As discussed below, NHTSA has updated its ‘‘analysis fleet’’ from a model year 2017 reference to a model year 2020 reference, updated estimates of manufacturers’ compliance credit ‘‘holdings,’’ updated fuel price projections to reflect the U.S. Energy Information Administration’s (EIA’s) 2021 Annual Energy Outlook (AEO), updated projections of GDP and related macroeconomic measures, and updated projections of future highway travel. In addition, through Executive Order 13990, President Biden has required the formation of an Interagency Working Group (IWG) on the Social Cost of Greenhouse Gases and charged this body with updating estimates of the social costs of carbon, nitrous oxide, and methane. As discussed in the TSD, NHTSA has applied the IWG’s interim guidance, which contains cost estimates (per ton of emissions) considerably greater than those applied in the analysis supporting the 2020 SAFE rule. These and other updated analytical inputs are discussed in detail in the TSD. NHTSA seeks comment on the above discussion. B. What is NHTSA analyzing? As in the CAFE and CO2 rulemakings in 2010, 2012, and 2020, NHTSA is proposing to set attribute-based CAFE standards defined by a mathematical function of vehicle footprint, which has observable correlation with fuel economy. 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.35 Thus, the proposed standards (and regulatory alternatives) take the form of fuel economy targets expressed as functions of vehicle footprint (the product of vehicle wheelbase and average track width) that are separate for passenger cars and light trucks. Chapter 1.2.3 of the TSD discusses in detail NHTSA’s continued 35 49 E:\FR\FM\03SEP2.SGM U.S.C. 32902(a)(3)(A). 03SEP2 49628 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules reliance on footprint as the relevant attribute in this proposal. Under the footprint-based standards, the function defines a 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 average standard for each year that is almost certainly unique to each of its fleets,36 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, consistent with 49 U.S.C. 32902(b)’s direction that NHTSA must set separate 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 mpg targets than smaller vehicles. This is because, generally speaking, smaller vehicles are more capable of achieving higher levels of fuel economy, 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 with 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.37 For passenger cars, consistent with prior rulemakings, NHTSA is proposing to define fuel economy targets as shown in Equation III–1. 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. Here, MIN and MAX are functions that take the minimum and maximum values, respectively, of the set of included 36 EPCA/EISA requires NHTSA and EPA to separate passenger cars into domestic and import passenger car fleets for CAFE compliance purposes (49 U.S.C. 32904(b)), whereas EPA combines all passenger cars into one fleet. 37 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 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00028 Fmt 4701 Sfmt 4702 values. For example, MIN[40, 35] = 35 and MAX(40, 25) = 40, such that MIN[MAX(40, 25), 35] = 35. For the preferred alternative, this equation is represented graphically as the curves in Figure III–2. BILLING CODE 4910–59–P (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). E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.030</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Equation 111-1-Passenger Car Fuel Economy Footprint Target Curve 49629 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 60 '' ---------.. ' ' ',',,.... ' ' .. .... . ........ ' ', .. .......... ' ' ... .... ........ ' . . .... ---------------________________________________________ _ ..... '\,, '\,. 55 ···················· ... 'co .. g. ···•.... '-" 50 -----------, ···..... ~ ~ •···········. ·•··--· ···<:'::,, ~ 8 ... , 0 ' ~45 ····.... ] .... .............................................................................................................................................. 40 35 30 25 35 40 45 50 55 60 Footprint (sf) 65 70 75 80 ............. 2020 ------- 2021 ---- 2022 -2023 ....... 2024 ----2025 - -2026 Figure 111-2 - Preferred Alternative, Fuel Economy Target Curves, Passenger Cars For light trucks, also consistent with prior rulemakings, NHTSA is proposing to define fuel economy targets as shown in Equation III–2. TARGETFE -MAX ( MIN [MAX 1 1 (c X FOOTPRINT+ d,¼) ,¼], MIN [MAX (n X FOOTPRINT+ h,¼) ,f] ) VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 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 PO 00000 Frm 00029 Fmt 4701 Sfmt 4702 h is an intercept (in gpm) of the same second line. For the preferred alternative, this equation is represented graphically as the curves in Figure III–3. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.032</GPH> 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, EP03SE21.031</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Equation 111-2 - Light Truck Fuel Economy Target Curve 49630 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 60 35 30 25 35 40 45 50 60 55 Footprint (sf) 65 70 75 80 2020 -------2021 ----2022 -2023 ·······2024 ----2025 - -2026 Figure 111-3 - Preferred Alternative, Fuel Economy Target Curves, Light Trucks lotter on DSK11XQN23PROD with PROPOSALS2 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. The actual parameters for both the preferred alternative and the other regulatory alternatives are presented in Section IV.B of this preamble. As has been the case since NHTSA began establishing attribute-based standards, no vehicle need meet the specific applicable fuel economy target, because compliance with CAFE VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 standards is determined based on corporate average fuel economy. In this respect, CAFE standards are unlike, for example, Federal Motor Vehicle Safety Standards (FMVSS) and certain vehicle criteria pollutant emissions standards where each car must meet the requirements. CAFE standards apply to the average fuel economy levels achieved by manufacturers’ entire fleets of vehicles produced for sale in the U.S. Safety standards apply on a vehicle-byvehicle basis, such that every single vehicle produced for sale in the U.S. must, on its own, comply with minimum FMVSS. When first PO 00000 Frm 00030 Fmt 4701 Sfmt 4702 mandating CAFE standards in the 1970s, Congress specified a more flexible averaging-based approach that allows some vehicles to ‘‘under comply’’ (i.e., fall short of the overall flat standard, or fall short of their target under attribute-based 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 shown in Equation III–3. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.033</GPH> BILLING CODE 4910–59–C Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules CAFErequired = 49631 Li PRODUCTIONi PRODUCTION· L·l TARGETFEi l ' lotter on DSK11XQN23PROD with PROPOSALS2 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 is the fuel economy target (as defined above) for model configuration i. Chapter 1 of the TSD describes the use of attribute-based standards, generally, and explains the specific decision, in past rules and for the current rule, to continue to use vehicle footprint as the attribute over which to vary stringency. That chapter also discusses the policy in selecting the specific mathematical function; the methodologies used to develop the current attribute-based standards; and methodologies previously used to reconsider the mathematical function for CAFE standards. NHTSA refers readers to the TSD for a full discussion of these topics. While Chapter 1 of the TSD explains why the proposed standards for MYs 2024–2026 continue to be footprintbased, the question has arisen periodically of whether NHTSA should instead consider multi-attribute standards, such as those that also depend on weight, torque, power, towing capability, and/or off-road capability. To date, every time NHTSA has considered options for which attribute(s) to select, the agency has concluded that a properly-designed footprint-based approach provides the best means of achieving the basic policy goals (i.e., by increasing the likelihood of improved fuel economy across the entire fleet of vehicles; by reducing disparities between manufacturers’ compliance burdens; and by reducing incentives for manufacturers to respond to standards in ways that could compromise overall highway safety) involved in applying an attribute-based standard. At the same time, footprintbased standards need also to be structured in a way that furthers the energy and environmental policy goals of EPCA without creating inappropriate incentives to increase vehicle size in ways that could increase fuel consumption or compromise safety. That said, as NHTSA moves forward VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 with the CAFE program, and continues to refine our understanding of the lightduty vehicle market and trends in vehicle and highway safety, NHTSA will also continue to revisit whether other approaches (or other ways of applying the same basic approaches) could foreseeably provide better means of achieving policy goals. For example, in the 2021 NAS Report, the committee recommended that if Congress does not act to remove the prohibition at 49 U.S.C. 32902(h) on considering the fuel economy of dedicated alternative fuel vehicles (like BEVs) in determining maximum feasible CAFE standards, then NHTSA should account for the fuel economy benefits of ZEVs by ‘‘setting the standard as a function of a second attribute in addition to footprint—for example, the expected market share of ZEVs in the total U.S. fleet of new light-duty vehicles—such that the standards increase as the share of ZEVs in the total U.S. fleet increases.’’ 38 DOE seconded this suggestion in its comments during interagency review of this proposal. Chapter 1 of the TSD contains an examination of this suggestion, and NHTSA seeks comment on whether and how NHTSA might consider adding electrification as an attribute on which to base CAFE standards. Changes in the market that have occurred since NHTSA last examined the appropriateness of the footprint curves have been, for the most part, consistent with the trends that the agency identified in 2018. For the most part, the fleet has continued to grow somewhat in vehicle size, as vehicle manufacturers have continued over the past several years to reduce their offerings of smaller footprint vehicles and increase their sales of larger footprint vehicles and continue to sell many small to mid-size crossovers and SUVs, some of which are classified as passenger cars and some of which are 38 National Academies of Sciences, Engineering, and Medicine, 2021. Assessment of Technologies for Improving Fuel Economy of Light-Duty Vehicles—2025–2035, Washington, DC: The National Academies Press (hereafter, ‘‘2021 NAS Report’’), at Summary Recommendation 5. Available at https://www.nationalacademies.org/ our-work/assessment-of-technologies-for-improvingfuel-economy-of-light-duty-vehicles-phase-3 and for hard-copy review at DOT headquarters. PO 00000 Frm 00031 Fmt 4701 Sfmt 4702 light trucks. Although this trend has had the effect of reducing the achieved fuel economy of the fleet (and thus increasing its carbon dioxide emissions) as compared to if vehicles had instead remained the same size or gotten smaller, NHTSA does not believe that there have been sufficiently major changes in the relationship between footprint and fuel economy over the last three years to warrant a detailed reexamination of that relationship as part of this proposal. Moreover, changes to the footprint curves can significantly affect manufacturers’ ability to comply. Given the available lead time between now and the beginning of MY 2024, NHTSA believes it is unlikely any potential benefit of changing the shape of the footprint curves (when we are already proposing to change standard stringency) would outweigh the costs of doing so. NHTSA seeks comment on the choice of footprint as the attribute on which the proposed standards are based, and particularly seeks comment on the 2021 NAS report recommendation described above. If commenters wish to provide comments on possible changes to the attribute(s) on which fuel economy standards should be based, including approaches for considering vehicle electrification in ways that would further a zero emissions fleet as discussed in Chapter 1 of the TSD, NHTSA would appreciate commenters including a discussion of the timeframe in which those changes should be made—for example, whether and how much lead time would be preferable for making such changes, particularly recognizing the available lead time for MY 2024. NHTSA also seeks comment on whether, to the extent that vehicle upsizing trends and fuel economy curves are causally related instead of correlated, it is the curve shape versus the choice of footprint that creates this relationship (or, alternatively, whether the relationship if any derives from vehicle classification). Again, if commenters wish to provide comments on possible changes to the curve shapes, NHTSA would appreciate commenters including a discussion of the timeframe in which those changes should be made. NHTSA seeks comment on the discussion above and in the TSD. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.034</GPH> Equation 111-3 - Calculation for Required CAFE Level lotter on DSK11XQN23PROD with PROPOSALS2 49632 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules C. What inputs does the compliance analysis require? The CAFE 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 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 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,39 until the manufacturer exhausts all available technologies, or, if the manufacturer is assumed to be willing to pay civil penalties or acquire credits from another manufacturer, until paying civil penalties or purchasing credits 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/credits purchased by each manufacturer. This compliance simulation process is repeated for each model year included in the study period (through model year 2050 in this analysis). At the conclusion of the compliance simulation for a given regulatory scenario the system transitions between compliance simulation and effects calculations. This is the point where the system produces a full representation of the registered light-duty vehicle population in the United States. The CAFE Model then uses this fleet to generate estimates of the following (for each model year and calendar year included in the analysis): Lifetime travel, fuel consumption, carbon dioxide and criteria pollutant emissions, the magnitude of various economic externalities related to vehicular travel (e.g., congestion and noise), and energy consumption (e.g., the economic costs of short-term increases in petroleum prices, or social damages associated 39 Generally, the model considers a technology cost-effective if it pays for itself in fuel savings within 30 months. Depending on the settings applied, the model can continue to apply technologies that are not cost-effective rather than choosing other compliance options; if it does so, it will apply those additional technologies in order of cost-effectiveness (i.e., most cost-effective first). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 with GHG emissions). The system then uses these estimates to measure the benefits and costs associated with each regulatory alternative (relative to the noaction alternative). To perform this analysis, the CAFE Model uses millions of data points contained in several input files that have been populated by engineers, economists, and safety and environmental program analysts at both NHTSA and the DOT’s Volpe National Transportations Systems Center (Volpe). In addition, some of the input data comes from modeling and simulation analysis performed by experts at Argonne National Laboratory using their Autonomie full vehicle simulation model and BatPaC battery cost model. Other inputs are derived from other models, such as the U.S. Energy Information Administration’s (EIA’s) National Energy Modeling System (NEMS), Argonne’s ‘‘GREET’’ fuel-cycle emissions analysis model, and U.S. EPA’s ‘‘MOVES’’ vehicle emissions analysis model. As NHTSA and Volpe are both organizations within DOT, we use DOT throughout these sections to refer to the collaborative work performed for this analysis. This section and Section III.D describe the inputs that the compliance simulation requires, including an indepth discussion of the technologies used in the analysis, how they are defined in the CAFE Model, how they are characterized on vehicles that already exist in the market, how they can be applied to realistically simulate manufacturer’s decisions, their effectiveness, and their cost. The inputs and analyses for the effects calculations, including economic, safety, and environmental effects, are discussed later in Sections III.C through III.H. NHTSA seeks comment on the following discussion. 1. Overview of Inputs to the Analysis As discussed above, the current analysis involves estimating four major swaths of effects. First, the analysis estimates how the application of various combinations of technologies could impact vehicles’ costs and fuel economy levels (and CO2 emission rates). Second, the analysis estimates how vehicle manufacturers might respond to standards by adding fuel-saving technologies to new vehicles. Third, the analysis estimates how changes in new vehicles might impact vehicle sales and operation. Finally, the analysis estimates how the combination of these changes might impact national-scale energy consumption, emissions, highway safety, and public health. PO 00000 Frm 00032 Fmt 4701 Sfmt 4702 There are several CAFE Model input files important to the discussion these first two steps, and these input files are discussed in detail later in this section and in Section III.D. The Market Data file contains the detailed description 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 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 CAFE Model also makes use of databases of estimates of fuel consumption impacts and, as applicable, battery costs for different combinations of fuel saving technologies.40 These databases are termed the FE1 and FE2 Adjustments databases (the main database and the database specific to plug-in hybrid electric vehicles, applicable to those vehicles’ operation on electricity) and the Battery Costs database. DOT developed these databases using a large set of full vehicle and accompanying battery cost model simulations developed by Argonne National Laboratory. The Argonne simulation outputs, battery costs, and other reference materials are also discussed in the following sections.41 The following discussion in this section and in Section III.D expands on the inputs used in the compliance analysis. Further detail is included in Chapters 2 and 3 of the TSD accompanying this proposal, and all input values relevant to the compliance analysis can be seen in the Market Data, Technologies, fuel consumption and battery cost database files, and Argonne 40 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 memory and speed model operation, DOT has 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. 41 The Argonne workbooks included in the docket for this proposal include ten databases that contain the outputs of the Autonomie full vehicle simulations, two summary workbooks of assumptions used for the full vehicle simulations, a data dictionary, and the lookup tables for battery costs generated using the BatPaC battery cost model. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules summary files included in the docket for this proposal. As previously mentioned, other model input files underlie the effects analysis, and these are discussed in detail in Sections III.C through III.H. NHTSA seeks comment on the above discussion. 2. The Market Data File The Market Data file contains the detailed description of the vehicle models and model configurations each manufacturer produces for sale in the U.S. This snapshot of the recent light duty vehicle market, termed the analysis fleet, or baseline fleet, is the starting point for the evaluation of different stringency levels for future fuel economy standards. The analysis fleet provides a reference from which to project how manufacturers could apply additional technologies to vehicles to cost-effectively improve vehicle fuel economy, in response to regulatory action and market conditions.42 For this analysis, the MY 2020 light duty fleet was selected as the baseline for further evaluation of the effects of different fuel economy standards. The Market Data file also contains a range of other inputs that, though not specific to individual vehicle models, may be specific to individual manufacturers. The Market Data file is an Excel spreadsheet that contains five worksheets. Three worksheets, the Vehicles worksheet, Engines worksheet, and Transmissions worksheet, characterize the baseline fleet for this analysis. The three worksheets contain a characterization of every vehicle sold in MY 2020 and their relevant technology content, including the engines and transmissions that a manufacturer uses in its vehicle platforms and how those technologies are shared across platforms. In addition, the Vehicles worksheet includes lotter on DSK11XQN23PROD with PROPOSALS2 42 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 21:48 Sep 02, 2021 Jkt 253001 baseline economic and safety inputs linked to each vehicle that allow the CAFE Model to estimate economic and safety impacts resulting from any simulated compliance pathway. The remaining two worksheets, the Manufacturers worksheet and Credits and Adjustments worksheet, include baseline compliance positions for each manufacturer, including each manufacturer’s starting CAFE credit banks and whether the manufacturer is willing to pay civil penalties for noncompliance with CAFE standards, among other inputs. New inputs have been added for this analysis in the Vehicles worksheet and Manufacturers worksheet. The new inputs indicate which vehicles a manufacturer may reasonably be expected to convert to a zero emissions vehicle (ZEV) at first redesign opportunity, to comply with several States’ ZEV program provisions. The new inputs also indicate if a manufacturer has entered into an agreement with California to achieve more stringent CO2 emissions reductions targets than those promulgated in the 2020 final rule. The following sections discuss how we built the Market Data file, including characterizing vehicles sold in MY 2020 and their technology content, and baseline safety, economic, and manufacturer compliance positions. A detailed discussion of the Market Data file development process is in TSD Chapter 2.2. NHTSA seeks comment on the below discussion and the agency’s approach to developing the Market Data file for this proposal. (a) Characterizing Vehicles and Their Technology Content The Market Data file integrates information from many sources, including manufacturer compliance submissions, publicly available information, and confidential business information. At times, DOT must populate inputs using analyst judgment, either because information is still incomplete or confidential, or because PO 00000 Frm 00033 Fmt 4701 Sfmt 4702 49633 the information does not yet exist.43 For this analysis DOT uses mid-model year 2020 compliance data as the basis of the analysis fleet. The compliance data is supplemented for each vehicle nameplate with manufacturer specification sheets, usually from the manufacturer media website, or from online marketing brochures.44 For additional information about how specification sheets inform MY 2020 vehicle technology assignments, see the technology specific assignments sections in Section III.D. DOT uses the mid-model year 2020 compliance data to create a row on the Vehicles worksheet in the Market Data file for each vehicle (or vehicle variant 45) that lists a certification fuel economy, sales volume, regulatory class, and footprint. DOT identifies which combination of modeled technologies reasonably represents the fuel saving technologies already on each vehicle, and assigns those technologies to each vehicle, either on the Vehicles worksheet, the Engines worksheet, or the Transmissions worksheet. The fuel saving technologies considered in this analysis are listed in Table III–1. BILLING CODE 4910–59–P 43 Forward looking refresh/redesign cycles are one example of when analyst judgement is necessary. 44 The catalogue of reference specification sheets (broken down by manufacturer, by nameplate) used to populate information in the market data file is available in the docket. 45 The market data file often includes a few rows for vehicles that may have identical certification fuel economies, regulatory classes, and footprints (with compliance sales volumes divided out among rows), because other pieces of information used in the CAFE Model may be dissimilar. For instance, in the reference materials used to create the Market Data file, for a nameplate curb weight may vary by trim level (with premium trim levels often weighing more on account of additional equipment on the vehicle), or a manufacturer may provide consumers the option to purchase a larger fuel tank size for their vehicle. These pieces of information may not impact the observed compliance position directly, but curb weight (in relation to other vehicle attributes) is important to assess mass reduction technology already used on the vehicle, and fuel tank size is directly relevant to saving time at the gas pump, which the CAFE Model uses when calculating the value of avoided time spent refueling. E:\FR\FM\03SEP2.SGM 03SEP2 49634 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-1- Fuel Saving Technologies that the CAFE Model May Apply lotter on DSK11XQN23PROD with PROPOSALS2 Electric Power Steering Improved Accessorv Devices Start-Stop system Belt Integrated Starter Generator Strong Hvbrid Electric Vehicle, Parallel Strong Hybrid Electric Vehicle, Power Split with Atkinson Engine Strong Hybrid Electric Vehicle, Parallel with HCRO Engine (Alternative path for Turbo Engine Vehicles) Strong Hybrid Electric Vehicle, Parallel with HCRl Engine (Alternative path for Turbo Engine Vehicles) Strong Hybrid Electric Vehicle, Parallel with HCRlD Engine (Alternative path for Turbo Engine Vehicles) VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 EPS IACC 12VSS BISG SHEVP2 Additional technologies Additional technologies Electrification Electrification Electrification SHEVPS Vehicles Electrification P2HCRO Vehicles Electrification P2HCR1 Vehicles Electrification P2HCR1D Vehicles Electrification Abbreviation Frm 00034 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM Technology Group 03SEP2 EP03SE21.035</GPH> Technology Name Market Data File Worksheet Vehicles Vehicles Vehicles Vehicles Vehicles lotter on DSK11XQN23PROD with PROPOSALS2 Technology Name Strong Hybrid Electric Vehicle, Parallel with HCR2 Engine (Alternative path for Turbo Engine Vehicles) Plug-in Hybrid Vehicle with Atkinson Engine and 20 miles of electric range Plug-in Hybrid Vehicle with Atkinson Engine and 50 miles of electric range Plug-in Hybrid Vehicle with TURBOl Engine and 20 miles of electric range Plug-in Hybrid Vehicle with TURBOl Engine and 50 miles of electric range Plug-in Hybrid Vehicle with Atkinson Engine and 20 miles of electric range (Alternative path for Turbo Engine Vehicles) Plug-in Hybrid Vehicle with Atkinson Engine and 50 miles of electric range (Alternative path for Turbo Engine Vehicles) Battery Electric Vehicle with 200 miles ofrange Battery Electric Vehicle with 300 miles ofrange Battery Electric Vehicle with 400 miles ofrange Battery Electric Vehicle with 500 miles ofrange Fuel Cell Vehicle Low Dra2: Brakes Secondary Axle Disconnect Baseline Tire Rolling Resistance Tire Rolling Resistance, 10% Improvement Tire Rolling Resistance, 20% Improvement Baseline Aerodynamic Drag Technologv Aerodynamic Drag, 5% Drag Coefficient Reduction Aerodynamic Drag, 10% Drag Coefficient Reduction Aerodynamic Drag, 15% Drag Coefficient Reduction Aerodynamic Drag, 20% Drag Coefficient Reduction Baseline Mass Reduction Technologv Mass Reduction - 5.0% of Glider Mass Reduction - 7.5% of Glider Mass Reduction - 10.0% of Glider Mass Reduction - 15.0% of Glider Mass Reduction - 20.0% of Glider VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00035 Abbreviation Market Data File Worksheet Technology Group P2HCR2 Vehicles Electrification PHEV20 Vehicles Electrification PHEV50 Vehicles Electrification PHEV20T Vehicles Electrification PHEV50T Vehicles Electrification PHEV20H Vehicles Electrification PHEV50H Vehicles Electrification BEV200 Vehicles Electrification BEV300 Vehicles Electrification BEV400 Vehicles Electrification BEV500 Vehicles Electrification FCV LOB SAX ROLLO Vehicles Vehicles Vehicles Vehicles Electrification Additional technologies Additional technologies Rolling Resistance ROLLl0 Vehicles Rolling Resistance ROLL20 Vehicles Rolling Resistance AERO0 Vehicles Aerodynamic Drag AERO5 Vehicles Aerodynamic Drag AEROl0 Vehicles Aerodynamic Drag AERO15 Vehicles Aerodynamic Drag AERO20 Vehicles Aerodynamic Drag MR0 MRI MR2 MR3 MR4 MRS Vehicles Vehicles Vehicles Vehicles Vehicles Vehicles Mass Reduction Mass Reduction Mass Reduction Mass Reduction Mass Reduction Mass Reduction Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 49635 EP03SE21.036</GPH> Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49636 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Mass Reduction- 28.2% of Glider Single Overhead Cam Dual Overhead Cam Engine Friction Reduction Variable Valve Timing Variable Valve Lift Stoichiometric Gasoline Direct Injection Cylinder Deactivation Turbocharged Engine Advanced Turbocharged Engine Turbocharged Engine with Cooled Exhaust Gas Recirculation Advanced Cylinder Deactivation High Compression Ratio Engine (Atkinson Cvcle) Advanced High Compression Ratio Engine (Atkinson Cvcle) Advanced High Compression Ratio Engine (Atkinson Cycle) with Cylinder Deactivation EPA, 2016 Vintage Characterization High Compression Ratio Engine (Atkinson Cycle), with Cylinder Deactivation Variable Compression Ratio Engine Variable Turbo Geometry Engine Variable Turbo Geometry Engine with eBooster Turbocharged Engine with Cylinder Deactivation Turbocharged Engine with Advanced Cylinder Deactivation Advanced Diesel Engine Advanced Diesel Engine with Improvements Advanced Diesel Engine with Improvements and Advanced Cylinder Deactivation Compressed Natural Gas Engine lotter on DSK11XQN23PROD with PROPOSALS2 BILLING CODE 4910–59–C For additional information on the characterization of these technologies (including the cost, prevalence in the 2020 fleet, effectiveness estimates, and considerations for their adoption) see the appropriate technology section in Section III.D or TSD Chapter 3. DOT also assigns each vehicle a technology class. The CAFE Model uses the technology class (and engine class, discussed below) in the Market Data file VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Market Data File Worksheet Technology Group TURBOl TURBO2 Vehicles Engines Engines Engines Engines Engines Engines Engines Engines Engines Mass Reduction Basic Engines Basic Engines Engine Improvements Basic Engines Basic Engines Basic Engines Basic Engines Advanced Engines Advanced Engines CEGRl Engines Advanced Engines ADEAC Engines Advanced Engines HCR0 Engines Advanced Engines HCRl Engines Advanced Engines HCRlD Engines Advanced Engines HCR2 Engines Advanced Engines VCR VTG Engines Engines Advanced Engines Advanced Engines VTGE Engines Advanced Engines TURBOD Engines Advanced Engines TURBOAD Engines Advanced Engines ADSL Engines Advanced Engines DSLI Engines Advanced Engines DSLIAD Engines Advanced Engines CNG Engines Advanced Engines Abbreviation MR6 SOHC DOHC EFR VVT VVL SGDI DEAC to reference the most relevant technology costs for each vehicle, and fuel saving technology combinations. We assign each vehicle in the fleet a technology class using a two-step algorithm that takes into account key characteristics of vehicles in the fleet compared to the baseline characteristics PO 00000 Frm 00036 Fmt 4701 Sfmt 4702 of each technology class.46 As discussed further in Section III.C.4.b), there are ten technology classes used in the CAFE analysis that span five vehicle types and two performance variants. The 46 Baseline 0 to 60 mph accelerations times are assumed for each technology class as part of the Autonomie full vehicle simulations. DOT calculates class baseline curb weights and footprints by averaging the curb weights and footprints of vehicles within each technology class as assigned in previous analyses. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.037</GPH> Technology Name lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules technology class algorithm and assignment process is discussed in more detail in TSD Chapter 2.4.2. We also assign each vehicle an engine technology class so that the CAFE Model can reference the powertrain costs in the Technologies file that most reasonably align with the observed vehicle. DOT assigns engine technology classes for all vehicles, including electric vehicles. If an electric powertrain replaces and internal combustion engine, the electric motor specifications may be different (and hence costs may be different) depending on the capabilities of the internal combustion engine it is replacing, and the costs in the technologies file (on the engine tab) account for the power output and capability of the gasoline or electric drivetrain. Parts sharing helps manufacturers achieve economies of scale, deploy capital efficiently, and make the most of shared research and development expenses, while still presenting a wide array of consumer choices to the market. The CAFE Model simulates part sharing by implementing shared engines, shared transmissions, and shared mass reduction platforms. Vehicles sharing a part (as recognized in the CAFE Model), will adopt fuel saving technologies affecting that part together. To account for parts sharing across products, vehicle model/configurations that share engines are assigned the same engine code,47 vehicle model/configurations that share transmissions have the same transmission code, and vehicles that adopt mass reduction technologies together share the same platform. For more information about engine codes, transmission codes, and mass reduction platforms see TSD Chapter 3. Manufacturers often introduce fuel saving technologies at a major redesign of their product or adopt technologies at minor refreshes in between major product redesigns. To support the CAFE Model accounting for new fuel saving technology introduction as it relates to product lifecycle, the Market Data file includes a projection of redesign and refresh years for each vehicle. DOT projects future redesign years and refresh years based on the historical cadence of that vehicle’s product lifecycle. For new nameplates, DOT considers the manufacturer’s treatment of product lifecycles for past products in similar market segments. When considering year-by-year analysis of standards, the sizing of redesign and refresh intervals will affect projected compliance pathways and how quickly manufacturers can respond to standards. TSD Chapter 2.2.1.7 includes additional information about the product design cycles assumed for this proposal based on historical manufacturer product design cycles. The Market Data file also includes information about air conditioning (A/ C) and off-cycle technologies, but the information is not currently broken out at a row level, vehicle by vehicle.48 Instead, historical data (and forecast projections, which are used for analysis regardless of regulatory scenario) are listed by manufacturer, by fleet on the Credits and Adjustments worksheet of the Market Data file. Section III.D.8 shows model inputs specifying estimated adjustments (all in grams/ mile) for improvements to air conditioner efficiency and other offcycle energy consumption, and for reduced leakage of air conditioner refrigerants with high global warming potential (GWP). DOT estimated future values based on an expectation that manufacturers already relying heavily on these adjustments would continue do so, and that other manufacturers would, over time, also approach the limits on adjustments allowed for such improvements. 47 Engines (or transmissions) may not be exactly identical, as specifications or vehicle integration features may be different. However, the architectures are similar enough that it is likely the powertrain systems share research and development (R&D), tooling, and production resources in a meaningful way. 48 Regulatory provisions regarding off-cycle technologies are new, and manufacturers have only recently begun including related detailed information in compliance reporting data. For this analysis, though, such information was not sufficiently complete to support a detailed representation of the application of off-cycle VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 (b) Characterizing Baseline Safety, Economic, and Compliance Positions In addition to characterizing vehicles and their technology content, the Market Data file contains a range of other inputs that, though not specific to individual vehicle models, may be specific to individual manufacturers, or that characterize baseline safety or economic information. First, the CAFE Model considers the potential safety effect of mass reduction technologies and crash compatibility of different vehicle types. Mass reduction technologies lower the vehicle’s curb weight, which may improve crash compatibility and safety, or not, depending on the type of vehicle. DOT assigns each vehicle in the Market Data file a safety class that best aligns with the mass-size-safety analysis. This PO 00000 Frm 00037 Fmt 4701 Sfmt 4702 49637 analysis is discussed in more detail in Section III.H of this proposal and TSD Chapter 7. The CAFE Model also includes procedures to consider the direct labor impacts of manufacturer’s response to CAFE regulations, considering the assembly location of vehicles, engines, and transmissions, the percent U.S. content (that reflects percent U.S. and Canada content),49 and the dealership employment associated with new vehicle sales. The Market Data file therefore includes baseline labor information, by vehicle. Sales volumes also influence total estimated direct labor projections in the analysis. We hold the percent U.S. content constant for each vehicle row for the duration of the analysis. In practice, this may not be the case. Changes to trade policy and tariff policy may affect percent U.S. content in the future. Also, some technologies may be more or less likely to be produced in the U.S., and if that is the case, their adoption could affect future U.S. content. NHTSA does not have data at this time to support varying the percent U.S. content. We also hold the labor hours projected in the Market Data file per unit transacted at dealerships, per unit produced for final assembly, per unit produced for engine assembly, and per unit produced for transmission assembly constant for the duration of the analysis, and project that the origin of these activities to remain unchanged. In practice, it is reasonable to expect that plants could move locations, or engine and transmission technologies are replaced by another fuel saving technology (like electric motors and fixed gear boxes) that could require a meaningfully different amount of assembly labor hours. NHTSA does not have data at this time to support varying labor hours projected in the Market Data file, but we will continue to explore methods to estimate the direct labor impacts of manufacturer’s responses to CAFE standards in future analyses. As observed from Table III–2, manufacturers employ U.S. labor with varying intensity. In many cases, vehicles certifying in the light truck (LT) regulatory class have a larger percent U.S. content than vehicles certifying in the passenger car (PC) regulatory class. technology to specific vehicle model/configurations in the MY 2020 fleet. 49 Percent U.S. content was informed by the 2020 Part 583 American Automobile Labeling Act Reports, appearing on NHTSA’s website. E:\FR\FM\03SEP2.SGM 03SEP2 49638 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules PC LT Total MY 2020 Sales Weighted Percent U.S. Content BMW 7.1% 29.3% 15.4% 42.4% 0.0% 0.0% Daimler 19.1% 36.2% 28.1% 41.2% 39.8% 0.0% Fiat Chrysler Automobiles (FCA) 47.7% 52.9% 52.2% 68.0% 41.3% 45.7% Ford 35.2% 47.5% 44.2% 83.4% 32.9% 88.5% General Motors (GM) 39.8% 47.0% 44.7% 68.3% 69.8% 86.1% Honda 55.8% 61.7% 58.3% 74.9% 85.9% 58.6% Hyundai Kia-H 21.8% 0.0% 19.4% 46.0% 46.0% 34.3% Hyundai Kia-K 12.8% 33.3% 20.7% 38.4% 17.2% 37.8% JLR 2.6% 6.3% 6.2% 0.0% 0.0% 31.7% Mazda 1.1% 1.1% 1.1% 0.0% 0.0% 0.0% Mitsubishi 0.0% 0.3% 0.2% 0.0% 0.0% 0.0% Nissan 29.0% 32.6% 30.1% 49.9% 47.5% 0.0% Subaru 35.5% 22.9% 25.6% 53.2% 0.0% 0.0% Tesla50 50.6% 50.0% 50.6% 100.0% 100.0% 100.0% Toyota 35.2% 42.7% 38.7% 42.4% 46.0% 19.4% Volvo 10.2% 1.1% 3.4% 12.4% 0.0% 0.0% VWA 10.3% 8.8% 9.4% 13.5% 0.0% 0.0% TOTAL 32.4% 41.2% 37.4% 57.1% 44.1% 44.1% lotter on DSK11XQN23PROD with PROPOSALS2 Manufacturer Next, manufacturers may over-comply with CAFE standards and bank so-called over compliance credits. As discussed further in Section III.C.7, manufacturers may use these credits later, sell them to other manufacturers, or let them expire. The CAFE Model does not explicitly trade credits between and among manufacturers, but staff have adjusted starting credit banks in the Market Data file to reflect trades that are likely to happen when the simulation begins (in MY 2020). Considering information manufacturers have reported regarding compliance credits, and considering recent manufacturers’ compliance 50 Tesla does not have internal combustion engines, or multi-speed transmissions, even though they are identified as producing engine and transmission systems in the United States in the Market Data file. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Portion of Vehicles Assembled in the U.S. Portion of Engines Assembled in the U.S. Portion of Transmissions Assembled in the U.S. positions, DOT estimates manufacturers’ potential use of compliance credits in earlier MYs. This aligns to an extent that represents how manufacturers could deplete their credit banks rather than producing high volume vehicles with fuel saving technologies in earlier MYs. This also avoids the unrealistic application of technologies for manufacturers in early analysis years that typically rely on credits. For a complete discussion about how this data is collected and assigned in the Market Data file, see TSD Chapter 2.2.2.3. The Market Data file also includes assumptions about a vehicle manufacturer’s preferences towards civil penalty payments. EPCA requires that if a manufacturer does not achieve compliance with a CAFE standard in a PO 00000 Frm 00038 Fmt 4701 Sfmt 4702 given model year and cannot apply credits sufficient to cover the compliance shortfall, the manufacturer must pay civil penalties (i.e., fines) to the Federal Government. If inputs indicate that a manufacturer treats civil penalty payment as an economic choice (i.e., one to be taken if doing so would be economically preferable to applying further technology toward compliance), the CAFE Model, when evaluating the manufacturer’s response to CAFE standards in a given model year, will apply fuel-saving technology only up to the point beyond which doing so would be more expensive (after subtracting the value of avoided fuel outlays) than paying civil penalties. For this analysis, DOT exercises the CAFE Model with inputs treating all manufacturers as treating civil penalty E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.038</GPH> Table 111-2- Sales Weighted Percent U.S. Content by Manufacturer, by Regulatory Class lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules payment as an economic choice through model year 2023. While DOT expects that only manufacturers with some history of paying civil penalties would actually treat civil penalty payment as an acceptable option, the CAFE Model does not currently simulate compliance credit trading between manufacturers, and DOT expects that this treatment of civil penalty payment will serve as a reasonable proxy for compliance credit purchases some manufacturers might actually make through model year 2023. These input assumptions for model years through 2023 reduce the potential that the model will overestimate technology application in the model years leading up to those for which the agency is proposing new standards. As in past CAFE rulemaking analyses (except that supporting the 2020 final rule), DOT has treated manufacturers with some history of civil penalty payment (i.e., BMW, Daimler, FCA, Jaguar-Land Rover, Volvo, and Volkswagen) as continuing to treat civil penalty payment as an acceptable option beyond model year 2023, but has treated all other manufacturers as unwilling to do so beyond model year 2023. Next, the CAFE Model uses an ‘‘effective cost’’ metric to evaluate options to apply specific technologies to specific engines, transmissions, and vehicle model configurations. Expressed on a $/gallon basis, the analysis computes this metric by subtracting the estimated values of avoided fuel outlays and civil penalties from the corresponding technology costs, and then dividing the result by the quantity of avoided fuel consumption. The analysis computes the value of fuel outlays over a ‘‘payback period’’ representing the manufacturer’s expectation that the market will be willing to pay for some portion of fuel savings achieved through higher fuel economy. Once the model has applied enough technology to a manufacturer’s fleet to achieve compliance with CAFE standards (and CO2 standards and ZEV mandates) in a given model year, the model will apply any further fuel economy improvements estimated to produce a negative effective cost (i.e., any technology applications for which avoided fuel outlays during the payback period are larger than the corresponding technology costs). As discussed above in Section III.A and below in Section III.C, DOT anticipates that manufacturers are likely to act as if the market is willing to pay for avoided fuel outlays expected during the first 30 months of vehicle operation. We seek comment on whether this expectation is appropriate, or whether VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 some other amount of time should be used. If commenters believe a different amount of time should be used for the payback assumption, it would be most helpful if commenters could define the amount of time, provide an explanation of why that amount of time is preferable, provide any data or information on which the amount of time is based, and provide any discussion of how changing this assumption would interact with other elements in the analysis. In addition, the Market Data file includes two new sets of inputs for this analysis. In 2020, five vehicle manufacturers reached a voluntary commitment with the state of California to improve the fuel economy of their future nationwide fleets above levels required by the 2020 final rule. For this analysis, compliance with this agreement is in the baseline case for designated manufacturers. The Market Data file contains inputs indicating whether each manufacturer has committed to exceed Federal requirements per this agreement. Finally, when considering other standards that may affect fuel economy compliance pathways, DOT includes projected zero emissions vehicles (ZEV) that would be required for manufacturers to meet standards in California and Section 177 States, per the waiver granted under the Clean Air Act. To support the inclusion of the ZEV program in the analysis, DOT identifies specific vehicle model/ configurations that could adopt BEV technology in response to the ZEV program, independent of CAFE standards, at the first redesign opportunity. These ZEVs are identified in the Market Data file as future BEV200s, BEV300s, or BEV400s. Not all announced BEV nameplates appear in the MY 2020 Market Data file; in these cases, in consultation with CARB, DOT used the volume from a comparable vehicle in the manufacturer’s Market Data file portfolio as a proxy. The Market Data file also includes information about the portion of each manufacturer’s sales that occur in California and Section 177 states, which is helpful for determining how many ZEV credits each manufacturer will need to generate in the future to comply with the ZEV program with their own portfolio in the rulemaking timeframe. These new procedures are described in detail below and in TSD Chapter 2.3. 3. Simulating the Zero Emissions Vehicle Program California’s Zero Emissions Vehicle (ZEV) program is one part of a program of coordinated standards that the PO 00000 Frm 00039 Fmt 4701 Sfmt 4702 49639 California Air Resources Board (CARB) has enacted to control emissions of criteria pollutants and greenhouse gas emissions from vehicles. The program began in 1990, within the low-emission vehicle (LEV) regulation,51 and has since expanded to include eleven other states.52 These states may be referred to as Section 177 states, in reference to Section 177 of the Clean Air Act’s grant of authority to allow these states to adopt California’s air quality standards,53 but it is important to note that not all Section 177 states have adopted the ZEV program component.54 In the following discussion of the incorporation of the ZEV program into the CAFE Model, any reference to the Section 177 states refers to those states that have adopted California’s ZEV program requirements. To account for the ZEV program, and particularly as other states have recently adopted California’s ZEV standards, DOT includes the main provisions of the ZEV program in the CAFE Model’s analysis of compliance pathways. As explained below, incorporating the ZEV program into the model includes converting vehicles that have been identified as potential ZEV candidates into battery-electric vehicles (BEVs) at the first redesign opportunity, so that a manufacturer’s fleet meets calculated ZEV credit requirements. Since ZEV program compliance pathways happen independently from the adoption of fuel saving technology in response to increasing CAFE standards, the ZEV program is considered in the baseline of the analysis, and in all other regulatory alternatives. Through its ZEV program, California requires that all manufacturers that sell cars within the state meet ZEV credit standards. The current credit requirements are calculated based on manufacturers’ California sales volumes. Manufacturers primarily earn ZEV credits through the production of BEVs, fuel cell vehicles (FCVs), and 51 California Air Resource Board (CARB), ZeroEmission Vehicle Program. California Air Resources Board. Accessed April 12, 2021. https:// ww2.arb.ca.gov/our-work/programs/zero-emissionvehicle-program/about. 52 At the time of writing, the Section 177 states that have adopted the ZEV program are Colorado, Connecticut, Maine, Maryland, Massachusetts, New Jersey, New York, Oregon, Rhode Island, Vermont, and Washington. See Vermont Department of Environmental Conservation, Zero Emission Vehicles. Accessed April 12, 2021. https:// dec.vermont.gov/air-quality/mobile-sources/zev#:∼ :text=To%20date%2C%2012%20states%20have, ZEVs%20over%20the%20next%20decade. 53 Section 177 of the Clean Air Act allows other states to adopt California’s air quality standards. 54 At the time of writing, Delaware and Pennsylvania are the two states that have adopted the LEV standards, but not the ZEV portion. E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 49640 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules transitional zero-emissions vehicles (TZEVs), which are vehicles with partial electrification, namely plug-in hybrids (PHEVs). Total credits are calculated by multiplying the credit value each ZEV receives by the vehicle’s volume. The ZEV and PHEV/TZEV credit value per vehicle is calculated based on the vehicle’s range; ZEVs may earn up to 4 credits each and PHEVs with a US06 all-electric range capability of 10 mi or higher receive an additional 0.2 credits on top of the credits received based on all-electric range.55 The maximum PHEV credit amount available per vehicle is 1.10.56 Note however that CARB only allows intermediate-volume manufacturers to meet their ZEV credit requirements through PHEV production.57 DOT’s method for simulating the ZEV program involves several steps; first, DOT calculates an approximate ZEV credit target for each manufacturer based on the manufacturer’s national sales volumes, share of sales in Section 177 states, and the CARB credit requirements. Next, DOT identifies a general pathway to compliance that involves accounting for manufacturers’ potential use of ZEV overcompliance credits or other credit mechanisms, and the likelihood that manufacturers would choose to comply with the requirements with BEVs rather than PHEVs or other types of compliant vehicles, in addition to other factors. For this analysis, as discussed further below, DOT consulted with CARB to determine reasonable assumptions for this compliance pathway. Finally, DOT identifies vehicles in the MY 2020 analysis fleet that manufacturers could reasonably adapt to comply with the ZEV standards at the first opportunity for vehicle redesign, based on publicly announced product plans and other information. Each of these steps is discussed in turn, below, and a more detailed description of DOT’s simulation of the ZEV program is included in TSD Chapter 2.3. The CAFE Model is designed to present outcomes at a national scale, so the ZEV analysis considers the Section 177 states as a group as opposed to estimating each state’s ZEV credit requirements individually. To capture the appropriate volumes subject to the ZEV requirement, DOT calculates each manufacturer’s total market share in Section 177 states. DOT also calculates 55 US06 is one of the drive cycles used to test fuel economy and all-electric range, specifically for the simulation of aggressive driving. See Dynamometer Drive Schedules | Vehicle and Fuel Emissions Testing | U.S. EPA for more information, as well as Section III.C.4 and Section III.D.3.d). 56 13 CCR 1962.2(c)(3). 57 13 CCR 1962.2(c)(3). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 the overall market share of ZEVs in Section 177 states, in order to estimate as closely as possible the number of predicted ZEVs we expect all manufacturers to sell in those states. These shares are then used to scale down national-level information in the CAFE Model to ensure that we represent only Section 177 states in the final calculation of ZEV credits that we project each manufacturer to earn in future years. DOT uses model year 2019 National Vehicle Population Profile (NVPP) from IHS Markit—Polk to calculate these percentages.58 These data include vehicle characteristics such as powertrain, fuel type, manufacturer, nameplate, and trim level, as well as the state in which each vehicle is sold, which allows staff to identify the different types of ZEVs manufacturers sell in the Section 177 state group. DOT may make use of future Polk data in updating the analysis for the final rule and may include other states that join the ZEV program after the publication of this proposal, if necessary. We calculate sales volumes for the ZEV credit requirement based on each manufacturer’s future assumed market share in Section 177 states. DOT decided to carry each manufacturer’s ZEV market shares forward to future years, after examination of past market share data from model year 2016, from the 2017 version of the NVPP.59 Comparison of these data to the 2020 version showed that manufacturers’ market shares remain fairly constant in terms of geographic distribution. Therefore, we determined that it was reasonable to carry forward the recently calculated market shares to future years. We calculate total credits required for ZEV compliance by multiplying the percentages from CARB’s ZEV requirement schedule by the Section 177 state volumes. CARB’s credit percentage requirement schedule for the years covered in this analysis begins at 9.5% in 2020 and ramps up in increments to 22% by 2025.60 Note that the requirements do not currently change after 2025.61 We generate national sales volume predictions for future years using the 58 National Vehicle Population Profile (NVPP) 2020, IHS Markit—Polk. At the time of the analysis, model year 2019 data from the NVPP contained the most current estimate of market shares by manufacturer, and best represented the registered vehicle population on January 1, 2020. 59 National Vehicle Population Profile (NVPP) 2017, IHS Markit—Polk. 60 See 13 CCR 1962.2(b). The percentage credit requirements are as follows: 9.5% in 2020, 12% in 2021, 14.5% in 2022, 17% in 2023, 19.5% in 2024, and 22% in 2025 and onward. 61 13 CCR 1962.2(b). PO 00000 Frm 00040 Fmt 4701 Sfmt 4702 Compliance Report, a CAFE Model output file that includes simulated sales by manufacturer, fleet, and model year. We use a Compliance Report that corresponds to the baseline scenario of 1.5% per year increases in standards for both passenger car and light truck fleets. The resulting national sales volume predictions by manufacturer are then multiplied by each manufacturer’s total market share in the Section 177 states to capture the appropriate volumes in the ZEV credits calculation. Required credits by manufacturer, per year, are determined by multiplying the Section 177 state volumes by CARB’s ZEV credit percentage requirement. These required credits are subsequently added to the CAFE Model inputs as targets for manufacturer compliance with ZEV standards in the CAFE baseline. The estimated ZEV credit requirements serve as a target for simulating ZEV compliance in the baseline. To achieve this, DOT determines a modeling philosophy for ZEV pathways, reviews various sources for information regarding upcoming ZEV programs, and inserts those programs into the analysis fleet inputs. As manufacturers can meet ZEV standards in a variety of different ways, using various technology combinations, the analysis must include certain simplifying assumptions in choosing ZEV pathways. We made these assumptions in conjunction with guidance from CARB staff. The following sections discuss the approach used to simulate a pathway to ZEV program compliance in this analysis. First, DOT targeted 2025 compliance, as opposed to assuming manufacturers would perfectly comply with their credit requirements in each year prior to 2025. This simplifying assumption was made upon review of past history of ZEV credit transfers, existing ZEV credit banks, and redesign schedules. DOT focused on integrating ZEV technology throughout that timeline with the target of meeting 2025 obligations; thus, some manufacturers are estimated to overcomply or under-comply, depending on their individual situations, in the years 2021–2024. Second, DOT determined that the most reasonable way to model ZEV compliance would be to allow undercompliance in certain cases and assume that some manufacturers would not meet their ZEV obligation on their own in 2025. Instead, these manufacturers were assumed to prefer to purchase credits from another manufacturer with a credit surplus. Reviews of past ZEV credit transfers between manufacturers informed the decision to make this E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 simplifying assumption.62 CARB advised that for these manufacturers, the CAFE Model should still project that each manufacturer meet approximately 80% of their ZEV requirements with technology included in their own portfolio. Manufacturers that were observed to have generated many ZEV credits in the past or had announced major upcoming BEV initiatives were projected to meet 100% of their ZEV requirements on their own, without purchasing ZEV credits from other manufacturers.63 Third, DOT agreed that manufacturers would meet their ZEV credit requirements in 2025 though the production of BEVs. As discussed above, manufacturers may choose to build PHEVs or FCVs to earn some portion of their required ZEV credits. However, DOT projected that manufacturers would rely on BEVs to meet their credit requirements, based on reviews of press releases and industry news, as well as discussion with CARB. Since nearly all manufacturers have announced some plans to produce BEVs at a scale meaningful to future ZEV requirements, DOT agreed that this was a reasonable assumption.64 Furthermore, as CARB only allows intermediate-volume manufacturers to meet their ZEV credit requirements through the production of PHEVs, and the volume status of these few manufacturers could change over the years, assuming BEV production for ZEV compliance is the most straightforward path. Fourth, to account for the new BEV programs announced by some manufacturers, DOT identified vehicles in the 2020 fleet that closely matched the upcoming BEVs, by regulatory class, market segment, and redesign schedule. DOT made an effort to distribute ZEV candidate vehicles by CAFE regulatory class (light truck, passenger car), by manufacturer, in a manner consistent with the 2020 manufacturer fleet mix. Since passenger car and light truck mixes by manufacturer could change in response to the CAFE policy alternative under consideration, this effort was deemed necessary in order to avoid redistributing the fleet mix in an 62 See https://ww2.arb.ca.gov/our/work/ programs/advanced-clean-cars-program/zevprogram-zero-emission-vehicle-credit-balances for past credit balances and transfer information. 63 The following manufacturers were assumed to meet 100% ZEV compliance: Ford, General Motors, Hyundai, Kia, Jaguar Land Rover, and Volkswagen Automotive. Tesla was also assumed to meet 100% of its required standards, but the analyst team did not need to add additional ZEV substitutes to the baseline for this manufacturer. 64 See TSD Chapter 2.3 for a list of potential BEV programs recently announced by manufacturers. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 unrealistic manner. However, there were some exceptions to this assumption, as some manufacturers are already closer to meeting their ZEV obligation through 2025 with BEVs currently produced, and some manufacturers underperform their compliance targets more so in one fleet than another. In these cases, DOT deviated from keeping the LT/PC mix of BEVs evenly distributed across the manufacturer’s portfolio.65 DOT then identified future ZEV programs that could plausibly contribute towards the ZEV requirements for each manufacturer by 2025. To obtain this information, DOT examined various sources, including trade press releases, industry announcements, and investor reports. In many cases, these BEV programs are in addition to programs already in production.66 Some manufacturers have not yet released details of future electric vehicle programs at the time of writing, but have indicated goals of reaching certain percentages of electric vehicles in their portfolios by a specified year. In these cases, DOT reviewed the manufacturer’s current fleet characteristics as well as the aspirational information in press releases and other news in order to make reasonable assumptions about the vehicle segment and range of those future BEVs. DOT may reassign some manufacturer’s ZEV programs in the analysis fleet for the final rule based on stakeholder comments or other public information releases that occur in time for the final rule analysis. Overall, analysts assumed that manufacturers would lean towards producing BEV300s rather than BEV200s, based on the information reviewed and an initial conversation with CARB.67 Phase-in caps were also considered, especially for BEV200, with the understanding that the CAFE Model will always pick BEV200 before BEV300 or BEV400, until the quantity of BEV200s is exhausted. See Section III.D.3.c) for details regarding BEV phase-in caps. BEVs, especially BEVs with smaller battery packs and less range, are less likely to meet all the performance needs of traditional pickup truck owners today. However, new markets for BEVs may emerge, potentially in the form of 65 The GM light truck and passenger car distribution is one such example. 66 Examples of BEV programs already in production include the Nissan Leaf and the Chevrolet Bolt. 67 BEV300s are 300-mile range battery-electric vehicles. See Section III.D.3.b) for further information regarding electrification fleet assignments. PO 00000 Frm 00041 Fmt 4701 Sfmt 4702 49641 electric delivery trucks and some lightduty electric truck applications in state and local government. The extent to which BEVs will be used in these and other new markets is difficult to project. DOT did identify certain trucks as upcoming BEVs for ZEV compliance, and these BEVs were expected to have higher ranges, due to the specific performance needs associated with these vehicles. Outside of the ZEV inputs described here, the CAFE Model does not handle the application of BEV technology with any special considerations as to whether the vehicle is a pickup truck or not. Comments from manufacturers are solicited on this issue. Finally, in order to simulate manufacturers’ compliance with their particular ZEV credits target, 142 rows in the analysis fleet were identified as substitutes for future ZEV programs. As discussed above, the analysis fleet summarizes the roughly 13.6 million light-duty vehicles produced and sold in the United States in the 2020 model year with more than 3,500 rows, each reflecting information for one vehicle type observed. Each row includes the vehicle’s nameplate and trim level, the sales volume, engine, transmission, drive configuration, regulatory class, projected redesign schedule, and fuel saving technologies, among other attributes. As the goal of the ZEV analysis is to simulate compliance with the ZEV program in the baseline, and the analysis fleet only contains vehicles produced during model year 2020, DOT identified existing models in the analysis fleet that shared certain characteristics with upcoming BEVs. DOT also focused on identifying substitute vehicles with redesign years similar to the future BEV’s introduction year. The sales volumes of those existing models, as predicted for 2025, were then used to simulate production of the upcoming BEVs. DOT identified a combination of rows that would meet the ZEV target, could contribute productively towards CAFE program obligations (by manufacturer and by fleet), and would introduce BEVs in each manufacturer’s portfolio in a way that reasonably aligned with projections and announcements. DOT tagged each of these rows with information in the Market Data file, instructing the CAFE Model to apply the specified BEV technology to the row at the first redesign year, regardless of the scenario or type of CAFE or GHG simulation. The CAFE Model does not optimize compliance with the ZEV mandate; it relies upon the inputs described in this section in order to estimate each E:\FR\FM\03SEP2.SGM 03SEP2 49642 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 manufacturer’s resulting ZEV credits. The resulting amount of ZEV credits earned by manufacturer for each model year can be found in the CAFE Model’s Compliance file. Not all ZEV-qualifying vehicles in the U.S. earn ZEV credits, as they are not all sold in states that have adopted ZEV regulations. In order to reflect this in the CAFE Model, which only estimates sales volumes at the national level, the percentages calculated for each manufacturer are used to scale down the national-level volumes. Multiplying national-level ZEV sales volumes by these percentages ensures that only the ZEVs sold in Section 177 states count towards the ZEV credit targets of each manufacturer.68 See Section 5.8 of the CAFE Model Documentation for a detailed description of how the model applied these ZEV technologies and any changes made to the model’s programming for the incorporation of the ZEV program into the baseline. As discussed above, DOT made an effort to distribute the newly identified ZEV candidates between CAFE regulatory classes (light truck and passenger car) in a manner consistent with the proportions seen in the 2020 analysis fleet, by manufacturer. As mentioned previously, there were a few exceptions to this assumption in cases where manufacturers’ regulatory class distribution of current or planned ZEV programs clearly differed from their regulatory class distribution as a whole. In some instances, the regulatory distribution of flagged ZEV candidates leaned towards a higher portion of PCs. The reasoning behind this differs in each case, but there is an observed pattern in the 2020 analysis fleet of fewer BEVs being light trucks, especially pickups. The 2020 analysis fleet contains no BEV pickups in the light truck segment. The slow emergence of electric pickups could be linked to the specific performance needs associated with pickup trucks. However, the market for BEVs may emerge in unexpected ways that are difficult to project. Examples of this include anticipated electric delivery trucks and light-duty electric trucks used by state and local governments. Due to these considerations, DOT tagged some trucks as BEVs for ZEV, and expected that 68 The single exception to this assumption is Mazda, as Mazda has not yet produced any ZEVqualifying vehicles at the time of writing. Thus, the percentage of ZEVs sold in Section 177 states cannot be calculated from existing data. However, Mazda has indicated its intention to produce ZEVqualifying vehicles in the future, so DOT assumed that 100% of future ZEVs would be sold in Section 177 states for the purposes of estimating ZEV credits in the CAFE Model. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 these would generally be of higher ranges. TSD Chapter 2.3 includes more information about the process we use to simulate ZEV program compliance in this analysis. 4. Technology Effectiveness Values The next input we use to simulate manufacturers’ decision-making processes for the year-by-year application of technologies to specific vehicles are estimates of how effective each technology would be at reducing fuel consumption. For this analysis, we use full-vehicle modeling and simulation to estimate the fuel economy improvements manufacturers could make to a fleet of vehicles, considering the vehicles’ technical specifications and how combinations of technologies interact. Full-vehicle modeling and simulation uses physics-based models to predict how combinations of technologies perform as a full system under defined conditions. We use 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.69 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,70 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. We simulate the model’s behavior over test cycles, including the 2-cycle laboratory compliance tests (or 2-cycle tests),71 to determine how the individual components interact. 69 Islam, E. S., A. Moawad, N. Kim, R. Vijayagopal, and A. Rousseau. A Detailed Vehicle Simulation Process to Support CAFE Standards for the MY 2024–2026 Analysis. ANL/ESD–21/9 [hereinafter Autonomie model documentation]. 70 Each full vehicle model in this analysis is composed of sub-models, which is why the full vehicle model could also be referred to as a full system model, composed of sub-system models. 71 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’’ or ‘‘HWFET’’), 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 PO 00000 Frm 00042 Fmt 4701 Sfmt 4702 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 the way in which 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 combination 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 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 offer 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 should be accurate and relative to a consistent baseline vehicle. For this analysis, the baseline absolute fuel economy value for each vehicle in the analysis fleet is based on CAFE compliance data for each make and model.72 The absolute fuel economy values of the full vehicle simulations are driving, and the HFET is meant roughly to simulate steady flowing highway driving at about 50 mph. 72 See Section III.C.2 for further discussion of CAFE compliance data in the Market Data file. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 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 subsequent technology changes, we apply the incremental effectiveness values of one or more technologies to the baseline fuel economy value to determine the absolute fuel economy achieved for applying the technology change. As an example, if a Ford F–150 2wheel drive crew cab and short bed in the analysis 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 node 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 F–150’s 30 mpg absolute value. We determine the incremental effectiveness of technologies as applied to the thousands of unique vehicle and technology combinations 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 discussed in the following sections, the Autonomie analysis relies on ten vehicle technology class models that are representative of large portions of the analysis fleet vehicles. The vehicle technology classes ensure that key vehicle characteristics are reasonably represented in the full vehicle models. The next sections discuss the details of the technology effectiveness analysis input specifications and assumptions. NHTSA seeks comment on the following discussion. (a) Full Vehicle Modeling and Simulation As discussed above, for this analysis we use Argonne’s full vehicle modeling tool, Autonomie, to build vehicle models with different technology combinations and simulate the performance of those models over regulatory test cycles. The difference in the simulated performance between full vehicle models, with differing technology combination, is used to determine effectiveness values. We consider over 50 individual VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 technologies as inputs to the Autonomie modeling.73 These inputs consist of engine technologies, transmission technologies, powertrain electrification, lightweighting, aerodynamic improvements, and tire rolling resistance improvements. Section III.D broadly discusses each of the technology groupings definitions, inputs, and assumptions. A deeper discussion of the Autonomie modeled subsystems, and how inputs feed the sub models resulting in outputs, is contained in the Autonomie model documentation that accompanies this analysis. The 50 individual technologies, when considered with the ten vehicle technology classes, result in over 1.1 million individual vehicle technology combination models. For additional discussion on the full vehicle modeling used in this analysis see TSD Chapter 2. While Argonne built full-vehicle models and ran simulations for many combinations of technologies, it did not simulate literally every single vehicle model/configuration in the analysis fleet. Not only would it be impractical to assemble the requisite detailed information specific to each vehicle/ model configuration, much of which would likely only be provided on a confidential basis, doing so would increase the scale of the simulation effort by orders of magnitude. Instead, Argonne simulated ten 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. In this analysis the technology classes are compact cars, midsize cars, small SUVs, large SUVs, and pickup trucks. In addition, for each vehicle class there are two levels of performance attributes (for a total of 10 technology 73 See Autonomie model documentation; ANL— All Assumptions_Summary_NPRM_022021.xlsx; ANL—Data Dictionary_January 2021.xlsx. PO 00000 Frm 00043 Fmt 4701 Sfmt 4702 49643 classes). The high performance and low performance vehicles classifications allow for better diversity in estimating technology effectiveness across the fleet. For additional discussion on the development of the vehicle technology classes used in this analysis and the attributes used to characterize each vehicle technology class, see TSD Chapter 2.4 and the Autonomie model documentation. 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. 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 (DOHC, VVT, port fuel injection (PFI)), a baseline transmission (AT5), 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. 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. Argonne collected over a hundred baseline vehicle attributes to build the baseline vehicle for each technology class. In addition, to account for the weight of different engine sizes, like 4cylinder versus 8-cylinder or turbocharged versus naturally aspirated engines, Argonne developed a relationship curve between peak power and engine weight based on the A2Mac1 benchmarking data. Argonne uses the developed relationship to estimate mass for all engines. For additional discussion on the development and optimization of the baseline vehicle models and the baseline attributes used in this analysis see TSD Chapter 2.4 and the Autonomie model documentation. 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 (time required to accelerate from 0–60 mph), high-speed passing 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), and towing capacity. Together, these performance criteria are widely used by the automotive industry as metrics to quantify vehicle performance attributes E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 49644 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules that consumers observe and that are important for vehicle utility and customer satisfaction. As with conventional vehicle models, electrified vehicle models were also built from the ground up. For MY 2020, the U.S. market has an expanded number of available hybrid and electric vehicle models. To capture improvements for electrified vehicles for this analysis, DOT applied a mass regression analysis process that considers electric motor weight versus electric motor power (similar to the regression analysis for internal combustion engine weights) for vehicle models that have adopted electric motors. Benchmarking data for hybrid and electric vehicles from the A2Mac1 database were analyzed to develop a regression curve of electric motor peak power versus electric motor weight.74 We maintain performance neutrality in the full vehicle simulations 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. This is intended to reflect manufacturers’ comments to DOT on how they consider engine resizing and product complexity, and DOT’s observations on industry product complexity. A detailed discussion on powertrain sizing can be found in TSD Chapter 2.4 and in the Autonomie model documentation. After all vehicle class and technology combination models have been built, Autonomie simulates the vehicles’ performance on test cycles to calculate the effectiveness improvement of adding fuel-economy-improving technologies to the vehicle. Simulating vehicles’ performance using tests and procedures specified by Federal law and regulations minimizes the potential variation in determining technology effectiveness. For vehicles with conventional powertrains and micro hybrids, Autonomie simulates the vehicles per EPA 2-cycle test procedures and guidelines.75 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 per similar 74 See Autonomie model documentation, Chapter 5.2.10 Electric Machines System Weight. 75 40 CFR part 600. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 procedures and guidelines as prescribed in SAE J1711.76 For BEVs Autonomie simulates vehicles per similar procedures and guidelines as prescribed in SAE J1634.77 (b) Performance Neutrality The purpose of the CAFE analysis is to examine the impact of technology application that can improve fuel economy. When the fuel economyimproving technology is applied, often the manufacturer must choose how the technology will affect the vehicle. The advantages of the new technology can either be completely applied to improving fuel economy or be used to increase vehicle performance while maintaining the existing fuel economy, or some mix of the two effects. Historically, vehicle performance has improved over the years as more technology is applied to the fleet. The average horsepower is the highest that it has ever been; all vehicle types have improved horsepower by at least 42 percent compared to the 1978 model year, and pickup trucks have improved by 48 percent.78 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 will, at a minimum, demand vehicles that offer the same utility as today’s fleet. For this rulemaking analysis, DOT analyzed technology pathways manufacturers could use for compliance that attempt to maintain vehicle attributes, utility, and performance. Using this approach allows DOT to assess the 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 attributes that consumers may value across the analyzed regulatory alternatives. This allows for a streamlined accounting of costs and benefits by not requiring the 76 PHEV testing is broken into several phases based on SAE J1711: Charge-sustaining on the city cycle and HWFET cycle, and charge-depleting on the city and HWFET cycles. 77 SAE J1634. ‘‘Battery Electric Vehicle Energy Consumption and Range Test Procedure.’’ July 12, 2017. 78 ‘‘The 2020 EPA Automotive Trends Report, Greenhouse Gas Emissions, Fuel Economy, and Technology since 1975,’’ EPA–420–R–21–003, January 2021 [hereinafter 2020 EPA Automotive Trends Report]. PO 00000 Frm 00044 Fmt 4701 Sfmt 4702 values of other vehicle attributes that trade off with fuel economy. To confirm minimal differences in performance metrics across regulatory alternatives, DOT analyzed the salesweighted average 0–60 mph acceleration performance of the entire simulated vehicle fleet for MYs 2020 and 2029. The analysis compared performance under the baseline standards and preferred alternative. This analysis identified that the analysis fleet under no action standards in MY 2029 had a 0.77 percent worse 0–60 mph acceleration time than under the preferred alternative, indicating there is minimal difference in performance between the alternatives. This assessment shows that for this analysis, the performance difference is minimal across regulatory alternatives and across the simulated model years, which allows for fair, direct comparison among the alternatives. Further details about this assessment can be found in TSD Chapter 2.4.5. (c) Implementation in the CAFE Model The CAFE Model uses two elements of information from the large amount of data generated by the Autonomie simulation runs: Battery costs, and fuel consumption on the city and highway cycles. DOT combines the fuel economy information from the two cycles to produce a composite fuel economy for each vehicle, and for each fuel used in dual fuel vehicles. The fuel economy information for each simulation run is converted into a single value for use in the CAFE Model. In addition to the technologies in the Autonomie simulation, the CAFE Model also incorporated a handful of technologies not explicitly simulated in Autonomie. These technologies’ performance either could not be captured on the 2-cycle test, or there was no robust data usable as an input for full-vehicle modeling and simulation. The specific technologies are discussed in the individual technology sections below and in TSD Chapter 3. To calculate fuel economy improvements attributable to these additional technologies, estimates of fuel consumption improvement factors were developed and scale multiplicatively when applied together. See TSD Chapter 3 for a complete discussion on how these factors were developed. The Autonomie-simulated results and additional technologies are combined, forming a single dataset used by the CAFE Model. Each line in the CAFE Model dataset represents a unique combination of technologies. DOT organizes the records using a unique technology state vector, E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules or technology key (tech key), that describes the technology content associated with each unique record. The modeled 2-cycle fuel economy (miles per gallon) of each combination is converted into fuel consumption (gallons per mile) and then normalized relative to a baseline tech key. The improvement factors used by the model are a given combination’s fuel consumption improvement relative to the baseline tech key in its technology class. The tech key format was developed by recognizing that most of the technology pathways are unrelated and are only logically linked to designate the direction in which technologies are allowed to progress. As a result, it is possible to condense the paths into groups based on the specific technology. These groups are used to define the technology vector, or tech key. The following technology groups defined the tech key: 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). This summarizes to a tech key with the following fields: CONFIG; VVT; VVL; SGDI; DEAC; ADVENG; TRANS; ELEC; ROLL; AERO; MR; EFR; ELECACC; LDB; SAX. It should be noted that some of the fields may be blank for some tech key combinations. These fields will be left visible for the examples below, but blank fields may be omitted from tech keys shown elsewhere in the documentation. 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; AERO20; MR1; ; EPS; LDB ; .’’ 79 79 In the example tech key, 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 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Once a vehicle is assigned (or mapped) to an appropriate tech key, adding a new technology to the vehicle simply represents progress from a previous tech key to a new tech key. The previous tech key refers to the technologies that are currently in use on a vehicle. The new tech key is determined, in the simulation, by adding a new technology to the combination represented by the previous state vector while simultaneously removing any technologies that are superseded by the newly added one. For example, start with a vehicle with the tech key: SOHC; VVT; AT6; BISG; ROLL10; AERO20; MR1; EPS; LDB. Assume the simulation is evaluating PHEV20 as a candidate technology for application on this vehicle. The new tech key for this vehicle is computed by removing SOHC, VVT, AT6, and BISG technologies from the previous state vector,80 and adding PHEV20, resulting a tech key that looks like this: PHEV20; ROLL10; AERO20; MR1; EPS; LDB. From here, the simulation obtains a fuel economy improvement factor for the new combination of technologies and applies that factor to the fuel economy of a vehicle in the analysis fleet. The resulting improvement is applied to the original compliance fuel economy value for a discrete vehicle in the MY 2020 analysis fleet. 5. Defining Technology Adoption in the Rulemaking Timeframe As discussed in Section III.C.2, starting with a fixed analysis fleet (for this analysis, the model year 2020 fleet indicated in manufacturers’ early CAFE compliance data), the CAFE Model estimates ways each manufacturer could potentially apply specific fuel-saving technologies to specific vehicle model/ configurations in response to, among other things (such as fuel prices), CAFE standards, CO2 standards, commitments some manufacturers have made to CARB’s ‘‘Framework Agreement’’, and ZEV mandates imposed by California and several other States. The CAFE Model follows a year-by-year approach to simulating manufacturers’ potential decisions to apply technology, accounting for multiyear planning within the context of estimated schedules for future vehicle redesigns and refreshes during which significant technology changes may most practicably be implemented. example for clarity and emphasis and will not be included in future examples. 80 For more discussion of how the CAFE Model handles technology supersession, see S4.5 of the CAFE Model Documentation. PO 00000 Frm 00045 Fmt 4701 Sfmt 4702 49645 The modeled technology adoption for each manufacturer under each regulatory alternative depends on this representation of multiyear planning, and on a range of other factors represented by other model characteristics and inputs, such as the logical progression of technologies defined by the model’s technology pathways; the technologies already present in the analysis fleet; inputs directing the model to ‘‘skip’’ specific technologies for specific vehicle model/ configurations in the analysis fleet (e.g., because secondary axle disconnect cannot be applied to 2-wheel-drive vehicles, and because manufacturers already heavily invested in engine turbocharging and downsizing are unlikely to abandon this approach in favor of using high compression ratios); inputs defining the sharing of engines, transmissions, and vehicle platforms in the analysis fleet; the model’s logical approach to preserving this sharing; inputs defining each regulatory alternative’s specific requirements; inputs defining expected future fuel prices, annual mileage accumulation, and valuation of avoided fuel consumption; and inputs defining the estimated efficacy and future cost (accounting for projected future ‘‘learning’’ effects) of included technologies; inputs controlling the maximum pace the simulation is to ‘‘phase in’’ each technology; and inputs further defining the availability of each technology to specific technology classes. Two of these inputs—the ‘‘phase-in cap’’ and the ‘‘phase-in start year’’— apply to the manufacturer’s entire estimated production and, for each technology, define a share of production in each model year that, once exceeded, will stop the model from further applying that technology to that manufacturer’s fleet in that model year. The influence of these inputs varies with regulatory stringency and other model inputs. For example, setting the inputs to allow immediate 100% penetration of a technology will not guarantee any application of the technology if stringency increases are low and the technology is not at all cost effective. Also, even if these are set to allow only very slow adoption of a technology, other model aspects and inputs may nevertheless force more rapid application than these inputs, alone, would suggest (e.g., because an engine technology propagates quickly due to sharing across multiple vehicles, or because BEV application must increase quickly in response to ZEV requirements). For this analysis, nearly E:\FR\FM\03SEP2.SGM 03SEP2 49646 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 all of these inputs are set at levels that do not limit the simulation at all. As discussed below, for the most advanced engines (advanced cylinder deactivation, variable compression ratio, variable turbocharger geometry, and turbocharging with cylinder deactivation), DOT has specified phasein caps and phase-in start years that limit the pace at which the analysis shows the technology being adopted in the rulemaking timeframe. For example, this analysis applies a 34% phase-in cap and MY 2019 phase-in start year for advanced cylinder deactivation (ADEAC), meaning that in MY 2021 (using a MY 2020 fleet, the analysis begins simulating further technology application in MY 2021), the model will stop adding ADEAC to a manufacturer’s MY 2021 fleet once ADEAC reaches more than 68% penetration, because 34% × (2021¥2019) = 34% × 2 = 68%. This analysis also applies phase-in caps and corresponding start years to prevent the simulation from showing inconceivable rates of applying batteryelectric vehicles (BEVs), such as showing that a manufacturer producing very few BEVs in MY 2020 could plausibly replace every product with a 300- or 400-mile BEV by MY 2025. Also, as discussed in Section III.D.4, this analysis applies phase-in caps and corresponding start years intended to ensure that the simulation’s plausible application of the highest included levels of mass reduction (20% and 28.2% reductions of vehicle ‘‘glider’’ weight) do not, for example, outpace plausible supply of raw materials and development of entirely new manufacturing facilities. These model logical structures and inputs act together to produce estimates of ways each manufacturer could potentially shift to new fuel-saving technologies over time, reflecting some measure of protection against rates of change not reflected in, for example, technology cost inputs. This does not mean that every modeled solution would necessarily be economically practicable. Using technology adoption features like phase-in caps and phase-in start years is one mechanism that can be used so that the analysis better represents the potential costs and benefits of technology application in the rulemaking timeframe. 6. Technology Costs DOT estimates 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, direct manufacturing costs (DMCs), or the VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 component and labor costs of producing and assembling the physical parts and systems, are estimated 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. DOT accounts 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. To reflect this, DOT estimates 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 and assembly costs of the physical parts and systems that make up a complete vehicle. The analysis used agency-sponsored teardown 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, DOT scrutinized the study assumptions, and sometimes revised or updated the analysis accordingly. Due to the variety of technologies and their applications, and the cost and time required to conduct detailed tear-down analyses, the agency did not sponsor teardown studies for every technology. In addition, some fuel-saving technologies were considered that are pre-production or are sold in very small pilot volumes. For those technologies, DOT could not conduct a tear-down study to assess costs because the PO 00000 Frm 00046 Fmt 4701 Sfmt 4702 product is not yet in the marketplace for evaluation. In these cases, DOT relied upon third-party estimates and confidential information from suppliers and manufacturers; however, there are some common pitfalls with relying on confidential business information to estimate costs. The agency 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 DOT, 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 CAFE Model as not all manufacturers may have access to proprietary technologies at stated costs. The agency carefully evaluates new information in light of these common pitfalls, especially regarding emerging 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, DOT uses the best information available at the time of the analysis and will continue to update cost assumptions for any future analysis. The discussion of each category of technologies in Section III.D (e.g., engines, transmissions, electrification) and corresponding TSD Chapter 3 summarizes the specific cost estimates DOT applied for this analysis. (b) Indirect Costs (Retail Price Equivalent) 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 standards. They include materials, labor, and variable energy costs required to produce and assemble the vehicle. However, they do not 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 III–3 below. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49647 Table 111-3 - Retail Price Components Direct Costs Cost of materials, labor, and variable energy needed for production Manufacturing Cost Indirect Costs Production Overhead Warranty Research and Development Cost of providing product warranty Cost of developing and engineering the product Depreciation and amortization of manufacturing facilities and equipment Cost of maintaining and operating manufacturing facilities and equipment Depreciation and amortization Maintenance, repair, operations Corporate Overhead General and Administrative Retirement Health Care Selling Costs Transportation Salaries of nonmanufacturing labor, operations of corporate offices, etc. Cost of pensions for nonmanufacturing labor Cost of health care for nonmanufacturing labor Cost of transporting manufactured goods Manufacturer costs of advertising manufactured goods Marketing Dealer Costs Dealer selling expense Dealer profit lotter on DSK11XQN23PROD with PROPOSALS2 Net income To estimate the impact of higher vehicle prices on consumers, both direct and indirect costs must be considered. To estimate total consumer costs, DOT multiplies direct manufacturing costs by an indirect cost factor to represent the average price for fuel-saving technologies at retail. 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. Figure III–4 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 1.4.81 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 III–4 illustrates the historical relationship between retail prices and direct manufacturing costs.82 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. 81 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. 82 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, NC. 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 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00047 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.039</GPH> Dealer selling and advertising expense Net Income to dealers from sales of new vehicles Net income to manufacturers from production and sales of new vehicles 49648 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 2.0 ---------··------···--------+~----- 1.9 1.8 - - - - - - - - - - - - - - - - - - - - - - - - - 1.7 - i - - - - - i - - - - 1 - - - - 1 - - - - - 1 1 - - - - - - - 1 - - - - r - - - - - 1 - - - - - 1 1.3 1.2 1.1 -t---------------1----11---------+ - - - - + - - - - - - - 1 - - - +.. ~·............,...-+.......,.. - .......- ......~ -~-+---•--1---•·••·••••··•..,•..l +---------------------------lI 1.01970 - - -1975 - - - 1980 ------ - -1995 - - -2000 - - -2005 ---1985 1990 2010 Vear 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 that 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. In 2011, the National Academy of Sciences recommended RPEs of 1.5 for suppliers and 2.0 for in-house production be used to estimate total costs.83 The Alliance of Automobile Manufacturers also advocates these values as appropriate markup factors for estimating costs of technology changes.84 In their 2015 report, the National Academy of Sciences recommend 1.5 as an overall RPE markup.85 An RPE of 2.0 has also been adopted by a coalition of environmental and research groups (Northeast States Center for a Clean Air Future (NESCCAF), International Council on Clean Transportation (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 III– 4 below). Table III–4 below also 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. Table III–4—Alternate Estimates of the RPE 86 83 Effectiveness and Impact of Corporate Average Fuel Economy Standards, Washington, DC—The National Academies Press; NRC, 2011. 84 Communication from Chris Nevers (Alliance) to Christopher Lieske (EPA) and James Tamm (NHTSA), https://www.regulations.gov Docket ID Nos. NHTSA–2018–0067; EPA–HQ–OAR–2018– 0283, p.143. 85 National Research Council 2015. Cost, Effectiveness, and Deployment of Fuel Economy Technologies for Light Duty Vehicles. Washington, DC: The National Academies Press. https://doi.org/ 10.17226/21744 [hereinafter 2015 NAS report]. 86 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; Cost, Effectiveness, and Deployment of Fuel Economy Technologies in Light Duty Vehicles. Washington, DC—The National Academies Press, 2015; 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 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00048 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.040</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure 111-4- Historical Data for Retail Price Equivalent (RPE), 1972-1997 and 2007 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49649 Table 111-4 - Alternate Estimates of the RPE 86 Author and Year Value, Comments Jack Faucett Associates for EPA, 1985 1.26 initial value, later corrected to 1.7+ by Sierra research 1.5 for outsourced, 2.0 for original equipment manufacturer (OEM). electric and hvbrid vehicles 1.4 (corrected to> by Duleep) 1. 7 based on European study 1.4 (derived using the JFA initial 1.26 value, not the corrected 1.7+ value) 2.0 or>, based on Chrysler data 1.4, 1.56, 1. 7 based on integration complexity 1.5 for Tier 1 supplier, 2.0 for OEM 1.5 for OEM NRC,2002 McKinsey and Company, 2003 CARB,2004 Sierra Research for AAA, 2007 Duleep, 2008 NRC, NAS 2011 NRC, NAS 2015 lotter on DSK11XQN23PROD with PROPOSALS2 The RPE has thus enjoyed widespread use and acceptance by a variety of governmental, academic, and industry organizations. In past rulemakings, a second type of indirect cost multiplier has also been examined. Known as the ‘‘Indirect Cost Multiplier’’ (ICM) approach, ICMs were first examined alongside the RPE approach in the 2010 rulemaking regarding standards for MYs 2012–2016 (75 FR 25324, May 7, 2010). Both methods have been examined in subsequent rulemakings. Consistent with the 2020 final rule, we continue to employ the RPE approach 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. A detailed discussion of indirect cost methods and the basis for our use of the RPE to reflect these costs is available in the Final Regulatory Impact Analysis (FRIA) for the 2020 final rule.87 (c) Stranded Capital Costs 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 the equipment and research and 87 Final Regulatory Impact Analysis, The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Year 2021–2026 Passenger Cars and Light Trucks, USDOT, EPA, March 2020, at 354–76. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 development investments have been fully paid off, there will be unrecouped, or stranded, capital costs. Quantifying stranded capital costs accounts for such lost investments. As DOT has observed previously, manufacturers may be shifting their investment strategies in ways that may alter how stranded capital could be 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. As a proxy for stranded capital in recent CAFE analyses, the CAFE Model has accounted for platform and engine sharing and includes redesign and refresh cycles for significant and less significant vehicle updates. This 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. In addition, confining some manufacturers to specific advanced technology pathways through technology adoption features acts as a proxy to indirectly account for stranded capital. Adoption features specific to each technology, if applied on a manufacturer-by-manufacturer basis, are discussed in each technology section. The agency will monitor these trends to assess the role of stranded capital moving forward. PO 00000 Frm 00049 Fmt 4701 Sfmt 4702 (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 methods to maximize efficiency and reduce production costs. Typically, a representation of this cost learning, or learning curves, reflects 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. We estimate cost learning by considering methods established by T.P. Wright and later expanded upon by J.R. Crawford.88 89 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 88 Wright, T.P., Factors Affecting the Cost of Airplanes. Journal of Aeronautical Sciences, Vol. 3 (1936), at 124–25. Available at https:// www.uvm.edu/pdodds/research/papers/others/ 1936/wright1936a.pdf. 89 Crawford, J.R., Learning Curve, Ship Curve, Ratios, Related Data, Burbank, California-Lockheed Aircraft Corporation (1944). E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.041</GPH> Vyas et al., 2000 49650 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules As pictured in Figure III–5, 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 production increases. J.R. Crawford expanded upon Wright’s learning curve theory to develop a single unit cost model, 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. , $1,000 $900 \ $800 \ $700 !::: $600 z ::, 0:: $500 LJ.J 0. \ '-. "'~- IVl $400 0 u 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. ~ $300 - $200 I $100 $0 0 10 5 15 20 25 30 35 40 45 so CUMULATIVE PRODUCTION Figure 111-5- Wright's Learning Curve (Progress Ratio= 0.8) (1) Time Versus Volume-Based Learning For the 2012 joint CAFE and GHG rulemaking, DOT developed learning curves as a function of vehicle model year.90 Although the concept of this methodology is derived from Wright’s cumulative production volume-based learning curve, its application for CAFE technologies was more of a function of time. More than a dozen learning curve schedules were developed, varying 90 77 FR 62624 (Oct. 15, 2012). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 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 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 NHTSA 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.’’ 91 In response, we incorporated statically projected cumulative volume production data of fuel economy91 National Research Council 2015. Cost, Effectiveness, and Deployment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC: The National Academies Press. https://doi.org/ 10.17226/21744. PO 00000 Frm 00050 Fmt 4701 Sfmt 4702 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. We obtained historical cumulative production data for many technologies produced and/or sold in the U.S. 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 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 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.042</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 The analysis accounts for learning effects with model year-based cost learning forecasts for each technology that reduces direct manufacturing costs over time. We evaluate the historical use of technologies, and reviews industry forecasts to estimate future volumes to develop the model year-based technology cost learning curves. The following section discusses the development of model year-based cost learning forecasts for this analysis, 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, we discuss how these learning effects are applied in the CAFE Model. Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules percent, where 100 percent indicates no learning can be achieved.92 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. We incorporated findings from automotive cost-teardown studies with EPA’s 2015 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.93 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 III–5, 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 49651 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, we note that unit costs continued to fall as the organization gained experience operating with both shifts. We 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 reestimated average progress ratio of 85 percent from those five studies (equally weighted). Author (Publication Date) Industry Progress Ratio (Cumulative Output Approach) Argote et al. (1997) 94 Benkard (2000) 95 Epple et al. (1991) 96 Epple et al. (1996) 97 Levitt et al. (2013) 98 Trucks Aircraft (commercial) Trucks Trucks Automobiles 85% 82% 90% 85% 82% In addition to EPA’s literature review, this progress ratio estimate was informed based on 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 engages contractors to perform detailed engineering ‘‘teardown’’ 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, the agency examines cost and production volume for automotive safety technologies. In particular, we estimated costs from multiple cost teardown 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).99 We 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 III–6 includes these five technologies and yields an average progress rate of 92 percent. 92 Martin, J., ‘‘What is a Learning Curve?’’ Management and Accounting Web, University of South Florida, available at: https://www.maaw.info/ LearningCurveSummary.htm. 93 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:// 19january2017snapshot.epa.gov/sites/production/ files/2016–11/documents/420r16018.pdf. 94 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). 95 Benkard, C. L., Learning and Forgetting—The Dynamics of Aircraft Production, The American Economic Review, Vol. 90(4), at 1034–54 (2000). 96 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), at 58–70 (1991). 97 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), at 77–86 (1996). 98 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), at 643–81 (2013). 99 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 30–33. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00051 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.043</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table 111-5-Progress Ratios from EPA's Literature Review 49652 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-6 - Progress Ratios Researched by NHTSA Anti-lock Brake Systems Driver Airbags Ratio 87% 93% Manual 3-pt lap shoulder safety belts Adjustable Head Restraints Dual Master Cylinder 96% 91% 95% lotter on DSK11XQN23PROD with PROPOSALS2 For the 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. We placed equal weight on progress ratios from all 10 sources. More specifically, we placed equal weight 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. (3) Obtaining Appropriate Baseline Years for Direct Manufacturing Costs DOT obtained direct manufacturing costs for each fuel economy-improving technology from various sources, as discussed above. To establish a consistent basis for direct manufacturing costs in the rulemaking analysis, we adjusted each technology cost 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, we can solve for an implied cost for the first unit produced. For some technologies, we used modestly different progress ratios to match detailed cost projections if available from another source (for instance, batteries for plug-in hybrids and battery electric vehicles). This approach produces reasonable estimates for technologies already in production, and some additional steps are required to set appropriate learning rates for technologies not yet in production. Specifically, for technologies not yet in production in MY 2017, the cumulative production volume in MY 2017 is zero, because VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 manufacturers have not yet produced the technologies. For pre-production cost estimates in previous CAFE rulemakings, we 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, we carefully examined direct costs with learning, and made adjustments to the starting point for those technologies on the learning curve to better align with the assumptions used for the initial direct cost estimate. (4) Cost Learning Applied in the CAFE Model For this analysis, we applied learning effects to the incremental cost over the null technology state on the applicable technology tree. After this step, we calculated year-by-year incremental costs over preceding technologies on the tech tree to create the CAFE Model inputs.100 The shift from incremental cost accounting to absolute cost accounting in recent CAFE analyses made cost inputs more transparently relatable to detailed model output, and relevant to this discussion, made it easier to apply learning curves in the course of developing inputs to the CAFE Model. We grouped certain technologies, such as advanced engines, advanced transmissions, and non-battery electric components and assigned them to the same learning schedule. While these grouped technologies differ in operating 100 These costs are located in the CAFE Model Technologies file. PO 00000 Frm 00052 Fmt 4701 Sfmt 4702 characteristics and design, we 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 consider 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 III.D. In addition, we expanded model inputs to extend the explicit simulation of technology application through MY 2050. Accordingly, we updated the learning curves for each technology group to cover MYs through 2050. For MYs 2017–2032, we 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, we 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 nonbattery electrification components, we estimated a steeper learning curve that E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.044</GPH> I Prog~ess I Technology Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules will gradually flatten after MY 2040. For a more detailed discussion of the electrification learning curves, see Section III.D.3. Each technology in the CAFE Model is assigned a learning schedule developed from the methodology explained previously. For example, the following chart shows learning rates for several technologies applicable to midsize sedans, demonstrating that while we estimate that such learning effects have already been almost entirely realized for engine turbocharging (a 1.0 technology that has been in production for many years), we 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, we 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 49653 technologies, we developed specific learning estimates that may diverge from the 0.89 progress rate. As shown in Figure III–6, 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 (ROLL20) over the baseline, and battery integrated starter/ generator (BISG). l'S!!~.-------------------- 0.9 \ \ 0.8 \ 0.7 -TURBOl ...,._VTGE ·········AEROl5 -0-MRS --ROLL20 _..,_BlSG -0-Batteries 0.2 0.1 2015 2020 2025 2030 2035 Model Year 2040 2045 2050 Figure 111-6- Examples of Year-by-Year Cost Learning Effects (Midsize Sedan) To facilitate specification of detailed model inputs and review of detailed model outputs, the CAFE Model continues to use absolute cost inputs relative to a known base component cost, 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 a 7-speed transmission is specified relative to a 5speed transmission, as is the cost of every other transmission technology. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Conversely, in some earlier versions of the CAFE Model, incremental cost inputs were estimated relative to the technology immediately preceding on the relevant technology pathway. For our 7-speed transmission example, the incremental cost would be relative to a 6-speed transmission. 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. The CAFE Model Documentation accompanying our PO 00000 Frm 00053 Fmt 4701 Sfmt 4702 analysis presents details of the structure for model cost inputs.101 The individual technology sections in Section III.D provide a detailed discussion of cost accounting for each technology. 7. Manufacturer’s Credit Compliance Positions This proposed rule involves a variety of provisions regarding ‘‘credits’’ and other compliance flexibilities. Some regulatory provisions allow a manufacturer to earn ‘‘credits’’ that will 101 CAFE E:\FR\FM\03SEP2.SGM Model Documentation, S4.7. 03SEP2 EP03SE21.045</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 (e) Cost Accounting lotter on DSK11XQN23PROD with PROPOSALS2 49654 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 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. 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.’’ We described in the 2020 final rule how the CAFE Model simulates these compliance credit provisions for both the CAFE program and for EPA’s CO2 standards.102 For this analysis, we modeled the no-action and action alternatives as a set of CAFE standards in place simultaneously with EPA baseline (i.e., 2020 final) CO2 standards, related CARB agreements with five manufacturers, and ZEV mandates in place in California and some other states. The modeling of CO2 standards and standard-like contractual obligations includes our representation of applicable credit provisions. 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 the EPA with broad standardsetting authority for the CO2 program, with no specific directives regarding 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.103 Therefore, this analysis excludes model years 2024– 2026 from those in which carriedforward or transferred credits can be applied for the CAFE program. 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 102 See 103 49 85 FR 24174, 24303 (April 30, 2020). U.S.C. 32902(h)(3). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 the real impact of any alternative. Under 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 Draft Supplemental Environmental Impact Statement (SEIS) accompanying this proposed rule, like the corresponding SEIS analysis, presents ‘‘unconstrained’’ modeling results. Also, because the CAA provides no direction regarding consideration of any CO2 credit provisions, this analysis includes simulation of carried-forward and transferred CO2 credits in all model years. The CAFE Model, therefore, 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. On the other hand, 49 U.S.C. 32902(h) prevents NHTSA from, in its standard setting analysis, considering the potential that manufacturers could use compliance credits in model years for which the agency is establishing maximum feasible CAFE standards. Further, as discussed below, we 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 CAFE 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 simulate credit carry-forward (i.e., banking) between model years and transfers between the passenger car and light truck fleets but not credit carry-back (i.e., borrowing) from future model years or trading between manufacturers. While NHTSA’s ‘‘unconstrained’’ evaluation can consider the potential to carry back compliance credits from later to earlier model years, past examples of failed attempts to carry back CAFE credits (e.g., a MY 2014 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, and we find it reasonable and prudent to refrain from attempting to simulate such ‘‘borrowing’’ in rulemaking analysis. Like the previous version, the current CAFE Model provides a basis to specify (in model inputs) CAFE credits available from model years earlier than those being explicitly simulated. For PO 00000 Frm 00054 Fmt 4701 Sfmt 4702 example, with this analysis representing model years 2020–2050 explicitly, credits earned in the model year 2015 are made available for use through the model year 2020 (given the current fiveyear limit on carry-forward of credits). The banked credits are specific to both the model year and fleet in which they were earned. To increase the realism with which the model transitions between the early model years (MYs 2020–2023) and the later years that are the subject of this action, we have accounted for the potential that some manufacturers might trade credits earned prior to 2020 to other manufacturers. However, the analysis refrains from simulating the potential that manufacturers might continue to trade credits during and beyond the model years covered by this action. In 2018 and 2020, the analysis included idealized cases simulating ‘‘perfect’’ (i.e., wholly unrestricted) trading of CO2 compliance credits by treating all vehicles as being produced by a single manufacturer. Even for CO2 compliance credit trading, these scenarios were not plausible, because it is exceedingly unlikely that some pairs of manufacturers would trade compliance credits. NHTSA did not include such cases for CAFE compliance credits, because EPCA provisions (such as the minimum domestic passenger car standard requirement) make such scenarios impossible. At this time, we remain concerned that any realistic simulation of such trading would require assumptions regarding which specific pairs of manufacturers might trade compliance credits, and the evidence to date makes it clear that the credit market is far from fully ‘‘open.’’ We 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. 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 E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules already evidenced cases of compliance credit trades that were planned and subsequently aborted, reinforcing our 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. 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 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 cost-effective. The model attempts to use credits that will expire within the next three years as a means to smooth out technology applications 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 the 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 (PIC) to provide public access to a range of information regarding the CAFE program,104 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.105 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 40 104 CAFE Public Information Center, https:// one.nhtsa.gov/cafe_pic/cafe_pic_home.htm (last visited May 11, 2021). 105 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. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 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 (e.g., when another manufacturer uses credits acquired from Tesla, the manufacturer may only be able to offset a 1 mpg compliance shortfall, even though the credits’ ‘‘face value’’ suggests the manufacturer could offset a 10 mpg compliance shortfall). Specific inputs accounting for manufacturers’ accumulated compliance credits are discussed in TSD Chapter 2.2.2.3. 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 CAFE rulemakings have evaluated the effects of standards over longer time periods, the early actions taken by manufacturers required more nuanced representation. Accordingly, the CAFE Model now provides means to exclude the simulated application of CAFE compliance credits only from specific model years for which standards are being set (for this analysis, 2024–2026), while allowing CAFE credits to be applied in other model years. In addition to more rigorous accounting of CAFE and CO2 compliance credits, the model 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 specific estimates of each manufacturer’s reliance on these adjustments are discussed above in Section III.C.2.a). Because air conditioning efficiency and off-cycle adjustments are not credits in NHTSA’s program, but rather adjustments to compliance fuel economy, 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 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 PO 00000 Frm 00055 Fmt 4701 Sfmt 4702 49655 over-compliance with earlier years’ standards, A/C efficiency credits, A/C leakage credits, and off-cycle credits. 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 cap. As discussed further in Section III.D.8, this analysis considers that some manufacturers may apply up to 15.0 g/mi of off-cycle credit by MY 2032. We considered the potential to model the application of off-cycle technologies 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 are currently too limited to support explicit modeling of these technologies and adjustments. When establishing maximum feasible fuel economy standards, NHTSA is prohibited from considering the availability of alternatively fueled vehicles,106 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 107) are not available in the compliance simulation to improve fuel economy. Under the ‘‘unconstrained’’ perspective, such as is documented in the SEIS, 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 could be produced in response to CAFE standards outside the model years for which standards are being set, or for other reasons (e.g., ZEV mandates, as accounted for in this analysis). EPCA also provides that CAFE levels may, subject to limitations, be adjusted upward to reflect the sale of flexible fuel vehicles (FFVs). Because these adjustments ended in model year 2020, this analysis assumes no manufacturer 106 49 U.S.C. 32902(h). 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. 107 Dedicated E:\FR\FM\03SEP2.SGM 03SEP2 49656 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 will earn FFV credits within the modeling horizon. Also, the CAA provides no direction regarding consideration of alternative fuels, and EPA has provided that manufacturers selling 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.’’ 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 2024–2026 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. D. Technology Pathways, Effectiveness, and Cost Vehicle manufacturers meet increasingly more stringent fuel economy standards by applying increasing levels of fuel-economyimproving technologies to their vehicles. An appropriate characterization of the technologies available to manufacturers to meet fuel economy standards is, therefore, an important input required to assess the levels of standards that manufacturers can achieve. Like previous CAFE standards analyses, this proposal considers over 50 fuel-economyimproving technologies that manufacturers could apply to their MY 2020 fleet of vehicles to meet proposed levels of CAFE standards in MYs 2024– 2026. The characterization of these technologies, the technology effectiveness values, and technology cost assumptions build on work performed by DOT, EPA, the National Academy of Sciences, and other Federal and state government agencies including the Department of Energy’s Argonne National Laboratory and the California Air Resources Board. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 After spending approximately a decade refining the technology pathways, effectiveness, and cost assumptions used in successive CAFE Model analyses, DOT has developed guiding principles to ensure that the CAFE Model’s simulation of manufacturer compliance pathways results in impacts that we would reasonably expect to see in the real world. These guiding principles are as follows: Even though the analysis considers over 50 individual technologies, the fuel economy improvement from any individual technology must be considered in conjunction with the other fuel-economy-improving technologies applied to the vehicle. For example, there is an obvious fuel economy benefit that results from converting a vehicle with a traditional internal combustion engine to a battery electric vehicle; however, the benefit of the electrification technology depends on the other road load reducing technologies (i.e., mass reduction, aerodynamic, and rolling resistance) on the vehicle. Technologies added in combination to a vehicle will not result in a simply additive fuel economy improvement from each individual technology. As discussed in Section III.C.4, full vehicle modeling and simulation provides the required degree of accuracy to project how different technologies will interact in the vehicle system. For example, as discussed further in Sections III.D.1 and III.D.3, a parallel hybrid architecture powertrain improves fuel economy, in part, by allowing the internal combustion engine to spend more time operating at efficient engine speed and load conditions. This reduces the advantage of adding advanced internal combustion engine technologies, which also improve fuel economy, by broadening the range of speed and load conditions for the engine to operate at high efficiency. This redundancy in fuel savings mechanism results in a reduced effectiveness improvement when the technologies are added to each other. The effectiveness of a technology depends on the type of vehicle the technology is being applied to. For example, applying mass reduction technology results in varying effectiveness as the absolute mass reduced is a function of the starting vehicle mass, which varies across technology classes. See Section III.D.4 for more details. The cost and effectiveness values for each technology should be reasonably representative of what can be achieved across the entire industry. Each technology model employed in the PO 00000 Frm 00056 Fmt 4701 Sfmt 4702 analysis is designed to be representative of a wide range of specific technology applications used in industry. Some vehicle manufacturer’s systems may perform better and cost less than our modeled systems and some may perform worse and cost more. However, employing this approach will ensure that, on balance, the analysis captures a reasonable level of costs and benefits that would result from any manufacturer applying the technology. The baseline for cost and effectiveness values must be identified before assuming that a cost or effectiveness value could be employed for any individual technology. For example, as discussed further in Section III.D.1.d) below, this analysis uses a set of engine map models that were developed by starting with a small number of baseline engine configurations, and then, in a very systematic and controlled process, adding specific well-defined technologies to create a new map for each unique technology combination. The following sections discuss the engine, transmission, electrification, mass reduction, aerodynamic, tire rolling resistance, and other vehicle technologies considered in this analysis. Each section discusses how we define the technology in the CAFE Model,108 how we assigned the technology to vehicles in the MY 2020 analysis fleet used as a starting point for this analysis, any adoption features applied to the technology so the analysis better represents manufacturers’ real-world decisions, the technology effectiveness values, and technology cost. Please note that the following technology effectiveness sections provide examples of the range of effectiveness values that a technology could achieve when applied to the entire vehicle system, in conjunction with the other fuel-economy-improving technologies already on or also applied at the same time to the vehicle. To see the incremental effectiveness values for any particular vehicle moving from one technology key to a more advanced technology key, see the FE_1 and FE_2 Adjustments files that are integrated in the CAFE Model executable file. Similarly, the technology costs provided in each section are examples of absolute costs seen in specific model years (MYs 2020, 2025, and 2030 for most technologies), for specific vehicle classes. To see all absolute technology costs used in the analysis across all model years, see the Technologies file. 108 Note, due to the diversity of definitions industry sometimes employs for technology terms, or in describing the specific application of technology, the terms defined here may differ from how the technology is defined in the industry. E:\FR\FM\03SEP2.SGM 03SEP2 49657 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules NHTSA seeks comment on the following discussion. 1. Engine Paths For this analysis, the extensive variety of light duty vehicle internal combustion (IC) engine technologies are classified into discrete engine technology paths. These paths are used to model the most representative characteristics, costs, and performance of the fuel-economy improving technologies most likely available during the rulemaking time frame, MYs 2024–2026. Due to uncertainties in the cost and capabilities of emerging technologies, some new and preproduction technologies are not part of this analysis. We did not include technologies unlikely to be feasible in the rulemaking timeframe, technologies unlikely to be compatible with U.S. fuels, or technologies for which there was not appropriate data available to allow the simulation of effectiveness across all vehicle technology classes in this analysis. The following sections discuss IC engine technologies considered in this analysis, general technology categories used by the CAFE Model, and how the engine technologies are assigned in the MY 2020 analysis fleet. The following sections also discuss adoption features applicable to engine technologies, engine technologies’ effectiveness when combined in a full vehicle model, and the engine technologies’ costs. (a) Engine Modeling in the CAFE Model DOT models IC engine technologies that manufacturers can use to improve fuel economy. Some engine technologies can be incorporated into existing engines with minor or moderate changes to the engines, but many engine technologies require an entirely new engine architecture. We divide engine technologies into two categories, ‘‘basic engine technologies’’ and ‘‘advanced engine Engine Configuration Path Turbo Eng. HCREng. technologies.’’ ‘‘Basic engine technologies’’ refer to technologies adaptable to an existing engine with minor or moderate changes to the engine. ‘‘Advanced engine technologies’’ refer to technologies that generally require significant changes or an entirely new engine architecture. The words ‘‘basic’’ and ‘‘advanced’’ are not meant to confer any information about the level of sophistication of the technology. Many advanced engine technology definitions also include some basic engine technologies, and these basic technologies are accounted for in the costs and effectiveness values of the advance engine. Figure III–7 shows how the basic and other engines are laid out on pathways evaluated in the compliance simulation. Each engine technology is briefly described, below. It is important to note the ‘‘Basic Engine Path’’ shows that every engine starts with VVT and can add one, some, or all the technologies in the dotted box, as discussed in Section III.D.1.a)(1). ADEACEng. Diesel Eng. ADSL Basic Engine Path I 0 VVT J, DSLIAD I B B VCR Eng. VTG Eng. Adv. Turbo Alt. Fuel s B Figure 111-7 - Engine Technology Paths in the CAFE Model In the CAFE Model, basic engine technologies may be applied individually or in combination with other basic engine technologies. The basic engine technologies include variable valve timing (VVT), variable valve lift (VVL), stoichiometric gasoline direct injection (SGDI), and cylinder deactivation. Cylinder deactivation VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 includes a basic level (DEAC) and an advanced level (ADEAC). DOT applies the basic engine technologies across two engine architectures: dual over-head camshaft (DOHC) engine architecture and single over-head camshaft (SOHC) engine architecture. VVT: Variable valve timing is a family of valve-train designs that dynamically adjusts the timing of the intake valves, exhaust valves, or both, in relation to PO 00000 Frm 00057 Fmt 4701 Sfmt 4702 piston position. VVT can reduce pumping losses, provide increased engine torque and horsepower over a broad engine operating range, and allow unique operating modes, such as Atkinson cycle operation, to further enhance efficiency.109 VVT is nearly universally used in the MY 2020 fleet. VVT enables more control of in-cylinder 109 2015 E:\FR\FM\03SEP2.SGM NAS report, at 31. 03SEP2 EP03SE21.046</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 (1) Basic Engines lotter on DSK11XQN23PROD with PROPOSALS2 49658 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules air flow for exhaust scavenging and combustion relative to fixed valve timing engines. Engine parameters such as volumetric efficiency, effective compression ratio, and internal exhaust gas recirculation (iEGR) can all be enabled and accurately controlled by a VVT system. VVL: Variable valve lift dynamically adjusts the distance a valve travels from the valve seat. The dynamic adjustment can optimize airflow over a broad range of engine operating conditions. The technology can increase effectiveness by reducing pumping losses and by affecting the fuel and air mixture motion and combustion in-cylinder.110 VVL is less common in the MY 2020 fleet than VVT, but still prevalent. Some manufacturers have implemented a limited, discrete approach to VVL. The discrete approach allows only limited (e.g., two) valve lift profiles versus allowing a continuous range of lift profiles. SGDI: Stoichiometric gasoline direct injection sprays fuel at high pressure directly into the combustion chamber, which provides cooling of the incylinder charge via in-cylinder fuel vaporization to improve spark knock tolerance and enable an increase in compression ratio and/or more optimal spark timing for improved efficiency.111 SGDI is common in the MY 2020 fleet, and the technology is used in many advanced engines as well. DEAC: Basic cylinder deactivation disables intake and exhaust valves and turns off fuel injection for the deactivated cylinders during light load operation. DEAC is characterized by a small number of discrete operating configurations.112 The engine runs temporarily as though it were a smaller engine, reducing pumping losses and improving efficiency. DEAC is present in the MY 2020 baseline fleet. ADEAC: Advanced cylinder deactivation systems, also known as rolling or dynamic cylinder deactivation systems, allow a further degree of cylinder deactivation than the base DEAC. ADEAC allows the engine to vary the percentage of cylinders deactivated and the sequence in which cylinders are deactivated, essentially providing ‘‘displacement on demand’’ for low load operations. A small number of vehicles have ADEAC in the MY 2020 baseline fleet. Section III.D.1.d) contains additional information about each basic engine technology used in this analysis, including information about the engine NAS report, at 32. 111 2015 NAS report, at 34. 112 2015 NAS report, at 33. 21:48 Sep 02, 2021 (2) Advanced Engines DOT defines advanced engine technologies in the analysis as technologies that require significant changes in engine structure, or an entirely new engine architecture.113 The advanced engine technologies represent the application of alternate combustion cycles or changes in the application of forced induction to the engine. Each advanced engine technology has a discrete pathway for progression to improved versions of the technology, as seen above in Figure III–7. The advanced engine technology pathways include a turbocharged pathway, a high compression ratio (Atkinson) engine pathway, a variable turbo geometry (Miller Cycle) engine pathway, a variable compression ratio pathway, and a diesel engine pathway. Although the CAFE Model includes a compressed natural gas (CNG) pathway, that technology is a baseline-only technology and was not included in the analysis; currently, there are no dedicated CNG vehicles in the MY 2020 analysis fleet. TURBO: Forced induction engines, or turbocharged downsized engines, are characterized by technology that can create greater-than-atmospheric pressure in the engine intake manifold when higher output is needed. The raised pressure results in an increased amount of airflow into the cylinder supporting combustion, increasing the specific power of the engine. Increased specific power means the engine can generate more power per unit of cylinder volume. The higher power per cylinder volume allows the overall engine volume to be reduced, while maintaining performance. The overall engine volume decrease results in an increase in fuel efficiency by reducing parasitic loads associated with larger engine volumes.114 Cooled exhaust gas recirculation is also part of the advanced forced induction technology path. The basic recycling of exhaust gases using VVT is called internal EGR (iEGR) and is included as part of the performance improvements provided by the VVT basic engine technology. Cooled EGR (cEGR) is a second method for diluting the incoming air that takes exhaust gases, passes them through a heat exchanger to reduce their temperature, and then mixes them with incoming air 113 Examples of this include but are not limited to changes in cylinder count, block geometry or combustion cycle changes. 114 2015 NAS report, at 34. 110 2015 VerDate Sep<11>2014 map models used in the full vehicle technology effectiveness modeling. Jkt 253001 PO 00000 Frm 00058 Fmt 4701 Sfmt 4702 in the intake manifold.115 As discussed in Section III.D.1.d), many advanced engine maps include EGR. Five levels of turbocharged engine downsizing technologies are considered in this analysis: A ‘basic’ level of turbocharged downsized technology (TURBO1), an advanced turbocharged downsized technology (TURBO2), an advanced turbocharged downsized technology with cooled exhaust gas recirculation applied (cEGR), a turbocharged downsized technology with basic cylinder deactivation applied (TURBOD), and a turbocharged downsized technology with advanced cylinder deactivation applied (TURBOAD). HCR: Atkinson engines, or high compression ratio engines, represent a class of engines that achieve a higher level of fuel efficiency by implementing an alternate combustion cycle.116 Historically, the Otto combustion cycle has been used by most gasoline-based spark ignition engines. Increased research into improving fuel economy has resulted in the development of alternate combustion cycles that allow for greater levels of thermal efficiency. One such alternative combustion cycle is the Atkinson cycle. Atkinson cycle operation is achieved by allowing the expansion stroke of the engine to overextend allowing the combustion products to achieve the lowest possible pressure before the exhaust stroke.117 118 119 Descriptions of Atkinson cycle engines and Atkinson mode or Atkinson-enabled engine technologies have been used interchangeably in association with high compression ratio (HCR) engines, for past rulemaking analyses. Both technologies achieve a higher thermal efficiency than traditional Otto cycle-only engines, however, the two engine types operate differently. For purposes of this analysis, Atkinson technologies can be categorized into two groups to reduce confusion: (1) Atkinson-enabled engines and (2) Atkinson engines. Atkinson-enabled engines, or high compression ratio engines (HCR), 115 2015 NAS report, at 35. the 2015 NAS report, Appendix D, for a short discussion on thermodynamic engine cycles. 117 Otto cycle is a four-stroke cycle that has four piston movements over two engine revolutions for each cycle. First stroke: Intake or induction; seconds stroke: Compression; third stroke: Expansion or power stroke; and finally, fourth stroke: Exhaust. 118 Compression ratio is the ratio of the maximum to minimum volume in the cylinder of an internal combustion engine. 119 Expansion ratio is the ratio of maximum to minimum volume in the cylinder of an IC engine when the valves are closed (i.e., the piston is traveling from top to bottom to produce work). 116 See E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 dynamically swing between operating closer to an Otto cycle or an Atkinson cycle based on engine loads. During high loads the engine will use the lower-efficiency, power-dense Otto cycle mode, while at low loads the engine will use the higher-efficiency, lower power-dense Atkinson cycle mode. The hybrid combustion cycle operation is used to address the low power density issues that can limit the Atkinson-only engine and allow for a wider application of the technology. The level of efficiency improvement experienced by a vehicle employing Atkinson cycle operation is directly related to how much of the engine’s operation time is spent in Atkinson mode. Vehicles that can experience operation at a high load for long portions of their operating cycle will see little to no benefit from this technology. This limitation to performance results in manufacturers typically limiting the application of this technology to vehicles with a use profile that can take advantage of the technology’s behavior. Three HCR or Atkinson-enabled engines are available in the analysis: (1) The baseline Atkinson-enabled engine (HCR0), (2) the enhanced Atkinson enabled engine (HCR1), and finally, (3) the enhanced Atkinson enabled engine with cylinder deactivation (HCR1D). In contrast, Atkinson engines in this analysis are defined as engines that operate full-time in the Atkinson cycle. The most common method of achieving Atkinson operation is the use of late intake valve closing. This method allows backflow from the combustion chamber into the intake manifold, reducing the dynamic compression ratio, and providing a higher expansion ratio. The higher expansion ratio improves thermal efficiency but reduces power density. The low power density generally relegates these engines to hybrid vehicle (SHEVPS) applications only in this analysis. Coupling the engines to electric motors and significantly reducing road loads can compensate for the lower power density and maintain desired performance levels for the vehicle.120 The Toyota Prius is an example of a vehicle that uses an Atkinson engine. The 2017 Toyota Prius achieved a peak thermal efficiency of 40 percent.121 120 Toyota. ‘‘Under the Hood of the All-new Toyota Prius.’’ Oct. 13, 2015. Available at https:// global.toyota/en/detail/9827044. Last accessed Nov. 22, 2019. 121 Matsuo, S., Ikeda, E., Ito, Y., and Nishiura, H., ‘‘The New Toyota Inline 4 Cylinder 1.8L ESTEC 2ZR–FXE Gasoline Engine for Hybrid Car,’’ SAE Technical Paper 2016–01–0684, 2016, https:// doi.org/10.4271/2016-01-0684. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 NHTSA seeks comment on whether and how to consider ‘‘HCR2’’ in the analysis for the final rule. VTG: The Miller cycle is another type of overexpansion combustion cycle, similar to the Atkinson cycle. The Miller cycle, however, operates in combination with a forced induction system that helps address the impacts of reduced power density during high load operating conditions. Miller cycleenabled engines use a similar technology approach as seen in Atkinson-enabled engines to effectively create an expanded expansion stroke of the combustion cycle. In the analysis, the baseline Miller cycle-enabled engine includes the application of a variable turbo geometry technology (VTG). The advanced Miller cycle enabled system includes the application of a 48V-based electronic boost system (VTGE). VTG technology allows the system to vary boost level based on engine operational needs. The use of a variable geometry turbocharger also supports the use of cooled exhaust gas recirculation.122 An electronic boost system has an electric motor added to assist a turbocharger at low engine speeds. The motor assist mitigates turbocharger lag and low boost pressure at low engine speeds. The electronic assist system can provide extra boost needed to overcome the torque deficits at low engine speeds.123 VCR: Variable compression ratio (VCR) engines work by changing the length of the piston stroke of the engine to optimize the compression ratio and improve thermal efficiency over the full range of engine operating conditions. Engines using VCR technology are currently in production, but appear to be targeted primarily towards limited production, high performance applications. Nissan is the only manufacturer to use this technology in the MY 2020 baseline fleet. Few manufacturers and suppliers provided information about VCR technologies, and DOT reviewed several design concepts that could achieve a similar functional outcome. In addition to design concept differences, intellectual property ownership complicates the ability to define a VCR hardware system that could be widely adopted across the industry. Because of these issues, adoption of the VCR engine technology is limited to Nissan only. ADSL: Diesel engines have several characteristics that result in superior fuel efficiency over traditional gasoline engines. These advantages include reduced pumping losses due to lack of 122 2015 123 2015 PO 00000 NAS report, at 116. NAS report, at 62. Frm 00059 Fmt 4701 Sfmt 4702 49659 (or greatly reduced) throttling, high pressure direct injection of fuel, a more efficient combustion cycle,124 and a very lean air/fuel mixture relative to an equivalent-performance gasoline engine.125 However, diesel technologies require additional enablers, such as a NOx adsorption catalyst system or a urea/ammonia selective catalytic reduction system, for control of NOx emissions. DOT considered three levels of diesel engine technology: the baseline diesel engine technology (ADSL) is based on a standard 2.2L turbocharged diesel engine; the more advanced diesel engine (DSLI) starts with the ADSL system and incorporates a combination of low pressure and high pressure EGR, reduced parasitic loss, friction reduction, a highly-integrated exhaust catalyst with low temp light off temperatures, and closed loop combustion control; and finally the most advanced diesel system (DSLIAD) is the DSLI system with advanced cylinder deactivation technology added. EFR: Engine friction reduction technology is a general engine improvement meant to represent future technologies that reduce the internal friction of an engine. EFR technology is not available for application until MY 2023. The future technologies do not significantly change the function or operation of the engine but reduce the energy loss due to the rotational or rubbing friction experienced in the bearings or cylinder during normal operation. These technologies can include improved surface coatings, lower-tension piston rings, roller cam followers, optimal thermal management and piston surface treatments, improved bearing design, reduced inertial loads, improved materials, or improved geometry. (b) Engine Analysis Fleet Assignments As a first step in assigning baseline levels of engine technologies in the analysis fleet, DOT used data for each manufacturer to determine which platforms shared engines. Within each manufacturer’s fleet, DOT assigned unique identification designations (engine codes) based on configuration, technologies applied, displacement, compression ratio, and power output. DOT used power output to distinguish between engines that might have the same displacement and configuration 124 Diesel cycle is also a four-stroke cycle like the Otto Cycle, except in the intake stroke no fuel is injected and fuel is injected late in the compression stroke at higher pressure and temperature. 125 See the 2015 NAS report, Appendix D, for a short discussion on thermodynamic engine cycles. E:\FR\FM\03SEP2.SGM 03SEP2 49660 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules but significantly different horsepower ratings. The CAFE Model identifies leaders and followers for a manufacturer’s vehicles that use the same engine, indicated by sharing the same engine code. The model automatically determines which engines are leaders by using the highest sales volume row of the highest sales volume nameplate that is assigned an engine code. This leaderfollower relationship allows the CAFE Model simulation to maintain engine sharing as more technology is applied to engines. DOT accurately represents each engine using engine technologies and engine technology classes. The first step is to assign engine technologies to each engine code. Technology assignment is based on the identified characteristics of the engine being modeled, and based on technologies assigned, the engine will be aligned with an engine map model that most closely corresponds. The engine technology classes are a second identifier used to accurately account for engine costs. The engine technology class is formatted as number of cylinders followed by the letter C, number of banks followed by the letter B, and an engine head configuration designator, which is _SOHC for single overhead cam, _ohv for overhead valve, or blank for dual overhead cam. As an example, one variant of the GMC Acadia has a naturally aspirated DOHC inline 4-cylinder engine, so DOT assigned the vehicle to the ‘4C1B’ engine technology class and assigned the technology VVT and SGDI. Table III–7 shows examples of observed engines with their corresponding assigned engine technologies as well as engine technology classes. Table 111-7 - Examples of Observed Engines and Their Corresponding Engine Technology Class and Technology Assignments GMCAcadia VW Arteon Bentley Bentayga Honda Passport Honda Civic Cadillac CT5 Ford Escape lotter on DSK11XQN23PROD with PROPOSALS2 Chevrolet Silverado Engine Observed 21:48 Sep 02, 2021 Engine Technology Assigned 4C1B VVT,SGDI 6C2B TURBOl 16C4B TURBOD 6C2B SOHC VVT, VVL, SGDI, DEAC 4C1B TURBOl 8C2B TURBOD 4C1B L TURBOl 8C2B ohv ADEAC Naturally Aspirated DOHC Inline 4 cylinder Turbocharged DOHC Inline 4 cylinder Turbocharged DOHC Wl2 w/ cylinder deactivation Naturally Aspirated SOHC V6 Turbocharged DOHC Inline 4 cylinder Turbocharged DOHC V6 w/ cylinder deactivation Turbocharged DOHC Inline 3 cylinder Naturally Aspirated OHV V8 w/ skip fire The cost tables for a given engine class include downsizing (to an engine architecture with fewer cylinders) when turbocharging technology is applied, and therefore, the turbocharged engines observed in the 2020 fleet (that have already been downsized) often map to an engine class with more cylinders. For instance, an observed TURBO1 V6 engine would map to an 8C2B (V8) engine class, because the turbo costs on the 8C2B engine class worksheet assume a V6 (6C2B) engine architecture. Diesel engines map to engine technology classes that match the observed cylinder count since naturally aspirated diesel engines are not found in new light duty vehicles in the U.S. market. Similarly, as indicated above, the TURBO1 I3 in the Ford Escape maps to the 4C1B_L (I4) engine class, because the turbo costs on VerDate Sep<11>2014 Engine Technology Class Assigned Jkt 253001 the 4C1B_L engine class worksheet assume a I3 (3C1B) engine architecture. Some instances can be more complex, including low horsepower variants for 4-cylinder engines, and are shown in Table III–8. For this analysis, we have allowed additional downsizing beyond what has been previously modeled. We allow enhanced downsizing because manufacturers have downsized low output naturally aspirated engines to turbo engines with smaller architectures than traditionally observed.126 127 128 To 126 Richard Truett, ‘‘GM Brining 3-Cylinder back to North America.’’ Automotive News, December 01, 2019. https://www.autonews.com/carsconcepts/gm-bringing-3-cylinder-back-na. 127 Stoklosa, Alexander, ‘‘2021 Mini Cooper Hardtop.’’ Car and Driver, December 2, 2014. PO 00000 Frm 00060 Fmt 4701 Sfmt 4702 capture this new level of turbo downsizing we created a new category of low output naturally aspirated engines, which is only applied to 4cylinder engines in the MY 2020 fleet. These engines use the costing tabs in the Technologies file with the ‘L’ designation and are assumed to downsize to turbocharged 3-cylinder engines for costing purposes. We seek comment regarding the expected further application of this technology to larger cylinder count engines, such as 8cylinder engines that may be turbo https://www.caranddriver.com/reviews/a15109143/ 2014-mini-cooper-hardtop-manual-test-review/. 128 Leanse, Alex ‘‘2020 For Escape Options: Hybrid vs. 3-Cylinder EcoBoost vs. 4-Cylinder EcoBoost.’’ MotorTrend, Sept 24, 2019. https:// www.motortrend.com/news/2020-ford-escapeengine-options-pros-and-cons-comparison/. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.047</GPH> Vehicle Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules downsized to 4-cylinder engines. We would also like comment on how to define the characteristic of an engine 49661 that may be targeted for enhanced downsizing. Table 111-8 - Examples of Engine Technology Class Assignment Logic w Observed Number of Cvlinders 3 3 4 4 4 4 4 4 4 5 16 lotter on DSK11XQN23PROD with PROPOSALS2 TSD Chapter 3.1.2 includes more details about baseline engine technology assignment logic, and details about the levels of engine technology penetration in the MY 2020 fleet. (c) Engine Adoption Features Engine adoption features are defined through a combination of (1) refresh and redesign cycles, (2) technology path logic, (3) phase-in capacity limits, and (4) SKIP logic. Figure III–7 above shows the technology paths available for engines in the CAFE Model. Engine technology development and application typically results in an engine design moving from the basic engine tree to one of the advanced engine trees. Once an engine design moves to the advanced engine tree it is not allowed to move to alternate advanced engine trees. Specific path logic, phase-in caps, and SKIP logic applied to each engine technology are discussed by engine technology, in turn. Refresh and redesign cycles dictate when engine technology can be applied. Technologies applicable only during a platform redesign can be applied during a platform refresh if another vehicle platform that shares engine codes (uses the same engine) has already applied the technology during a redesign. For example, models of the GMC Acadia and the Cadillac XT4 use the same engine (assigned engine code 112011 in the Market Data file); if the XT4 adds a new engine technology during a redesign, then the Acadia may also add the same engine technology during the VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Horsepower Any Any <=180 <=180 <=180 <=180 >180 >180 >180 Any Any Naturally Aspirated or Turbo NA Turbo NA Turbo NA Turbo NA Turbo Turbo Turbo Turbo next refresh or redesign. This allows the model to maintain engine sharing relationships while also maintaining refresh and redesign schedules.129 For engine technologies, DOHC, OHV, VVT, and CNG engine technologies are baseline only, while all other engine technologies can only be applied at a vehicle redesign. Basic engine technologies in the CAFE Model are represented by four technologies: VVT, VVL, SGDI, and DEAC. DOT assumes that 100% of basic engine platforms use VVT as a baseline, based on wide proliferation of the technology in the U.S. fleet. The remaining three technologies, VVL, SGDI, and DEAC, can all be applied individually or in any combination of the three. An engine can jump from the basic engines path to any other engine path except the Alternative Fuel Engine Path. Turbo downsizing allows manufacturers to maintain vehicle performance characteristics while reducing engine displacement and cylinder count. Any basic engine can adopt one of the turbo engine technologies (TURBO1, TURBO2 and CEGR1). Vehicles that have turbocharged engines in the baseline fleet will stay on the turbo engine path to prevent unrealistic engine technology change in the short timeframe considered in the rulemaking analysis. Turbo technology is a mutually 129 See Section III.C.2.a) for more discussion on platform refresh and redesign cycles. PO 00000 Frm 00061 Fmt 4701 Sfmt 4702 Engine Technology Class Assigned 3C1B 4C1B L 4C1B L 4C1B 4C2B L 4C2B 4C1B 6C2B 6C2B 6C2B 16C4B exclusive technology in that it cannot be adopted for HCR, diesel, ADEAC, or CNG engines. Non-HEV Atkinson mode engines are a collection of engines in the HCR engine pathway (HCR0, HCR1, HCR1D and HCR2). Atkinson engines excel in lower power applications for lower load conditions, such as driving around a city or steady state highway driving without large payloads, thus their adoption is more limited than some other technologies. DOT expanded the availability of HCR technology compared to the 2020 final rule because of new observed applications in the market.130 However, there are three categories of adoption features specific to the HCR engine pathway: 131 • DOT does not allow vehicles with 405 or more horsepower to adopt HCR engines due to their prescribed duty cycle being more demanding and likely not supported by the lower power density found in HCR-based engines.132 • Pickup trucks and vehicles that share engines with pickup trucks are 130 For example, the Hyundai Palisade and Kia Telluride have a 291 hp V6 HCR1 engine. The specification sheets for these vehicles are located in the docket for this action. 131 See Section III.D.1.d)(1) Engine Maps, for a discussion of why HCR2 and P2HCR2 were not used in the central analysis. ‘‘SKIP’’ logic was used to remove this engine technology from application, however as discussed below, we maintain HCR2 and P2HCR2 in the model architecture for sensitivity analysis and for future engine map model updates. 132 Heywood, John B. Internal Combustion Engine Fundamentals. McGraw-Hill Education, 2018. Chapter 5. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.048</GPH> Observed Gasoline Engine Confo?:uration Inline Inline Inline Inline Boxer Boxer Inline Inline Boxer Inline 49662 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 also excluded from receiving HCR engines; the duty cycle for these heavy vehicles, particularly when hauling cargo or towing, are likely unable to take full advantage of Atkinson cycle use, and would ultimately spend the majority of operation as an Otto cycle engine, negating the benefits of HCR technology.133 • HCR engine application is also restricted for some manufacturers that are heavily performance-focused and have demonstrated a significant commitment to power dense technologies such as turbocharged downsizing.134 NHTSA seeks comment on the appropriateness of these restrictions for the final rule. Advanced cylinder deactivation technology (ADEAC), or dynamic cylinder deactivation (e.g., Dynamic Skip Fire), can be applied to any engine with basic technology. This technology represents a naturally aspirated engine with ADEAC. Additional technology can be applied to these engines by moving to the Advanced Turbo Engine Path. Miller cycle (VTG and VTGE) engines can be applied to any basic and turbocharged engine. VTGE technology is enabled by the use of a 48V system that presents an improvement from traditional turbocharged engines, and accordingly VTGE includes the application of a mild hybrid (BISG) system. VCR engines can be applied to basic and turbocharged engines, but the technology is limited to Nissan and Mitsubishi.135 VCR technology requires a complete redesign of the engine, and in the analysis fleet, only two of Nissan’s models had incorporated this technology. The agency does not believe any other manufacturers will invest to develop and market this technology in their fleet in the rulemaking time frame. Advanced turbo engines are becoming more prevalent as the technologies mature. TURBOD combines TURBO1 and DEAC technologies and represents the first advanced turbo. TURBOAD combines TURBO1 and ADEAC technologies and is the second and last level of advanced turbos. Engines from either the Turbo Engine Path or the 133 This is based on CBI conversation with manufacturers that currently employ HCR-based technology but saw no benefit when the technology was applied to truck platforms in their fleet. 134 There are three manufacturers that met the criteria (near 100% turbo downsized fleet, and future hybrid systems are based on turbodownsized engines) described and were excluded: BMW, Daimler, and Jaguar Land Rover. 135 Nissan and Mitsubishi are strategic partners and members of the Renault-Nissan-Mitsubishi Alliance. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 ADEAC Engine Path can adopt these technologies. Any basic engine technologies (VVT, VVL, SGDI, and DEAC) can adopt ADSL and DSLI engine technologies. Any basic engine and diesel engine can adopt DSLIAD technology in this analysis; however, DOT applied a phase in cap and year for this technology at 34 percent and MY 2023, respectively. In DOT’s engineering judgement, this is a rather complex and costly technology to adopt and it would take significant investment for a manufacturer to develop. For more than a decade, diesel engine technologies have been used in less than one percent of the total lightduty fleet production and have been found mostly on medium and heavyduty vehicles. Finally, DOT allows the CAFE Model to apply EFR to any engine technology except for DSLI and DSLIAD. DSLI and DSLIAD inherently have incorporated engine friction technologies from ADSL. In addition, friction reduction technologies that apply to gasoline engines cannot necessarily be applied to diesel engines due to the higher temperature and pressure operation in diesel engines. (d) Engine Effectiveness Modeling Effectiveness values used for engine technologies were simulated in two ways. The value was either calculated based on the difference in full vehicle simulation results created using the Autonomie modeling tool, or effectiveness values were determined using an alternate calculation method, including analogous improvement or fuel economy improvement factors. (1) Engine Maps Most effectiveness values used as inputs for the CAFE Model were determined by comparing results of full vehicle simulations using the Autonomie simulation tool. For a full discussion about how Autonomie was used, see Section III.C.4 and TSD Chapter 2.4, in addition to the Autonomie model documentation. Engine map models were the primary inputs used to simulate the effects of different engine technologies in the Autonomie full vehicle simulations. Engine maps provide a threedimensional representation of engine performance characteristics at each engine speed and load point across the operating range of the engine. Engine maps have the appearance of topographical maps, typically with engine speed on the horizontal axis and engine torque, power, or brake mean PO 00000 Frm 00062 Fmt 4701 Sfmt 4702 effective pressure (BMEP) 136 on the vertical axis. A third engine characteristic, such as brake-specific fuel consumption (BSFC),137 is displayed using contours overlaid across the speed and load map. The contours provide the values for the third characteristic in the regions of operation covered on the map. Other characteristics typically overlaid on an engine map include engine emissions, engine efficiency, and engine power. The engine maps developed to model the behavior of the engines used in this analysis are referred to as engine map models. The engine map models used in this analysis are representative of technologies that are currently in production or are expected to be available in the rulemaking timeframe, MYs 2024–2026. The engine map models were developed to be representative of the performance achievable across industry for a given technology and are not intended to represent the performance of a single manufacturer’s specific engine. The broadly representative performance level was targeted because the same combination of technologies produced by different manufacturers will have differences in performance, due to manufacturer-specific designs for engine hardware, control software, and emissions calibration. Accordingly, DOT expects that the engine maps developed for this analysis will differ from engine maps for manufacturers’ specific engines. However, DOT intends and expects that the incremental changes in performance modeled for this analysis, due to changes in technologies or technology combinations, will be similar to the incremental changes in performance observed in manufacturers’ engines for the same changes in technologies or technology combinations. The analysis never applies absolute BSFC levels from the engine maps to any vehicle model or configuration for the rulemaking analysis. The absolute fuel economy values from the full vehicle Autonomie simulations are used only to determine incremental effectiveness for switching from one technology to another technology. The incremental effectiveness is applied to the absolute fuel economy of vehicles in the analysis fleet, which are based on CAFE compliance data. For subsequent 136 Brake mean effective pressure is an engineering measure, independent of engine displacement, that indicates the actual work an engine performs. 137 Brake-specific fuel consumption is the rate of fuel consumption divided by the power being produced. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 technology changes, incremental effectiveness is applied to the absolute fuel economy level of the previous technology configuration. Therefore, for a technically sound analysis, it is most important that the differences in BSFC among the engine maps be accurate, and not the absolute values of the individual engine maps. However, achieving this can be challenging. For this analysis, DOT used a small number of baseline engine configurations with well-defined BSFC maps, and then, in a very systematic and controlled process, added specific well-defined technologies to create a BSFC map for each unique technology combination. This could theoretically be done through engine or vehicle testing, but testing would need to be conducted on a single engine, and each configuration would require physical parts and associated engine calibrations to assess the impact of each technology configuration, which is impractical for the rulemaking analysis because of the extensive design, prototype part fabrication, development, and laboratory resources that are required to evaluate each unique configuration. Modeling is an approach used by industry to assess an array of technologies with more limited testing. Modeling offers the opportunity to isolate the effects of individual technologies by using a single or small number of baseline engine VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 configurations and incrementally adding technologies to those baseline configurations. This provides a consistent reference point for the BSFC maps for each technology and for combinations of technologies that enables the differences in effectiveness among technologies to be carefully identified and quantified. The Autonomie model documentation provides a detailed discussion on how the engine map models were used as inputs to the full vehicle simulations performed using the Autonomie tool. The Autonomie model documentation contains the engine map model topographic figures, and additional engine map model data can be found in the Autonomie input files.138 Most of the engine map models used in this analysis were developed by IAV GmbH (IAV) Engineering. IAV is one of the world’s leading automotive industry engineering service partners with an over 35-year history of performing research and development for powertrain components, electronics, and vehicle design.139 The primary outputs of IAV’s work for this analysis are engine maps that model the operating characteristics of engines equipped with specific technologies. 138 See additional Autonomie supporting materials in docket number NHTSA–2021–0053 for this proposal. 139 IAV Automotive Engineering, https:// www.iav.com/en/. PO 00000 Frm 00063 Fmt 4701 Sfmt 4702 49663 The generated engine maps were validated against IAV’s global database of benchmarked data, engine test data, single cylinder test data, prior modeling studies, technical studies, and information presented at conferences.140 The effectiveness values from the simulation results were also validated against detailed engine maps produced from the Argonne engine benchmarking programs, as well as published information from industry and academia, ensuring reasonable representation of simulated engine technologies.141 The engine map models used in this analysis and their specifications are shown in Table III–9. BILLING CODE 4910–59–P 140 Friedrich, I., Pucher, H., and Offer, T., ‘‘Automatic Model Calibration for Engine-Process Simulation with Heat-Release Prediction,’’ SAE Technical Paper 2006–01–0655, 2006, https:// doi.org/10.4271/2006-01-0655. Rezaei, R., Eckert, P., Seebode, J., and Behnk, K., ‘‘Zero-Dimensional Modeling of Combustion and Heat Release Rate in DI Diesel Engines,’’ SAE Int. J. Engines 5(3):874– 885, 2012, https://doi.org/10.4271/2012-01-1065. Multistage Supercharging for Downsizing with Reduced Compression Ratio (2015). MTZ Rene Berndt, Rene Pohlke, Christopher Severin and Matthias Diezemann IAV GmbH. Symbiosis of Energy Recovery and Downsizing (2014). September 2014 MTZ Publication Heiko Neukirchner, Torsten Semper, Daniel Luederitz and Oliver Dingel IAV GmbH. 141 Bottcher,. L, Grigoriadis, P. ‘‘ANL—BSFC map prediction Engines 22–26.’’ IAV (April 30, 2019). 20190430_ANL_Eng 22–26 Updated_Docket.pdf. E:\FR\FM\03SEP2.SGM 03SEP2 49664 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-9 - Engine Map Models used in This Analysis Technologies Eng0l DOHC+VVT Eng02 Eng03 DOHC+VVT+VVL DOHC+VVT+VVL+SGDI DOHC+VVT+VVL+SGDI +DEAC Eng04 SOHC+VVT+PFI Eng5a Eng5b Eng6a Eng7a Eng8a SOHC+VVT (level 1 Red. Friction) SOHC+VVT+VVL (level 1 Red. Friction) SOHC+VVT+VVL+SGDI (level 1 Red. Friction) SOHC+VVT+VVL+SGDI +DEAC (level 1 Red. Friction) Engl2 DOHC Turbo 1.6118bar Engl2 DEAC DOHC Turbo 1.6118bar Eng13 DOHC Turbo 1.21 24bar Engl7 Engl8 Engl9 Eng20 Eng21 Eng22b DOHC Turbo l .2124bar + CooledEGR Diesel DOHC+VVT+SGDI DOHC+VVT+DEAC DOHC+VVT+VVL+DEAC DOHC+VVT+SGDl+DEAC DOHC+VVT Eng24 Current SkyActiv 2.0193AKI Eng25 Future SkyActiv 2.01 CEGR 93AKl+DEAC Engl4 Eng26 lotter on DSK11XQN23PROD with PROPOSALS2 Eng23b Eng23c Eng26a Atkinson Cycle Engine DOHC+VTG+VVT+VVL+SGD I +cEGR DOHC+VTG+VVT+SGDI +cEGR+Eboost DOHC+VCR+VVT+SGDI +Turbo+cEGR BILLING CODE 4910–59–C VerDate Sep<11>2014 21:48 Sep 02, 2021 Notes Parent NA engine, Gasoline, 2.0L, 4 cyl, NA, PFI, DOHC, dual cam VVT, CRl0.2 VVL added to Eng0 1 SGDI added to Eng02, CRl 1 Cylinder deactivation added to Eng03 Eng0l converted to SOHC (gasoline, 2.0L, 4cyl, NA, PFI, single cam vvn For Reference Onlv Eng5a with valvetrain friction reduction (small friction reduction) Eng02 with valvetrain friction reduction (small friction reduction) Eng03 with valvetrain friction reduction (small friction reduction), addition of VVL and SGDI Eng04 with valvetrain friction reduction (small friction reduction). addition ofDEAC Parent Turbocharged Engine, Gasoline, l .6L, 4 cyl, turbocharged, SGDI, DOHC, dual cam VVT, VVL En_gine BMEP: 18 bar Engl2 with DEAC applied, Engine BMEP 18bar Engl2 downsized to l.2L, En_gine BMEP 24 bar Cooled external EGR added to Eng 13 Engine BMEP 24 bar Diesel, 2.2L (measured on test bed) Gasoline, 2.0L, 4 cyl, NA, SGDI, DOHC, VVT Cylinder deactivation added to Eng0 1 Cylinder deactivation added to Eng02 Cylinder deactivation added to Eng 18 Atkinson-enabled 2.5L DOHC, VVT, PFI, CR14 Non-HEV Atkinson mode, Gasoline, 2.0L, 4 cyl, DOHC, NA SGDL VVT. CR 13.L 93 AKI Non-HEV Atkinson mode, Gasoline, 2.0L, 4 cyl, DOHC, NA, SGDI, VVT, cEGR, DEAC CR 14.1, 93AKI For Reference Only HEV and PHEV Atkinson Cycle Engine l.8L Miller Cycle, 2.0L DOHC, VTG, SGDI, cEGR, VVT, VVL, CR12 Eng23b with an 48V Electronic supercharger and battery pack VVT, SGDI, Turbo, cEGR, VCR CR 9-12 Two engine map models shown in Table III–9, Eng24 and Eng25, were not Jkt 253001 PO 00000 Frm 00064 Fmt 4701 Sfmt 4702 developed as part of the IAV modeling effort and only Eng24 is used in this E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.049</GPH> Engines Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules analysis. The Eng24 and Eng25 engine maps are equivalent to the ATK and ATK2 models developed for the 2016 Draft Technical Assessment Report (TAR), EPA Proposed Determination, and Final Determination.142 The ATK1 engine model is based directly on the 2.0L 2014 Mazda SkyActiv-G (ATK) engine. The ATK2 represents an Atkinson engine concept based on the Mazda engine, adding cEGR, cylinder deactivation, and an increased compression ratio (14:1). In this analysis, Eng24 and Eng25 correspond to the HCR1 and HCR2 technologies. The HCR2 engine map model application in this analysis follows the approach of the 2020 final rule.143 The agency believes the use of HCR0, HCR1, and the new addition of HCR1D reasonably represents the application of Atkinson Cycle engine technologies within the current light-duty fleet and the anticipated applications of Atkinson Cycle technology in the MY 2024–2026 timeframe. We are currently developing an updated family of HCR engine map models that will include cEGR, cylinder deactivation and a combination thereof. The new engine map models will closely align with the baseline assumptions used in the other IAVbased HCR engine map models used for the agency’s analysis. The updated engine map models will likely not be available for the final rule associated with this proposal because of engine map model testing and validation requirements but will be available for future CAFE analyses. We believe the timing for including the new engine map models is reasonable, because a manufacturer that could apply this technology in response to CAFE standards is likely not do so before MY 2026, as the application of this technology will require an engine redesign. We also believe this is reasonable given manufacturer’s statements that there are diminishing returns to additional conventional engine technology improvements considering vehicle electrification commitments. NHTSA seeks comment on whether and how to change our engine maps for HCR2 in the analysis for the final rule. (2) Analogous Engine Effectiveness Improvements and Fuel Economy Improvement Factors For some technologies, the effectiveness for applying an incremental engine technology was determined by using the effectiveness values for applying the same engine technology to a reasonably similar base engine. An example of this can be seen in the determination of the application 49665 of SGDI to the baseline SOHC engine. Currently there is no engine map model for the SOHC+VVT+SGDI engine configuration. To create the effectiveness data required as an input to the CAFE Model, first, a pairwise comparison between technology configurations that included the DOHC+VVT engine (Eng1) and the DOHC+VVT+SGDI (Eng18) engine was conducted. Then, the results of that comparison were used to generate a data set of emulated performance values for adding the SGDI technology to the SOHC+VVT engine (Eng5b) systems. The pairwise comparison is performed by finding the difference in fuel consumption performance between every technology configuration using the analogous base technology (e.g., Eng1) and every technology configuration that only changes to the analogous technology (e.g., Eng18). The individual changes in performance between all the technology configurations are then added to the same technology configurations that use the new base technology (e.g., Eng5b) to create a new set of performance values for the new technology (e.g., SOHC+VVT+SGDI). Table III–10 shows the engine technologies where analogous effectiveness values were used. Table ID-to-Engine Technology Performance Values Determined by Analogous Effectiveness Values Analogous Technology Engl DOHC+VVT Engl DOHC+VVT Engl8 DOHC+VVT+SGDI Engl9 SOHC+VVT+DEAC Eng20 DOHC+VVT+VVL+ DEAC Eng21 DOHC+VVT+SGDl+DE AC Engl DOHC+VVT Engl DOHC+VVT lotter on DSK11XQN23PROD with PROPOSALS2 Engl2 (TURBOl) Engl2DEAC (TURBOD) New Base Technolo!!V Eng5b SOHC+VVT Eng5b SOHC+VVT Eng5b SOHC+VVT SOHC+VVT+SGDI+ DEAC Eng24 (HCRl) HCRlD 142 Ellies, B., Schenk, C., and Dekraker, P., ‘‘Benchmarking and Hardware-in-the-Loop Operation of a 2014 MAZDA SkyActiv 2.0L 13:1 Compression Ratio Engine,’’ SAE Technical Paper 2016–01–1007, 2016, doi:10.4271/2016–01–1007. Jkt 253001 PO 00000 Frm 00065 Fmt 4701 SOHC+VVT+DEAC SOHC+VVT+VVL+ DEAC either no appropriate analogous technology or there were not enough data to create a full engine map model. The improvement factors were generally 21:48 Sep 02, 2021 SOHC+VVT+SGDI Eng5b SOHC+VVT DOT also developed a static fuel efficiency improvement factor to simulate applying an engine technology for some technologies where there was VerDate Sep<11>2014 New Technology Sfmt 4702 developed based on literature review or confidential business information (CBI) provided by stakeholders. Table III–11 provides a summary of the technology 143 85 E:\FR\FM\03SEP2.SGM FR 24425–27 (April 30, 2020). 03SEP2 EP03SE21.050</GPH> Analogous Baseline 49666 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules effectiveness values simulated using improvement factors, and the value and rules for how the improvement factors were applied. Advanced cylinder deactivation (ADEAC, TURBOAD, DSLIAD), advanced diesel engines (DSLIA) and engine friction reduction (EFR) are the three technologies modeled using improvement factors. The application of the advanced cylinder deactivation is responsible for three of the five technologies using an improvement factor in this analysis. The initial review of the advanced cylinder deactivation technology was based on a technical publication that used a MY 2010 SOHC VVT basic engine.144 Additional information about the technology effectiveness came from a benchmarking analysis of preproduction 8-cylinder OHV prototype systems.145 However, at the time of the analysis no studies of production versions of the technology were available, and the only available technology effectiveness came from existing studies, not operational information. Thus, only estimates of effect could be developed and not a full model of operation. No engine map model could be developed, and no other technology pairs were analogous. To model the effects of advanced cylinder deactivation, an improvement factor was determined based on the lotter on DSK11XQN23PROD with PROPOSALS2 144 Wilcutts, M., Switkes, J., Shost, M., and Tripathi, A., ‘‘Design and Benefits of Dynamic Skip Fire Strategies for Cylinder Deactivated Engines,’’ SAE Int. J. Engines 6(1):278–288, 2013, available at https://doi.org/10.4271/2013-01-0359. EisazadehFar, K. and Younkins, M., ‘‘Fuel Economy Gains through Dynamic-Skip-Fire in Spark Ignition Engines,’’ SAE Technical Paper 2016–01–0672, 2016, available at https://doi.org/10.4271/2016-010672. 145 EPA, 2018. ‘‘Benchmarking and Characterization of a Full Continuous Cylinder Deactivation System.’’ Presented at the SAE World Congress, April 10–12, 2018. Retrieved from https:// www.regulations.gov/document?D=EPA-HQ-OAR2018-0283-0029. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 information referenced above and applied across the engine technologies. The effectiveness values for naturally aspirated engines were predicted by using full vehicle simulations of a basic engine with DEAC, SGDI, VVL, and VVT, and adding 3 percent or 6 percent improvement based on engine cylinder count: 3 percent for engines with 4 cylinders or less and 6 percent for all other engines. Effectiveness values for turbocharged engines were predicted using full vehicle simulations of the TURBOD engine and adding 1.5 percent or 3 percent improvement based on engine cylinder count: 1.5 percent for engines with 4 cylinders or less and 3 percent for all other engines. For diesel engines, effectiveness values were predicted by using the DSLI effectiveness values and adding 4.5 percent or 7.5 percent improvement based on vehicle technology class: 4.5 percent improvement was applied to small and medium non-performance cars, small performance cars, and small non-performance SUVs. 7.5 percent improvement was applied to all other vehicle technology classes. The analysis modeled advanced engine technology application to the baseline diesel engine by applying an improvement factor to the ADSL engine technology combinations. A 12.8 percent improvement factor was applied to the ADSL technology combinations to create the DSLI technology combinations. The improvement in performance was based on the application of a combination of low pressure and high pressure EGR, reduced parasitic loss, advanced friction reduction, incorporation of highlyintegrated exhaust catalyst with low temp light off temperatures, and closed loop combustion control.146 147 148 149 146 2015 NAS report, at 104. J., Fukushima, H., Sasaki, Y., Nishimori, K., Tabuchi, T., Ishihara, Y. ‘‘The New 147 Hatano, PO 00000 Frm 00066 Fmt 4701 Sfmt 4702 As discussed above, the application of the EFR technology does not simulate the application of a specific technology, but the application of an array of potential improvements to an engine. All reciprocating and rotating components in the engine are potential candidates for friction reduction, and minute improvements in several components can add up to a measurable fuel economy improvement.150 151 152 153 Because of the incremental nature of this analysis, a range of 1–2 percent improvement was identified initially, and narrowed further to a specific 1.39% improvement. The final value is likely representative of a typical value industry may be able to achieve in future years. 1.6L 2-Stage Turbo Diesel Engine for HONDA CR– V.’’ 24th Aachen Colloquium—Automobile and Engine Technology 2015. 148 Steinparzer, F., Nefischer, P., Hiemesch, D., Kaufmann, M., Steinmayr, T. ‘‘The New SixCylinder Diesel Engines from the BMW In-Line Engine Module.’’ 24th Aachen Colloquium— Automobile and Engine Technology 2015. 149 Eder, T., Weller, R., Spengel, C., Bo ¨ hm, J., Herwig, H., Sass, H. Tiessen, J., Knauel, P. ‘‘Launch of the New Engine Family at Mercedes-Benz.’’ 24th Aachen Colloquium—Automobile and Engine Technology 2015. 150 ‘‘Polyalkylene Glycol (PAG) Based Lubricant for Light- & Medium-Duty Axles,’’ 2017 DOE Annual Merit Review. Ford Motor Company, Gangopadhyay, A., Ved, C., Jost, N. https:// energy.gov/sites/prod/files/2017/06/f34/ft023_ gangopadhyay_2017_o.pdf. 151 ‘‘Power-Cylinder Friction Reduction through Coatings, Surface Finish, and Design,’’ 2017 DOE Annual Merit Review. Ford Motor Company. Gangopadhay, A. Erdemir, A. https://energy.gov/ sites/prod/files/2017/06/f34/ft050_gangopadhyay_ 2017_o.pdf. 152 ‘‘Nissan licenses energy-efficient engine technology to HELLER,’’ https://newsroom.nissanglobal.com/releases/170914-01-e?lang=enUS&rss&la=1&downloadUrl=%2F releases%2F170914-01-e%2Fdownload. Last accessed April 2018. 153 ‘‘Infiniti’s Brilliantly Downsized V–6 Turbo Shines,’’ https://wardsauto.com/engines/infiniti-sbrilliantly-downsized-v-6-turbo-shines. Last Accessed April 2018. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49667 Table 111-11- Engine Technologies Modeled Using Efficiency Improvement Factors Baseline Technology DEAC TURBOD ADSL DSLI New Technolo2v Fuel Efficiency Improvement Factor 3% for::; 4 Cylinders 6% for> 4 Cylinders 1.5% for::; 4 Cylinders 3% for > 4Cylinders 12.8% 4.5% for small and medium non-performance cars and SUVs, and small performance cars; 7.5% for all other technology classes All Engine Technologies ADEAC TURBOAD 1.39% (3) Engine Effectiveness Values The effectiveness values for the engine technologies, for all ten vehicle technology classes, are shown in Figure III–8. Each of the effectiveness values shown is representative of the improvements seen for upgrading only the listed engine technology for a given DSLI DSLIAD EFR combination of other technologies. In other words, the range of effectiveness values seen for each specific technology (e.g., TURBO1) represents the addition of the TURBO1 technology to every technology combination that could select the addition of TURBO1. See Table III–12 for several specific examples. It must be emphasized, the change in fuel consumption values between entire technology keys is used,154 and not the individual technology effectiveness values. Using the change between whole technology keys captures the complementary or non-complementary interactions among technologies. Table 111-12-Example of Effectiveness Calculations Shown in Figure 111-8* Tech Vehicle Tech Class Initial Technology Key Fuel Consumption Initial New (gal/mile) (gal/mile) Effectiveness (%) DOHC;VVT;;;;;AT8L2;SS12V; 0.0282 0.0248 12.15 ROLL10;AERO5;MR2 DOHC;VVT;;;;;AT8L2;CONV; TURBOl Medium Car 0.0292 0.0254 13.13 ROLL10;AERO5;MR2 DOHC;VVT;;;;;AT8L2;BISG; TURBOl Medium Car 0.0275 0.0237 13.80 ROLL10;AERO5;MR2 DOHC;VVT;;;;;AT6;SS 12V; TURBOl Medium Car 0.0312 0.0269 13.80 ROLL10;AERO5;MR2 *The 'Tech' is added to the 'Initial Technology Key' replacing the existing engine technology, resulting in the new fuel consumption value. The percent effectiveness is found by determining the percent improved fuel consumption of the new value versus the initial value. 155 Medium Car modeling to capture interactions between technologies and capture instances of both complimentary technologies and non-complimentary technologies. In this instance, the SHEVP2 powertrain improves fuel economy, in part, by allowing the engine to spend more time operating at efficient engine speed and load 154 Technology key is the unique collection of technologies that constitutes a specific vehicle, see Section III.C.4.c). 155 The full data set we used to generate this example can be found in the FE_1 Improvements file. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00067 Fmt 4701 Sfmt 4702 conditions. This reduces the advantage of adding advanced engine technologies, which also improve fuel economy, by broadening the range of speed and load conditions for the engine to operate at high efficiency. This redundancy in fuel savings mechanism results in a lower effectiveness when the technologies are added to each other. E:\FR\FM\03SEP2.SGM EP03SE21.052</GPH> Some of the advanced engine technologies have values that indicate seemingly low effectiveness. Investigation of these values shows the low effectiveness was a result of applying the advanced engines to existing SHEVP2 architectures. This effect is expected and illustrates the importance of using the full vehicle 03SEP2 EP03SE21.051</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 TURBOl 49668 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules ,,.,,..._ ,!I ___ 0.05 o.oo' (e) Engine Costs lotter on DSK11XQN23PROD with PROPOSALS2 The CAFE Model considers both cost and effectiveness in selecting any technology changes. We have allocated considerable resources to sponsoring research to determine direct manufacturing costs (DMCs) for fuel saving technologies. As discussed in detail in TSD Chapter 3.1.5, the engine costs used in this analysis build on estimates from the 2015 NAS report, agency-funded teardown studies, and work performed by non-government organizations.157 Absolute costs of the engine technology are used in this analysis 156 The box shows the inner quartile range (IQR) of the effectiveness values and whiskers extend out 1.5 × IQR. The dots outside this range show effectiveness values outside those thresholds. The VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 instead of relative costs, which were used prior to the 2020 final rule. The absolute costs are used to ensure the full cost of the IC engine is removed when electrification technologies are applied specifically for the transition to BEVs. This analysis models the cost of adoption of BEV technology by first removing the costs associated with IC powertrain systems, then applying the BEV systems costs. Relative costs can still be determined through comparison of the absolute costs for the initial technology combination and the new technology combination. As discussed in detail in TSD Chapter 3.1.5, engine costs are assigned based on the number of cylinders in the engine and whether the engine is naturally aspirated or turbocharged and downsized. Table III–13 below shows an example of absolute costs for engine technologies in 2018$. The example costs are shown for a straight 4-cylinder DOHC engine and V-6-cylinder DOHC engine. The table shows costs declining across successive years due to the learning rate applied to each engine technology. For a full list of all absolute engine costs used in the analysis across all model years, see the Technologies file. data used to create this figure can be found in the FE_1 Improvements file. 157 FEV prepared several cost analysis studies for EPA on subjects ranging from advanced 8-speed transmissions to belt alternator starters or start/stop systems. NHTSA contracted Electricore, EDAG, and Southwest Research for teardown studies evaluating mass reduction and transmissions. The 2015 NAS report also evaluated technology costs developed based on these teardown studies. PO 00000 Frm 00068 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.053</GPH> Figure 111-8-Engine Technologies Effectiveness Values for all Vehicle Technology Classes 156 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49669 Table 111-13- Examples of Absolute Costs for Engine Technologies in 2018$ for a Straight 4-Cylinder DOHC Engine and a V-6-Cylinder DOHC Engine for Select Model Years Technology 4C1B Costs (2018$) MY2020 MY2025 MY2030 6C2B Costs (2018$) MY2020 MY2025 MY2030 EFR VVT VVL SGDI DEAC TURBOl TURB02 CEGRl ADEAC HCR0 HCRl HCRlD VCR VTG VTGE TURBOD TURBOAD ADSL DSLI DSLIAD CNG 66.61 5,205.13 5,402.62 5,435.72 5,268.59 6,228.96 6,807.16 7,221.06 6,292.36 5,819.86 5,863.02 6,040.68 7,370.02 7,592.44 8,892.07 6,406.61 6,971.41 9,726.31 10,226.67 10,791.47 11,822.52 99.92 6,059.15 6,298.29 6,347.93 6,040.39 7,073.58 7,673.21 8,087.11 7,633.14 6,953.63 6,996.80 7,206.43 8,214.65 8,457.91 9,757.54 7,251.23 7,816.03 11,384.74 12,036.41 12,883.61 12,676.54 For this analysis, DOT classified all light duty vehicle transmission technologies into discrete transmission technology paths. These paths are used to model the most representative characteristics, costs, and performance of the fuel-economy improving transmissions most likely available during the rulemaking time frame, MYs 2024–2026. The following sections discuss how transmission technologies considered in this analysis are defined, the general technology categories used by the CAFE Model, and the transmission technologies’ relative effectiveness and costs. The following sections also provide an overview of how the transmission technologies were assigned to the MY 2020 fleet, as well as the adoption features applicable to the transmission technologies. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 57.83 5,199.02 5,385.95 5,417.27 5,259.08 6,152.15 6,538.33 6,887.39 6,174.57 5,801.18 5,825.45 5,993.60 7,124.07 7,241.61 8,097.54 6,320.30 6,801.38 9,362.48 9,823.56 10,304.64 11,471.76 (a) Transmission Modeling in the CAFE Model DOT modeled two major categories of transmissions for this analysis: Automatic and manual. Automatic transmissions are characterized by automatically selecting and shifting between transmission gears for the driver during vehicle operation. Automatic transmissions are further subdivided into four subcategories: Traditional automatic transmissions (AT), dual clutch transmissions (DCT), continuously variable transmissions (CVT), and direct drive transmissions (DD). ATs and CVTs also employ different levels of high efficiency gearbox (HEG) technology. HEG improvements for transmissions represent incremental advancement in technology that improve efficiency, such as reduced friction seals, bearings and clutches, super finishing of gearbox parts, and improved lubrication. These advancements are all aimed at reducing PO 00000 Frm 00069 Fmt 4701 Sfmt 4702 95.96 6,052.31 6,284.28 6,332.43 6,034.11 7,020.02 7,498.58 7,873.26 7,521.16 6,928.79 6,958.18 7,161.53 8,048.82 8,234.25 9,257.62 7,192.35 7,701.57 11,065.55 11,679.77 12,443.61 12,462.91 86.74 6,046.93 6,273.28 6,320.26 6,029.18 6,989.71 7,384.60 7,733.67 7,456.45 6,924.86 6,949.13 7,147.55 7,961.63 8,088.26 8,944.19 7,157.85 7,638.93 10,948.81 11,549.33 12,270.94 12,319.67 frictional and other parasitic loads in transmissions to improve efficiency. DOT considered three levels of HEG improvements in this analysis, based on 2015 recommendations by the National Academy of Sciences and CBI data.158 HEG efficiency improvements are applied to ATs and CVTs, as those transmissions inherently have higher friction and parasitic loads related to hydraulic control systems and greater component complexity, compared to MTs and DCTs. HEG technology improvements are noted in the transmission technology pathways by increasing ‘‘levels’’ of a transmission technology; for example, the baseline 8speed automatic transmission is termed ‘‘AT8’’, while an AT8 with level 2 HEG technology is ‘‘AT8L2’’ and an AT8 with level 3 HEG technology is ‘‘AT8L3.’’ AT: Conventional planetary gear automatic transmissions are the most 158 2015 E:\FR\FM\03SEP2.SGM NAS report, at 191. 03SEP2 EP03SE21.054</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 2. Transmission Paths 63.97 5,201.71 5,393.28 5,425.38 5,263.27 6,179.91 6,644.50 7,019.17 6,217.71 5,803.73 5,833.12 6,005.45 7,208.71 7,380.16 8,403.54 6,352.24 6,861.47 9,459.91 9,931.51 10,440.74 11,612.31 49670 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules popular transmission.159 ATs typically contain three or four planetary gear sets that provide the various gear ratios. Gear ratios are selected by activating solenoids which engage or release multiple clutches and brakes as needed. ATs are packaged with torque converters, which provide a fluid coupling between the engine and the driveline and provide a significant increase in launch torque. When transmitting torque through this fluid coupling, energy is lost due to the churning fluid. These losses can be eliminated by engaging the torque convertor clutch to directly connect the engine and transmission (‘‘lockup’’). For the Draft TAR and 2020 final rule, EPA and DOT surveyed automatic transmissions in the market to assess trends in gear count and purported fuel economy improvements.160 Based on that survey, and also EPA’s more recent 2019 and 2020 Automotive Trends Reports,161 DOT concluded that modeling ATs with a range of 5 to 10 gears, with three levels of HEG technology for this analysis was reasonable. CVT: Conventional continuously variable transmissions consist of two cone-shaped pulleys, connected with a belt or chain. Moving the pulley halves allows the belt to ride inward or outward radially on each pulley, effectively changing the speed ratio between the pulleys. This ratio change is smooth and continuous, unlike the step changes of other transmission varieties.162 DOT modeled two types of CVT systems in the analysis, the baseline CVT and a CVT with HEG technology applied. DCT: Dual clutch transmissions, like automatic transmissions, automate shift and launch functions. DCTs use separate clutches for even-numbered and odd-numbered gears, allowing the next gear needed to be pre-selected, resulting in faster shifting. The use of multiple clutches in place of a torque converter results in lower parasitic losses than ATs.163 Because of a history of limited appeal,164 165 DOT constrains application of additional DCT technology to vehicles already using DCT technology, and only models two types of DCTs in the analysis. MT: Manual transmissions are transmissions that require direct control by the driver to operate the clutch and shift between gears. In a manual transmission, gear pairs along an output shaft and parallel layshaft are always engaged. Gears are selected via a shift lever, operated by the driver. The lever operates synchronizers, which speed match the output shaft and the selected gear before engaging the gear with the shaft. During shifting operations (and during idle), a clutch between the engine and transmission is disengaged to decouple engine output from the transmission. Automakers today offer a minimal selection of new vehicles with manual transmissions.166 As a result of reduced market presence, DOT only included three variants of manual transmissions in the analysis. The transmission model paths used in this analysis are shown in Figure III–9. Baseline-only technologies (MT5, AT5, AT7L2, AT9L2, and CVT) are grayed and can only be assigned as initial vehicle transmission configurations. Further details about transmission path modeling can be found in TSD Chapter 3.2. Automatic Transmission Path MT Path ocrs CVTL2 AT10l3 159 2020 EPA Automotive Trends Report, at 57– 61. 160 Draft TAR at 5–50, 5–51; Final Regulatory Impact Analysis accompanying the 2020 final rule, at 549. 161 The 2019 EPA Automotive Trends Report, EPA–420–R–20–006, at 59 (March 2020), https:// VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 nepis.epa.gov/Exe/ZyPDF.cgi?Dockey= P100YVFS.pdf [hereinafter 2019 EPA Automotive Trends Report]; 2020 EPA Automotive Trends Report, at 57. 162 2015 NAS report, at 171. 163 2015 NAS report, at 170. 164 2020 EPA Automotive Trends Report, at 57. PO 00000 Frm 00070 Fmt 4701 Sfmt 4725 165 National Academies of Sciences, Engineering, and Medicine 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy 2025– 2035. Washington, DC: The National Academies Press. https://doi.org/10.17226/26092, at 4–56 [hereinafter 2021 NAS report]. 166 2020 EPA Automotive Trends Report, at 61. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.055</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure 111-9 - CAFE Model Pathways for Transmission Technologies Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 (b) Transmission Analysis Fleet Assignments The wide variety of transmissions on the market are classified into discrete transmission technology paths for this analysis. These paths are used to model the most representative characteristics, costs, and performance of the fuel economy-improving technologies most likely available during the rulemaking time frame. For the 2020 analysis fleet, DOT gathered data on transmissions from manufacturer mid-model year CAFE compliance submissions and publicly available manufacturer specification sheets. These data were used to assign transmissions in the analysis fleet and determine which platforms shared transmissions. Transmission type, number of gears, and high-efficiency gearbox (HEG) level are all specified for the baseline fleet assignment. The number of gears in the assignments for automatic and manual transmissions usually match the number of gears listed by the data sources, with some exceptions. Four-speed transmissions were not modeled in Autonomie for this analysis due to their rarity and low likelihood of being used in the future, so DOT assigned 2020 vehicles with an AT4 or MT4 to an AT5 or MT5 baseline, respectively. Some dual-clutch transmissions were also an exception; dual-clutch transmissions with seven gears were assigned to DCT6. For automatic and continuously variable transmissions, the identification of the most appropriate transmission path model required additional steps; this is because highefficiency gearboxes are considered in the analysis but identifying HEG level from specification sheets alone was not always straightforward. DOT conducted a review of the age of the transmission design, relative performance versus previous designs, and technologies incorporated and used the information obtained to assign an HEG level. No automatic transmissions in the MY 2020 analysis fleet were determined to be at HEG Level 3. In addition, no six-speed automatic transmissions were assigned HEG Level 2. However, DOT found all 7-speed, all 9-speed, all 10-speed, and some 8-speed automatic transmissions to be advanced transmissions operating at HEG Level 2 equivalence. Eight-speed automatic transmissions developed after MY 2017 are assigned HEG Level 2. All other transmissions are assigned to their respective transmission’s baseline level. The baseline (HEG level 1) technologies available include AT6, AT8, and CVT. DOT assigned any vehicle in the analysis fleet with a hybrid or electric VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 powertrain a direct drive (DD) transmission. This designation is for informational purposes; if specified, the transmission will not be replaced or updated by the model. In addition to technology type, gear count, and HEG level, transmissions are characterized in the analysis fleet by drive type and vehicle architecture. Drive types considered in the analysis include front-, rear-, all-, and four-wheel drive. The definition of drive types in the analysis does not always align with manufacturers’ drive type designations; see the end of this subsection for further discussion. These characteristics, supplemented by information such as gear ratios and production locations, showed that manufacturers use transmissions that are the same or similar on multiple vehicle models. Manufacturers have told the agency they do this to control component complexity and associated costs for development, manufacturing, assembly, and service. If multiple vehicle models share technology type, gear count, drive configuration, internal gear rations, and production location, the transmissions are treated as a single group for the analysis. Vehicles in the analysis fleet with the same transmission configuration adopt additional fuelsaving transmission technology together, as described in Section III.C.2.a). Shared transmissions are designated and tracked in the CAFE Model input files using transmission codes. Transmission codes are six-digit numbers that are assigned to each transmission and encode information about them. This information includes the manufacturer, drive configuration, transmission type, and number of gears. TSD Chapter 3.2.2 includes more information on the transmission codes designated in the MY 2020 analysis fleet. Different transmission codes are assigned to variants of a transmission that may have appeared to be similar based on the characteristics considered in the analysis but are not mechanically identical. DOT analysts distinguish among transmission variants by comparing their internal gear ratios and production locations. For example, several Ford nameplates carry a rearwheel drive, 10-speed automatic transmission. These nameplates comprise a wide variety of body styles and use cases, and so DOT assigned different transmission codes to these different nameplates. Because they have different transmission codes, they are not treated as ‘‘shared’’ for the purposes of the analysis and have the opportunity PO 00000 Frm 00071 Fmt 4701 Sfmt 4702 49671 to adopt transmission technologies independently. Note that when determining the drive type of a transmission, the assignment of all-wheel drive versus four-wheel drive is determined by vehicle architecture. This assignment does not necessarily match the drive type used by the manufacturer in specification sheets and marketing materials. Vehicles with a powertrain capable of providing power to all wheels and a transverse engine (front-wheel drive architecture) are assigned all-wheel drive. Vehicles with power to all four wheels and a longitudinal engine (rearwheel drive architecture) are assigned four-wheel drive. (c) Transmission Adoption Features Transmission technology pathways are designed to prevent ‘‘branch hopping’’—changes in transmission type that would correspond to significant changes in transmission architecture—for vehicles that are relatively advanced on a given pathway. For example, any automatic transmission with more than five gears cannot move to a dual-clutch transmission. For a more detailed discussion of path logic applied in the analysis, including technology supersession logic and technology mutual exclusivity logic, please see CAFE Model Documentation S4.5 Technology Constraints (Supersession and Mutual Exclusivity). Additionally, the CAFE Model prevents ‘‘branch hopping’’ to prevent stranded capital associated with moving from one transmission architecture to another. Stranded capital is discussed in Section III.C.6. Some technologies that are modeled in the analysis are not yet in production, and therefore are not assigned in the baseline fleet. Nonetheless, these technologies, which are projected to be available in the analysis timeframe, are available for future adoption. For instance, an AT10L3 is not observed in the baseline fleet, but it is plausible that manufacturers that employ AT10L2 technology may improve the efficiency of those AT10L2s in the rulemaking timeframe. The following sections discuss specific adoption features applied to each type of transmission technology. When electrification technologies are adopted, the transmissions associated with those technologies will supersede the existing transmission on a vehicle. The transmission technology is superseded if P2 hybrids, plug-in hybrids, or battery electric vehicle technologies are applied. For more information, see Section III.D.3.c). E:\FR\FM\03SEP2.SGM 03SEP2 49672 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules The automatic transmission path precludes adoption of other transmission types once a platform progresses past an AT6. This restriction is used to avoid the significant level of stranded capital loss that could result from adopting a completely different transmission type shortly after adopting an advanced transmission, which would occur if a different transmission type were adopted after AT6 in the rulemaking timeframe. Vehicles that did not start out with AT7L2 or AT9L2 transmissions cannot adopt those technologies in the model. The agency observed that MY 2017 vehicles with those technologies were primarily luxury performance vehicles and concluded that other vehicles would likely not adopt those technologies. DOT concluded that this was also a reasonable assumption for the MY 2020 analysis fleet because vehicles that have moved to more advanced automatic transmissions have overwhelmingly moved to 8-speed and 10-speed transmissions.167 CVT adoption is limited by technology path logic. CVTs cannot be adopted by vehicles that do not originate with a CVT or by vehicles with multispeed transmissions beyond AT6 in the baseline fleet. Vehicles with multispeed transmissions greater than AT6 demonstrate increased ability to operate the engine at a highly efficient speed and load. Once on the CVT path, the platform is only allowed to apply improved CVT technologies. The analysis restricts the application of CVT technology on larger vehicles because of the higher torque (load) demands of those vehicles and CVT torque limitations based on durability constraints. Additionally, this restriction is used to avoid the significant level of stranded capital. The analysis allows vehicles in the baseline fleet that have DCTs to apply an improved DCT and allows vehicles with an AT5 to consider DCTs. Drivability and durability issues with some DCTs have resulted in a low relative adoption rate over the last decade; this is also broadly consistent with manufacturers’ technology choices.168 Manual transmissions can only move to more advanced manual transmissions for this analysis, because other transmission types do not provide a similar driver experience (utility). Manual transmissions cannot adopt AT, CVT, or DCT technologies under any circumstance. Other transmissions cannot move to MT because manual transmissions lack automatic shifting associated with the other transmission types (utility) and in recognition of the low customer demand for manual transmissions.169 (d) Transmission Effectiveness Modeling For this analysis, DOT used the Autonomie full vehicle simulation tool to model the interaction between transmissions and the full vehicle system to improve fuel economy, and how changes to the transmission subsystem influence the performance of the full vehicle system. The full vehicle simulation approach clearly defines the contribution of individual transmission technologies and separates those contributions from other technologies in the full vehicle system. The modeling approach follows the recommendations of the National Academy of Sciences in its 2015 light duty vehicle fuel economy technology report to use full vehicle modeling supported by application of collected improvements at the submodel level.170 See TSD Chapter 3.2.4 for more details on transmission modeling inputs and results. The only technology effectiveness results that were not directly calculated using the Autonomie simulation results were for the AT6L2. DOT determined that the model for this specific technology was inconsistent with the 168 Ibid. lotter on DSK11XQN23PROD with PROPOSALS2 167 2020 EPA Automotive Trends Report, at 64, figure 4.18. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 169 Ibid. 170 2015 PO 00000 NAS report, at 292. Frm 00072 Fmt 4701 Sfmt 4702 other transmission models and overpredicted effectiveness results. Evaluation of the AT6L2 transmission model revealed an overestimated efficiency map was developed for the AT6L2 model. The high level of efficiency assigned to the transmission surpassed benchmarked advanced transmissions.171 To address the issue, DOT replaced the effectiveness values of the AT6L2 model. DOT replaced the effectiveness for the AT6L2 technology with analogous effectiveness values from the AT7L2 transmission model. For additional discussion on how analogous effectiveness values are determined please see Section III.D.1.d)(2). The effectiveness values for the transmission technologies, for all ten vehicle technology classes, are shown in Figure III–10. Each of the effectiveness values shown is representative of the improvements seen for upgrading only the listed transmission technology for a given combination of other technologies. In other words, the range of effectiveness values seen for each specific technology, e.g., AT10L3, represents the addition of the AT10L3 technology to every technology combination that could select the addition of AT10L3. It must be emphasized that the graph shows the change in fuel consumption values between entire technology keys,172 and not the individual technology effectiveness values. Using the change between whole technology keys captures the complementary or noncomplementary interactions among technologies. In the graph, the box shows the inner quartile range (IQR) of the effectiveness values and whiskers extend out 1.5 × IQR. The dots outside of the whiskers show values for effectiveness that are outside these bounds. 171 Autonomie model documentation, Chapter 5.3.4. Transmission Performance Data. 172 Technology key is the unique collection of technologies that constitutes a specific vehicle, see Section III.C.4.c). E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49673 0.25 0.00 ~ < N i,.J N \,0 t--- 00 ~ i,.J ~ < f... N i,.J 00 ("t') i,.J 00 N lo-I' °' < < ~ r-' f... N ~ , I'.""" ..... > u < ~ i,.J 0,..... i,.J 0 f... Figure 111-10-Transmission Technologies Effectiveness Values for all Vehicle Technology Classes 173 (e) Transmission Costs This analysis uses transmission costs drawn from several sources, including the 2015 NAS report and NAS-cited studies. TSD Chapter 3.2.5 provides a detailed description of the cost sources used for each transmission technology. Table III–14 shows an example of absolute costs for transmission technologies in 2018$ across select model years, which demonstrates how cost learning is applied to the transmission technologies over time. Note, because transmission hardware is often shared across vehicle classes, transmission costs are the same for all vehicle classes. For a full list of all absolute transmission costs used in the analysis across all model years, see the Technologies file. 173 The data used to create this figure can be found the FE_1 Improvements file. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00073 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.056</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Note that the effectiveness for the MT5, AT5 and DD technologies are not shown. The DD transmission does not have a standalone effectiveness because it is only implemented as part of electrified powertrains. The MT5 and AT5 also have no effectiveness values because both technologies are baseline technologies against which all other technologies are compared. 49674 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-14-Examples of Absolute Costs for Transmission Technologies in 2018$ for Select Model Years Technology MY2020 MY2025 MY2030 MT5 MT6 MT7 AT5 AT6 AT6L2 AT7L2 ATS AT8L2 AT8L3 AT9L2 AT10L2 AT10L3 DCT6 DCT8 CVT CVTL2 1,563.97 1,928.41 2,226.75 2,085.30 2,063.19 2,331.44 2,298.63 2,195.36 2,442.32 2,649.15 2,546.03 2,546.03 2,753.44 2,115.89 2,653.91 2,332.83 2,518.80 1,563.97 1,917.08 2,100.64 2,085.30 2,063.19 2,303.65 2,276.53 2,195.18 2,405.33 2,590.74 2,498.29 2,498.29 2,684.21 2,115.84 2,653.15 2,322.63 2,500.94 1,563.97 1,910.70 2,034.88 2,085.30 2,063.19 2,293.25 2,268.26 2,195.15 2,391.49 2,568.89 2,480.43 2,480.43 2,658.31 2,115.84 2,653.02 2,315.25 2,488.02 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 assumptions used in the Autonomie and CAFE Model analysis. (a) Electrification Modeling in the CAFE Model The CAFE Model defines the technology pathway for each type of electrification grouping in a logical progression. Whenever the CAFE Model converts a vehicle model to one of the available electrified systems, both effectiveness and costs are updated according to the specific components’ modeling algorithms. Additionally, all technologies on the different electrification paths are mutually exclusive and are evaluated in parallel. For example, the model may evaluate PHEV20 technology prior to having to apply 12-volt stop-start (SS12V) or strong hybrid technology. The specific set of algorithms and rules are discussed further in the sections below, and more detailed discussions are included in the CAFE Model Documentation. The PO 00000 Frm 00074 Fmt 4701 Sfmt 4702 specifications for each electrification technology used in the analysis is discussed below. The technologies that are included on the three vehicle-level paths pertaining to the electrification and electric improvements defined within the modeling system are illustrated in Figure III–11. As shown in the Electrification path, the baseline-only CONV technology is grayed out. This technology is used to denote whether a vehicle comes in with a conventional powertrain (i.e., a vehicle that does not include any level of hybridization) and to allow the model to properly map to the Autonomie vehicle simulation database results. If multiple branches converge on a single technology, the subset of technologies that will be disabled from further adoption is extended only up the point of convergence. BILLING CODE 4910–59–P E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.057</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 3. Electrification Paths The electric paths include a large set of technologies that share the common element of using electrical power for certain vehicle functions that were traditionally powered mechanically by engine power. Electrification technologies thus can range from electrification of specific accessories (for example, electric power steering to reduce engine loads by eliminating parasitic losses) to electrification of the entire powertrain (as in the case of a battery electric vehicle). The following subsections discuss how each electrification technology is defined in the CAFE Model and the electrification pathways down which a vehicle can travel in the compliance simulation. The subsections also discuss how the agency assigned electrified vehicle technologies to vehicles in the MY 2020 analysis fleet, any limitations on electrification technology adoption, and the specific effectiveness and cost Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Elec. Path 49675 Electric Path SHEVP2 SHEVPS P2HCRO BISG P2HCR2 Elec. lmprv. PHEV20T PHEV20H FCV BEVSOO lotter on DSK11XQN23PROD with PROPOSALS2 BILLING CODE 4910–59–C SS12V: 12-volt stop-start (SS12V), sometimes referred to as start-stop, idlestop, or a 12-volt micro hybrid system, is the most basic hybrid system that facilitates idle-stop capability. In this system, the integrated starter generator is coupled to the internal combustion (IC) engine. When the vehicle comes to an idle-stop the IC engine completely shuts off, and, with the help of the 12volt battery, the engine cranks and starts again in response to throttle to move the vehicle, application or release of the brake pedal to move the vehicle. The 12volt battery used for the start-stop system is an improved unit compared to a traditional 12-volt battery, and is capable of higher power, increased life cycle, and capable of minimizing voltage drop on restart. This technology is beneficial to reduce fuel consumption VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 and emissions when the vehicle frequently stops, such as in city driving conditions or in stop and go traffic. 12VSS can be applied to all vehicle technology classes. BISG: The belt integrated starter generator, sometimes referred to as a mild hybrid system or P0 hybrid, provides idle-stop capability and uses a higher voltage battery with increased energy capacity over conventional automotive batteries. These higher voltages allow the use of a smaller, more powerful and efficient electric motor/ generator which replaces the standard alternator. In BISG systems, the motor/ generator is coupled to the engine via belt (similar to a standard alternator). In addition, these motor/generators can assist vehicle braking and recover braking energy while the vehicle slows down (regenerative braking) and in turn PO 00000 Frm 00075 Fmt 4701 Sfmt 4702 can propel the vehicle at the beginning of launch, allowing the engine to be restarted later. Some limited electric assist is also provided during acceleration to improve engine efficiency. Like the micro hybrids, BISG can be applied to all vehicles in the analysis except for Engine 26a (VCR). We assume all mild hybrids are 48-volt systems with engine belt-driven motor/ generators. SHEVP2/SHEVPS: A strong hybrid vehicle is a vehicle that combines two or more propulsion systems, where one uses gasoline (or diesel), and the other captures energy from the vehicle during deceleration or braking, or from the engine and stores that energy for later used by the vehicle. This analysis evaluated the following strong hybrid systems: Hybrids with ‘‘P2’’ parallel E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.058</GPH> Figure 111-11- Electrification Paths in the CAFE Model 49676 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules drivetrain architectures (SHEVP2),174 and hybrids with power-split architectures (SHEVPS). Both types provide start-stop or idle-stop functionality, regenerative braking capability, and vehicle launch assist. A SHEVPS has a higher potential for fuel economy improvement than a SHEVP2, although its cost is also higher and engine power density is lower.175 P2 parallel hybrids (SHEVP2) are a type of hybrid vehicle that use a transmission-integrated electric motor placed between the engine and a gearbox or CVT, with a clutch that allows decoupling of the motor/ transmission from the engine. Although similar to the configuration of the crank mounted integrated starter generator (CISG) system discussed previously, a P2 hybrid is typically equipped with a larger electric motor and battery in comparison to the CISG. Disengaging the clutch allows all-electric operation and more efficient brake-energy recovery. Engaging the clutch allows coupling of the engine and electric motor and, when combined with a transmission, reduces gear-train losses relative to power-split or 2-mode hybrid systems. P2 hybrid systems typically rely on the internal combustion engine to deliver high, sustained power levels. Electric-only mode is used when power demands are low or moderate. An important feature of the SHEVP2 system is that it can be applied in conjunction with most engine technologies. Accordingly, once a vehicle is converted to a SHEVP2 powertrain in the compliance simulation, the CAFE Model allows the vehicle to adopt the conventional engine technology that is most cost effective, regardless of relative location of the existing engine on the engine technology path. For example, a vehicle in the MY 2020 analysis fleet that starts with a TURBO2 engine could adopt a TURBO1 engine with the SHEVP2 system, if that TURBO1 engine allows the vehicle to meet fuel economy standards more cost effectively. The power-split hybrid (SHEVPS) is a hybrid electric drive system that replaces the traditional transmission with a single planetary gear set (the power-split device) and a motor/ generator. This motor/generator uses the engine either to charge the battery or to supply additional power to the drive motor. A second, more powerful motor/ generator is connected to the vehicle’s final drive and always turns with the wheels. The planetary gear splits engine power between the first motor/generator and the drive motor either to charge the battery or to supply power to the wheels. During vehicle launch, or when the battery state of charge (SOC) is high, the engine is turned off and the electric motor propels the vehicle.176 During normal driving, the engine output is used both to propel the vehicle and to generate electricity. The electricity generated can be stored in the battery and/or used to drive the electric motor. During heavy acceleration, both the engine and electric motor (by consuming battery energy) work together to propel the vehicle. When braking, the electric motor acts as a generator to convert the kinetic energy of the vehicle into electricity to charge the battery. Table III–15 below shows the configuration of conventional engines and transmissions used with strong hybrids for this analysis. The SHEVPS powertrain configuration was paired with a planetary transmission (eCVT) and Atkinson engine (Eng26). This configuration was designed to maximize efficiency at the cost of reduced towing capability and real-world acceleration performance.177 In contrast, the SHEVP2 powertrains were paired with an advanced 8-speed automatic transmissions (AT8L2) and could be paired with most conventional engines.178 CAFE Model Technologies SHEVPS Transmission Options Engine Options (PC/SUV) Engine Options (LT) Planetary - eCVT Eng 26 - Atkinson NIA AT8L2 All Engines except for VTGE and VCR All Engines except for VTGE and VCR lotter on DSK11XQN23PROD with PROPOSALS2 SHEVP2 179 PHEV: Plug-in hybrid electric vehicles are hybrid electric vehicles with the means to charge their battery packs from an outside source of electricity (usually the electric grid). These vehicles have larger battery packs with more energy storage and a greater capability to be discharged than other non-plug-in hybrid electric vehicles. PHEVs also generally use a control system that allows the battery pack to be substantially depleted under electriconly or blended mechanical/electric operation and batteries that can be cycled in charge-sustaining operation at a lower state of charge than non-plug-in hybrid electric vehicles. These vehicles generally have a greater all-electric range than typical strong HEVs. Depending on how these vehicles are operated, they can use electricity exclusively, operate like a conventional hybrid, or operate in some combination of these two modes. 174 Depending on the location of electric machine (motor with or without inverter), the parallel hybrid technologies are classified as P0-motor located at the primary side of the engine, P1-motor located at the flywheel side of the engine, P2-motor located between engine and transmission, P3-motor located at the transmission output, and P4-motor located on the axle. 175 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. 176 Autonomie model documentation, Chapter 4.13.2. 177 Kapadia, J., D, Kok, M. Jennings, M. Kuang, B. Masterson, R. Isaacs, A. Dona. 2017. Powersplit or Parallel—Selecting the Right Hybrid Architecture. SAE International Journal of Alternative Powertrains 6 (1): 68–76. https://doi.org/10.4271/ 2017-01-1154. 178 We did not model SHEVP2s with VTGe (Eng23c) and VCR (Eng26a). 179 Engine 01, 02, 03, 04, 5b, 6a, 7a, 8a, 12, 12DEAC, 13, 14, 17, 18, 19, 20, 21, 22b, 23b, 24, 24Deac. See Section III.D.1 for these engine specifications. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00076 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.059</GPH> Table 111-15 - Configuration of Strong Hybrid Architectures with Transmissions and Engines Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules There are four PHEV architectures included in this analysis that reflect combinations of two levels of all-electric range (AER) and two engine types. DOT selected 20 miles AER and 50 miles AER to reasonably span the various AER in the market, and their effectiveness and cost. DOT selected an Atkinson engine and a turbocharged downsized engine to span the variety of engines in the market. PHEV20/PHEV20H and PHEV50/ PHEV50H are essentially a SHEVPS with a larger battery and the ability to drive with the engine turned off. In the CAFE Model, the designation for ‘‘H’’ in PHEVxH could represent another type of engine configuration, but for this analysis DOT used the same effectiveness values as PHEV20 and PHEV50 to represent PHEV20H and PHEV50H, respectively. The PHEV20/ PHEV20H represents a ‘‘blended-type’’ plug-in hybrid, which can operate in allelectric (engine off) mode only at light loads and low speeds, and must blend electric motor and engine power together to propel the vehicle at medium or high loads and speeds. The PHEV50/PHEV50H represents an extended range electric vehicle (EREV), which can travel in all-electric mode even at higher speeds and loads. Further discussion of engine sizing, batteries, and motors for these PHEVs is discussed in Section III.D.3.d). PHEV20T and PHEV50T are 20 mile and 50 mile AER vehicles based on the 49677 SHEVP2 engine architecture. The PHEV versions of these architectures include larger batteries and motors to meet performance in charge sustaining mode at higher speeds and loads as well as similar performance and range in all electric mode in city driving, at higher speeds and loads. For this analysis, the CAFE Model considers these PHEVs to have an advanced 8-speed automatic transmission (AT8L2) and TURBO1 (Eng12) in the powertrain configuration. Further discussion of engine sizing, batteries, and motors for these PHEVs is discussed in Section III.D.3.d). Table III–16 shows the different PHEV configurations used in this analysis. Table 111-16- Configuration of Plug-in Hybrid Architectures with Transmissions and Engines Transmission Options PHEV20/PHEV20H Planetary eCVT PHEV20T AT8L2 PHEV50/PHEV50H lotter on DSK11XQN23PROD with PROPOSALS2 PHEV50T AT8L2 BEV: Battery electric vehicles are equipped with all-electric drive systems powered by energy-optimized batteries charged primarily by electricity from the grid. BEVs do not have a combustion engine or traditional transmission. Instead, BEVs rely on all electric powertrains, with an advanced transmission packaged with the powertrain. The range of battery electric vehicles vary by vehicle and battery pack size. DOT simulated BEVs with ranges of 200, 300, 400, and 500 miles in the CAFE Model. BEV range is measured pursuant to EPA test procedures and guidance.180 The CAFE Model assumes that BEVs transmissions are unique to each vehicle (i.e., the transmissions are not shared by any other vehicle) and 180 BEV electric ranges are determined per EPA guidance Document. ‘‘EPA Test Procedure for Electric Vehicles and Plug-in Hybrids.’’ https:// fueleconomy.gov/feg/pdfs/EPA%20test %20procedure%20for%20EVs-PHEVs-11–14– 2017.pdf. November 14, 2017. Last Accessed May 3, 2021. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Planetary eCVT Engine Options (PC/SUV) Eng 26Atkinson Engine Eng 12 TURBOl Eng 26 Atkinson Eng 12 TURBOl that no further improvements are available. A key note about the BEVs offered in this analysis is that the CAFE Model does not account for vehicle range when considering additional BEV technology adoption. That is, the CAFE Model does not have an incentive to build BEV300, 400, and 500s, because the BEV200 is just as efficient as those vehicles and counts the same toward compliance, but at a significantly lower cost because of the smaller battery. While manufacturers have been building 200mile range BEVs, those vehicles have generally been passenger cars. Manufacturers have told DOT that greater range is important for meeting the needs of broader range of consumers and to increase consumer demand. More recently, there has been a trend towards manufacturers building higher range BEVs in the market, and manufacturers building CUV/SUV and pickup truck BEVs. To simulate the potential relationship of BEV range to consumer demand, DOT has included several PO 00000 Frm 00077 Fmt 4701 Sfmt 4702 Engine Options (LT) NIA Eng 12TURBOl NIA Eng 12TURBOl adoption features for BEVs. These are discussed further in Section III.D.3.c). Fuel cell electric vehicle (FCEV): Fuel cell electric vehicles are equipped with an all-electric drivetrain, but unlike BEVs, FCEVs do not solely rely on batteries; rather, electricity to run the FCEV electric motor is mainly generated by an onboard fuel cell system. FCEV architectures are similar to series hybrids,181 but with the engine and generator replaced by a fuel cell. Commercially available FCEVs consume hydrogen to generate electricity for the fuel cell system, with most automakers using high pressure gaseous hydrogen storage tanks. FCEVs are currently produced in limited numbers and are available in limited geographic areas where hydrogen refueling stations are accessible. For reference, in MY 2020, only four FCV models were offered for 181 Series hybrid architecture is a strong hybrid that has the engine, electric motor and transmission in series. The engine in a series hybrid drives a generator that charges the battery. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.060</GPH> CAFE Model Technologies 49678 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules sale, and since 2014 only 9,975 FCVs have been sold.182 183 For this analysis, the CAFE Model simulates a FCEV with a range of 320 miles. Any type of powertrain could adopt a FCEV powertrain; however, to account for limited market penetration and unlikely increased adoption in the rulemaking timeframe, technology phase in caps were used to control how many FCEVs a manufacturer could build. The details of this concept are further discussed in Section III.D.3.c). (b) Electrification Analysis Fleet Assignments DOT identified electrification technologies present in the baseline fleet and used these as the starting point for the regulatory analysis. These assignments were based on manufacturer-submitted CAFE compliance information, publicly available technical specifications, marketing brochures, articles from reputable media outlets, and data from Wards Intelligence.184 Table III–17 gives the baseline fleet penetration rates of electrification technologies eligible to be assigned in the baseline fleet. Over half the fleet had some level of electrification, with the vast majority of these being micro hybrids. BEVs represented less than 2% of MY 2020 baseline fleet; BEV300 was the most common BEV technology, while no BEV500s were observed. Electrification Technology lotter on DSK11XQN23PROD with PROPOSALS2 None SS12V BISG SHEVP2 SHEVPS PHEV20 PHEV20T PHEV50 PHEV50T BEV200 BEV300 BEV400 BEV500 FCV Sales Volume with this Technology Penetration Rate in 2020 Baseline Fleet 5,791,220 6,837,257 258,629 6,409 378,523 46,393 18,943 2,392 18 72,123 145,900 34,000 0 744 42.61% 50.30% 1.90% 0.05% 2.78% 0.34% 0.14% 0.02% 0.0001% 0.53% 1.07% 0.25% 0% 0.005% Micro and mild hybrids refer to the presence of SS12V and BISG, respectively. The data sources discussed above were used to identify the presence of these technologies on vehicles in the fleet. Vehicles were assigned one of these technologies only if its presence could be confirmed with manufacturer brochures or technical specifications. Strong hybrid technologies included SHEVPS and SHEVP2. Note that P2HCR0, P2HCR1, P2HCR1D, and P2HCR2 are not assigned in the fleet and are only available to be applied by the model. When possible, manufacturer specifications were used to identify the strong hybrid architecture type. In the absence of more sophisticated information, hybrid architecture was determined by number of motors. Hybrids with one electric motor were assigned P2, and those with two were assigned power-split (PS). DOT seeks comment on additional ways the agency could perform initial hybrid assignments based on publicly available information. Plug-in hybrid technologies PHEV20/ 20T and PHEV50/50T are assigned in the baseline fleet. PHEV20H and PHEV50H are not assigned in the fleet and are only available to be applied by the model. Vehicles with an electriconly range of 40 miles or less were assigned PHEV20; those with a range above 40 miles were assigned PHEV50. They were respectively assigned PHEV20T/50T if the engine was turbocharged (i.e., if it would qualify for one of technologies on the turbo engine technology pathway). DOT also had to calculate baseline fuel economy values for PHEV technologies as part of the PHEV analysis fleet assignments; that process is described in detail in TSD Chapter 3.3.2. Fuel cell and battery electric vehicle technologies included BEV200/300/400/ 500 and FCV. Vehicles with all-electric powertrains that used hydrogen fuel were assigned FCV. The BEV technologies were assigned to vehicles based on range thresholds that best account for vehicles’ existing range capabilities while allowing room for the model to potentially apply more advanced electrification technologies. 182 Argonne National Laboratory, ‘‘Light Duty Electric Drive Vehicles Monthly Sales Update.’’ Energy Systems Division, https://www.anl.gov/es/ light-duty-electric-drive-vehicles-monthly-salesupdates. Last Accessed May 4, 2021. 183 See the MY 2020 Market Data file. The four vehicles are the Honda Clarity, Hyundai Nexo and Nexo Blue, and Toyota Mirai. 184 ‘‘U.S. Car and Light Truck Specifications and Prices, ’20 Model Year.’’ Wards Intelligence, 3 Aug. 2020, wardsintelligence.informa.com/WI964244/ US-Car-and-Light-Truck-Specifications-and-Prices20-Model-Year. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00078 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.061</GPH> Table 111-17 - Penetration Rate of Electrification Technologies in the MY 2020 Fleet Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules (c) Electrification Adoption Features Multiple types of adoption features applied to the electrification technologies. The hybrid/electric technology path logic dictated how vehicles could adopt different levels of electrification technology. Broadly speaking, more advanced levels of hybridization or electrification superseded all prior levels, with certain technologies within each level being mutually exclusive. The analysis modeled (from least to most electrified) micro hybrids, mild hybrids, strong hybrids, plug-in hybrids, and fully electric vehicles. As discussed further below, SKIP logic—restrictions on the adoption of certain technologies—applied to plug-in (PHEV) and strong hybrid vehicles (SHEV). Some technologies on these pathways were ‘‘skipped’’ if a vehicle was high performance, required high towing capabilities as a pickup truck, or belonged to certain manufacturers who have demonstrated that their future product plans will more than likely not include the technology. The specific criteria for SKIP logic for each applicable electrification technology will be expanded on later in this section. This section also discusses the supersession of engines and transmissions on vehicles that adopt SHEV or PHEV powertrains. To manage the complexity of the analysis, these types of hybrid powertrains were modeled with several specific engines and transmissions, rather than in multiple configurations. Therefore, the cost and effectiveness values SHEV and PHEV technologies take into account these specific engines and transmissions. Finally, phase-in caps limited the adoption rates of battery electric (BEV) and fuel cell vehicles (FCV). These phase-in caps were set by DOT, taking into account current market share, scalability, and reasonable consumer adoption rates of each technology. TSD Chapter 3.3.3 discusses the electrification phase-in caps and the reasoning behind them in detail. The only adoption feature applicable to micro and mild hybrid technologies was path logic. The pathway consists of a linear progression starting with a conventional powertrain with no electrification at all, which is superseded by SS12V, which in turn is superseded by BISG. Vehicles could only adopt micro and mild hybrid technology if the vehicle did not already have a more advanced level of electrification. The adoption features applied to strong hybrid technologies included path logic, powertrain substitution, and vehicle class restrictions. Per the defined technology pathways, SHEVPS, SHEVP2, and the P2HCR technologies were considered mutually exclusive. In other words, when the model applies one of these technologies, the others are immediately disabled from future application. However, all vehicles on the strong hybrid pathways could still advance to one or more of the plug-in hybrid technologies. When the model applied any strong hybrid technology to a vehicle, the transmission technology on the vehicle was superseded. Regardless of the transmission originally present, P2 hybrids adopt an 8-speed automatic transmission (AT8L2), and PS hybrids adopt a continuously variable transmission (eCVT). When the model applies the SHEVP2 technology, the model can consider various engine options to pair with the SHEVP2 architecture according to existing engine path constraints, taking into account relative cost effectiveness. For SHEVPS technology, the existing engine was replaced with Eng26, a full Atkinson cycle engine. SKIP logic was also used to constrain adoption for SHEVPS, P2HCR0, P2HCR1, and P2HCR1D. No SKIP logic applied to SHEVP2; P2HCR2 was restricted from all vehicles in the 2020 fleet, as discussed further in Section III.D.1.d)(1). These technologies were ‘‘skipped’’ for vehicles with engines 185 that met one of the following conditions: • The engine belonged to an excluded manufacturer; 186 • The engine belonged to a pickup truck (i.e., the engine was on a vehicle assigned the ‘‘pickup’’ body style); • The engine’s peak horsepower was more than 405 HP; or if • The engine was on a non-pickup vehicle but was shared with a pickup. The reasons for these conditions are similar to those for the SKIP logic applied to HCR engine technologies, discussed in more detail above. In the real world, pickups and performance vehicles with certain powertrain configurations cannot adopt the technologies listed above and maintain vehicle performance without redesigning the entire powertrain. SKIP logic was put in place to prevent the model from pursuing compliance pathways that are ultimately unrealistic. PHEV technologies superseded the micro, mild, and strong hybrids, and could only be replaced by full electric technologies. Plug-in hybrid technology paths were also mutually exclusive, with the PHEV20 technologies able to progress to the PHEV50 technologies. The engine and transmission technologies on a vehicle were superseded when PHEV technologies were applied to a vehicle. For all plugin technologies, the model applied an AT8L2 transmission. For PHEV20/50 and PHEV20H/50H, the vehicle received a full Atkinson cycle engine, Eng26. For PHEV20T/50T, the vehicle received a TURBO1 engine, Eng12. SKIP logic applied to PHEV20/20H and PHEV50/50H under the same four conditions listed for the strong hybrid technologies in the previous section, for the same reasons previously discussed. For the analysis, the adoption of BEVs and FCEVs was limited by both path logic and phase in caps. BEV200/300/ 400/500 and FCEV were applied as endof-path technologies that superseded previous levels of electrification. The main adoption feature applicable to BEVs and FCEVs is phase-in caps, which are defined in the CAFE Model input files as percentages that represent the maximum rate of increase in penetration rate for a given technology. They are accompanied by a phase-in start year, which determines the first year the phase-in cap applies. Together, the phase-in cap and start year determine the maximum penetration rate for a given technology in a given year; the maximum penetration rate equals the phase-in cap times the number of years elapsed since the phase-in start year. Note that phase-in caps do not inherently dictate how much a technology is applied by the model. Rather, they represent how much of the fleet could have a given technology by a given year. Because BEV200 costs less and has higher effectiveness values than other advanced electrification technologies,187 the model will have vehicles adopt it first, until it is restricted by the phase-in cap. Table III–18 shows the phase-in caps, phase-in year, and maximum penetration rate through 2050 for BEV and FCEV technologies. For comparison, the actual penetration rate of each technology in the 2020 baseline fleet is also listed in the fourth column from the left. 185 This refers to the engine assigned to the vehicle in the 2020 baseline fleet. 186 Excluded manufacturers included BMW, Daimler, and Jaguar Land Rover. 187 This is because BEV200 uses fewer batteries and weighs less than BEVs with greater ranges. For more detail about the electrification analysis fleet assignment process, see TSD Chapter 3.3.2. lotter on DSK11XQN23PROD with PROPOSALS2 49679 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00079 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 49680 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-18- Phase-In Caps for Fuel Cell and Battery Electric Vehicle Technologies ~ Q) 8~ z >. Oil 0 0 ..= (j = Q) >-..... Q. ~ u FCV 00 ..... "" ""~ I ..= Q) ""~ ~ ..= ~ :.. QJ Q) ~ lotter on DSK11XQN23PROD with PROPOSALS2 QJ QJ = 8 -~ =... -~ ~ 1998 2009 2016 2021 0.53% 1.07% 0.25% 1.98% 7.70% 5.00% - 0.018% 2016 0.005% Jkt 253001 .....:..QJ 0 ~ 0 5N = 8 -~ =... = 8 -~ =... ~ ~ ~ ~ 0.09% 0.70% 1.25% 4.25% 21:48 Sep 02, 2021 :.. l£l .,._.N 0 5N ~ ~ (j 188 AAA. ‘‘AAA Electric Vehicle Range Testing.’’ February 2019. https://www.aaa.com/AAA/ common/AAR/files/AAA-Electric-Vehicle-RangeTesting-Report.pdf. 189 Baldwin, Roberto. ‘‘Tesla Model Y Standard Range Discontinued; CEO Musk Tweets Explanation.’’ Car and Driver, 30 Apr. 2021, www.caranddriver.com/news/a35602581/elonmusk-model-y-discontinued-explanation/. Accessed May 20, 2020. 190 2020 EPA Automotive Trends Report, at 53, figure 4.14. = .8 .....~ = .8 .....~ .....:..QJ 0l£l = .8 .....~ 0 .....:.. ...,. ~ QJ 5N 0 5N = 8 -~ =... = 8 -~ =... ~ ~ = .8 .....~ l£l .....:.. ...,. QJ 0 5N = 8 -~ =... ~ = .8 .....~ :.. 0 QJ 0 ..... l£l 5N = = ... ~ 8 -~ =~ -~=~ -~=~ -~=~ -~=~ -~=~ -~=~ - ~o -N ~o .EN < ...= The BEV200 phase-in cap was informed by manufacturers’ tendency to move away from low-range vehicle offerings, in part because of consumer hesitancy to adopt this technology. The advertised range on most electric vehicles does not reflect extreme cold and hot real-world driving conditions, affecting the utility of already low-range vehicles.188 Many manufacturers have told DOT that the portion of consumers willing to accept a vehicle with less than 300 miles of electric range is extremely small, and many manufacturers do not plan to offer vehicles with less than 300 miles of electric range. For example, in February 2021, Tesla, the U.S.’ highest-selling BEV manufacturer, discontinued the Standard Range Model Y because its range did not meet the company’s ‘‘standard of excellence.’’ 189 Tesla does sell long-range versions of many of its vehicles. Furthermore, the average BEV range has steadily increased over the past decade,190 perhaps in part as batteries become more cost effective. EPA observed in its 2020 Automotive Trends Report that ‘‘the average range of new EVs has climbed substantially. In model year 2019 the average new EV is projected to have a 252-mile range, or VerDate Sep<11>2014 = .8 .....~ ~ =~ Q) = .8 .....~ :.. 0 .,._.N 0 5N =i;... 0 QJ Q) E-< BEV200 BEV300 BEV400 BEV500 ~1 ·..:=:-== .....~ I ..... - :.. - -= = Q) ~ ~ ~ ~ ~ ~ ~ ~ - 2.43% 11.20% 11.25% 17.00% 2.88% 14.70% 17.50% 38.25% 3.33% 18.20% 23.75% 59.50% 3.78% 21.70% 30.00% 80.75% 4.23% 25.20% 36.25% 102.00% 4.68% 28.70% 42.50% 123.25% 0.072% 0.162% 0.252% 0.342% 0.432% 0.522% 0.612% about three and a half times the range of an average EV in 2011. This difference is largely attributable to higher production of new EVs with much longer ranges.’’ 191 The maximum growth rate for BEV200 in the model was set accordingly low to less than 0.1% per year. While this rate is significantly lower than that of the other BEV technologies, the BEV200 phase-in cap allows the penetration rate of lowrange BEVs to grow by a multiple of what is currently observed in the market. For BEV300, 400, and 500, phase-in caps are largely a reflection of the challenges facing the scalability of BEV manufacturing, and implementing BEV technology on many vehicle configurations, including larger vehicles. In the short term, the penetration of BEVs is largely limited by battery availability.192 For example, Tesla has struggled to scale production of new cells for its vehicles, and it remains a bottleneck in the company’s production capability.193 The Director of Energy and Environmental Research at Toyota acknowledged in March 2021 that BEV adoption faces many challenges beyond battery availability, including ‘‘the cost of batteries, the need for national infrastructure, long recharging times, limited driving range 191 2020 EPA Automotive Trends Report, at 53. e.g., Cohen, Ariel. ‘‘Manufacturers Are Struggling To Supply Electric Vehicles With Batteries.’’ Forbes, Forbes Magazine, 25 March 2020, www.forbes.com/sites/arielcohen/2020/03/25/ manufacturers-are-struggling-to-supply-electricvehicles-with-batteries. Accessed May 20, 2021. 193 Hyatt, Kyle. ‘‘Tesla Will Build an Electric Van Eventually, Elon Musk Says.’’ Roadshow, CNET, 28 Jan. 2021, www.cnet.com/roadshow/news/teslaelectric-van-elon-musk/. Accessed May 20, 2021. 192 See, PO 00000 ~ ~ Frm 00080 Fmt 4701 Sfmt 4702 and the need for consumer behavioral change.’’ 194 Incorporating battery packs that provide greater amounts of electric range into vehicles also poses its own engineering challenges. Heavy batteries and large packs may be difficult to integrate for many vehicle configurations. Pickup trucks and large SUVs in particular require higher levels of energy as the number of passengers and/or payload increases, for towing and other high-torque applications. DOT selected the BEV400 and 500 phase-in caps to reflect these concerns. The phase-in cap for FCEVs was assigned based on existing market share as well as historical trends in FCEV production. FCEV production share in the past five years has been extremely low, and DOT set the phase-in cap accordingly.195 As with BEV200, however, the phase-in cap still allows for the market share of FCVs to grow several times over. (d) Electrification Effectiveness Modeling For this analysis, DOT considers a range of electrification technologies which, when modeled, result in varying levels of effectiveness at reducing fuel consumption. As discussed above, the modeled electrification technologies include micro hybrids, mild hybrids, two different strong hybrids, two different plug-in hybrids with two separate all electric ranges, full electric vehicles and FCEVs. Each electrification technology consists of many complex sub-systems with unique component 194 https://www.energy.senate.gov/services/files/ E2EA0E4F-BAD9-452D-99CC-35BC204DE6F0. 195 2020 EPA Automotive Trends Report, at 52, figure 4.13. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.062</GPH> :.. Q) Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 characteristics and operational modes. As discussed further below, the systems that contribute to the effectiveness of an electrified powertrain in the analysis include the vehicle’s battery, electric motors, power electronics, and accessory loads. Procedures for modeling each of these sub-systems are broadly discussed below, in Section III.C.4, and the Autonomie model documentation. Argonne used data from their Advanced Mobility Technology Laboratory (AMTL) to develop Autonomie’s electrified powertrain models. The modeled powertrains are not intended to represent any specific manufacturer’s architecture but are intended to act as surrogates predicting representative levels of effectiveness for each electrification technology. Autonomie determines the effectiveness of each electrified powertrain type by modeling the basic components, or building blocks, for each powertrain, and then combining the components modularly to determine the overall efficiency of the entire powertrain. The basic building blocks that comprise an electrified powertrain in the analysis include the battery, electric motors, power electronics, and accessory loads. Autonomie identifies components for each electrified powertrain type, and then interlinks those components to create a powertrain architecture. Autonomie then models each electrified powertrain architecture and provides an effectiveness value for each architecture. For example, Autonomie determines a BEV’s overall efficiency by considering the efficiencies of the battery, the electric traction drive system (the electric machine and power electronics) and VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 mechanical power transmission devices. Or, for a SHEVP2, Autonomie combines a very similar set of components to model the electric portion of the hybrid powertrain, and then also includes the combustion engine and related power for transmission components. See TSD Chapter 3.3.4 for a complete discussion of electrification component modeling. As discussed earlier in Section III.C.4, Autonomie applies different powertrain sizing algorithms depending on the type of vehicle considered because different types of vehicles not only contain different powertrain components to be optimized, but they must also operate in different driving modes. While the conventional powertrain sizing algorithm must consider only the power of the engine, the more complex algorithm for electrified powertrains must simultaneously consider multiple factors, which could include the engine power, electric machine power, battery power, and battery capacity. Also, while the resizing algorithm for all vehicles must satisfy the same performance criteria, the algorithm for some electric powertrains must also allow those electrified vehicles to operate in certain driving cycles, like the US06 cycle, without assistance of the combustion engine, and ensure the electric motor/ generator and battery can handle the vehicle’s regenerative braking power, all-electric mode operation, and intended range of travel. To establish the effectiveness of the technology packages, Autonomie simulates the vehicles’ performance on compliance test cycles, as discussed in Section III.C.4.196 197 198 The range of 196 See U.S. EPA, ‘‘How Vehicles are Tested.’’ https://www.fueleconomy.gov/feg/how_ tested.shtml. Last accessed May 6, 2021. PO 00000 Frm 00081 Fmt 4701 Sfmt 4702 49681 effectiveness for the electrification technologies in this analysis is a result of the interactions between the components listed above and how the modeled vehicle operates on its respective test cycle. This range of values will result in some modeled effectiveness values being close to realworld measured values, and some modeled values that will depart from measured values, depending on the level of similarity between the modeled hardware configuration and the realworld hardware and software configurations. This modeling approach comports with the National Academy of Science 2015 recommendation to use full vehicle modeling supported by application of lumped improvements at the sub-model level.199 The approach allows the isolation of technology effects in the analysis supporting an accurate assessment. The range of effectiveness values for the electrification technologies, for all ten vehicle technology classes, is shown in Figure III–12. In the graph, the box shows the inner quartile range (IQR) of the effectiveness values and whiskers extend out 1.5 x IQR. The dots outside of the whiskers show values outside these bounds. BILLING CODE 4910–59–P 197 See Autonomie model documentation, Chapter 6: Test Procedures and Energy Consumption Calculations. 198 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 May 6, 2021. 199 2015 NAS report, at 292. E:\FR\FM\03SEP2.SGM 03SEP2 49682 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 0.9 0.8 0.7 0.6···· VJ is ii> 0.5 ·.a ~ ~ 0.3 0.2•· 0.1 0.0 ~·.···;···· ~ ..... 00 {/) - I 0 {I) S! ·i:Q {/) I 0 ~ ti! ~ 0 > ti! ~ E-< 0 N > ~ I""' 0 on ~ (> p... ~ 0 0 0 0 ~ ~ i:Q ~ t<') 0 0 0 0 > ~ ~ If') BILLING CODE 4910–59–C lotter on DSK11XQN23PROD with PROPOSALS2 (e) Electrification Costs The total cost to electrify a vehicle in this analysis is based on the battery the vehicle requires, the non-battery electrification component costs the vehicle requires, and the traditional powertrain components that must be added or removed from the vehicle to build the electrified powertrain. We worked collaboratively with the experts at Argonne National Laboratory to generate battery costs using BatPaC, which is a model designed to calculate the cost of a vehicle battery for a specified battery power, energy, and type. Argonne used BatPaC v4.0 (October 2020 release) to create lookup tables for battery cost and mass that the Autonomie simulations referenced when a vehicle received an electrified powertrain. The BatPaC battery cost estimates are generated for a base year, in this case for MY 2020. Accordingly, our BatPaC inputs characterized the state of the market in MY 2020 and employed a widely utilized cell 200 The data used to create this figure can be found in the FE_1 Adjustments file. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 chemistry (NMC622),201 average estimated battery pack production volume per plant (25,000), and a plant efficiency or plant cell yield value of 95%. For two specific electrified vehicle applications, BEV400 and BEV500, we did not use BatPaC to generate battery pack costs. Rather, we scaled the BatPaC-generated BEV300 costs to match the range of BEV400 and BEV500 vehicles to compute a direct manufacturing cost for those vehicles’ batteries. We initially examined using BatPaC to model the cost and weight of BEV400 and BEV500 packs, however, initial values from the model could not 201 Autonomie model documentation, Chapter 5.9. Argonne surveyed A2Mac1 and TBS teardown reports for electrified vehicle batteries and of the five fully electrified vehicles surveyed, four of those vehicles used NMC622 and one used NMC532. See also Georg Bieker, A Global Comparison of the LifeCycle Greenhouse Gas Emissions of Combustion Engine and Electric Passenger Cars, International Council on Clean Transportation (July 2021), https://theicct.org/sites/default/files/publications/ Global-LCA-passenger-cars-jul2021_0.pdf (‘‘For cars registered in 2021, the GHG emission factors of the battery production are based on the most common battery chemistry, NMC622-graphite batteries. . . .’’); 2021 NAS report, at 5–92 (‘‘. . . NMC622 is the most common cathode chemistry in 2019. . . .’’). PO 00000 Frm 00082 Fmt 4701 Sfmt 4702 be validated and were based on assumptions for smaller sized battery packs. The initial results provided cost and weight estimates for BEV400 battery packs out of alignment with current examples of BEV400s in the market, and there are currently no examples of BEV500 battery packs in the market against which to validate the pack results. Finally, to reflect how we expect batteries could fall in cost over the timeframe considered in the analysis, we applied a learning rate to the direct manufacturing cost. Broadly, the learning rate applied in this analysis reflects middle-of-the-road year-overyear improvements until MY 2032, and then the learning rates incrementally become shallower as battery technology is expected to mature in MY 2033 and beyond. Applying learning curves to the battery pack DMC in subsequent analysis years lowers the cost such that the cost of a battery pack in any future model year could be representative of the cost to manufacture a battery pack, regardless of potentially diverse parameters such as cell chemistry, cell format, or production volume. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.063</GPH> Figure 111-12 - Electrification Technology Effectiveness Values for All the Vehicle Technology Classes200 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules TSD Chapter 3.3.5.1 includes more detail about the process we used to develop battery costs for this analysis. In addition, all BatPaC-generated direct manufacturing costs for all technology keys can be found in the CAFE Model’s Battery Costs file, and the Argonne BatPaC Assumptions file includes the assumptions used to generate the costs, and pack costs, pack mass, cell capacity, $/kW at the pack level, and W/kg at the pack level for all vehicle classes. Table III–19 and Table III–20 show an example of our battery pack direct manufacturing costs per kilowatt hour for BEV300s for all vehicle classes for the base year, MY 2020. The tables shown here demonstrate how the cost 49683 per kWh varies with the size of the battery pack. While the overall cost of a battery pack will go up for larger kWh battery packs, the cost per kWh goes down. The amortization of costs for components required in all battery packs across a larger number of cells results in this reduced cost per kWh. BILLING CODE 4910–59–P Table III-19-BEV300 Battery Pack Direct Manufacturing Costs per Kilowatt/Hour for Compact - Medium Car Classes in MY 2020 ,..-.._ >. ell <l) s:: ~ "a ..... 0 E-< '-" Q) ;:,. ~ 1-t <l) <l) ~ ~ ~ ~ ~ ~ ~ lotter on DSK11XQN23PROD with PROPOSALS2 th VerDate Sep<11>2014 0 ~ 21:48 Sep 02, 2021 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0 240.0 280.0 320.0 400.0 Jkt 253001 PO 00000 30.0 50.0 70.0 90.0 120.0 $244 $245 $246 $248 $249 $250 $251 $252 $254 $255 $258 $261 $267 $280 $186 $187 $188 $188 $189 $190 $190 $191 $192 $193 $194 $196 $197 $201 $160 $161 $161 $162 $162 $163 $163 $164 $164 $165 $166 $167 $168 $170 $145 $145 $146 $146 $146 $147 $147 $147 $148 $148 $149 $150 $151 $152 $131 $132 $132 $132 $132 $133 $133 $133 $134 $134 $134 $135 $136 $137 Frm 00083 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.064</GPH> Energy,kWh BEV300 49684 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-20 - BEV300 Battery Pack Direct Manufacturing Costs per Kilowatt/Hour for SUV and Pickup Classes in MY 2020 Energy,kWh >-. e.o <l) ~ J-<l) :> <l) ~ ~ p.. ~ 1-t <l) ~ 0 p.. tii ! ~ 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0 240.0 280.0 320.0 400.0 30.0 50.0 70.0 90.0 120.0 140.0 160.0 $252 $253 $254 $255 $257 $258 $259 $260 $261 $262 $265 $268 $273 $286 $191 $192 $193 $193 $194 $194 $195 $196 $196 $197 $198 $200 $201 $204 $164 $164 $165 $165 $166 $166 $167 $167 $167 $168 $169 $170 $171 $173 $148 $148 $148 $149 $149 $149 $150 $150 $151 $151 $152 $152 $153 $155 $133 $133 $134 $134 $134 $134 $135 $135 $135 $135 $136 $136 $137 $138 $127 $127 $127 $127 $128 $128 $128 $128 $129 $129 $129 $130 $130 $131 $122 $122 $122 $122 $122 $123 $123 $123 $123 $123 $124 $124 $125 $125 BILLING CODE 4910–59–C lotter on DSK11XQN23PROD with PROPOSALS2 A range of parameters can ultimately influence battery pack manufacturing costs, including other vehicle improvements (e.g., mass reduction technology, aerodynamic improvements, or tire rolling resistance improvements all affect the size and energy of a battery required to propel a vehicle where all else is equal), and the availability of materials required to manufacture the battery.202 203 Or, if manufacturers adopt more electrification technology than projected in this analysis, increases in battery pack production volume will likely lower actual battery pack costs. Like the 2020 final rule, we compared our battery pack costs in future years to battery pack costs from other sources that may or may not account for some of these additional parameters, including varying potential future battery chemistry and learning rates. As 202 The cost of raw material also has a meaningful influence on the future cost of the battery pack. As the production volume goes up, the demand for battery critical raw materials also goes up, which has an offsetting impact on the efficiency gains achieved through economies of scale, improved plant efficiency, and advanced battery cell chemistries. We do not consider future battery raw material price fluctuations for this analysis, however that may be an area for further exploration in future analyses. 203 See, e.g., Jacky Wong, EV Batteries: The Next Victim of High Commodity Prices?, The Wall Street Journal (July 22, 2021), https://www.wsj.com/ articles/ev-batteries-the-next-victim-of-highcommodity-prices-11626950276. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 discussed in TSD Chapter 3.3.5.1.4, our battery pack costs in 2025 and 2030 fell fairly well in the middle of other sources’ cost projections, with Bloomberg New Energy Finance (BNEF) projections presenting the highest yearover-year cost reductions,204 and MIT’s Insights into Future Mobility report providing an upper bound of potential future costs.205 ICCT presented a similar comparison of costs from several sources in its 2019 working paper, Update on Electric Vehicle Costs in the United States through 2030, and predicted battery pack costs in 2025 and 2030 would drop to approximately $104/kWh and $72/kWh, respectively,206 which put their projections slightly higher than BNEF’s 2019 projections. BNEF’s more recent 2020 Electric Vehicle Outlook projected average pack cost to fall below $100/ kWh by 2024,207 while the 2021 NAS 204 See Logan Goldie-Scot, A Behind the Scenes Take on Lithium-ion Battery Prices, Bloomberg New Energy Finance (March 5, 2019), https:// about.bnef.com/blog/behind-scenes-take-lithiumion-battery-prices/. 205 MIT Energy Initiative. 2019. Insights into Future Mobility. Cambridge, MA: MIT Energy Initiative. Available at https://energy.mit.edu/ insightsintofuturemobility. 206 Nic Lutsey and Michael Nicholas, Update on electric vehicle costs in the United States through 2030, ICCT (April 2, 2019), available at https:// theicct.org/publications/update-US-2030-electricvehicle-cost. 207 Bloomberg New Energy Finance (BNEF), ‘‘Electric Vehicle Outlook 2020,’’ https:// PO 00000 Frm 00084 Fmt 4701 Sfmt 4702 report projected that pack costs are projected to reach $90–115 kWh by 2025.208 That our projected costs seem to fall between several projections gives us some confidence that the costs in this NPRM could reasonably represent future battery pack costs across the industry during the rulemaking time frame. That said, we recognize that battery technology is currently under intensive development, and that characteristics such as cost and capability are rapidly changing. These advances are reflected in recent aggressive projections, like those from ICCT, BNEF, and the 2021 NAS report. As a result, we would like to seek comments, supported by data elements as outlined below, on these characteristics. We seek comment on the input assumptions used to generate battery pack costs in BatPaC and the BatPaCgenerated direct manufacturing costs for the base year (MY 2020). If commenters believe that different input assumptions should be used for battery chemistry,209 about.bnef.com/electric-vehicle-outlook/, last accessed July 29, 2021. 208 2021 NAS report, at 5–121. The 2021 NAS report assumed a 7 percent cost reduction per year from 2018 through 2030. 209 Note that stakeholders had commented to the 2020 final rule that batteries using NMC811 chemistry had either recently come into the market or was imminently coming into the market, and therefore DOT should have selected NMC811 as the E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.065</GPH> BEV300 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 plant manufacturing volume, or plant efficiency in MY 2020, they should provide data or other information validating such assumptions. In addition, commenters should explain how these assumptions reasonably represent applications across the industry in MY 2020. This is important to align with our guiding principles to ensure that the CAFE Model’s simulation of manufacturer compliance pathways results in impacts that we would reasonably expect to see in the real world. As discussed above, each technology model employed in the analysis is designed to be representative of a wide range of specific technology applications used in industry. Some vehicle manufacturer’s systems may perform better and cost less than our modeled systems and some may perform worse and cost more. However, employing this approach will ensure that, on balance, the analysis captures a reasonable level of costs and benefits that would result from any manufacturer applying the technology. In this case, vehicle and battery manufacturers use different chemistries, cell types, and production processes to manufacture electric vehicle battery packs. Any proposed alternative costs for base year direct manufacturing costs should be able to represent the range of costs across the industry in MY 2020 based on different manufacturers using different approaches. We also seek comment on the scaling used to generate direct manufacturing costs for BEV400 and BEV500 technologies. If commenters have additional data or information on the relationship between cost and weight for heavier battery packs used for these higher-range BEV applications, particularly in light truck vehicle segments, that would be helpful as well. In addition, we seek comment on the learning rates applied to the battery pack costs and on the battery pack costs in future years. Recognizing that any battery pack cost projections for future appropriate chemistry for modeling battery pack costs. Similar to the other technologies considered in this analysis, DOT endeavors to use technology that is a reasonable representation of what the industry could achieve in the model year or years under consideration, in this case the base DMC year of 2020, as discussed above. At the time of this current analysis, the referenced A2Mac1 teardown reports and other reports provided the best available information about the range of battery chemistry actually employed in the industry. At the time of writing, DOT still has not found examples of NMC811 in commercial application across the industry in a way that DOT believes selecting NMC811 would have represented industry average performance in MY 2020. As discussed in TSD Chapter 3.3.5.1.4, DOT did analyze the potential future cost of NMC811 in the composite learning curve generated to ensure the battery learning curve projections are reasonable. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 years from our analysis or external analyses will involve assumptions that may or may not come to pass, it would be most helpful if commenters thoroughly explained the basis for any recommended learning rates, including references to publicly available data or models (and if such models are peer reviewed) where appropriate. Similarly, it would be helpful for commenters to note where external analyses may or may not take into account certain parameters in their battery pack cost projections, and whether we should attempt to incorporate those parameters in our analysis. For example, as discussed above, our analysis does not consider raw material price fluctuations; however, the price of battery pack raw materials will put a lower bound on NMC-based battery prices.210 It would also be helpful if commenters explained how learning rates or future cost projections could represent the state of battery technology across the industry. Like other technologies considered in this analysis, some battery and vehicle manufacturers have more experience manufacturing electric vehicle battery packs, and some have less, meaning that different manufacturers will be at different places along the learning curve in future years. Note also that comments should specify whether their referenced costs, either for MY 2020 or for future years, are for the battery cell or the battery pack. Ensuring our learning rates encompass these diverse parameters will ensure that the analysis best predicts the costs and benefits associated with future standards. We will incorporate any new information received to the extent possible for the final rule and future analyses. Recognizing again that battery technology is a rapidly evolving field and there are a range of external analyses that project battery pack costs declining at different rates across the next decade, as discussed above and further in the TSD, we performed four sensitivity studies around battery pack costs that are described in PRIA Chapter 7.2.2.5. The sensitivity studies examined the impacts of increasing and decreasing the direct cost of batteries and battery learning costs by 20 percent from central analysis levels, based on our survey of external analyses’ battery pack cost projections that fell generally within +/¥20% of our central analysis costs. We found that changing the battery direct manufacturing costs in 210 See, e.g., MIT Energy Initiative. 2019. Insights into Future Mobility. Cambridge, MA: MIT Energy Initiative. Available at https://energy.mit.edu/ insightsintofuturemobility, at 78–9. PO 00000 Frm 00085 Fmt 4701 Sfmt 4702 49685 MY 2020 without changing the learning rate did not produce meaningfully different outcomes for electric vehicle technology penetration in later years, although it resulted in the lowest technology costs. Keeping the same direct manufacturing costs and using a steeper battery learning rate produced slightly higher technology costs, compared to the sensitivity results that changed battery pack direct manufacturing cost and kept learning rate the same. We seek comment on these conclusions, their implications for any potential updates to battery pack costs for the final rule, and any other external analyses that the agency should consider when validating future battery pack cost projections. Next, each vehicle powertrain type also receives different non-battery electrification components. When researching costs for different nonbattery electrification components, DOT found that different reports vary in components considered and cost breakdown. This is not surprising, as vehicle manufacturers use different nonbattery electrification components in different vehicle’s systems, or even in the same vehicle type, depending the application.211 DOT developed costs for the major non-battery electrification components on a dollar per kilowatt hour basis using the costs presented in two reports. DOT used a $/kW cost metric for non-battery components to align with the normalized costs for a system’s peak power rating as presented in U.S. DRIVE’s Electrical and Electronics Technical Team (EETT) Roadmap report.212 This approach captures components in some manufacturer’s systems, but not all systems; however, DOT believes this is a reasonable metric and approach to use for this analysis given the differences in non-battery electrification component systems. This approach allows us to scale the cost of non-battery electrification components based on the requirements of the system. We also relied on a teardown study of a MY 2016 Chevrolet Bolt for non-battery component costs that were not explicitly estimated in the EETT Roadmap report.213 211 For example, the MY 2020 Nissan Leaf does not have an active cooling system whereas Chevy Bolt uses an active cooling system. 212 U.S. DRIVE, Electrical and Electronics Technical Team Roadmap (Oct. 2017), available at https://www.energy.gov/sites/prod/files/2017/11/ f39/EETT%20Roadmap%2010-27-17.pdf. 213 Hummel et al., UBS Evidence Lab Electric Car Teardown—Disruption Ahead?, UBS (May 18, 2017), https://neo.ubs.com/shared/ d1wkuDlEbYPjF/. E:\FR\FM\03SEP2.SGM 03SEP2 49686 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules To develop the learning curves for non-battery electrification components, DOT used cost information from Argonne’s 2016 Assessment of Vehicle Sizing, Energy Consumption, and Cost through Large-Scale Simulation of Advanced Vehicle Technologies report.214 The report provided estimated cost projections from the 2010 lab year to the 2045 lab year for individual vehicle components.215 216 DOT considered the component costs used in electrified vehicles, and determined the learning curve by evaluating the year over year cost change for those components. Argonne recently published a 2020 version of the same report that included high and low cost estimates for many of the same components, that also included a learning rate.217 DOT’s learning estimates generated using the 2016 report fall fairly well in the middle of these two ranges, and therefore staff decided that continuing to apply the learning curve estimates based on the 2016 report was reasonable. There are many sources that DOT staff could have picked to develop learning curves for non-battery electrification component costs, however given the uncertainty surrounding extrapolating costs out to MY 2050, DOT believes these learning curves provide a reasonable estimate. Table III–21 shows an example of how the non-battery electrification component costs are computed for the Medium Car and Medium SUV nonperformance vehicle classes. BILLING CODE 4910–59–P Table ill-21-Example Non-Battery Components for Medium Car and SUV NonPerformance Classes ...="' ~ ~ .5 "' ... ... .!!- = ~ ~ :r. t: ~ ~ Q ... =--CJ -;_ ... _ :r. :r. ~ ~~ 0,.::i: ~ :r. :r. =~ ~ =--"' ~ Q == ~ Q ~ ~ Q Q ... =-t: .::ii: ~ ~~ ~ =CJ Q ~ Q = = :r. .5 5 I Q ~ Q - .t: ~ ..... ...,,.,_ Q :r. u~ ~8 ~ ~ ~ = Q u u ... .5 2$ :r. -== u "i::I :r. = ~ ~ = -~= :r.> ... :r. ~ Q Q .,Q I ~ Q ~ :r. ~ ~ Q Q. u ... "' ~ 5 ~ - E,-; -~ <:r. ..: E,-; ...... Q CJ .,Q ......"' QC) Q ~ Q M ~ = r-l :r. QC) =-i::i=: CJ :r. M ~ ~ .,Q ~ rJJ. ~ E,-; ~ Q - -= u= ... ......= -t: ...= ..... "' Q .§ ~ "3CJ t: :r. :r. ~ "i::I ...== = = = "0 - -... -... u "i::I s !! 5C. ~8 > u :r. -= CJ ..... u ~ r-l Q E,-; <:r. Q E,-; > u .....Q ..."' Q u I ~ r-l ~ ~ "'Q u u"' ~ Q "' ~ = -~ Q Q ...=..:- ~-Sb ...... • .. ~ ~ CJ ..: :r. ~~ Q :r. -== -- --; 5 = CJ t: CJ r-l ~ r-l E,-; ~ ~ ~ ~ ; $1,655 $1,655 $1,655 $1,686 $1,686 $2,473 $2,473 $2,473 $2,518 $2,518 $2,815 $3,191 $4,227 $4,360 $4,596 $4,006 $4,457 $5,817 $6,088 $6,345 $1,655 $1,655 $1,655 $1,686 $1,686 $2,473 $2,473 $2,473 $2,518 $2,518 $2,836 $3,271 $4,512 $4,559 $4,803 $4,034 $4,563 $6,194 $6,355 $6,618 ~ Q ..!: Medium Car - Non-Performance SHEVP2 28.01 PHEV20T 38.95 PHEV50T 95.21 SHEVPS 72.62 PHEV20 74.66 0 0 0 37.61 38.92 $516 $717 $1,753 $2,030 $2,091 $184 $184 $184 $184 $184 $0 $174 $174 $0 $174 $460 $460 $460 $460 $460 $1,160 $1,536 $2,572 $2,674 $2,910 $1,566.37 $2,027.04 $3,394.53 $3,570.16 $3,841.04 lotter on DSK11XQN23PROD with PROPOSALS2 SHEVP2 29.14 PHEV20T 43.32 PHEV50T 110.72 SHEVPS 79.32 PHEV20 81.81 0 0 0 41.74 43.01 $537 $798 $2,039 $2,229 $2,298 214 Moawad, Ayman, Kim, Namdoo, Shidore, Neeraj, and Rousseau, Aymeric. Assessment of Vehicle Sizing, Energy Consumption and Cost Through Large Scale Simulation of Advanced Vehicle Technologies (ANL/ESD–15/28). United States (2016). Available at https:// www.autonomie.net/pdfs/Report%20ANL%20ESD1528%20-%20Assessment%20of%20 Vehicle%20Sizing,%20Energy%20 Consumption%20and%20Cost%20through %20Large%20Scale%20Simulation%20of%20 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 $184 $184 $184 $184 $184 $0 $174 $174 $0 $174 $460 $460 $460 $460 $460 $1,181 $1,616 $2,857 $2,874 $3,117 $1,594.46 $2,133.26 $3,771.52 $3,836.40 $4,114.25 Advanced%20Vehicle%20Technologies%20%201603.pdf. 215 ANL/ESD–15/28 at 116. 216 DOE’s lab year equates to five years after a model year, e.g., DOE’s 2010 lab year equates to MY 2015. 217 Islam, E., Kim, N., Moawad, A., Rousseau, A. ‘‘Energy Consumption and Cost Reduction of Future Light-Duty Vehicles through Advanced Vehicle Technologies: A Modeling Simulation Study PO 00000 Frm 00086 Fmt 4701 Sfmt 4725 Through 2050’’, Report to the U.S. Department of Energy, Contract ANL/ESD–19/10, June 2020 https://www.autonomie.net/pdfs/ANL%20%20Islam%20-%202020%20-%20Energy%20 Consumption%20and %20Cost%20Reduction%20of%20Future%20LightDuty%20Vehicles%20through%20Advanced%20 Vehicle%20Technologies%20A%20 Modeling%20Simulation%20Study%20 Through%202050.pdf. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.066</GPH> Medium SUV-Non-Performance 49687 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules TSD Chapter 3.3.5.2 contains more information about the non-battery electrification components relevant to each specific electrification technology and the sources used to develop these costs. We seek comment on these costs, the appropriateness of the sources used to develop these costs, and the $/kW metric used to size specific non-battery electrification components. In addition, we seek comment on the learning rate applied to non-battery electrification components. Finally, the cost of electrifying a vehicle depends on the other powertrain components that must be added or removed from a vehicle with the addition of the electrification technology. Table III–22 below provides a breakdown of each electrification component included for each electrification technology type, as well as where to find the costs in each CAFE Model input file. Table 111-22 - Breakdown of the Electrification Costs by Electrification Technology Type Electrification Technology Tvoe Micro Hybrid Mild Hybrid P2 Strong Hybrid PS Strong Hybrid Plug-in Hybrid (PHEV20T/5on Plug-in Hybrid (PHEV 20/50 and 20H/50H) BEVs FCEVs Technologies File Vehicle Tabs Technologies File Engine Tabs Battery Cost File Motor/generator -NIA Battery Pack Motor/generator, DC/DC converter, other components DC/DC converter, on-board charger, high voltage cables, e-motor, A T8L2 transmission, and power electronics DC/DC converter, on-board charger, high voltage cables, e-motor, CVTL2 transmission, and power electronics DC/DC converter, on-board charger, high voltage cables, e-motor, A T8L2 transmission, and power electronics DC/DC converter, on-board charger, high voltage cables, e-motor, CVTL2 transmission, and power electronics DC/DC converter, on-board charger, high voltage cables, e-motor -NIA Battery Pack IC engine* Battery Pack IC engine Battery Pack IC engine Battery Pack IC engine Battery Pack ETD System Battery Pack -NIA NIA Fuel cell system, e-motor, H2 Tank, transmission, and power electronics As shown in Table III–22, DOT used the cost of the CVTL2 as a proxy for the cost of an eCVT used in PS hybrid vehicles. In its recent 2021 report, the NAS estimated the cost of eCVTs to be lower than DOT’s cost estimate for CVTL2.218 DOT is investigating the cost assumptions used for the PS hybrid transmission and may update those costs for the final rule depending on information submitted by stakeholders or other research. DOT seeks comment on the appropriateness of the cost estimate for eCVTs in the 2021 NAS report, or any other data that could be made public on the costs of eCVTs. The following example in Table III–23 shows how the costs are computed for a vehicle that progresses from a lower level to a higher level of electrified powertrain. The table shows the components that are removed and the components that are added as a GMC Acadia progresses from a MY 2024 vehicle with only SS12V electrification technology to a BEV300 in MY 2025. The total cost in MY 2025 is a net cost addition to the vehicle. The same methodology could be used for any other technology advancement in the electric technology tree path.219 218 A detailed cost comparison between our costs and the 2021 NAS report costs is discussed in TSD Chapter 3.3.5.3.3. 219 Please note that in this calculation the CAFE Model accounts for the air conditioning and offcycle technologies (g/mile) applied to each vehicle model. The cost for the AC/OC adjustments are located in the CAFE Model Scenarios file. The air conditioning and off-cycle cost values are discussed further in TSD Chapter 3.8. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00087 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.067</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 *The engine cost for a P2 Hybrid is based on engine technology that is used in the conventional powertrain. 49688 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-23 - Technology Cost Change for GMC Acadia Example Technology Removed Technology Added MY2025 Cost of Technology (2018$) (5830.76) (221.54) (501.67) (203.35) MY 2025 Overall Technology Cost (2018$) 888.7 (5482.2) (5703.74) (6205.41) (6408.76) (2498.29) (8907.05) 017.28) (247.43) (308.44) (0) 3581.65 146.68 (9024.33) (9271.76) (9580.2) (9580.2) (5998.55) (5851.87) 1137.67 (4714.2) 17955.29 248.9 13241.09 13489.99 72.71 13562.7 MY2024 BEV300 - ETDS IACC Non-battery components Battery Pack Cost AERO20 Total Air Conditioning/OffCycle (AC/OC) Adiustments 219 Added Technologies MY2025 13562.7 BILLING CODE 4910–59–C lotter on DSK11XQN23PROD with PROPOSALS2 TSD Chapter 3.3.5.3 includes more details about how the costs associated with the internal combustion engine, transmission, electric machine(s), nonbattery electrification components, and battery pack for each electrified technology type are combined to create a full electrification system cost. 4. Mass Reduction Mass reduction is a relatively costeffective means of improving fuel economy, and vehicle manufacturers are expected to apply various mass reduction technologies to meet fuel economy standards. Reducing vehicle mass can be accomplished through several different techniques, such as modifying and optimizing vehicle component and system designs, part consolidation, and adopting lighter weight materials (advanced high strength steel, aluminum, magnesium, and plastics including carbon fiber reinforced plastics). The cost for mass reduction depends on the type and amount of materials used, the manufacturing and assembly processes required, and the degree to which changes to plants and new manufacturing and assembly equipment VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 is needed. In addition, manufacturers may develop expertise and invest in certain mass reduction strategies that may affect the approaches for mass reduction they consider and the associated costs. Manufacturers may also consider vehicle attributes like noise-vibration-harshness (NVH), ride quality, handling, crash safety and various acceleration metrics when considering how to implement any mass reduction strategy. These are considered to be aspects of performance, and for this analysis any identified pathways to compliance are intended to maintain performance neutrality. Therefore, mass reduction via elimination of, for example, luxury items such as climate control, or interior vanity mirrors, leather padding, etc., is not considered in the mass reduction pathways for this analysis. The automotive industry uses different metrics to measure vehicle weight. Some commonly used measurements are vehicle curb weight,220 gross vehicle weight 220 This is the weight of the vehicle with all fluids and components but without the drivers, passengers, and cargo. PO 00000 Frm 00088 Fmt 4701 Sfmt 4702 (GVW),221 gross vehicle weight rating (GVWR),222 gross combined weight (GCVW),223 and equivalent test weight (ETW),224 among others. The vehicle curb weight is the most commonly used measurement when comparing vehicles. A vehicle’s curb weight is the weight of the vehicle including fluids, but without a driver, passengers, and cargo. A vehicle’s glider weight, which is vehicle curb weight minus the powertrain weight, is used to track the potential opportunities for weight reduction not including the powertrain. A glider’s subsystems may consist of the vehicle body, chassis, interior, steering, 221 This weight includes all cargo, extra added equipment, and passengers aboard. 222 This is the maximum total weight of the vehicle, passengers, and cargo to avoid damaging the vehicle or compromising safety. 223 This weight includes the vehicle and a trailer attached to the vehicle, if used. 224 For the EPA two-cycle regulatory test on a dynamometer, an additional weight of 300 lbs is added to the vehicle curb weight. This additional 300 lbs represents the weight of the driver, passenger, and luggage. Depending on the final test weight of the vehicle (vehicle curb weight plus 300 lbs), a test weight category is identified using the table published by EPA according to 40 CFR 1066.805. This test weight category is called ‘‘Equivalent Test Weight’’ (ETW). E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.068</GPH> Removed Technologies Engine (DOHC) VVT SGDI DEAC Transmission (AT9L2) EPS SS12V SS 12V battery AERO0 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules electrical accessory, brake, and wheels systems. The percentage of weight assigned to the glider will remain constant for any given rule but may change overall. For example, as electric powertrains including motors, batteries, inverters, etc. become a greater percent of the fleet, glider weight percentage will change compared to earlier fleets with higher dominance of internal combustion engine (ICE) powertrains. For this analysis, DOT considered six levels of mass reduction technology that include increasing amounts of advanced materials and mass reduction techniques applied to the glider. The mass change associated with powertrain changes is accounted for separately. The following sections discuss the assumptions for the six mass reduction technology levels, the process used to assign initial analysis fleet mass reduction assignments, the effectiveness 49689 for applying mass reduction technology, and mass reduction costs. (a) Mass Reduction in the CAFE Model The CAFE Model considers six levels of mass reduction technologies that manufacturers could use to comply with CAFE standards. The magnitude of mass reduction in percent for each of these levels is shown in Table III–24 for mass reductions for light trucks, passenger cars and for gliders. Table 111-24 - Mass Reduction Technology Level and Associated Glider and Curb Mass Reduction MR Level Percent Glider Weight Percent Vehicle Curb Weight (Passenger Cars) Percent Vehicle Curb Weight (Light Trucks) MRO MRI MR2 MR3 MR4 MR5 MR6 0% 5% 7.5% 10% 15% 20% 28% 0.00% 3.55% 5.33% 7.10% 10.65% 14.20% 20.00% 0.00% 3.55% 5.33% 7.10% 10.65% 14.20% 20.00% 225 When the mass of the vehicle is reduced by an appropriate amount, the engine may be downsized to maintain performance. See Section III.C.4 for more details. 226 Since powertrains are sized based on the glider weight for the analysis, glider weight reduction beyond a threshold amount during a redesign will lead to re-sizing of the powertrain. For the analysis, the glider was used as a base for the application of any type of powertrain. A conventional powertrain consists of an engine, transmission, exhaust system, fuel tank, radiator and associated components. A hybrid powertrain also includes a battery pack, electric motor(s), VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 DOT uses glider weight to apply nonpowertrain mass reduction technology in the CAFE Model and use Autonomie simulations to determine the size of the powertrain and corresponding powertrain weight for the respective glider weight. The combination of glider weight (after mass reduction) and resized powertrain weight equal the vehicle curb weight. While there are a range of specific mass reduction technologies that may be applied to vehicles to achieve each of the six mass reduction levels, there are some general trends that are helpful to illustrate some of the more widely used approaches. Typically, MR0 reflects vehicles with widespread use of mild steel structures and body panels, and very little or no use of high strength steel or aluminum. MR0 reflects materials applied to average vehicles in the MY 2008 timeframe. MR1–MR3 can be achieved with a steel body structure. In going from MR1 to MR3, expect that mild steel to be replaced by high strength and then advanced high strength steels. In going from MR3 to MR4 aluminum is required. This will start at using aluminum closure panels and then to get to MR4 the vehicle’s primary structure will need to be mostly generator, high voltage wiring harness, high voltage connectors, inverter, battery management system(s), battery pack thermal system, and electric motor thermal system. PO 00000 Frm 00089 Fmt 4701 Sfmt 4702 made from aluminum. In the vast majority of cases, carbon fiber technology is necessary to reach MR5, perhaps with a mix of some aluminum. MR6 can really only be attained in anything resembling a passenger car by make nearly every structural component from carbon fiber. This mean the body structure and closure panels like hoods and door skins are wholly made from carbon fiber. There may be some use of aluminum in the suspension. TSD Chapter 3.4 includes more discussion of the challenges involved with adopting large amounts of carbon fiber in the vehicle fleet in the coming years. As discussed further below, the cost studies used to generate the cost curves assume mass can be reduced in levels that require different materials and different components to be utilized, in a specific order. DOT’s mass reduction levels are loosely based on what materials and components that would be required to be used for each percent of mass reduction, based on the conclusions of those studies. (b) Mass Reduction Analysis Fleet Assignments To assign baseline mass reduction levels (MR0 through MR6) for vehicles in the MY 2020 analysis fleet, DOT used previously developed regression models to estimate curb weight for each vehicle based on observable vehicle attributes. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.069</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 For this analysis, DOT considers mass reduction opportunities from the glider subsystems of a vehicle first, and then consider associated opportunities to downsize the powertrain, which are accounted for separately.225 As explained below, in the Autonomie simulations, the glider system includes both primary and secondary systems from which a percentage of mass is reduced for different glider weight reduction levels; specifically, the glider includes the body, chassis, interior, electrical accessories, steering, brakes and wheels. In this analysis, DOT assumed the glider share is 71% of vehicle curb weight. The Autonomie model sizes the powertrain based on the glider weight and the mass of some of the powertrain components in an iterative process. The mass of the powertrain depends on the powertrain size. Therefore, the weight of the glider impacts the weight of the powertrain.226 49690 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 DOT used these models to establish a baseline (MR0) curb weight for each vehicle, and then determined the existing mass reduction technology level by finding the difference between the vehicles actual curb weight to the estimated regression-based value, and comparing the difference to the values in Table III–24. DOT originally developed the mass reduction regression models using MY 2015 fleet data; for this analysis, DOT used MY 2016 and 2017 analysis fleet data to update the models. DOT believes the regression methodology is a technically sound approach for estimating mass reduction levels in the analysis fleet. For a detailed discussion about the regression development and use please see TSD Chapter 3.4.2. Manufacturers generally apply mass reduction technology at a vehicle platform level (i.e., using the same components across multiple vehicle models that share a common platform) to leverage economies of scale and to manage component and manufacturing complexity, so conducting the regression analysis at the platform level leads to more accurate estimates for the real-world vehicle platform mass reduction levels. The platform approach also addresses the impact of potential weight variations that might exist for specific vehicle models, as all the individual vehicle models are aggregated into the platform group, and are effectively averaged using sales weighting, which minimizes the impact of any outlier vehicle configurations. (c) Mass Reduction Adoption Features Given the degree of commonality among the vehicle models built on a single platform, manufacturers do not have complete freedom to apply unique technologies to each vehicle that shares the platform. While some technologies (e.g., low rolling resistance tires) are very nearly ‘‘bolt-on’’ technologies, others involve substantial changes to the structure and design of the vehicle, and therefore affect all vehicle models that share a platform. In most cases, mass reduction technologies are applied to platform level components and therefore the same design and components are used on all vehicle models that share the platform. Each vehicle in the analysis fleet is associated with a specific platform. Similar to the application of engine and transmission technologies, the CAFE Model defines a platform ‘‘leader’’ as the vehicle variant of a given platform that has the highest level of observed mass reduction present in the analysis fleet. If there is a tie, the CAFE Model VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 begins mass reduction technology on the vehicle with the highest sales volume in model year 2020. If there remains a tie, the model begins by choosing the vehicle with the highest manufacturer suggested retail price (MSRP) in MY 2020. As the model applies technologies, it effectively levels up all variants on a platform to the highest level of mass reduction technology on the platform. For example, if the platform leader model is already at MR3 in MY 2020, and a ‘‘follower’’ platform model starts at MR0 in MY 2020, the follower platform model will get MR3 at its next redesign, assuming no further mass reduction technology is applied to the leader model before the follower models next redesign. In addition to the platform-sharing logic employed in the model, DOT applied phase-in caps for MR5 and MR6 (15 percent and 20 percent reduction of a vehicle’s curb weight, respectively), based on the current state of mass reduction technology. As discussed above, for nearly every type of vehicle, with the exception of the smallest sports cars, a manufacturer’s strategy to achieve mass reduction consistent with MR5 and MR6 will require extensive use of carbon fiber technologies in the vehicles’ primary structures. For example, one way of using carbon fiber technology to achieve MR6 is to develop a carbon fiber monocoque structure. A monocoque structure is one where the outer most skins support the primary loads of the vehicle. For example, they do not have separate non-load bearing aero surfaces. All of the vehicle’s primary loads are supported by the monocoque. In the most structurally efficient automotive versions, the monocoque is made from multiple wellconsolidated plies of carbon fiber infused with resin. Such structures can require low hundreds of pounds of carbon fiber for most passenger vehicles. Add to this another roughly equivalent mass of petroleum-derived resins and even at aspirational prices for dry carbon fiber of $10–20 per pound it is easy to see how direct materials alone can easily climb into the five-figure dollar range per vehicle. High CAFE stringency levels will push the CAFE Model to select compliance pathways that include these higher levels of mass reduction for vehicles produced in the mid and high hundreds of thousands of vehicles per year. DOT assumes, based on material costs and availability, that achieving MR6 levels of mass reduction will cost tens of thousands of dollars per car. Therefore, application of such technology to high volume vehicles is PO 00000 Frm 00090 Fmt 4701 Sfmt 4702 unrealistic today and will, with certainty, remain so for the next several years. The CAFE Model applies technologies to vehicles that provide a cost-effective pathway to compliance. In some cases, the direct manufacturing cost, indirect costs, and applied learning factor do not capture all the considerations that make a technology more or less costly for manufacturers to apply in the real world. For example, there are direct labor, R&D overhead, manufacturing overhead, and amortized tooling costs that will likely be higher for carbon fiber production than current automotive steel production, due to fiber handling complexities. In addition, R&D overhead will also increase because of the knowledge base for composite materials in automotive applications is simply not as deep as it is for steel and aluminum. Indeed, the intrinsic anisotropic mechanical properties of composite materials compared to the isotropic properties of metals complicates the design process. Added testing of these novel anisotropic structures and their associated costs will be necessary for decades. Adding up all these contributing costs, the price tag for a passenger car or truck monocoque would likely be multiple tens of thousands of dollars per vehicle. This would be significantly more expensive than transitioning to hybrid or fully electric powertrains and potentially less effective at achieving CAFE compliance. In addition, the CAFE Model does not currently enable direct accounting for the stranded capital associated with a transition away from stamped sheet metal construction to molded composite materials construction. For decades, or in some cases half-centuries, car manufacturers have invested billions of dollars in capital for equipment that supports the industry’s sheet metal forming paradigm. A paradigm change to tooling and equipment developed to support molding carbon fiber panels and monocoque chassis structures would leave that capital stranded in equipment that would be rendered obsolete. Doing this is possible, but the financial ramifications are not currently reflected in the CAFE Model for MR5 and MR6 compliance pathways. Financial matters aside, carbon fiber technology and how it is best used to produce lightweight primary automotive structures is far from mature. In fact, no car company knows for sure the best way to use carbon fiber to make a passenger car’s primary structure. Using this technology in passenger cars is far more complex than using it in racing cars where passenger egress, longevity, corrosion protection, crash protection, E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 etc. are lower on the list of priorities for the design team. BMW may be the manufacturer most able accurately opine on the viability of carbon fiber technology for primary structure on high-volume passenger cars, and even it decided to use a mixed materials solution for their next generation of EVs (the iX and i4) after the i3, thus eschewing a wholly carbon fiber monocoque structure. Another factor limiting the application of carbon fiber technology to mass volume passenger vehicles is indeed the availability of dry carbon fibers. There is high global demand from a variety of industries for a limited supply of carbon fibers. Aerospace, military/defense, and industrial applications demand most of the carbon fiber currently produced. Today, only roughly 10% of the global dry fiber supply goes to the automotive industry, which translates to the global supply base only being able to support approximately 70k cars.227 To account for these cost and production considerations, including the limited global supply of dry carbon fiber, DOT applied phase-in caps that limited the number of vehicles that can achieve MR5 and M6 levels of mass reduction in the CAFE Model. DOT applied a phase-in cap for MR5 level technology so that 75 percent of the vehicle fleet starting in 2020 could employ the technology, and the technology could be applied to 100 percent of the fleet by MY 2022. DOT also applied a phase-in cap for MR6 technology so that five percent of the vehicle fleet starting in MY 2020 could employ the technology, and the technology could be applied to 10 percent of the fleet by MY 2025. To develop these phase-in caps, DOT chose a 40,000 unit thresholds for both MR5 and MR6 technology (80,000 units total), because it roughly reflects the number of BMW i3 cars produced per year worldwide.228 As discussed above, the BMW i3 is the only high-volume vehicle currently produced with a primary structure mostly made from carbon fiber (except the skateboard, which is aluminum). Because mass 227 J. Sloan, ‘‘Carbon Fiber Suppliers Gear up for Next Generation Growth,’’ compositesworld.com, February 11, 2020. 228 However, even this number is optimistic because only a small fraction of i3 cars are sold in the U.S. market, and combining MR5 and MR6 allocations equates to 80k vehicles, not 40k. Regardless, if the auto industry ever seriously committed to using carbon fiber in mainstream high-volume vehicles, competition with the other industries would rapidly result in a dramatic increase in price for dry fiber. This would further stymie the deployment of this technology in the automotive industry. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 reduction is applied at the platform level (meaning that every car of a given platform would receive the technology, not just special low volume versions of that platform), only platforms representing 40,000 vehicles or less are eligible to apply MR5 and MR6 toward CAFE compliance. Platforms representing high volume sales, like a Chevrolet Traverse, for example, where hundreds of thousands are sold per year, are therefore blocked from access to MR5 and MR6 technology. There are no phase in caps for mass reduction levels MR1, MR2, MR3, or MR4. In addition to determining that the caps were reasonable based on current global carbon fiber production, DOT determined that the MR5 phase-in cap is consistent with the DOT lightweighting study that found that a 15 percent curb weight reduction for the fleet is possible within the rulemaking timeframe.229 These phase-in caps appropriately function as a proxy for the cost and complexity currently required (and that likely will continue to be required until manufacturing processes evolve) to produce carbon fiber components. Again, MR6 technology in this analysis reflects the use of a significant share of carbon fiber content, as seen through the BMW i3 and Alfa Romeo 4c as discussed above. Given the uncertainty and fluid nature of knowledge around higher levels of mass reduction technology, DOT welcomes comments on how to most cost effectively use carbon fiber technology in high-volume passenger cars. Financial implementation estimates for this technology are equally as welcome. (d) Mass Reduction Effectiveness Modeling As discussed in Section III.C.4, Argonne developed a database of vehicle attributes and characteristics for each vehicle technology class that included over 100 different attributes. Some examples from these 100 attributes include frontal area, drag coefficient, fuel tank weight, transmission housing weight, transmission clutch weight, hybrid vehicle components, and weights for components that comprise engines and electric machines, tire rolling resistance, transmission gear ratios, and final drive ratio. Argonne used these attributes to ‘‘build’’ each vehicle that it used for the effectiveness modeling and simulation. 229 Singh, Harry. (2012, August). Mass Reduction for Light-Duty Vehicles for Model Years 2017–2025. (Report No. DOT HS 811 666). Program Reference: DOT Contract DTNH22–11–C–00193. Contract Prime: Electricore, Inc, at 356, Figure 397. PO 00000 Frm 00091 Fmt 4701 Sfmt 4702 49691 Important for precisely estimating the effectiveness of different levels of mass reduction is an accurate list of initial component weights that make up each vehicle subsystem, from which Autonomie considered potential mass reduction opportunities. As stated above, glider weight, or the vehicle curb weight minus the powertrain weight, is used to determine the potential opportunities for weight reduction irrespective of the type of powertrain.230 This is because weight reduction can vary depending on the type of powertrain. For example, an 8speed transmission may weigh more than a 6-speed transmission, and a basic engine without variable valve timing may weigh more than an advanced engine with variable valve timing. Autonomie simulations account for the weight of the powertrain system inherently as part of the analysis, and the powertrain mass accounting is separate from the application and accounting for mass reduction technology levels that are applied to the glider in the simulations. Similarly, Autonomie also accounts for battery and motor mass used in hybrid and electric vehicles separately. This secondary mass reduction is discussed further below. Accordingly, in the Autonomie simulations, mass reduction technology is simulated as a percentage of mass removed from the specific subsystems that make up the glider, as defined for that set of simulations (including the non-powertrain secondary mass systems such as the brake system). For the purposes of determining a reasonable percentage for the glider, DOT in consultation with Argonne examined glider weight data available in the A2Mac1 database,231 in addition to the NHTSA MY 2014 Chevrolet Silverado lightweighting study (discussed further below). Based on these studies, DOT assumed that the glider weight comprised 71 percent of the vehicle curb weight. TSD Chapter 3.4.4 includes a detailed breakdown of the components that DOT considered to arrive at the conclusion that a glider, on average, represents 71% of a vehicle’s curb weight. Any mass reduction due to powertrain improvements is accounted for separately from glider mass reduction. Autonomie considers several components for powertrain mass reduction, including engine downsizing, 230 Depending on the powertrain combination, the total curb weight of the vehicle includes glider, engine, transmission and/or battery pack and motor(s). 231 A2Mac1: Automotive Benchmarking, https:// a2mac1.com. E:\FR\FM\03SEP2.SGM 03SEP2 49692 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 and transmission, fuel tank, exhaust systems, and cooling system lightweighting. The 2015 NAS report suggested an engine downsizing opportunity exists when the glider mass is lightweighted by at least 10%. The 2015 NAS report also suggested that 10% lightweighting of the glider mass alone would boost fuel economy by 3% and any engine downsizing following the 10% glider mass reduction would provide an additional 3% increase in fuel economy.232 The 2011 Honda Accord and 2014 Chevrolet Silverado lightweighting studies applied engine downsizing (for some vehicle types but not all) when the glider weight was reduced by 10 percent. Accordingly, this analysis limited engine resizing to several specific incremental technology steps as in the 2018 CAFE NPRM (83 FR 42986, Aug. 24, 2018) and 2020 final rule; important for this discussion, engines in the analysis were only resized when mass reduction of 10% or greater was applied to the glider mass, or when one powertrain architecture was replaced with another architecture. Specifically, we allow engine resizing upon adoption of 7.1%, 10.7%, 14.2%, and 20% curb weight reduction, but not at 3.6% and 5.3%.233 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. Argonne performed a regression analysis of engine peak power versus weight for a previous analysis based on attribute data taken from the A2Mac1 benchmarking database, to account for the difference in weight for different engine types. For example, to account for weight of different engine sizes like 232 National Research Council. 2015. Cost, Effectiveness, and Deployment of Fuel Economy Technologies for Light-Duty Vehicles. Washington, DC—The National Academies Press. https://doi.org/ 10.17226/21744. 233 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 III.C.4 and TSD Chapter 2.4. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 4-cylinder versus 8-cylinder, Argonne developed a relationship curve between peak power and engine weight based on the A2Mac1 benchmarking data. We use this relationship to estimate mass for all engine types regardless of technology type (e.g., variable valve lift and direct injection). DOT applied weight associated with changes in engine technology 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 8-cylinder engine adopted a more fuel-efficient 6cylinder engine, the total vehicle weight would reflect the updated engine weight with two less cylinders based on the peak power versus engine weight relationship. When Autonomie selects a powertrain combination for a lightweighted glider, the engine and transmission are selected such that there is no degradation in the performance of the vehicle relative to the baseline vehicle. The resulting curb weight is a combination of the lightweighted glider with the resized and potentially new engine and transmission. This methodology also helps in accurately accounting for the cost of the glider and cost of the engine and transmission in the CAFE Model. Secondary mass reduction is possible from some of the components in the glider after mass reduction has been incorporated in primary subsystems (body, chassis, and interior). Similarly, engine downsizing and powertrain secondary mass reduction is possible after certain level of mass reduction is incorporated in the glider. For the analysis, the agencies include both primary mass reduction, and when there is sufficient primary mass reduction, additional secondary mass reduction. The Autonomie simulations account for the aggregate of both primary and secondary glider mass reduction, and separately for powertrain mass. Note that secondary mass reduction is integrated into the mass reduction cost curves. Specifically, the NHTSA studies, upon which the cost curves depend, first generated costs for lightweighting the vehicle body, chassis, interior, and other primary components, and then calculated costs for lightweighting secondary components. Accordingly, the cost curves reflect that, for example, secondary mass reduction PO 00000 Frm 00092 Fmt 4701 Sfmt 4702 for the brake system is only applied after there has been sufficient primary mass reduction to allow the smaller brake system to provide safe braking performance and to maintain mechanical functionality. DOT enhanced the accuracy of estimated engine weights by creating two curves to represent separately naturally aspirated engine designs and turbocharged engine designs.234 This achieves 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 6cylinder engines that adopted turbocharging and downsized technologies would have lower weight due to having fewer engine cylinders. For this analysis, a naturally aspirated 8-cylinder engine that adopts turbocharging technology and is downsized to a 6-cylinder turbocharged engine appropriately reflects the added weight of the turbocharging components, and the lower weight of fewer cylinders. The range of effectiveness values for the mass reduction technologies, for all ten vehicle technology classes are shown in Figure III–13. In the graph, the box shows the inner quartile range (IQR) of the effectiveness values and whiskers extend out 1.5 × IQR. The dots outside of the whiskers show a few values outside these ranges. As discussed earlier, Autonomie simulates all possible combinations of technologies for fuel consumption improvements. For a few technology combinations mass reduction has minimal impact on effectiveness on the regulatory 2-cycle test. For example, if an engine is operating in an efficient region of the fuel map on the 2-cycle test further reduction of mass may have smaller improvement on the regulatory cycles. Figure III–13 shows the range improvements based on the full range of other technology combinations considered in the analysis. 234 See Autonomie model documentation, Chapter 5.2.9. Engine Weight Determination. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49693 0.20 lotter on DSK11XQN23PROD with PROPOSALS2 (e) Mass Reduction Costs The CAFE Model analysis handles mass reduction technology costs differently than all other technology costs. Mass reduction costs are calculated as an average cost per pound over the baseline (MR0) for a vehicle’s glider weight. While the definitions of glider may vary, DOT referenced the same dollar per pound of curb weight to develop costs for different glider definitions. In translating these values, DOT took care to track units ($/kg vs. $/lb) and the reference for percentage improvements (glider vs. curb weight). DOT calculated the cost of mass reduction on a glider weight basis so that the weight of each powertrain configuration could be directly and separately accounted for. This approach provides the true cost of mass reduction without conflating the mass change and costs associated with downsizing a powertrain or adding additional advanced powertrain technologies. Hence, the mass reduction costs in this proposal reflect the cost of mass reduction in the glider and do not VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 include the mass reduction associated with engine downsizing. The mass reduction and costs associated with engine downsizing are accounted for separately. A second reason for using glider share instead of curb weight is that it affects the absolute amount of curb weight reduction applied, and therefore cost per pound for the mass reduction changes with the change in the glider share. The cost for removing 20 percent of the glider weight when the glider represents 75 percent of a vehicle’s curb weight is not the same as the cost for removing 20 percent of the glider weight when the glider represents 50 percent of the vehicle’s curb weight. For example, the glider share of 79 percent of a 3,000pound curb weight vehicle is 2,370 lbs, while the glider share of 50 percent of a 3,000-pound curb weight vehicle is 1,500 lbs, and the glider share of 71 percent of a 3,000-pound curb weight vehicle is 2,130 lbs. The mass change associated with 20 percent mass reduction is 474 lbs for 79 percent glider share (=[3,000 lbs × 79% × 20%]), 300 lbs for 50 percent glider share (=[3,000 PO 00000 Frm 00093 Fmt 4701 Sfmt 4702 lbs × 50% × 20%]), and 426 lbs for 71 percent glider share (=[3,000 lbs × 71% × 20%]). The mass reduction cost studies that DOT relied on to develop mass reduction costs for this analysis show that the cost for mass reduction varies with the amount of mass reduction. Therefore, for a fixed glider mass reduction percentage, different glider share assumptions will have different costs. DOT considered several sources to develop the mass reduction technology cost curves. Several mass reduction studies have used either a mid-size passenger car or a full-size pickup truck as an exemplar vehicle to demonstrate the technical and cost feasibility of mass reduction. While the findings of these studies may not apply directly to different vehicle classes, the cost estimates derived for the mass reduction technologies identified in these studies can be useful for formulating general estimates of costs. As discussed further below, the mass reduction cost curves developed for this analysis are based on two lightweighting studies, and DOT also updated the curves based on more E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.070</GPH> Figure 111-13-Mass Reduction Technologies Effectiveness Values for all the Vehicle Technology Classes 49694 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules recent studies to better account for the cost of carbon fiber needed for the highest levels of mass reduction technology. The two studies used for MR1 through MR4 costs included the teardown of a MY 2011 Honda Accord and a MY 2014 Chevrolet Silverado pickup truck, and the carbon fiber costs required for MR5 and MR6 were updated based on the 2021 NAS report.235 Both teardown studies are structured to derive the estimated cost for each of the mass reduction technology levels. DOT relied on the results of those studies because they considered an extensive range of material types, material gauge, and component redesign while taking into account real world constraints such as manufacturing and assembly methods and complexity, platform-sharing, and maintaining vehicle utility, functionality and attributes, including safety, performance, payload capacity, towing capacity, handling, NVH, and other characteristics. In addition, DOT determined that the baseline vehicles and mass reduction technologies assessed in the studies are still reasonably representative of the technologies that may be applied to vehicles in the MY 2020 analysis fleet to achieve up to MR4 level mass reduction in the rulemaking timeframe. DOT adjusted the cost estimates derived from the two studies to reflect the assumption that a vehicle’s glider weight consisted of 71% of the vehicle’s curb weight, and mass reduction as it pertains to achieving MR0–MR6 levels would only come from the glider. As discussed above, achieving the highest levels of mass reduction often necessitates extensive use of advanced materials like higher grades of aluminum, magnesium, or carbon fiber. For the 2020 final rule, DOT provided a survey of information available regarding carbon fiber costs compared to the costs DOT presented in the final rule based on the Honda Accord and Chevrolet Silverado teardown studies. In the Honda Accord study, the estimated cost of carbon fiber was $5.37/kg, and the cost of carbon fiber used in the Chevy Silverado study was $15.50/kg. The $15.50 estimate closely matched the cost estimates from a BMW i3 teardown analysis,236 the cost figures provided by Oak Ridge National Laboratory for a study from the IACMI Composites Institute,237 and from a Ducker Worldwide presentation at the CAR Management Briefing Seminar.238 For this analysis, DOT relied on the cost estimates for carbon fiber construction that the National Academies detailed in the 2021 Assessment of Technologies for Improving Fuel Economy of Light-Duty Vehicles—Phase 3 recently completed by the National Academies.239 The study indicates that the sum of direct materials costs plus manufacturing costs for carbon fiber composite automotive components is $25.97 per pound in high volume production. In order to use this cost in the CAFE Model it must be put in terms of dollars per pound saved. Using an average vehicle curb weight of 4000 lbs, a 71% glider share and the percent mass savings associated with MR5 and MR6, it is possible to calculate the number of pounds to be removed to attain MR5 and MR6. Also taken from the NAS study is the assertion that carbon fiber substitution for steel in an automotive component results in a 50% mass reduction. Combining all this together, carbon fiber technology offers weight savings at $24.60 per pound saved. This dollar per pound savings figure must also be converted to a retail price equivalent (RPE) to account for various commercial costs associated with all automotive components. This is accomplished by multiplying $24.60 by the factor 1.5. This brings the cost per pound saved for using carbon fiber to $36.90 per pound saved.240 The analysis uses this cost for achieving MR5 and MR6. Table III–25 and Table III–26 show the cost values (in dollars per pound) used in the CAFE Model with MR1–4 costs based on the cost curves developed from the MY 2011 Honda Accord and MY 2014 Chevrolet Silverado studies, and the updated MR5 and MR6 values that account for the updated carbon fiber costs from the 2021 NAS report. Both tables assume a 71% glider share. Table 111-25-Mass Reduction Costs for MY 2020 in CAFE Model for Small Car, Small Car Performance, Medium Car, Medium Car Performance, Small SUV, Small SUV Performance lotter on DSK11XQN23PROD with PROPOSALS2 MR2 MR3 MR4 MR5 MR6 235 This analysis applied the cost estimates per pound derived from passenger cars to all passenger car segments, and the cost estimates per pound derived from full-size pickup trucks to all light-duty truck and SUV segments. The cost estimates per pound for carbon fiber (MR5 and MR6) were the same for all segments. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Percentage Reduction in Curb Wei2ht 0.00% 3.55% 5.33% 7.10% 10.65% 14.20% 20% Cost of Mass Reduction ($/lbs) 0.00 0.46 0.86 1.22 1.59 36.90 36.90 236 Singh, Harry, FSV Body Structure Comparison with 2014 BMW i3, Munro and Associates for World Auto Steel (June 3, 2015). 237 IACMI Baseline Cost and Energy Metrics (March 2017), available at https://iacmi.org/wpcontent/uploads/2017/12/IACMI-Baseline-Costand-Energy-Metrics-March-2017.pdf. PO 00000 Frm 00094 Fmt 4701 Sfmt 4725 238 Ducker Worldwide, The Road Ahead— Automotive Materials (2016), https:// societyofautomotiveanalysts.wildapricot.org/ resources/Pictures/SAA%20Sumit%20slides%20 for%20Abey%20Abraham%20of%20Ducker.pdf. 239 2021 NAS report, at 7–242–3. 240 See MR5 and MR6 CFRP Cost Increase Calculator.xlsx in the docket for this action. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.071</GPH> MR0 MRI Percentage Reduction in Glider Wei2ht 0.00% 5.00% 7.50% 10.00% 15.00% 20.00% 28.00% Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules There is a dramatic increase in cost going from MR4 to MR5 and MR6 for all classes of vehicles. However, while the increase in cost going from MR4 to MR5 and MR6 is dramatic, the MY 2011 Honda Accord study, the MY 2014 Chevrolet Silverado study, and the 2021 NAS report all included a steep increase to achieve the highest levels of mass reduction technology. As noted above, DOT seeks comment on any additional information about the costs of achieving the highest levels of mass reduction technology, including from publicly available sources or data that could be made publicly available. Table III–27 provides an example of mass reduction costs in 2018$ over 49695 select model years for the medium car and pickup truck technology classes as a dollar per pound value. The table shows how the $/lb value for each mass reduction level decreases over time because of cost learning. For a full list of the $/lb mass reduction costs used in the analysis across all model years, see the Technologies file. Table 111-27 - Examples of the $/lb Mass Reduction Costs in 2018$ for Medium Car and Pickup Truck Vehicle Classes MR0 MRI MR2 MR3 MR4 MR5 MR6 Medium Car Costs (2018$)/lbs MY2020 MY2025 MY2030 MY2020 MY2025 MY2030 0.00 0.00 0.00 0.00 0.00 0.00 0.46 0.42 0.39 0.30 0.27 0.25 0.86 0.78 0.73 0.70 0.63 0.59 1.22 1.11 1.03 1.25 1.13 1.06 1.59 1.34 1.21 1.70 1.44 1.30 36.90 31.44 26.93 36.90 31.44 26.93 36.90 31.44 26.93 36.90 31.44 26.93 lotter on DSK11XQN23PROD with PROPOSALS2 5. Aerodynamics The energy required to overcome aerodynamic drag accounts for a significant portion of the energy consumed by a vehicle and can become the dominant factor for a vehicle’s energy consumption at high speeds. Reducing aerodynamic drag can, therefore, be an effective way to reduce fuel consumption and emissions. Aerodynamic drag is proportional to the frontal area (A) of the vehicle and coefficient of drag (Cd), such that aerodynamic performance is often expressed as the product of the two values, CdA, which is also known as the drag area of a vehicle. The coefficient of drag (Cd) is a dimensionless value that essentially represents the aerodynamic efficiency of the vehicle shape. The frontal area (A) is the cross-sectional area of the vehicle as viewed from the front. It acts with the coefficient of drag as a sort of scaling factor, representing the relative size of the vehicle shape that the coefficient of drag describes. The force imposed by aerodynamic drag increases with the square of vehicle velocity, accounting for the largest contribution to road loads at higher speeds. Aerodynamic drag reduction can be achieved via two approaches, either by reducing the drag coefficient or VerDate Sep<11>2014 21:48 Sep 02, 2021 Pickup Costs (2018$)/lbs Jkt 253001 reducing vehicle frontal area, with two different categories of technologies, passive and active aerodynamic technologies. Passive aerodynamics refers to aerodynamic attributes that are inherent to the shape and size of the vehicle, including any components of a fixed nature. Active aerodynamics refers to technologies that variably deploy in response to driving conditions. These include technologies such as active grille shutters, active air dams, and active ride height adjustment. It is important to note that manufacturers may employ both passive and active aerodynamic technologies to achieve aerodynamic drag values. The greatest opportunity for improving aerodynamic performance is during a vehicle redesign cycle when significant changes to the shape and size of the vehicle can be made. Incremental improvements may also be achieved during mid-cycle vehicle refresh using restyled exterior components and addon devices. Some examples of potential technologies applied during mid-cycle refresh are restyled front and rear fascia, modified front air dams and rear valances, addition of rear deck lips and underbody panels, and low-drag exterior mirrors. While manufacturers may nudge the frontal area of the vehicle during redesigns, large changes in frontal area are typically not possible PO 00000 Frm 00095 Fmt 4701 Sfmt 4702 without impacting the utility and interior space of the vehicle. Similarly, manufacturers may improve Cd by changing the frontal shape of the vehicle or lowering the height of the vehicle, among other approaches, but the form drag of certain body styles and airflow needs for engine cooling often limit how much Cd may be improved. The following sections discuss the four levels of aerodynamic improvements considered in the CAFE Model, how the agency assigned baseline aerodynamic technology levels to vehicles in the MY 2020 fleet, the effectiveness improvements for the addition of aerodynamic technologies to vehicles, and the costs for adding that aerodynamic technology. (a) Aerodynamic Technologies in the CAFE Model DOT bins aerodynamic improvements into four levels—5%, 10%, 15% and 20% aerodynamic drag improvement values over a baseline computed for each vehicle body style—which correspond to AERO5, AERO10, AERO15, and AERO20, respectively. The aerodynamic improvements technology pathway consists of a linear progression, with each level superseding all previous levels, as seen in Figure III–14. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.073</GPH> Technology 49696 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules AERO Path AER015 I AE!zo I While the four levels of aerodynamic improvements are technology-agnostic, DOT built a pathway to compliance for each level based on aerodynamic data from a National Research Council (NRC) of Canada-sponsored wind tunnel testing program. The program included an extensive review of production vehicles utilizing these technologies, and industry comments.241 242 Again, these technology combinations are intended to show a potential way for a manufacturer to achieve each aerodynamic improvement level; however, in the real world, manufacturers may implement different combinations of aerodynamic technologies to achieve a percentage improvement over their baseline vehicles. Table III–28 and Table III–29 show the aerodynamic technologies that could be used to achieve 5%, 10%, 15% and 20% improvements in passenger cars, SUVs, and pickup trucks. As discussed further in Section III.D.5.c, AERO20 cannot be applied to pickup trucks in the model, which is why there is no pathway to AERO20 shown in Table III– 29. While some aerodynamic improvement technologies can be applied across vehicle classes, like active grille shutters (used in the 2015 Chevrolet Colorado),243 DOT determined that there are limitations that make it infeasible for vehicles with some body styles to achieve a 20% reduction in the coefficient of drag from their baseline. This technology path is an example of how a manufacturer could reach each AERO level, but they would not necessarily be required to use the technologies. 241 Larose, G., Belluz, L., Whittal, I., Belzile, M. et al., ‘‘Evaluation of the Aerodynamics of Drag Reduction Technologies for Light-duty Vehicles—a Comprehensive Wind Tunnel Study,’’ SAE Int. J. Passeng. Cars—Mech. Syst. 9(2):772–784, 2016, https://doi.org/10.4271/2016-01-1613. 242 Larose, Guy & Belluz, Leanna & Whittal, Ian & Belzile, Marc & Klomp, Ryan & Schmitt, Andreas. (2016). Evaluation of the Aerodynamics of Drag Reduction Technologies for Light-duty Vehicles—a Comprehensive Wind Tunnel Study. SAE International Journal of Passenger Cars— Mechanical Systems. 9. 10.4271/2016–01–1613. 243 Chevrolet Product Information, available at https://media.chevrolet.com/content/media/us/en/ chevrolet/vehicles/colorado/2015/_jcr_content/ iconrow/textfile/file.res/15-PG-Chevrolet-Colorado082218.pdf. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00096 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.074</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure 111-14- Technology Pathway for Levels of Aerodynamic Drag Reduction Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49697 Table 111-28 - Combinations of Technologies That Could Achieve Aerodynamic Improvements Used in the Current Analyses for Passenger Cars and SUVs Aero Improvement Level AERO5 AEROl0 AERO15 AERO20 Components Front Styling Roof Line raised at forward of B-pillar Faster A pillar rake angle Shorter C pillar Low drag wheels Rear Spoiler Wheel Deflector / Air outlet inside wheel housing Bumper Lip Rear Diffuser Underbody Cover Incl. Rear axle cladding) Lowering ride height by 10mm Active Grill Shutters Extend Air dam Effectiveness(%) 2.0% 0.5% 0.5% 1.0% 1.0% 1.0% 1.0% 1.0% 2.0% 3.0% 2.0% 3.0% 2.0% Table ill-29 - Combinations of Technologies That Could Achieve Aerodynamic Improvements Used in the Current Analyses for Pickup Trucks AEROlO AERO15 lotter on DSK11XQN23PROD with PROPOSALS2 BILLING CODE 4910–59–C As discussed further in Section III.D.8, this analysis assumes manufacturers apply off-cycle technology at rates defined in the Market Data file. While the AERO levels in the analysis are technology-agnostic, achieving AERO20 improvements does assume the use of active grille shutters, which is an off-cycle technology. (b) Aerodynamics Analysis Fleet Assignments DOT uses a relative performance approach to assign an initial level of aerodynamic drag reduction technology VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 to each vehicle. Each AERO level represents a percent reduction in a vehicle’s aerodynamic drag coefficient (Cd) from a baseline value for its body style. For a vehicle to achieve AERO5, the Cd must be at least 5% below the baseline for the body style; for AERO10, 10% below the baseline, and so on. Baseline aerodynamic assignment is therefore a three step process: Each vehicle in the fleet is assigned a body style, the average drag coefficient is calculated for each body style, and the drag coefficient for each vehicle model PO 00000 Frm 00097 Fmt 4701 Sfmt 4702 Effectiveness(%) 1.5% 0.5% 1.0% 1.0% 1.0% 2.0% 3.0% 3.0% 2.0% is compared to the average for the body style. Every vehicle in the fleet is assigned a body style; available body styles included convertible, coupe, sedan, hatchback, wagon, SUV, pickup, minivan, and van. These assignments do not necessarily match the body styles used by manufacturers for marketing purposes. Instead, they are assigned based on analyst judgement, taking into account how a vehicle’s AERO and vehicle technology class assignments are affected. Different body styles offer different utility and have varying levels E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.076</GPH> AERO5 Components Whole Body Styling (Shape Optimization) Faster A pillar rake angle Rear Spoiler Wheel Deflector I Air outlet inside wheel housing Bumper Lip Rear Diffuser Underbody Cover Incl. Rear axle claddinu:) Active Grill Shutters Extend Air dam EP03SE21.075</GPH> Aero Improvement Level 49698 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules As already discussed, DOT engineers use a relative performance approach to assign current aerodynamic technology (AERO) level to a vehicle. For some body styles with different utility, such as pickup trucks, SUVs and minivans, frontal area can vary, and this can affect the overall aerodynamic drag forces. In order to maintain vehicle utility and functionality related to passenger space and cargo space, we assume all technologies that improve aerodynamic drag forces do so by reducing Cd while maintaining frontal area. Technology pathway logic for levels of aerodynamic improvement consists of a linear progression, with each level superseding all previous ones. Technology paths for AERO are illustrated in Figure III–14. The highest levels of AERO are not considered for certain body styles. In these cases, this means that AERO20, and sometimes AERO15, can neither be assigned in the baseline fleet nor adopted by the model. For these body styles, there are no commercial examples of drag coefficients that demonstrate the required AERO15 or AERO20 improvement over baseline levels. DOT also deemed the most advanced levels of aerodynamic drag simulated as not technically practicable given the form drag of the body style and costed technology, especially given the need to maintain vehicle functionality and utility, such as interior volume, cargo area, and ground clearance. In short, DOT ‘skipped’ AERO15 for minivan body styles, and ‘skipped’ AERO20 for convertible, minivan, pickup, and wagon body styles. DOT also does not allow application of AERO15 and AERO20 technology to vehicles with more than 780 horsepower. There are two main types of vehicles that informed this threshold: performance internal combustion engine (ICE) vehicles and high-power battery electric vehicles (BEVs). In the case of the former, the agency recognizes that manufacturers tune aerodynamic features on these vehicles to provide desirable downforce at high speeds and to provide sufficient cooling for the powertrain, rather than reducing drag, resulting in middling drag coefficients despite advanced aerodynamic features. Therefore, manufacturers may have limited ability to improve aerodynamic drag coefficients for high performance vehicles with internal combustion engines without reducing horsepower. The baseline fleet includes 1,655 units of sales volume with limited application of aerodynamic technologies because of ICE vehicle performance.246 In the case of high-power battery electric vehicles, the 780-horsepower threshold is set above the highest peak system horsepower present on a BEV in the 2020 fleet. BEVs have different aerodynamic behavior and considerations than ICE vehicles, allowing for features such as flat underbodies that significantly reduce drag.247 BEVs are therefore more likely to achieve higher AERO levels, so the horsepower threshold is set high enough that it does not restrict AERO15 and AERO20 application. Note that the 244 See TSD Chapter 2.4.1 for a table of vehicle attributes used to build the Autonomie baseline vehicle models. That table includes a drag coefficient for each vehicle class. 245 See 83 FR 42986 (Aug. 24, 2018). The MY 2016 fleet was built to support the 2018 NPRM. 246 Market Data file. 247 2020 EPA Automotive Trends Report, at 227. of baseline form drag. In addition, frontal area is a major factor in aerodynamic forces, and the frontal area varies by vehicle. This analysis considers both frontal area and body style as utility factors affecting aerodynamic forces; therefore, the analysis assumes all reduction in aerodynamic drag forces come from improvement in the drag coefficient. Average drag coefficients for each body style were computed using the MY 2015 drag coefficients published by manufacturers, which were used as the baseline values in the analysis. DOT harmonizes the Autonomie simulation baselines with the analysis fleet assignment baselines to the fullest extent possible.244 The drag coefficients used for each vehicle in the MY 2020 analysis fleet are sourced from manufacturer specification sheets, when possible. However, drag coefficients for the MY 2020 vehicles were not consistently reported publicly. If no drag coefficient was reported, analyst judgment is sometimes used to assign an AERO level. If no level was manually assigned, the drag coefficient obtained from manufacturers to build the MY 2016 fleet,245 was used, if available. The MY 2016 drag coefficient values may not accurately reflect the current technology content of newer vehicles but are, in many cases, the most recent data available. lotter on DSK11XQN23PROD with PROPOSALS2 (c) Aerodynamics Adoption Features VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00098 Fmt 4701 Sfmt 4702 CAFE Model does not force high levels of AERO adoption; rather, higher AERO levels are usually adopted organically by BEVs because significant drag reduction allows for smaller batteries and, by extension, cost savings. BEVs represent 252,023 units of sales volume in the baseline fleet.248 (d) Aerodynamics Effectiveness Modeling To determine aerodynamic effectiveness, the CAFE Model and Autonomie used individually assigned road load technologies for each vehicle to appropriately assign initial road load levels and appropriately capture benefits of subsequent individual road load improving technologies. The current analysis included four levels of aerodynamic improvements, AERO5, AERO10, AERO15, and AERO20, representing 5, 10, 15, and 20 percent reduction in drag coefficient (Cd), respectively. DOT assumed that aerodynamic drag reduction could only come from reduction in Cd and not from reduction of frontal area, to maintain vehicle functionality and utility, such as passenger space, ingress/egress ergonomics, and cargo space. The effectiveness values for the aerodynamic improvement levels relative to AERO0, for all ten vehicle technology classes, are shown in Figure III–15. Each of the effectiveness values shown is representative of the improvements seen for upgrading only the listed aerodynamic technology level for a given combination of other technologies. In other words, the range of effectiveness values seen for each specific technology (e.g., AERO 15) represents the addition of AERO15 technology (relative to AERO0 level) for every technology combination that could select the addition of AERO15. It must be emphasized that the change in fuel consumption values between entire technology keys is used,249 and not the individual technology effectiveness values. Using the change between whole technology keys captures the complementary or non-complementary interactions among technologies. The box shows the inner quartile range (IQR) of the effectiveness values and whiskers extend out 1.5 x IQR. The dots outside the whiskers show effectiveness values outside those thresholds. 248 Market Data file. key is the unique collection of technologies that constitutes a specific vehicle, see TSD Chapter 2.4.7 for more detail. 249 Technology E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49699 0.12 0.11 ti') l; 5> 0.06 ",Q g 0.05 i:e 0.04····· '1l 0.03 0.02·· 0.01 0.00 -0.01 0 V") ~ ~ ~ ~ (e) Aerodynamics Costs lotter on DSK11XQN23PROD with PROPOSALS2 This analysis uses the AERO technology costs established in the 2020 final rule that are based on confidential business information submitted by the automotive industry in advance of the 2018 NPRM,251 and on DOT’s assessment of manufacturing costs for specific aerodynamic technologies.252 DOT received no additional comments 250 The data used to create this figure can be found in the FE_1 Improvements file. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 from stakeholders regarding the costs established in the 2018 NPRM, and continued to use the established costs for the 2020 final rule and this analysis. Table III–30 shows examples of costs for AERO technologies as applied to the medium car and pickup truck vehicle classes in select model years. The cost to achieve AERO5 is relatively low, as most of the improvements can be made through body styling changes. The cost to achieve AERO10 is higher than AERO5, due to the addition of several passive aerodynamic technologies, and the cost to achieve AERO15 and AERO20 is higher than AERO10 due to use of both passive and active aerodynamic technologies. For a full list of all absolute aerodynamic technology costs used in the analysis across all model years see the Technologies file. 251 See the PRIA accompanying the 2018 NPRM, Chapter 6.3.10.1.2.1.2 for a discussion of these cost estimates. 252 See the FRIA accompanying the 2020 final rule, Chapter VI.C.5.e. PO 00000 Frm 00099 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.077</GPH> Figure ill-15 -AERO Technology Effectiveness250 49700 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-30 - Examples of Costs for Aerodynamic Reduction Technologies in 2018$ for Medium Cars and Pickup Trucks for Select Model Years Medium Car Costs (2018$) MY2020 MY2025 MY2030 MY2020 MY2025 MY2030 AERO0 0.00 0.00 0.00 0.00 0.00 0.00 AERO5 53.96 48.70 45.73 53.96 48.70 45.73 AEROlO 110.32 99.56 93.49 110.32 99.56 93.49 AERO15 155.88 140.68 132.10 275.80 248.90 233.72 233.72 - - - AERO20 275.80 248.90 6. Tire Rolling Resistance Tire rolling resistance is a road load force that arises primarily from the energy dissipated by elastic deformation of the tires as they roll. Tire design characteristics (for example, materials, construction, and tread design) have a strong influence on the amount and type of deformation and the energy it dissipates. Designers can select these characteristics to minimize rolling resistance. However, these characteristics may also influence other performance attributes, such as durability, wet and dry traction, handling, and ride comfort. Lower-rolling-resistance tires have characteristics that reduce frictional losses associated with the energy dissipated mainly in the deformation of the tires under load, thereby improving fuel economy. Low rolling resistance tires are increasingly specified by OEMs in new vehicles and are also increasingly available from aftermarket tire vendors. They commonly include attributes such as higher inflation pressure, material changes, tire construction optimized for lower hysteresis, geometry changes (e.g., lotter on DSK11XQN23PROD with PROPOSALS2 Pickup Costs (2018$) VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 reduced aspect ratios), and reduced sidewall and tread deflection. These changes are commonly accompanied by additional changes to vehicle suspension tuning and/or suspension design to mitigate any potential impact on other performance attributes of the vehicle. DOT continues to assess the potential impact of tire rolling resistance changes on vehicle safety. DOT has been following the industry developments and trends in application of rolling resistance technologies to light duty vehicles. As stated in the National Academies Press (NAP) special report on Tires and Passenger Vehicle Fuel Economy,253 national crash data does not provide data about tire structural failures specifically related to tire rolling resistance, because the rolling resistance of a tire at a crash scene cannot be determined. However, other metrics like brake performance compliance test data are helpful to show trends like that stopping distance has 253 Tires and Passenger Vehicle Fuel Economy: Informing Consumers, Improving Performance— Special Report 286 (2006), available at https:// www.nap.edu/read/11620/chapter/6. PO 00000 Frm 00100 Fmt 4701 Sfmt 4702 not changed in the last ten years,254 during which time many manufacturers have installed low rolling resistance tires in their fleet—meaning that manufacturers were successful in improving rolling resistance while maintaining stopping distances through tire design, tire materials, and/or braking system improvements. In addition, NHTSA has addressed other tire-related issues through rulemaking,255 and continues to research tire problems such as blowouts, flat tires, tire or wheel deficiency, tire or wheel failure, and tire degradation.256 However, there are currently no data connecting low rolling resistance tires to accident or fatality rates. 254 See, e.g., NHTSA Office of Vehicle Safety Compliance, Compliance Database, https:// one.nhtsa.gov/cars/problems/comply/index.cfm. 255 49 CFR 571.138, Tire pressure monitoring systems. 256 Tire-Related Factors in the Pre-Crash Phase, DOT HS 811 617 (April 2012), available at https:// crashstats.nhtsa.dot.gov/Api/Public/View Publication/811617. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.078</GPH> Technology Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules NHTSA conducted tire rolling resistance tests and wet grip index tests on original equipment tires installed on new vehicles. The tests showed that there is no degradation in wet grip index values (no degradation in traction) for tires with improved rolling resistance technology. With better tire design, tire compound formulations and improved tread design, tire manufacturers have tools to balance stopping distance and reduced rolling resistance. Tire manufacturers can use ‘‘higher performance materials in the tread compound, more silica as reinforcing fillers and advanced tread design features’’ to mitigate issues related to stopping distance.257 The following sections discuss levels of tire rolling resistance technology considered in the CAFE Model, how the technology was assigned in the analysis fleet, adoption features specified to maintain performance, effectiveness, and cost. lotter on DSK11XQN23PROD with PROPOSALS2 (a) Tire Rolling Resistance in the CAFE Model DOT continues to consider two levels of improvement for low rolling resistance tires in the analysis: The first level of low rolling resistance tires considered reduced rolling resistance 10 percent from an industry-average baseline rolling resistance coefficient (RRC) value, while the second level reduced rolling resistance 20 percent from the baseline.258 DOT selected the industry-average RRC baseline of 0.009 based on a CONTROLTEC study prepared for the California Air Resources Board,259 in addition to confidential business information submitted by manufacturers prior to the 2018 NPRM analysis. The average RRC from the CONTROLTEC study, which surveyed 1,358 vehicle models, was 0.009.260 CONTROLTEC also compared the findings of their survey with values provided by Rubber Manufacturers Association (renamed as USTMA–U.S. Tire Manufacturers Association) for original equipment 257 Jesse Snyder, A big fuel saver: Easy-rolling tires (but watch braking) (July 21, 2008), https:// www.autonews.com/article/20080721/OEM01/ 307219960/a-big-fuel-saver-easy-rolling-tires-butwatch-braking. Last visited December 3, 2019. 258 To achieve ROLL10, the tire rolling resistance must be at least 10 percent better than baseline (.0081 or better). To achieve ROLL20, the tire rolling resistance must be at least 20 percent better than baseline (.0072 or better). 259 Technical Analysis of Vehicle Load Reduction by CONTROLTEC for California Air Resources Board (April 29, 2015). 260 The RRC values used in this study were a combination of manufacturer information, estimates from coast down tests for some vehicles, and application of tire RRC values across other vehicles on the same platform. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 tires. The average RRC from the data provided by RMA was 0.0092,261 compared to average of 0.009 from CONTROLTEC. In past agency actions, commenters have argued that based on available data on current vehicle models and the likely possibility that there would be additional tire improvements over the next decade, DOT should consider ROLL30 technology, or a 30 percent reduction of tire rolling resistance over the baseline.262 As stated in the Joint TSD for the MY 2017–2025 final rule (77 FR 62624, Oct. 15, 2012) and 2020 final rule, tire technologies that enable rolling resistance improvements of 10 and 20 percent have been in existence for many years.263 Achieving improvements of up to 20 percent involves optimizing and integrating multiple technologies, with a primary contributor being the adoption of a silica tread technology. Tire suppliers have indicated that additional innovations are necessary to achieve the next level of low rolling resistance technology on a commercial basis, such as improvements in material to retain tire pressure, tread design to manage both stopping distance and wet traction, and development of carbon black material for low rolling resistance without the use of silica to reduce cost and weight.264 The agency believes that the tire industry is in the process of moving automotive manufacturers towards higher levels of rolling resistance technology in the vehicle fleet. Importantly, as shown below, the MY 2020 fleet does include a higher percentage of vehicles with ROLL20 technology than the MY 2017 fleet. However, DOT believes that at this time, the emerging tire technologies that would achieve 30 percent improvement in rolling resistance, like changing tire profile, stiffening tire walls, or adopting improved tires along with active chassis control,265 among other technologies, will not be available for widespread commercial adoption in the fleet during the rulemaking timeframe. As a result, the agency continues to not to incorporate 30 percent reduction in rolling resistance technology. DOT will consider adding an advanced level of 261 Technical Analysis of Vehicle Load Reduction by CONTROLTEC for California Air Resources Board (April 29, 2015) at page 40. 262 NHTSA–2018–0067–11985. 263 EPA–420–R–12–901, at page 3–210. 264 2011 NAS report, at 103. 265 Mohammad Mehdi Davari, Rolling resistance and energy loss in tyres (May 20, 2015), available at https://www.sveafordon.com/media/42060/ SVEA-Presentation_Davari_public.pdf. Last visited December 30, 2019. PO 00000 Frm 00101 Fmt 4701 Sfmt 4702 49701 tire rolling resistance technology to future analyses, and invites comment on any updated information on manufacturers’ capabilities to add tires with higher levels of rolling resistance to their vehicles, and consumers’ willingness to accept these tires on their vehicles. (b) Tire Rolling Resistance Analysis Fleet Assignments Tire rolling resistance is not a part of tire manufacturers’ publicly released specifications and thus it is difficult to assign this technology to the analysis fleet. Manufacturers also often offer multiple wheel and tire packages for the same nameplates, further increasing the complexity of this assignment. DOT employed an approach consistent with previous rulemaking in assigning this technology. DOT relied on previously submitted rolling resistance values that were supplied by manufacturers in the process of building older fleets and bolstered it with agency-sponsored tire rolling testing by Smithers.266 DOT carried over rolling resistance assignments for nameplates where manufacturers had submitted data on the vehicles’ rolling resistance values, even if the vehicle was redesigned. If Smithers data was available, DOT replaced any older or missing values with that updated data. Those vehicles for which no information was available from either previous manufacturer submission or Smithers data were assigned to ROLL0. All vehicles under the same nameplate were assigned the same rolling resistance technology level even if manufacturers do outfit different trim levels with different wheels and tires. The MY 2020 analysis fleet includes the following breakdown of rolling resistance technology: 44% at ROLL0, 20% at ROLL10, and 36% at ROLL20, which shows that the majority of the fleet has now adopted some form of improved rolling resistance technology. The majority of the change from the MY 2017 analysis fleet has been in implementing ROLL20 technology. There is likely more proliferation of rolling resistance technology, but we would need further information from manufacturers in order to account for it. DOT invites comment from manufacturers on whether these rolling 266 See memo to Docket No. NHTSA–2021–0053, Evaluation of Rolling Resistance and Wet Grip Performance of OEM Stock Tires Obtained from NCAP Crash Tested Vehicles Phase One and Two. NHTSA used tire rolling resistance coefficient values from this project to assign baseline tire rolling resistance technology in the MY 2020 analysis fleet and is therefore providing the draft project appendices for public review and comment. E:\FR\FM\03SEP2.SGM 03SEP2 49702 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules resistance values are still applicable, or any updated rolling resistance values that could be incorporated in a publicly available analysis fleet. If manufacturers submit updated information on baseline rolling resistance assignments DOT may update those assignments for the final rule. (c) Tire Rolling Resistance Adoption Features lotter on DSK11XQN23PROD with PROPOSALS2 Rolling resistance technology can be adopted with either a vehicle refresh or redesign. In some cases, low rolling resistance tires can affect traction, which may adversely impact acceleration, braking, and handling characteristics for some highperformance vehicles. Similar to past rulemakings, the agency recognizes that to maintain performance, braking, and handling functionality, some highperformance vehicles would not adopt low rolling resistance tire technology. For cars and SUVs with more than 405 horsepower (hp), the agency restricted the application of ROLL20. For cars and SUVs with more than 500 hp, the agency restricted the application of any additional rolling resistance technology (ROLL10 or ROLL20). The agency developed these cutoffs based on a review of confidential business VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 information and the distribution of rolling resistance values in the fleet. (d) Tire Rolling Resistance Effectiveness Modeling As discussed above, the baseline rolling resistance value from which rolling resistance improvements are measured is 0.009, based on a thorough review of confidential business information submitted by industry, and a review of other literature. To achieve ROLL10, the tire rolling resistance must be at least 10 percent better than baseline (.0081 or better). To achieve ROLL20, the tire rolling resistance must be at least 20 percent better than baseline (.0072 or better). DOT determined effectiveness values for rolling resistance technology adoption using Autonomie modeling. Figure III–16 below shows the range of effectiveness values used for adding tire rolling resistance technology to a vehicle in this analysis. The graph shows the change in fuel consumption values between entire technology keys,267 and not the individual technology effectiveness values. Using the change between whole technology 267 Technology key is the unique collection of technologies that constitutes a specific vehicle, see TSD Chapter 2.4.7 for more information. PO 00000 Frm 00102 Fmt 4701 Sfmt 4702 keys captures the complementary or non-complementary interactions among technologies. In the graph, the box shows the interquartile range (IQR) of the effectiveness values and whiskers extend out 1.5 x IQR. The dots outside of the whiskers show values for effectiveness that are outside these bounds. The data points with the highest effectiveness values are almost all exclusively BEV and FCV technology combinations for medium sized nonperformance cars. The effectiveness for these vehicles, when the low rolling resistance technology is applied, is amplified by a complementary effect, where the lower rolling resistance reduces road load and allows a smaller battery pack to be used (and still meet range requirements). The smaller battery pack reduces the overall weight of the vehicle, further reducing road load, and improving fuel efficiency. This complimentary effect is experience by all the vehicle technology classes, but the strongest effect is on the midsized vehicle non-performance classes and is only captured in the analysis through the use of full vehicle simulations, demonstrating the full interactions of the technologies. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49703 0.12 0.11 0.10 '''"'"' 0.09 0.08 In ~ 0.07 "'''" .8~ 0.06 ~ 0.05 0.04 0.03 0.02 0.01 0.00 Figure 111-16-ROLL Technology Effectiveness (e) Tire Rolling Resistance Costs DOT continues to use the same DMC values for ROLL technology that were used for the 2020 final rule which are based on NHTSA’s MY 2011 CAFE final rule (74 FR 14196, March 30, 2009) and the 2006 NAS/NRC report.268 Table III– 31 shows the different levels of tire rolling resistance technology cost for all vehicle classes across select model years, which shows how the learning rate for ROLL technologies impacts the cost. For all ROLL absolute technology costs used in the analysis across all model years see the Technologies file. Table 111-31- Examples of Costs for Rolling Resistance Reduction Technologies in 2018$ for Select Model Years Technology MY 2020 MY2025 MY2030 ROLLO ROLLlO ROLL20 0.00 7.13 51.18 0.00 6.52 44.04 0.00 6.16 40.70 268 ‘‘Tires and Passenger Vehicle Fuel Economy,’’ Transportation Research Board Special Report 286, VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 reduces the number of Autonomie simulations that are needed. National Research Council of the National Academies, 2006, Docket No. EPA–HQ–OAR–2009– 0472–0146. PO 00000 Frm 00103 Fmt 4701 Sfmt 4702 (a) Electric Power Steering Electric power steering reduces fuel consumption by reducing load on the engine. Specifically, it reduces or eliminates the parasitic losses associated with engine-driven power E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.080</GPH> Four other vehicle technologies were included in the analysis—electric power steering (EPS), improved accessory devices (IACC), low drag brakes (LDB), and secondary axle disconnect (SAX). The effectiveness of these technologies was applied directly in the CAFE Model with unique effectiveness values for each technology and for each technology class, rather than using Autonomie effectiveness estimates. This methodology was used in these four cases because the effectiveness of these technologies varies little with combinations of other technologies. Also, applying these technologies directly in the CAFE Model significantly EP03SE21.079</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 7. Other Vehicle Technologies 49704 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules steering pumps, which pump hydraulic fluid continuously through the steering actuation system even when no steering input is present. By selectively powering the electric assist only when steering input is applied, the power consumption of the system is reduced in comparison to the traditional ‘‘alwayson’’ hydraulic steering system. Power steering may be electrified on light duty vehicles with standard 12V electrical systems and is also an enabler for vehicle electrification because it provides power steering when the engine is off (or when no combustion engine is present). Power steering systems can be electrified in two ways. Manufacturers may choose to eliminate the hydraulic portion of the steering system and provide electric-only power steering (EPS) driven by an independent electric motor, or they may choose to move the hydraulic pump from a belt-driven configuration to a stand-alone electrically driven hydraulic pump. The latter system is commonly referred to as electro-hydraulic power steering (EHPS). As discussed in the rulemakings, manufacturers have informed DOT that full EPS systems are being developed for all types of lightduty vehicles, including large trucks. DOT described in past rulemakings that, like low drag brakes, EPS can be difficult to observe and assign to the analysis fleet, however, it is found more frequently in publicly available information than low drag brakes. Based on comments received during the 2020 rulemaking, the agency increased EPS application rate to nearly 90 percent for the 2020 final rule. The agency is maintaining this level of EPS fleet penetration for this analysis, recognizing that some specialized, unique vehicle types or configurations still implement hydraulically actuated power steering systems for the baseline fleet model year. The effectiveness of both EPS and EHPS is derived from the decoupling of the pump from the crankshaft and is considered to be practically the same for both. Thus, a single effectiveness value is used for both EPS and EHPS. As indicated in the following table, the effectiveness of EPS and EHPS varies based on the vehicle technology class it is being applied to. This variance is a direct result of vehicle size and the amount of energy required to turn the vehicle’s two front wheels about their vertical axis. More simply put, more energy is required for vehicles that weigh more and, typically, have larger tire contact patches. Table 111-32- Fuel Consumption Improvement Values for Electric Power Steering Tech Class EPS SmallCar SmallCarPerf MedCar MedCarPerf SmallSUV SmallSUVPerf MedSUV MedSUVPerf Pickup PickupHT Engine accessories typically include the alternator, coolant pump, cooling fan, and oil pump, and are traditionally mechanically driven via belts, gears, or directly by other rotating engine components such as camshafts or the crankshaft. These can be replaced with improved accessories (IACC), which may include high efficiency alternators, electrically driven (i.e., on-demand) coolant pumps, electric cooling fans, variable geometry oil pumps, and a mild regeneration strategy. Replacing lowerefficiency and/or mechanically-driven components with these improved accessories results in a reduction in fuel consumption, as the improved accessories can conserve energy by being turned on/off ‘‘on demand’’ in some cases, driven at partial load as needed, or by operating more efficiently. For example, electric coolant pumps and electric powertrain cooling fans VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 1.30% 1.20% 1.00% 0.80% provide better control of engine cooling. Flow from an electric coolant pump can be varied, and the cooling fan can be shut off during engine warm-up or cold ambient temperature conditions, reducing warm-up time, fuel enrichment requirements, and, ultimately reducing parasitic losses. IACC technology is difficult to observe and therefore there is uncertainty in assigning it to the analysis fleet. As in the past, DOT relies on industry-provided information and comments to assess the level of IACC technology applied in the fleet. DOT believes there continues to be opportunity for further implementation of IACC. The MY 2020 analysis fleet has an IACC fleet penetration of approximately eight percent compared to the six percent value in the MY 2017 analysis fleet used for the 2020 final rule analysis. PO 00000 Frm 00104 Fmt 4701 Sfmt 4702 The agency believes improved accessories may be incorporated in coordination with powertrain related changes occurring at either a vehicle refresh or vehicle redesign. This coordination with powertrain changes enables related design and tooling changes to be implemented and systems development, functionality and durability testing to be conducted in a single product change program to efficiently manage resources and costs. This analysis carries forward work on the effectiveness of IACC systems conducted in the Draft TAR and EPA Proposed Determination that is originally founded in the 2002 NAS Report 269 and confidential manufacturer data. This work involved gathering information by monitoring 269 National Research Council 2002. Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards. Washington, DC: The National Academies Press. https://doi.org/10.17226/10172. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.081</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 (b) Improved Accessories 1.50% Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules based on the vehicle technology class it is being applied to. This variance, like EPS, is a direct result of vehicle size and the amount of energy required perform the work necessary for the vehicle to operate as expected. This variance is related to the amount energy generated press reports, holding meetings with suppliers and OEMs, and attending industry technical conferences. The resulting effectiveness estimates we use are shown below. As indicated in the following table, the effectiveness of IACC is simulated with differing values 49705 by the alternator, the size of the coolant pump to the cool the necessary systems, the size of the cooling fan required, among other characteristics and it directed related to a vehicle size and mass. Table III-33-Fuel Consumption Improvement Values for Improved Accessories Tech Class I SmallCar MedCar 2.36% MedCarPerf SmallSUV 1.74% SmallSUVPerf MedSUV 2.34% MedSUVPerf Pickup 2.15% PickupHT lotter on DSK11XQN23PROD with PROPOSALS2 Since 2009, for the MY 2011 CAFE final rule, DOT has defined low drag brakes (LDB) as brakes that reduce the sliding friction of disc brake pads on rotors when the brakes are not engaged because the brake pads are pulled away from the rotating disc either by mechanical or electric methods.270 DOT estimated the effectiveness of LDB technology to be a range from 0.5–1.0 percent, based on CBI data. DOT applied a learning curve to the estimated cost for LDB, but noted that the technology was considered high volume, mature, and stable. DOT explained that confidential manufacturer comments in response to the NPRM for MY 2011 (73 FR 24352, May 2, 2008) indicated that most passenger cars have already adopted LDB technology, but ladder frame trucks have not. DOT and EPA continued to use the same definition for LDB in the MY 2012–2016 rule (75 FR 25324, May 7, 2010), with an estimated effectiveness of up to 1 percent based on CBI data.271 DOT only allowed LDB technology to be applied to large car, minivan, medium 270 Final Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2011 Passenger Cars and Light Trucks (March 2009), at V–135. 271 Final Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2012–MY 2016 Passenger Cars and Light Trucks (March 2010), at 249. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 and large truck, and SUV classes because the agency determined the technology was already largely utilized in most other subclasses. The 2011 NAS committee also utilized NHTSA and EPA’s definition for LDB and added that most new vehicles have low-drag brakes.272 The committee confirmed that the impact over conventional brakes may be about a 1 percent reduction of fuel consumption. For the MY 2017–2025 rule, however, DOT and EPA updated the effectiveness estimate for LDB to 0.8 percent based on a 2011 Ricardo study and updated lumped-parameter model.273 The agencies considered LDB technology to be off the learning curve (i.e., the DMC does not change year-over-year). The 2015 NAS report continued to use the agencies’ definition for LDB and commented that the 0.8 percent effectiveness estimate is a reasonable estimate.274 The 2015 NAS committee did not opine on the application of LDB technology in the fleet. The agencies used the same definition, cost, and effectiveness estimates for LDB in the Draft TAR, but also noted the existence of zero drag brake systems which use 272 2011 NAS report, at 104. Technical Support Document: Final Rulemaking for 2017–2025 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards (August 2012), at 3–211. 274 2015 NAS report, at 231. 273 Joint PO 00000 Frm 00105 Fmt 4701 I 1.85% SmallCarPerf (c) Low Drag Brakes IACC Sfmt 4702 electrical actuators that allow brake pads to move farther away from the rotor.275 However, the agencies did not include zero drag brake technology in either compliance simulation. EPA continued with this approach in its first 2017 Final Determination that the standards through 2025 were appropriate.276 In the 2020 final rule, the agencies applied LDB sparingly in the MY 2017 analysis fleet using the same cost and effectiveness estimates from the 2011 Ricardo study, with approximately less than 15% of vehicles being assigned the technology. In addition, DOT noted the existence of zero drag brakes in production for some BEVs, similar to the summary in the Draft TAR, but did not opine on the existence of zero drag brakes in the fleet. Some stakeholders commented to the 2020 final rule that other vehicle technologies, including LDB, were actually overapplied in the analysis fleet. For this action, DOT considered the conflicting statements that LDB were both universally applied in new vehicles and that the new vehicle fleet still had space to improve LDB technology. DOT determined that LDB technology as previously defined going back to the MY 2011 rule (74 FR 14196, March 30, 2009) was universally 275 Draft 276 EPA E:\FR\FM\03SEP2.SGM TAR, at 5–207. Proposed Determination TSD, at 2–422. 03SEP2 EP03SE21.082</GPH> I 49706 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules applied in the MY 2020 fleet. However, DOT determined that zero drag brakes, the next level of brake technology, was sparingly applied in the MY 2020 analysis fleet. Currently, DOT does not believe that zero drag brake systems will be available for wide scale application in the rulemaking timeframe and did not include it as a technology for this analysis. DOT will consider how to define a new level of low drag brake technology that either encompasses the definition of zero drag brakes or similar technology in future rulemakings. We invite comment on the issue, and any available data regarding use of such systems on current and forthcoming production vehicles, any available data regarding system costs and efficacy in reducing drag (i.e., force at different speeds) and vehicle fuel economy levels (i.e., through coastdown testing). (d) Secondary Axle Disconnect All-wheel drive (AWD) and fourwheel drive (4WD) vehicles provide improved traction by delivering torque to the front and rear axles, rather than just one axle. When a second axle is rotating, it tends to consume more energy because of additional losses related to lubricant churning, seal friction, bearing friction, and gear train inefficiencies.277 Some of these losses may be reduced by providing a secondary axle disconnect function that disconnects one of the axles when driving conditions do not call for torque to be delivered to both. The terms AWD and 4WD are often used interchangeably, although they have also developed a colloquial distinction, and are two separate systems. The term AWD has come to be associated with light-duty passenger vehicles providing variable operation of one or both axles on ordinary roads. The term 4WD is often associated with larger truck-based vehicle platforms providing a locked driveline configuration and/or a low range gearing meant primarily for off-road use. Many 4WD vehicles provide for a single-axle (or two-wheel) drive mode that may be manually selected by the user. In this mode, a primary axle Systems, ‘‘AWD Component Analysis’’, Project Report, performed for Transport Canada, Contract T8080150132, May 31, 2016. lotter on DSK11XQN23PROD with PROPOSALS2 277 Pilot VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 (usually the rear axle) will be powered, while the other axle (known as the secondary axle) is not. However, even though the secondary axle and associated driveline components are not receiving engine power, they are still connected to the non-driven wheels and will rotate when the vehicle is in motion. This unnecessary rotation consumes energy,278 and leads to increased fuel consumption that could be avoided if the secondary axle components were completely disconnected and not rotating. Light-duty AWD systems are often designed to divide variably torque between the front and rear axles in normal driving to optimize traction and handling in response to driving conditions. However, even when the secondary axle is not necessary for enhanced traction or handling, in traditional AWD systems it typically remains engaged with the driveline and continues to generate losses that could be avoided if the axle was instead disconnected. The SAX technology observed in the marketplace disengages one axle (typically the rear axle) for twowheel drive (2WD) operation but detects changes in driving conditions and automatically engages AWD mode when it is necessary. The operation in 2WD can result in reduced fuel consumption. For example, Chrysler has estimated the secondary axle disconnect feature in the Jeep Cherokee reduces friction and drag attributable to the secondary axle by 80% when in disconnect mode.279 Observing SAX technology on actual vehicles is very difficult. Manufacturers do not typically identify the technology on technical specifications or other widely available information. The agency employed an approach consistent with previous rulemaking in assigning this technology. Specifically, the agency assigned SAX technology based on a combination of publicly available information and previously submitted confidential information. In the analysis fleet, 38% of the vehicles that had AWD or 4WD are determined to have SAX technology. All vehicles in the analysis fleet with front-wheel drive 278 Any time a drivetrain component spins it consumes some energy, primarily to overcome frictional forces. 279 Brooke, L. ‘‘Systems Engineering a new 4x4 benchmark’’, SAE Automotive Engineering, June 2, 2014. PO 00000 Frm 00106 Fmt 4701 Sfmt 4702 (FWD) or rear-wheel drive (RWD) have SAX skipped since SAX technology is a way to emulate FWD or RWD in AWD and 4WD vehicles, respectively. The agency does not allow for the application of SAX technology to FWD or RWD vehicles because they do not have a secondary driven axle to disconnect. SAX technology can be adopted by any vehicle in the analysis fleet, including those with a HEV or BEV powertrain,280 which was identified as having AWD or 4WD. It does not supersede any technology or result in any other technology being excluded for future implementation for that vehicle. SAX technology can be applied during any refresh or redesign. DOT seeks comment on whether it is appropriate for SAX technology to be allowed to be applied to BEVs, or if the technology only provides benefits to ICE vehicles. This analysis carries forward work on the effectiveness of SAX systems conducted in the Draft TAR and EPA Proposed Determination.281 This work involved gathering information by monitoring press reports, holding meetings with suppliers and OEMs, and attending industry technical conferences. DOT does not simulate SAX effectiveness in the Autonomie modeling because, similar to LDB, IACC, and EFR, the fuel economy benefits from the technology are not fully captured on the two-cycle test. The secondary axle disconnect effectiveness values, for the most part, have been accepted as plausible based on the rulemaking record and absence of contrary comments. As such, the agency has prioritized its extensive Autonomie vehicle simulation work toward other technologies that are emerging or considered more critical for total system effectiveness. The resulting effectiveness estimates we use are shown below. The agency welcomes comment on these effectiveness values and will consider any material data providing revised, or confirmatory, values for those being used in the analysis. 280 The inefficiencies addressed on ICEs by SAX technology may not be similar enough, or even present, in HEVs or BEVs. 281 Draft TAR, at 5–412; Proposed Determination TSD, at 2–422. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49707 Table 111-34-Fuel Consumption Improvement Values for Secondary Axle Disconnect I I Tech Class SAX SmallCar 1.40% SmallCarPerf MedCar 1.40% MedCarPerf SmallSUV 1.40% SmallSUVPerf MedSUV 1.30% MedSUVPerf Pickup 1.60% PickupHT (e) Other Vehicle Technology Costs The cost estimates for EPS, IACC, SAX, and LDB 282 rely on previous work published as part of past rulemakings with learning applied to those cost I values which is founded in the 2002 NAS report.283 The cost values are the same values that were used for the Draft TAR and 2020 final rule, updated to 2018 dollars. Table III–35 shows examples of costs for these technologies across select model years. Note that these costs are the same for all vehicle technology classes. For all absolute EPS, IACC, LDB, and SAX technology costs across all model years, see the Technologies file. MY2020 MY2025 MY2030 EPS IACC LDB SAX 126.53 169.70 86.42 88.69 117.28 146.67 78.35 80.34 110.90 135.17 73.12 75.15 lotter on DSK11XQN23PROD with PROPOSALS2 8. Simulating Air Conditioning Efficiency and Off-Cycle Technologies Off-cycle and air conditioning (A/C) efficiency technologies can provide fuel economy benefits in real-world vehicle operation, but those benefits cannot be fully captured by the traditional 2-cycle test procedures used to measure fuel economy.284 Off-cycle technologies include technologies like high efficiency alternators and high efficiency exterior lighting.285 A/C efficiency technologies are technologies that reduce the operation of or the loads on the compressor, which pressurizes A/C refrigerant. The less the compressor operates or the more efficiently it operates, the less load the compressor places on the engine, resulting in better fuel efficiency. Vehicle manufacturers have the option to generate credits for off-cycle technologies and improved A/C systems under the EPA’s CO2 program and receive a fuel consumption improvement value (FCIV) equal to the value of the benefit not captured on the 2-cycle test under NHTSA’s CAFE program. The FCIV is not a ‘‘credit’’ in the NHTSA CAFE program,286 but the FCIVs increase the reported fuel economy of a manufacturer’s fleet, which is used to determine compliance. EPA applies FCIVs during determination of a fleet’s final average fuel economy reported to NHTSA.287 282 Note that because LDB technology is applied universally as a baseline technology in the MY 2020 fleet, there is functionally zero costs for this technology associated with this proposed rulemaking. 283 National Research Council 2002. Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards. Washington, DC: The National Academies Press. https://doi.org/10.17226/10172. 284 See 49 U.S.C 32904(c) (‘‘The Administrator shall measure fuel economy for each model and calculate average fuel economy for a manufacturer under testing and calculation procedures prescribed by the Administrator. . . . the Administrator shall use the same procedures for passenger automobiles the Administrator used for model year 1975 (weighted 55 percent urban cycle and 45 percent highway cycle), or procedures that give comparable results.’’). 285 40 CFR 86.1869–12(b)—Credit available for certain off-cycle technologies. 286 Unlike, for example, the statutory overcompliance credits prescribed in 49 U.S.C. 32903. 287 49 U.S.C. 32904(c)–(e). EPCA granted EPA authority to establish fuel economy testing and calculation procedures. See Section VII for more information. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00107 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.083</GPH> Technology EP03SE21.084</GPH> Table 111-35 - Examples of Costs for EPS, IACC, LDB, and SAX Technologies in 2018$ for Select Model Years 49708 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules FCIVs are only calculated and applied at a fleet level for a manufacturer and are based on the volume of the manufacturer’s fleet that contain qualifying technologies.288 There are three pathways that can be used to determine the value of A/C efficiency and off-cycle adjustments. First, manufacturers can use a predetermined list or ‘‘menu’’ of g/mi values that EPA established for specific off-cycle technologies.289 Second, manufacturers can use 5-cycle testing to demonstrate off-cycle CO2 benefit; 290 the additional tests allow emissions benefits to be demonstrated over some elements of real-world driving not captured by the 2-cycle compliance tests, including high speeds, rapid accelerations, hot temperatures, and cold temperatures. Third, manufacturers can seek EPA approval, through a notice and comment process, to use an alternative methodology other than the menu or 5-cycle methodology for determining the off-cycle technology improvement values.291 For further discussion of the A/C and off-cycle compliance and application process, see Section VII. DOT and EPA have been collecting data on the application of these technologies since implementing the A/ C and off-cycle programs.292 293 Most manufacturers are applying A/C efficiency and off-cycle technologies; in MY 2019, 17 manufacturers employed A/C efficiency technologies and 20 manufacturers employed off-cycle 288 40 CFR 600.510–12(c). 40 CFR 86.1869–12(b). The TSD for the 2012 final rule for MYs 2017 and beyond provides technology examples and guidance with respect to the potential pathways to achieve the desired physical impact of a specific off-cycle technology from the menu and provides the foundation for the analysis justifying the credits provided by the menu. The expectation is that manufacturers will use the information in the TSD to design and implement off-cycle technologies that meet or exceed those expectations in order to achieve the real-world benefits of off-cycle technologies from the menu. 290 See 40 CFR 86.1869–12(c). EPA proposed a correction for the 5-cycle pathway in a separate technical amendments rulemaking. See 83 FR 49344 (Oct. 1, 2019). EPA is not approving credits based on the 5-cycle pathway pending the finalization of the technical amendments rule. 291 See 40 CFR 86.1869–12(d). 292 See 77 FR at 62832, 62839 (Oct. 15, 2012). EPA introduced A/C and off-cycle technology credits for the CO2 program in the MY 2012–2016 rule and revised the program in the MY 2017–2025 rule and NHTSA adopted equivalent provisions for MYs 2017 and later in the MY 2017–2025 rule. 293 Vehicle and Engine Certification. Compliance Information for Light-Duty Gas (GHG) Standards. Compliance Information for Light-Duty Greenhouse Gas (GHG) Standards | Certification and Compliance for Vehicles and Engines | U.S. EPA. Last Accessed May 24, 2021. lotter on DSK11XQN23PROD with PROPOSALS2 289 See VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 technologies, though the level of deployment varies by manufacturer.294 Manufacturers have only recently begun including detailed information on off-cycle and A/C efficiency technologies equipped on vehicles in compliance reporting data. For this analysis, though, such information was not sufficiently complete to support a detailed representation of the application of off-cycle technology to specific vehicle model/configurations in the MY 2020 fleet. To account for the A/ C and off-cycle technologies equipped on vehicles and the potential that manufacturers will apply additional A/ C and off-cycle technologies in the rulemaking timeframe, DOT specified model inputs for A/C efficiency and offcycle fuel consumption improvement values in grams/mile for each manufacturer’s fleet in each model year. DOT estimated future values based on an expectation that manufacturers already relying heavily on these adjustments would continue do so, and that other manufacturers would, over time, also approach the limits on adjustments allowed for such improvements. The next sections discuss how the CAFE Model simulates the effectiveness and cost for A/C efficiency and off-cycle technology adjustments. (a) A/C and Off-Cycle Effectiveness Modeling in the CAFE Model In this analysis, the CAFE Model applies A/C and off-cycle flexibilities to manufacturer’s CAFE regulatory fleet performance in a similar way to the regulation.295 In the analysis and after the first MY, A/C efficiency and offcycle FCIVs apply to each manufacturer’s regulatory fleet after the CAFE Model applies conventional technologies for a given standard. That is, conventional technologies are applied to each manufacturers’ vehicles in each MY to assess the 2-cycle sales weighted harmonic average CAFE rating. Then, the CAFE Model assesses the CAFE rating to use for a manufacturer’s compliance value after applying the A/C efficiency and offcycle FCIVs designated in the Market Data file. This assessment of adoption of conventional technology and the A/C efficiency and off-cycle technology occurs on a year-by-year basis in the CAFE Model. The CAFE Model attempts to apply technologies and flexibilities in a way that both minimizes cost and allows the manufacturer to meet their 294 See 2020 EPA Automotive Trends Report, at 91. 295 49 CFR 531.6 and 49 CFR 533.6 Measurement and Calculation procedures. PO 00000 Frm 00108 Fmt 4701 Sfmt 4702 standards without over or under complying. To determine how manufacturers might adopt A/C efficiency and off-cycle technologies in the rulemaking timeframe, DOT began with data from EPA’s 2020 Trends Report and CBI compliance material from manufacturers.296 297 DOT used manufacturer’s MY 2020 A/C efficiency and off-cycle FCIVs as a starting point, and then extrapolated values in each MY until MY 2026, for light trucks to the proposed regulatory cap, for each manufacturer’s fleets by regulatory class. To determine the rate at which to extrapolate the addition of A/C and offcycle technology adoption for each manufacturer, DOT reviewed historical A/C and off-cycle technology applications, each manufacturer’s fleet composition (i.e., breakdown between passenger cars (PCs) and light trucks (LTs)), availability of A/C and off-cycle technologies that manufacturers could still use, and CBI compliance data. Different manufacturers showed different levels of historical A/C efficiency and off-cycle technology adoption; therefore, different manufacturers hit the proposed regulatory caps for A/C efficiency technology for both their PC and LT fleets, and different manufacturers hit caps for off-cycle technologies in the LT regulatory class. DOT declined to extrapolate off-cycle technology adoption for PCs to the proposed regulatory cap for a few reasons. First, past EPA Trends Reports showed that many manufacturers did not adopt offcycle technology to their passenger car fleets. Next, manufacturers limited PC offerings in MY 2020 as compared to historical trends. Last, CBI compliance data available to DOT indicated a lower adoption of menu item off-cycle technologies to PCs compared to LTs. DOT accordingly limited the application of off-cycle FCIVs to 10 g/mi for PCs but allowed LTs to apply 15 g/mi of offcycle FCIVs. The inputs for A/C efficiency technologies were set to 5 g/ mi and 7.2 g/mi for PCs and LTs, respectively. DOT allowed A/C efficiency technologies to reach the regulatory caps by MY 2024, which is the first year of standards assessed in this analysis. DOT decided to apply the FCIVs in this way because the A/C and off-cycle 296 Vehicle and Engine Certification. Compliance Information for Light-Duty Gas (GHG) Standards. Compliance Information for Light-Duty Greenhouse Gas (GHG) Standards | Certification and Compliance for Vehicles and Engines | U.S. EPA. Last Accessed May 24, 2021. 297 49 U.S.C. 32907. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules technologies are generally more costeffective than other technologies. The details of this assessment (and the calculation) are further discussed in the CAFE Model Documentation.298 The A/C efficiency and off-cycle adjustment schedules used in this analysis are shown in TSD Chapter 3.8 and in the Market Data file’s Credits and Adjustments worksheet. (b) A/C and Off-Cycle Costs For this analysis, A/C and off-cycle technologies are applied independently of the decision trees using the extrapolated values shown above, so it is necessary to account for the costs of those technologies independently. Table III–36 shows the costs used for A/C and off-cycle FCIVs in this analysis. The costs are shown in dollars per gram of CO2 per mile ($ per g/mile). The A/C efficiency and off-cycle technology costs are the same costs used in the EPA Proposed Determination and described in the EPA Proposed Determination TSD.299 To develop the off-cycle technology costs, DOT selected the 2nd generic 3 gram/mile package estimated to cost $170 (in 2015$) to apply in this analysis in $ per gram/mile. DOT updated the costs used in the Proposed Determination TSD from 2015$ to 2018$, adjusted the costs for RPE, and applied a relatively flat learning rate. We seek comment on whether these costs are still appropriate, or whether a different $ per gram/mile cost should be used. If commenters believe a different 49709 $ per gram/mile cost should be used, we request commenters provide any data or information on which any alternative costs are based. This should include a description of how the alternative costs are representative of costs across the industry, and whether the $ per gram/ mile estimate is based on a package of specific off-cycle technologies. Similar to off-cycle technology costs, DOT used the cost estimates from EPA Proposed Determination TSD for A/C efficiency technologies that relied on the 2012 rulemaking TSD.300 DOT updated these costs to 2018$ and adjusted for RPE for this analysis, and applied the same mature learning rate that DOT applied for off-cycle technologies. Table 111-36 - Estimated Costs ($ per g/mi) for A/C and Off-Cycle Adjustments A/C Efficiency 4.30 3.89 3.52 2020 2025 2030 lotter on DSK11XQN23PROD with PROPOSALS2 E. Consumer Responses to Manufacturer Compliance Strategies The previous subsections in Section III have so far discussed how manufacturers might respond to changes to the standards. While the technology analysis is informative of the different compliance strategies available to manufactures, the tangible costs and benefits that accrue because of CAFE standards are dependent on how consumers respond to the decisions made by manufacturers. Many, if not most, of the benefits and costs resulting from changes to CAFE standards are private benefits that accrue to the buyers of new cars and trucks, produced in the model years under consideration. These benefits and costs largely flow from the changes to vehicle ownership and operating costs that result from improved fuel economy, and the cost of the technology required to achieve those improvements. The remaining external benefits are also derived from how consumers use—or do not use— vehicles. The next few subsections walk through how the analysis models consumer responses to changing vehicles and prices. NHTSA requests comment on the following discussion. 298 CAFE Model Documentation, S5. PD TSD. EPA–420–R–16–021. November 2016. At 2–423–2–245. https://nepis.epa.gov/Exe/ 299 EPA VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 A/C Leakage 10.76 9.72 8.79 Off-Cycle 83.79 77.47 71.83 1. Macroeconomic and Consumer Behavior Assumptions This proposal includes a comprehensive economic analysis of the impacts of altering the CAFE standards. Most of the effects measured are influenced by macroeconomic conditions that are exogenous to the agency’s influence. For example, fuel prices are mainly determined by global demand, and yet they determine how much fuel efficiency technology manufacturers will apply to U.S.-bound vehicles, how much consumers are willing to pay for a new vehicle, the amount of travel in which all users engage, and the value of each gallon saved from higher CAFE standards. Constructing these forecasts requires robust projections of macroeconomic variables that span the timeframe of the analysis, including real U.S. Gross Domestic Product (GDP), consumer confidence, U.S. population, and real disposable personal income. In order to ensure internal consistency within the analysis, relevant economic assumptions are derived from the same source. The analysis presented in this analysis employs forecasts developed by DOT using the U.S. Energy Information Administration’s (EIA’s) National Energy Model System (NEMS). EIA is an agency within the U.S. Department of Energy (DOE) which collects, analyzes, and disseminates independent and impartial energy information to promote sound policymaking, efficient markets, and public understanding of energy and its interaction with the economy and the environment. EIA uses NEMS to produce its Annual Energy Outlook (AEO), which presents forecasts of future fuel prices, among many other energy-related variables. The analysis employs forecasts of fuel prices, real U.S. GDP, real disposable personal income, U.S. population, and fuel prices from the AEO 2021 Reference Case. The agency also uses a forecast of consumer confidence to project sales from the IHS Markit Global Insight long-term macroeconomic model. The IHS Markit Global Insight model is also used by EIA for the AOE. While these macroeconomic assumptions are some of the most critical inputs to the analysis, they are also subject to the most uncertainty— particularly over the full lifetimes of the vehicles affected by this proposed rule. The agency uses low and high cases from the AEO as bounding cases for sensitivity analyses. The purpose of the sensitivity analyses, discussed in greater ZyPDF.cgi?Dockey=P100Q3L4.pdf. Last accessed May 24, 2021. 5.1. PO 00000 Frm 00109 Fmt 4701 Sfmt 4702 300 Joint E:\FR\FM\03SEP2.SGM NHTSA and EPA 2012 TSD, see Section 03SEP2 EP03SE21.085</GPH> Model Year 49710 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 detail in PRIA Chapter 6 and PRIA Chapter 7, is not to posit a more credible future state of the world than the central case assumes—we assume the central case is the most likely future state of the world—but rather to measure the degree to which important outcomes can change under different assumptions about fuel prices. The first year simulated in this analysis is 2020, though it is based on observational data (rather than forecasts) to the greatest extent possible. The elements of the analysis that rely most heavily on the macroeconomic inputs— aggregate demand for VMT, new vehicle sales, used vehicle retirement rates—all reflect the relatively rapid climb back to pre-pandemic growth rates (in all the regulatory alternatives). See TSD Chapter 4.1 for a more complete discussion of the macroeconomic assumptions made for the analysis. Another key assumption that permeates throughout the analysis is how much consumers are willing to pay for fuel economy. Increased fuel efficiency offers vehicle owners significant savings; in fact, the analysis shows that fuel savings exceed the technology cost to comply with even the most stringent standards analyzed by this proposal at a 3% discount rate. It would be reasonable to assume that consumers value the full value of fuel savings as they would be better off not having to spend more of their disposable income on fuel. If consumers did value the full amount of fuel savings, fuel-efficient vehicles would functionally be cheaper for consumers to own when considering both purchasing and operational costs, and thus making the vehicles offered under the stricter alternatives more attractive than similar models offered in the baseline. Recent econometric research remains divided between studies that conclude has shown that consumers may value most, if not all of potential fuel savings, and those that conclude that consumers significantly undervalue expected fuel savings (NASEM, 2021, p. 11–351).301 302 303 301 There is a great deal of work attempting to test the question whether consumers are adequately informed about, and sufficiently attentive to, potential fuel savings at the time of purchase. The existing research is not conclusive and leaves many open questions. On the one hand, there is significant support for the proposition that consumers are responsive to changes in fuel costs. See, e.g., Busse et al.; Sallee, et al. On the other hand, there is also support for the proposition that many consumers do not, in fact, give full or sufficient attention to potential savings from fuelefficient vehicles, and thus make suboptimal decisions. See Duncan et al.; Gillingham et al. 302 Allcott, H. and C. Knittel, 2019. ‘‘Are Consumers Poorly Informed about Fuel Economy? VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 If buyers fully value the savings in fuel costs that result from higher fuel economy, manufacturers would be expected to supply the improvements that buyers demand, and vehicle demand would be expected to fully consider both future fuel cost savings consumers would realize from owning— and potentially re-selling—more fuelefficient models and increased cost of vehicles due to technological and design changes made to increase fuel economy. If instead, consumers systematically undervalue future fuel savings, the result would be an underinvestment in fuel-saving technology. In that case, more stringent fuel economy standards would also lead manufacturers to adopt improvements in fuel economy that improve consumer welfare (e.g., Allcott et al., 2014; Heutel, 2015). There is substantial evidence that consumers do not fully value lifetime fuel savings. Even though the average fuel economy of new vehicles reached an all-time high in MY 2020 of 25.7 MPG,304 this is still significantly below the fuel economy of the fleet’s most efficient vehicles that are readily available to consumers.305 Manufacturers have repeatedly informed the agency that consumers only value between 2 to 3 years-worth of fuel savings when making purchasing decisions. The potential for car buyers voluntarily to forego improvements in fuel economy that offer savings exceeding their initial costs is one example of what is often termed the ‘‘energy-efficiency gap.’’ This appearance of such a gap, between the level of energy efficiency that would minimize consumers’ overall expenses and what they actually purchase, is typically based on engineering calculations that compare the initial cost for providing higher energy efficiency to the discounted present value of the resulting savings in future energy costs. There has long been an active debate about why such a gap might arise and whether it actually exists. Economic theory predicts that economically rational individuals will purchase more energy-efficient products only if the savings in future energy costs they offer promise to offset their higher initial costs. On the other hand, behavioral economics has documented numerous situations in which the decision-making of consumers differs in important ways from the predictions of economic consumer model (e.g., Dellavigna, 2009). A behavioral explanation of such ‘undervaluation’ of the savings from purchasing higher-mpg models is myopia or present bias; consumers may give undue focus to short-term costs and insufficient attention to long-term benefits.306 This situation could arise because they are unsure of the fuel savings that will be achieved in realworld driving, what future fuel prices will be, how long they will own a new vehicle, whether they will drive it enough to realize the promised savings. As a consequence, they may view choosing to purchase or not purchase a fuel-efficient technology as a risky bet; behavioral economics has demonstrated that faced with the decision to accept or reject a risky choice, some consumers weigh potential losses approximately twice as heavily as potential gains, significantly undervaluing the choice relative to its expected value (e.g., Kahneman and Tversky, 1979; Kahneman, 2011). In the context of a choice to pay more for a fuel-saving technology, loss aversion has been shown to have the potential to cause undervaluation of future fuel savings similar to that reported by manufacturers (Greene, 2011; Greene et al., 2013).307 The behavioral model holds that consumers’ decisions are affected by the context, or framing, of choices. As explained in NASEM (2021), Ch. 11.3.3, it is possible that consumers respond to changes in fuel economy regulations differently than they respond to manufacturers voluntarily offering the option to purchase fuel economy technology to new car buyers. We explain this differential more thoroughly in TSD Chapter 4.2.1.1, but here is the contextual explanation for the differential valuation. If a consumer is thinking about buying a new car and is looking at two models, one that includes voluntarily added fuel economy technology and is more expensive and another that does not, she may buy the cheaper, less fuel efficient version even if the more expensive model will save Evidence from Two Experiments’’, AEJ: Economic Policy, 11(1): 1–37. 303 D. Duncan, A. Ku, A. Julian, S. Carley, S. Siddiki, N. Zirogiannis and J. Graham, 2019. ‘‘Most Consumers Don’t Buy Hybrids: Is Rational Choice a Sufficient Explanation?’’, J. of Benefit-Cost Analysis, 10(1): 1–38. 304 See EPA 2020 Automotive Trends Report at 6, available at https://nepis.epa.gov/Exe/ZyPDF.cgi? Dockey=P1010U68.pdf. 305 Id. At 9. 306 Gillingham et al., 2021, which is an AEJ: Economic Policy paper, just published on consumer myopia in vehicle purchases; a standard reference on present bias generally is O’Donoghue and Rabin, AER: Papers and Proceedings, 2015. 307 Application of investment under uncertainty will yield similar results as costs may be more certain and up front while the fuel savings or benefits of the investment may be perceived as more uncertain and farther into future, thereby reducing investments in fuel saving technologies. PO 00000 Frm 00110 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules money in the long run. But if, instead, the consumer is faced with whether to buy a new car at all as opposed to keeping an older one, if all new cars contain technology to meet fuel economy standards, then she may view the decision differently. Will, for example, an extra $1,000 for a new car— a $1,000 that the consumer will more than recoup in fuel savings—deter her from buying the new car, especially when most consumers finance cars over a number of years rather than paying the $1,000 cost up front (therefore any increase in monthly payment would be partly or entirely offset with lower fuel costs)? In additon, the fact that standards generally increase gradually over a period of years allows time for consumers and other information sources to verify that fuel savings are real and of substantial value. Another alternative is that consumers view the increase in immediate costs associated with fuel economy technology in the context of tradeoffs they must make amongst their purchasing decisions. American households must choose how to spend their income amongst many competing goods and services, including how much to spend on a new vehicle. They may also decide to opt for another form of transportation. While a consumer may recognize and value the potential long-term value of fuel savings, they may also prefer to spend their money on other items, either in the form of other vehicle attributes—such as picking a truck with a larger flatbed or upgrading to a more luxurious trim package—or other unrelated goods and services. The same technologies that can be used to increase fuel economy can also be used to enable increased vehicle power or weight while maintaining fuel economy. While increased fuel efficiency will free up disposable income throughout the lifetime of the vehicle (and may even exceed the additional upfront costs to purchase a more expensive fuel-efficient vehicle), the value of owning a different good sooner may provide consumers even more benefit. As explained more thoroughly in TSD Chapter 4.2.1.1, the analysis assumes that potential car and light truck buyers value only the undiscounted savings in fuel costs from purchasing a higher-mpg model they expect to realize over the first 30 months they own it. Depending on the discount rate buyers are assumed to apply, this amounts to 25–30% of the expected savings in fuel costs over its entire lifetime. These savings would offset only a fraction of the expected increase in new car and light truck prices that the agency estimates will be required for manufacturers to recover VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 their increased costs for making required improvements to fuel economy. The agency seeks comment on whether 30 months of undiscounted fuel savings is an appropriate measure for the analysis of consumer willingness to pay for fuel economy. The assumption also has important implications for other outcomes of the model, including for VMT, safety, and air pollution emissions projections. If NHTSA is incorrect about the undervaluation of fuel economy in the context of regulatory standards and its effect on car sales, correcting the assumption should result in improved safety outcomes and additional declines in conventional air pollutants. If commenters believe a different amount of time should be used for the payback assumption, it would be most helpful to NHTSA if commenters could define the amount of time, provide an explanation of why that amount of time is preferable, provide any data or information on which the amount of time is based, and provide any discussion of how changing this assumption would interact with other elements in the analysis. 2. Fleet Composition The composition of the on-road fleet—and how it changes in response to CAFE standards—determines many of the costs and benefits of the proposal. For example, how much fuel the lightduty consumes is dependent on the number of new vehicles sold, older (and less efficient) vehicles retired, and how much those vehicles are driven. Prior to the 2020 CAFE standards, all previous CAFE rulemaking analyses used static fleet forecasts that were based on a combination of manufacturer compliance data, public data sources, and proprietary forecasts (or product plans submitted by manufacturers). When simulating compliance with regulatory alternatives, those analyses projected identical sales and retirements across the alternatives, for each manufacturer down to the make/model level—where the exact same number of each model variant was assumed to be sold in a given model year under both the least stringent alternative (typically the baseline) and the most stringent alternative considered (intended to represent ‘‘maximum technology’’ scenarios in some cases). To the extent that an alternative matched the assumptions made in the production of the proprietary forecast, using a static fleet based upon those assumptions may have been warranted. However, a fleet forecast is unlikely to be representative of a broad set of regulatory alternatives with significant PO 00000 Frm 00111 Fmt 4701 Sfmt 4702 49711 variation in the cost of new vehicles. A number of commenters on previous regulatory actions and peer reviewers of the CAFE Model encouraged consideration of the potential impact of fuel efficiency standards on new vehicle prices and sales, the changes to compliance strategies that those shifts could necessitate, and the downstream impact on vehicle retirement rates. In particular, the continued growth of the utility vehicle segment causes changes within some manufacturers’ fleets as sales volumes shift from one region of the footprint curve to another, or as mass is added to increase the ride height of a vehicle on a sedan platform to create a crossover utility vehicle, which exists on the same place of the footprint curve as the sedan upon which it might be based. The analysis now dynamically simulates changes in the vehicle fleet’s size, composition, and usage as manufacturers and consumers respond to regulatory alternatives, fuel prices, and macroeconomic conditions. The analysis of fleet composition is comprised of two forces, how new vehicle sales—the flow of new vehicles into the registered population—changes in response to regulatory alternatives, and the influence of economic and regulatory factors on vehicle retirement (otherwise known as scrappage). Below are brief descriptions that of how the agency models sales and scrappage. For a full explanation, refer to TSD Chapter 4.2. Particularly given the broad uncertainty discussed in TSD Chapter 4.2, NHTSA seeks comment on the discussion below and the associated discussions in the TSD, on the internal structure of the sales and scrappage modules, and whether and how to change the sales and scrappage analyses for the final rule. (a) Sales For the purposes of regulatory evaluation, the relevant sales metric is the difference between alternatives rather than the absolute number of sales in any of the alternatives. As such, the sales response model currently contains three parts: A nominal forecast that provides the level of sales in the baseline (based upon macroeconomic inputs, exclusively), a price elasticity that creates sales differences relative to that baseline in each year, and a fleet share model that produces differences in the passenger car and light truck market share in each alternative. The nominal forecast does not include price and is merely a (continuous) function of several macroeconomic variables that are provided to the model as inputs. The price elasticity is also specified as an E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 49712 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules input, but this analysis assumes a unit elastic response of ¥1.0—meaning that a one percent increase in the average price of a new vehicle produces a one percent decrease in total sales. NHTSA seeks comment on this assumption. The price change on which the elasticity acts is calculated net of some portion of the future fuel savings that accrue to new vehicle buyers (2.5 years’ worth, in this analysis, as discussed in the previous section). The current baseline sales module reflects the idea that total new vehicle sales are primarily driven by conditions in the economy that are exogenous to the automobile industry. Over time, new vehicle sales have been cyclical—rising when prevailing economic conditions are positive (periods of growth) and falling during periods of economic contraction. While the kinds of changes to vehicle offerings that occur as a result of manufacturers’ compliance actions exert some influence on the total volume of new vehicle sales, they are not determinative. Instead, they drive the kinds of marginal differences between regulatory alternatives that the current sales module is designed to simulate—more expensive vehicles, generally, reduce total sales but only marginally. The first component of the sales response model is the nominal forecast, which is a function (with a small set of inputs) that determines the size of the new vehicle market in each calendar year in the analysis for the baseline. It is of some relevance that this statistical model is intended only as a means to project a baseline sales series. Past reviewers expressed concerns about the possibility of econometrically estimating an industry average price elasticity in a way that isolates the causal effect of new vehicle prices on new vehicle sales (and properly addresses the issue of endogeneity between sales and price). The nominal forecast model does not include prices and is not intended for statistical inference around the question of price response in the new vehicle market. The economic response to the pandemic has created uncertainty, particularly in the near-term, around pace at which the market for automobiles will recover— and the scale and timing of the recovery’s peak—before returning to its long-term trend. DOT will continue to monitor macroeconomic data and new vehicle sales and update its baseline forecast as appropriate. The second component of the sales response model captures how price changes affect the number of vehicles sold. The price elasticity is applied to the percentage change in average price VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 (in each year). The price change does not represent an increase/decrease over the last observed year, but rather the percentage change relative to the baseline for that year. In the baseline, the average price is defined as the observed new vehicle price in 2019 (the last historical year before the simulation begins) plus the average regulatory cost associated with the baseline alternative.308 The central analysis in this proposal simulates multiple programs simultaneously (CAFE final standards, EPA final greenhouse gas standards, ZEV, and the California Framework Agreement), and the regulatory cost includes both technology costs and civil penalties paid for noncompliance (with CAFE standards) in a model year. Because the elasticity assumes no perceived change in the quality of the product, and the vehicles produced under different regulatory scenarios have inherently different operating costs, the price metric must account for this difference. The price to which the unit elasticity is applied in this analysis represents the residual price change between scenarios after accounting for 2.5 years’ worth of fuel savings to the new vehicle buyer. The third and final component of the sales model is the dynamic fleet share module (DFS). Some commenters to previous rules noted that the market share of SUVs continues to grow, while conventional passenger car body-styles continue to lose market share. For instance, in the 2012 final rule, the agencies projected fleet shares based on the continuation of the baseline standards (MYs 2012–2016) and a fuel price forecast that was much higher than the realized prices since that time. As a result, that analysis assumed passenger car body-styles comprising about 70 percent of the new vehicle market by 2025, which was internally consistent. The reality, however, has been quite different. The CAFE Model includes the DFS model in an attempt to address these market realities. The DFS distributes the total industry sales across two different body-types: ‘‘cars’’ and ‘‘light trucks.’’ While there are specific definitions of ‘‘passenger cars’’ and ‘‘light trucks’’ that determine a vehicle’s regulatory class, the distinction used in this phase of the analysis is more simplistic. All body308 The CAFE Model currently operates as if all costs incurred by the manufacturer as a consequence of meeting regulatory requirements, whether those are the cost of additional technology applied to vehicles in order to improve fleetwide fuel economy or civil penalties paid when fleets fail to achieve their standard, are ‘‘passed through’’ to buyers of new vehicles in the form of price increases. PO 00000 Frm 00112 Fmt 4701 Sfmt 4702 styles that are obviously cars—sedans, coupes, convertibles, hatchbacks, and station wagons—are defined as ‘‘cars’’ for the purpose of determining fleet share. Everything else—SUVs, smaller SUVs (crossovers), vans, and pickup trucks—are defined as ‘‘light trucks’’— even though they may not be treated as such for compliance purposes. The DFS uses two functions from the National Energy Modeling System (NEMS) used in the 2017 AEO to independently estimate the share of passenger cars and light trucks, respectively, given average new market attributes (fuel economy, horsepower, and curb weight) for each group and current fuel prices, as well as the prior year’s market share and prior year’s attributes. The two independently estimated shares are then normalized to ensure that they sum to one. These shares are applied to the total industry sales derived in the first stage of the sales response. This produces total industry volumes of car and light truck body styles. Individual model sales are then determined from there based on the following sequence: (1) Individual manufacturer shares of each body style (either car or light truck) times the total industry sales of that body style, then (2) each vehicle within a manufacturer’s volume of that bodystyle is given the same percentage of sales as appear in the 2020 fleet. This implicitly assumes that consumer preferences for particular styles of vehicles are determined in the aggregate (at the industry level), but that manufacturers’ sales shares of those body styles are consistent with MY 2020 sales. Within a given body style, a manufacturer’s sales shares of individual models are also assumed to be constant over time. This approach implicitly assumes that manufacturers are currently pricing individual vehicle models within market segments in a way that maximizes their profit. Without more information about each OEM’s true cost of production and operation, fixed and variables costs, and both desired and achievable profit margins on individual vehicle models, there is no basis to assume that strategic shifts within a manufacturer’s portfolio will occur in response to standards. The DFS model show passenger car styles gaining share with higher fuel prices and losing them when prices are decline. Similarly, as fuel economy increases in light truck models, which offer consumers other desirable attributes beyond fuel economy (ride height or interior volume, for example) their relative share increases. However, this approach does not suggest that consumers dislike fuel economy in passenger cars, but merely recognizes E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 the fact that fuel economy has diminishing returns in terms of fuel savings. As the fuel economy of light trucks increases, the tradeoff between passenger car and light truck purchases increasingly involves a consideration of other attributes. The coefficients also show a relatively stronger preference for power improvements in cars than light trucks because that is an attribute where trucks have typically outperformed cars, just as cars have outperformed trucks for fuel economy. For years, some commenters encouraged the agency to consider vehicle attributes beyond price and fuel economy when estimating a sales response to fuel economy standards, and suggested that a more detailed representation of the new vehicle market would allow the agency to simulate strategic mix shifting responses from manufacturers and diverse attribute preferences among consumers. Doing so would have required a discrete choice model (at some level). Discrete models are highly sensitive on their inputs and typically fit well on a single year of data (a cross-section of vehicles and buyers). This approach misses relevant trends that build over time, such as rising GDP or shifting consumer sentiment toward emerging technologies and are better used for analysis as opposed to prediction. While the agency believes that these challenges provide a reasonable basis for not employing a discrete choice model in the current CAFE Model, the agency also believes these challenges are not insurmountable, and that some suitable variant of such models may yet be developed for use in future fuel economy rulemakings. The agency has not abandoned the idea and plans to continue experimenting with econometric specifications that address heterogeneous consumer preferences in the new vehicle market as they further refine the analytical tools used for regulatory analysis. The agency seeks suggestions on how to incorporate other vehicle attributes into the current analysis, or, alternatively, methods to implement a discrete choice model that can capture changing technologies and consumer trends over an extended timeperiod. (b) Scrappage New and used vehicles are substitutes. When the price of a good’s substitute increases/decreases, the demand curve for that good shifts upwards/downwards and the equilibrium price and quantity supplied also increases/decreases. Thus, increasing the quality-adjusted price of new vehicles will result in an increase VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 in equilibrium price and quantity of used vehicles. Since, by definition, used vehicles are not being ‘‘produced’’ but rather ‘‘supplied’’ from the existing fleet, the increase in quantity must come via a reduction in their scrappage rates. Practically, when new vehicles become more expensive, demand for used vehicles increases (and they become more expensive). Because used vehicles are more valuable in such circumstances, they are scrapped at a lower rate, and just as rising new vehicle prices push marginal prospective buyers into the used vehicle market, rising used vehicle prices force marginal prospective buyers of used vehicles to acquire older vehicles or vehicles with fewer desired attributes. The effect of fuel economy standards on scrappage is partially dependent on how consumers value future fuel savings and our assumption that consumers value only the first 30 months of fuel savings. Many competing factors influence the decision to scrap a vehicle, including the cost to maintain and operate it, the household’s demand for VMT, the cost of alternative means of transportation, and the value that can be attained through reselling or scrapping the vehicle for parts. A car owner will decide to scrap a vehicle when the value of the vehicle is less than the value of the vehicle as scrap metal, plus the cost to maintain or repair the vehicle. In other words, the owner gets more value from scrapping the vehicle than continuing to drive it, or from selling it. Typically, the owner that scraps the vehicle is not the first owner. While scrappage decisions are made at the household level, the agency is unaware of sufficient household data to sufficiently capture scrappage at that level. Instead, the agency uses aggregate data measures that capture broader market trends. Additionally, the aggregate results are consistent with the rest of the CAFE Model as the model does not attempt to model how manufacturers will price new vehicles; the model instead assumes that all regulatory costs to make a particular vehicle compliant are passed onto the purchaser who buys the vehicle. It is more likely that manufacturers will defray a portion of the increased regulatory cost across its vehicles or to other manufacturers’ buyers through the sale of credits. The most predictive element of vehicle scrappage is ‘engineering scrappage.’ This source of scrappage is largely determined by the age of a vehicle and the durability of a specific model year vintage, which the agency uses proprietary vehicle registration data from IHS/Polk to collect vehicle PO 00000 Frm 00113 Fmt 4701 Sfmt 4702 49713 age and durability. Other factors include fuel economy and new vehicle prices. For historical data on new vehicle transaction prices, the agency uses National Automobile Dealers Association (NADA) Data.309 The data consists of the average transaction price of all light-duty vehicles; since the transaction prices are not broken-down by body style, the model may miss unique trends within a particular vehicle body style. The transaction prices are the amount consumers paid for new vehicles and exclude any tradein value credited towards the purchase. This may be particularly relevant for pickup trucks, which have experienced considerable changes in average price as luxury and high-end options entered the market over the past decade. Future models will further consider incorporating price series that consider the price trends for cars, SUVs and vans, and pickups separately. The other source of vehicle scrappage is from cyclical effects, which the model captures using forecasts of GDP and fuel prices. Vehicle scrappage follows a roughly logistic function with age—that is, when a vintage is young, few vehicles in the cohort are scrapped, as they age, more and more of the cohort are retired and the instantaneous scrappage (the rate at which vehicles are scrapped) reaches a peak, and then scrappage declines as vehicles enter their later years as fewer and fewer of the cohort remains on the road. The analysis uses a logistic function to capture this trend of vehicle scrappage with age. The data shows that the durability of successive model years generally increases over time, or put another way, historically newer vehicles last longer than older vintages. However, this trend is not constant across all vehicle ages—the instantaneous scrappage rate of vehicles is generally lower for later vintages up to a certain age, but increases thereafter so that the final share of vehicles remaining converges to a similar share remaining for historically observed vintages.310 The agency uses fixed effects to capture potential changes in durability across model years and to ensure that vehicles approaching the end of their life are scrapped in the analysis, the agency applies a decay function to vehicles after they reach age 30. The macroeconomic conditions variables discussed above are included 309 The data can be obtained from NADA. For reference, the data for MY 2020 may be found at https://www.nada.org/nadadata/. 310 Examples of why durability may have changed are new automakers entering the market or general changes to manufacturing practices like switching some models from a car chassis to a truck chassis. E:\FR\FM\03SEP2.SGM 03SEP2 49714 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 in the logistic model to capture cyclical effects. Finally, the change in new vehicle prices projected in the model (technology costs minus 30 months of fuel savings) are included which generates differing scrappage rates across the alternatives. In addition to the variables included in the scrappage model, the agency considered several other variables that likely either directly or indirectly influence scrappage in the real world including, maintenance and repair costs, the value of scrapped metal, vehicle characteristics, the quantity of new vehicles purchased, higher interest rates, and unemployment. These variables were excluded from the model either because of a lack of underlying data or modeling constraints. Their exclusion from the model is not intended to diminish their importance, but rather highlights the practical constraints of modeling intricate decisions like scrappage. 3. Changes in Vehicle Miles Traveled (VMT) In the CAFE Model, VMT is the product of average usage per vehicle in the fleet and fleet composition, which is itself a function of new vehicle sales and vehicle retirement decisions, otherwise known as scrappage. These three components—average vehicle usage, new vehicle sales, and older vehicle scrappage—jointly determine total VMT projections for each alternative. VMT directly influences many of the various effects of fuel economy standards that decisionmakers consider in determining what levels of standards to set. For example, the value of fuel savings is a function of a vehicle’s efficiency, miles driven, and fuel price. Similarly, factors like criteria pollutant emissions, congestion, and fatalities are direct functions of VMT. It is the agency’s perspective that the total demand for VMT should not vary excessively across alternatives. The basic travel needs for an average household are unlikely to be influenced heavily by the stringency of the CAFE standards, as the daily need for a vehicle will remain the same. That said, it is reasonable to assume that fleets with differing age distributions and inherent cost of operation will have slightly different annual VMT (even without considering VMT associated with rebound miles); however, the difference could conceivably be small. Based on the structure of the CAFE Model, the combined effect of the sales and scrappage responses would create small percentage differences in total VMT across the range of regulatory alternatives if steps are not taken to VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 constrain VMT. Because VMT is related to many of the costs and benefits of the program, even small magnitude differences in VMT across alternatives can have meaningful impacts on the incremental net benefit analysis. Furthermore, since decisions about alternative stringencies look at the incremental costs and benefits across alternatives, it is more important that the analysis capture the variation of VMT across alternatives than to accurately predict total VMT within a scenario. To ensure that travel demand remains consistent across the different regulatory scenarios, the CAFE Model begins with a model of aggregate VMT developed by the Federal Highway Administration (FHWA) that is used to produce their official annual VMT forecasts. These estimates provide the aggregate VMT of all model years and body styles for any given calendar year and are same across regulatory alternatives for each year in the analysis. Since vehicles of different ages and body styles carry different costs and benefits, to account properly for the average value of consumer and societal costs and benefits associated with vehicle usage under various CAFE alternatives, it is necessary to partition miles by age and body type. The agency created ‘‘mileage accumulation schedules’’ using IHS-Polk odometer data to construct mileage accumulation schedules as an initial estimate of how much a vehicle expected to drive at each age throughout its life. The agency uses simulated new vehicle sales, annual rates of retirement for used vehicles, and the mileage accumulation schedules to distribute VMT across the age distribution of registered vehicles in each calendar year to preserve the nonrebound VMT constraint. The fuel economy rebound effect—a specific example of the welldocumented energy efficiency rebound effect for energy-consuming capital goods—refers to the tendency of motor vehicles’ use (as measured by VMT) to increase when their fuel economy is improved and, as a result, the cost per mile (CPM) of driving declines. Establishing more stringent CAFE standards than the baseline level will lead to comparatively higher fuel economy for new cars and light trucks, thus decreasing the amount of fuel consumed and increasing the amount of travel in which new car and truck buyers engage. The agency recognizes that the value selected for the rebound effect influences overall costs and benefits associated with the regulatory alternatives under consideration as well as the estimates of lives saved under PO 00000 Frm 00114 Fmt 4701 Sfmt 4702 various regulatory alternatives, and that the rebound estimate, along with fuel prices, technology costs, and other analytical inputs, is part of the body of information that agency decisionmakers have considered in determining the appropriate levels of the CAFE standards in this proposal. We also note that the rebound effect diminishes the economic and environmental benefits associated with increased fuel efficiency. The agency conducted a review of the literature related to the fuel economy rebound effect, which is extensive and covers multiple decades and geographic regions. The totality of evidence, without categorically excluding studies on grounds that they fail to meet certain criteria, and evaluating individual studies based on their particular strengths, suggests that a plausible range for the rebound effect is 10–50 percent. The central tendency of this range appears to be at or slightly above its midpoint, which is 30 percent. Considering only those studies that the agency believes are derived from extremely robust and reliable data, employ identification strategies that are likely to prove effective at isolating the rebound effect, and apply rigorous estimation methods suggests a range of approximately 10–45 percent, with most of their estimates falling in the 15–30 percent range. A case can also be made to support values of the rebound effect falling in the 5–15 percent range. There is empirical evidence supported by theory, that the rebound effect has been declining over time due to factors such as increasing income that affects the value of time, increasing fuel economy that makes the fuel cost of driving a smaller share of the total costs of vehicle travel, as well as diminishing impacts of increased car ownership and rates of license holding on vehicle travel. Lower rebound estimates are associated with studies that include recently published analyses using U.S. data, and to accord the most weight to research that relies on measures of vehicle use derived from odometer readings, controls for the potential endogeneity of fuel economy, and estimates the response of vehicle use to variation in fuel economy itself, rather than to fuel cost per distance driven or fuel prices. This approach suggests that the rebound effect is likely in the range from 5–15 percent and is more likely to lie toward the lower end of that range. The agency selected a rebound magnitude of 15% for the analysis because it was well-supported by the totality of the evidence and aligned well with FHWA’s estimated elasticity for E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules travel (14.6%). However, recognizing the uncertainty surrounding the rebound value, we also examine the sensitivity of estimated impacts to values of the rebound ranging from 10 percent to 20 percent. NHTSA seeks comment on the above discussion, and whether to consider a different value for the rebound effect for the final rule analysis. In order to calculate total VMT with rebound, the CAFE Model applies the price elasticity of VMT (taken from the FHWA forecasting model) to the full change in CPM and the initial VMT schedule, but applies the (user defined) rebound parameter to the incremental percentage change in CPM between the non-rebound and full CPM calculations to the miles applied to each vehicle during the reallocation step that ensured adjusted non-rebound VMT matched the non-rebound VMT constraint. The approach in the model is a combination of top-down (relying on the FHWA forecasting model to determine total light-duty VMT in a given calendar year), and bottom-up (where the composition and utilization of the onroad fleet determines a base level of VMT in a calendar year, which is constrained to match the FHWA model). While the agency and the model developers agree that a joint household consumer choice model—if one could be developed adequately and reliably to capture the myriad circumstances under which families and individuals make decisions relating to vehicle purchase, use, and disposal—would reflect decisions that are made at the household level, it is not obvious, or necessarily appropriate, to model the national program at that scale in order to produce meaningful results that can be used to inform policy decisions. The most useful information for policymakers relates to national impacts of potential policy choices. No other element of the rulemaking analysis occurs at the household level, and the error associated with allocating specific vehicles to specific households over the course of three decades would easily dwarf any error associated with the estimation of these effects in aggregate. We have attempted to incorporate estimates of changes to the new and used vehicle markets at the highest practical levels of aggregation, and worked to ensure that these effects produce fleetwide VMT estimates that are consistent with the best, current projections given our economic assumptions. While future work will always continue to explore approaches to improve the realism of CAFE policy simulation, there are important differences between small-scale VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 econometric studies and the kind of flexibility that is required to assess the impacts of a broad range of regulatory alternatives over multiple decades. To assist with creating even more precise estimates of VMT, the agency requests comment on alternative approaches to simulate VMT demand. See TSD Chapter 4.3 for a complete accounting of how the agency models VMT. 4. Changes to Fuel Consumption The agency uses the fuel economy and age and body-style VMT estimates to determine changes in fuel consumption. The agency divides the expected vehicle use by the anticipated MPG to calculate the gallons consumed by each simulated vehicle, and when aggregated, the total fuel consumed in each alternative. F. Simulating Environmental Impacts of Regulatory Alternatives This proposal includes the adoption of electric vehicles and other fuel-saving technologies, which produce additional co-benefits. These co-benefits include reduced vehicle tailpipe emissions during operation as well as reduced upstream emissions during petroleum extraction, transportation, refining, and finally fuel transportation, storage, and distribution. This section provides an overview of how we developed input parameters for criteria pollutants, greenhouse gases, and air toxics. This section also describes how we generated estimates of how these emissions could affect human health, in particular criteria pollutants known to cause poor air quality and damage human health when inhaled. The rule implements an emissions inventory methodology for estimating impacts. Vehicle emissions inventories are often described as three-legged stools, comprised of activity (i.e., miles traveled, hours operated, or gallons of fuel burned), population (or number of vehicles), and emission factors. An emissions factor is a representative rate that attempts to relate the quantity of a pollutant released to the atmosphere per unit of activity.311 In this rulemaking, upstream emission factors are on a fuel volume basis and tailpipe emission factors are on a distance basis. Simply stated, the rule’s upstream emission inventory is the product of the per-gallon emission factor and the corresponding number of gallons of gasoline or diesel consumed. 311 USEPA, Basics Information of Air Emissions Factors and Quantification, https://www.epa.gov/ air-emissions-factors-and-quantification/basicinformation-air-emissions-factors-andquantification. PO 00000 Frm 00115 Fmt 4701 Sfmt 4702 49715 Similarly, the tailpipe emission inventory is the product of the per-mile emission factor and the appropriate miles traveled estimate. The only exceptions are that tailpipe sulfur oxides (SOX) and carbon dioxide (CO2) also use a per-gallon emission factor in the CAFE Model. The activity levels— both miles traveled and fuel consumption—are generated by the CAFE Model, while the emission factors have been incorporated from other Federal models. For this rule, vehicle tailpipe (downstream) and upstream emission factors and subsequent inventories were developed independently from separate data sources. Upstream emission factors are estimated from a lifecycle emissions model developed by the U.S. Department of Energy’s (DOE) Argonne National Laboratory, the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) Model.312 Tailpipe emission factors are estimated from the regulatory highway emissions inventory model developed by the U.S. Environmental Protection Agency’s (EPA) National Vehicle and Fuel Emissions Laboratory, the Motor Vehicle Emission Simulator (MOVES3). Data from GREET and MOVES3 have been utilized to update the CAFE Model for this rulemaking. The changes in adverse health outcomes due to criteria pollutants emitted, such as differences in asthmatic episodes and hospitalizations due to respiratory or cardiovascular distress, are generally reported in incidence per ton values. Incidence values were developed using several EPA studies and recently updated from the 2020 final rule to better account for the emissions source sectors used in the CAFE Model analysis. Chapter 5 of the TSD accompanying this proposal includes the detailed discussion of the procedures we used to simulate the environmental impact of regulatory alternatives, and the implementation of these procedures into the CAFE Model is discussed in detail in the CAFE Model Documentation. Further discussion of how the health impacts of upstream and tailpipe criteria pollutant emissions have been monetized in the analysis can be found in Section III.G.2.b)(2). The Supplemental Environmental Impact Statement accompanying this analysis also includes a detailed discussion of both criteria pollutant and GHG emissions and their impacts. NHTSA 312 U.S. Department of Energy, Argonne National Laboratory, Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) Model, Last Update: 9 Oct. 2020, https://greet.es.anl.gov/. E:\FR\FM\03SEP2.SGM 03SEP2 49716 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules seeks comment on the following discussion. 1. Activity Levels Used To Calculate Emissions Impacts lotter on DSK11XQN23PROD with PROPOSALS2 Emission inventories in this rule vary by several key activity parameters, especially relating to the vehicle’s model year and relative age. Most importantly, the CAFE Model accounts for vehicle sales, turnover, and scrappage as well as travel demands over its lifetime. Like other models, the CAFE Model includes procedures to estimate annual rates at which new vehicles are purchased, driven, and subsequently scrapped. Together, these procedures result in, for each vehicle model in each model year, estimates of the number remaining in service in each calendar year, as well as the annual mileage accumulation (i.e., VMT) at each age. Inventories by model year are derived from the annual mileage accumulation rates and corresponding emission factors. As discussed in Section III.C.2, for each vehicle model/configuration in each model year from 2020 to 2050 for upstream estimates and 2060 for tailpipe estimates, the CAFE Model estimates and records the fuel type (e.g., gasoline, diesel, electricity), fuel economy, and number of units sold in the U.S. The model also makes use of an aggregated representation of vehicles sold in the U.S. during 1975–2019. The model estimates the numbers of each cohort of vehicles remaining in service in each calendar year, and the amount of driving accumulated by each such cohort in each calendar year. The CAFE Model estimates annual vehicle-miles of travel (VMT) for each individual car and light truck model produced in each model year at each age of their lifetimes, which extend for a maximum of 40 years. Since a vehicle’s age is equal to the current calendar year minus the model year in which it was originally produced, the age span of each vehicle model’s lifetime corresponds to a sequence of 40 calendar years beginning in the calendar year corresponding to the model year it was produced.313 These estimates reflect the gradual decline in the fraction of each car and light truck model’s original model year production volume that is expected to remain in 313 In practice, many vehicle models bearing a given model year designation become available for sale in the preceding calendar year, and their sales can extend through the following calendar year as well. However, the CAFE Model does not attempt to distinguish between model years and calendar years; vehicles bearing a model year designation are assumed to be produced and sold in that same calendar year. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 service during each year of its lifetime, as well as the well-documented decline in their typical use as they age. Using this relationship, the CAFE Model calculates fleet-wide VMT for cars and light trucks in service during each calendar year spanned in this analysis. Based on these estimates, the model also calculates quantities of each type of fuel or energy, including gasoline, diesel, and electricity, consumed in each calendar year. By combining these with estimates of each model’s fuel or energy efficiency, the model also estimates the quantity and energy content of each type of fuel consumed by cars and light trucks at each age, or viewed another way, during each calendar year of their lifetimes. As with the accounting of VMT, these estimates of annual fuel or energy consumption for each vehicle model and model year combination are combined to calculate the total volume of each type of fuel or energy consumed during each calendar year, as well as its aggregate energy content. The procedures the CAFE Model uses to estimate annual VMT for individual car and light truck models produced during each model year over their lifetimes and to combine these into estimates of annual fleet-wide travel during each future calendar year, together with the sources of its estimates of their survival rates and average use at each age, are described in detail in Section III.E.2. The data and procedures it employs to convert these estimates of VMT to fuel and energy consumption by individual model, and to aggregate the results to calculate total consumption and energy content of each fuel type during future calendar years, are also described in detail in that same section. The model documentation accompanying this NPRM describes these procedures in detail.314 The quantities of travel and fuel consumption estimated for the cross section of model years and calendar years constitutes a set of ‘‘activity levels’’ based on which the model calculates emissions. The model does so by multiplying activity levels by emission factors. As indicated in the previous section, the resulting estimates of vehicle use (VMT), fuel consumption, and fuel energy content are combined with emission factors drawn from various sources to estimate emissions of GHGs, criteria air pollutants, and airborne toxic compounds that occur throughout the fuel supply and distribution process, as well as during 314 CAFE Model documentation is available at https://www.nhtsa.gov/corporate-average-fueleconomy/compliance-and-effects-modeling-system. PO 00000 Frm 00116 Fmt 4701 Sfmt 4702 vehicle operation, storage, and refueling. Emission factors measure the mass of each GHG or criteria pollutant emitted per vehicle-mile of travel, gallon of fuel consumed, or unit of fuel energy content. The following sections identifies the sources of these emission factors and explains in detail how the CAFE Model applies them to its estimates of vehicle travel, fuel use, and fuel energy consumption to estimate total annual emissions of each GHG, criteria pollutant, and airborne toxic. 2. Simulating Upstream Emissions Impacts Building on the methodology for simulating upstream emissions impacts used in prior CAFE rules, this analysis uses emissions factors developed with the U.S. Department of Energy’s Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) Model, specifically GREET 2020.315 The analysis includes emissions impacts estimates for regulated criteria pollutants,316 greenhouse gases,317 and air toxics.318 The upstream emissions factors included in the CAFE Model input files include parameters for 2020 through 2050 in five-year intervals (e.g., 2020, 2025, 2030, and so on). For gasoline and diesel fuels, each analysis year includes upstream emissions factors for the four following upstream emissions processes: Petroleum extraction, petroleum transportation, petroleum refining, and fuel transportation, storage, and distribution (TS&D). In contrast, the upstream electricity emissions factor is only a single value per analysis year. We briefly discuss the components included in each upstream emissions factor here, and a more detailed discussion is included in Chapter 5 of the TSD accompanying this proposal and the CAFE Model Documentation. The first step in the process for calculating upstream emissions includes any emissions related to the extraction, recovery, and production of petroleumbased feedstocks, namely conventional crude oil, oil sands, and shale oils. Then, the petroleum transportation process accounts for the transport 315 U.S. Department of Energy, Argonne National Laboratory, Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) Model, Last Update: 9 Oct. 2020, https://greet.es.anl.gov/. 316 Carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOX), sulfur oxides (SOX), and particulate matter with 2.5micron (mm) diameters or less (PM2.5). 317 Carbon dioxide (CO ), methane (CH ), and 2 4 nitrous oxide (N2O). 318 Acetaldehyde, acrolein, benzene, butadiene, formaldehyde, diesel particulate matter with 10micron (mm) diameters or less (PM10). E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 processes of crude feedstocks sent for domestic refining. The petroleum refining calculations are based on the aggregation of fuel blendstock processes rather than the crude feedstock processes, like the petroleum extraction and petroleum transportation calculations. The final upstream process after refining is the transportation, storage, and distribution (TS&D) of the finished fuel product. The upstream gasoline and diesel emissions factors are aggregated in the CAFE Model based on the share of fuel savings leading to reduced domestic oil fuel refining and the share of reduced domestic refining from domestic crude oil. The CAFE Model applies a fuel savings adjustment factor to the petroleum refining process and a combined fuel savings and reduced domestic refining adjustment to both the petroleum extraction and petroleum transportation processes for both gasoline and diesel fuels and for each pollutant. These adjustments are consistent across fuel types, analysis years, and pollutants, and are unchanged from the 2020 final rule. Additional discussion of the methodology for estimating the share of fuel savings leading to reduced domestic oil refining is located in Chapter 6.2.4.3 of the TSD. NHTSA seeks comment on the methodology used and specifically whether all of the change in refining would happen domestically, rather than the current division between domestic and nondomestic refining. Upstream electricity emissions factors are also calculated using GREET 2020. GREET 2020 projects a national default electricity generation mix for transportation use from the latest Annual Energy Outlook (AEO) data available from the previous year. As discussed above, the CAFE Model uses a single upstream electricity factor for each analysis year. 3. Simulating Tailpipe Emissions Impacts Tailpipe emission factors are generated using the latest regulatory model for on-road emission inventories from the U.S. Environmental Protection Agency, the Motor Vehicle Emission Simulator (MOVES3), November 2020 release. MOVES3 is a state-of-thescience, mobile-source emissions inventory model for regulatory applications.319 New MOVES3 tailpipe emission factors have been incorporated 319 U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Motor Vehicle Emission Simulator (MOVES), Last Updated: March 2021, https://www.epa.gov/moves/latest-versionmotor-vehicle-emission-simulator-moves. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 into the CAFE parameters, and these updates supersede tailpipe data previously provided by EPA from MOVES2014 for past CAFE analyses. MOVES3 accounts for a variety of processes related to emissions impacts from vehicle use, including running exhaust, start exhaust, refueling displacement vapor loss, brakewear, and tirewear, among others. The CAFE Model uses tailpipe emissions factors for all model years from 2020 to 2060 for criteria pollutants and air toxics. To maintain continuity in the historical inventories, only emission factors for model years 2020 and after were updated; all emission factors prior to MY 2020 were unchanged from previous CAFE rulemakings. In addition, the updated tailpipe data in the current CAFE reference case no longer account for any fuel economy improvements or changes in vehicle miles traveled from the 2020 final rule. In order to avoid double-counting effects from the previous rulemaking in the current rulemaking, the new tailpipe baseline backs out 1.5% year-over-year stringency increases in fuel economy, and 0.3% VMT increases assumed each year (20% rebound on the 1.5% improvements in stringency). Note that the MOVES3 data do not cover all the model years and ages required by the CAFE Model, MOVES only generates emissions data for vehicles made in the last 30 model years for each calendar year being run. This means emissions data for some calendar year and vehicle age combinations are missing. To remedy this, we take the last vehicle age that has emissions data and forward fill those data for the following vehicle ages. Due to incomplete available data for years prior to MY 2020, tailpipe emission factors for MY 2019 and earlier have not been modified and continue to utilize MOVES2014 data. For tailpipe CO2 emissions, these factors are defined based on the fraction of each fuel type’s mass that represents carbon (the carbon content) along with the mass density per unit of the specific type of fuel. To obtain the emission factors associated with each fuel, the carbon content is then multiplied by the mass density of a particular fuel as well as by the ratio of the molecular weight of carbon dioxide to that of elemental carbon. This ratio, a constant value of 44/12, measures the mass of carbon dioxide that is produced by complete combustion of mass of carbon contained in each unit of fuel. The resulting value defines the emission factor attributed to CO2 as the amount of grams of CO2 emitted during vehicle operation from each type of fuel. This calculation is repeated for gasoline, E85, diesel, and PO 00000 Frm 00117 Fmt 4701 Sfmt 4702 49717 compressed natural gas (CNG) fuel types. In the case of CNG, the mass density and the calculated CO2 emission factor are denoted as grams per standard cubic feet (scf), while for the remainder of fuels, these are defined as grams per gallon of the given fuel source. Since electricity and hydrogen fuel types do not cause CO2 emissions to be emitted during vehicle operation, the carbon content, and the CO2 emission factors for these two fuel types are assumed to be zero. The mass density, carbon content, and CO2 emission factors for each fuel type are defined in the Parameters file. The CAFE Model calculates CO2 tailpipe emissions associated with vehicle operation of the surviving onroad fleet by multiplying the number of gallons (or scf for CNG) of a specific fuel consumed by the CO2 emissions factor for the associated fuel type. More specifically, the amount of gallons or scf of a particular fuel are multiplied by the carbon content and the mass density per unit of that fuel type, and then applying the ratio of carbon dioxide emissions generated per unit of carbon consumed during the combustion process.320 4. Estimating Health Impacts From Changes in Criteria Pollutant Emissions The CAFE Model computes select health impacts resulting from three criteria pollutants: NOX, SOX,321 and PM2.5. Out of the six criteria pollutants currently regulated, NOX, SOX, and PM2.5 are known to be emitted regularly from mobile sources and have the most adverse effects to human health. These health impacts include several different morbidity measures, as well as low and high mortality estimates, and are measured by the number of instances predicted to occur per ton of emitted pollutant.322 The model reports total health impacts by multiplying the estimated tons of each criteria pollutant by the corresponding health incidence per ton value. The inputs that inform the calculation of the total tons of emissions resulting from criteria pollutants are discussed above. This section discusses how the health 320 Chapter 3, Section 4 of the CAFE Model Documentation provides additional description for calculation of CO2 tailpipe emissions with the model. 321 Any reference to SO in this section refers to X the sum of sulfur dioxide (SO2) and sulfate particulate matter (pSO4) emissions, following the methodology of the EPA papers cited. 322 The complete list of morbidity impacts estimated in the CAFE Model is as follows: Acute bronchitis, asthma exacerbation, cardiovascular hospital admissions, lower respiratory symptoms, minor restricted activity days, non-fatal heart attacks, respiratory emergency hospital admissions, respiratory emergency room visits, upper respiratory symptoms, and work loss days. E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 49718 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules incidence per ton values were obtained. See Section III.G.2.b)(2) and Chapter 6.2.2 of the TSD accompanying this proposal for information regarding the monetized damages arising from these health impacts. The SEIS that accompanies this proposal also includes a detailed discussion of the criteria pollutants and air toxics analyzed and their potential health effects. In addition, consistent with past analyses, NHTSA will perform full-scale photochemical air quality modeling and present those results in the Final SEIS associated with the final rule. That analysis will provide additional assessment of the human health impacts from changes in PM2.5 and ozone associated with this rule. NHTSA will also consider whether such modeling could practicably and meaningfully be included in the FRIA, noting that compliance with CAFE standards is based on the average performance of manufacturers’ production for sale throughout the U.S., and that the FRIA will involve sensitivity analysis spanning a range of model inputs, many of which impact estimates of future emissions from passenger cars and light trucks. Chapter 6 of the PRIA includes a discussion of overall changes in health impacts associated with criteria pollutant changes across the different rulemaking scenarios. In previous rulemakings, health impacts were split into two categories based on whether they arose from upstream emissions or tailpipe emissions. In the current analysis, these health incidence per ton values have been updated to reflect the differences in health impacts arising from each emission source sector, according to the latest publicly available EPA reports. Five different upstream emission source sectors (Petroleum Extraction, Petroleum Transportation, Refineries, Fuel Transportation, Storage and Distribution, and Electricity Generation) are now represented. As the health incidences for the different source sectors are all based on the emission of one ton of the same pollutants, NOX, SOX, and PM2.5, the differences in the incidence per ton values arise from differences in the geographic distribution of the pollutants, a factor which affects the number of people impacted by the pollutants.323 The CAFE Model health impacts inputs are based partially on the structure of EPA’s 2018 technical 323 See Environmental Protection Agency (EPA). 2018. Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. https:// www.epa.gov/sites/production/files/2018-02/ documents/sourceapportionmentbpttsd_2018.pdf. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 support document, Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors (referred to here as the 2018 EPA source apportionment TSD),324 which reported benefit per ton values for the years 2016, 2020, 2025, and 2030.325 For the years in between the source years used in the input structure, the CAFE Model applies values from the closest source year. For instance, 2020 values are applied for 2020–2022, and 2025 values are applied for 2023–2027. For further details, see the CAFE Model documentation, which contains a description of the model’s computation of health impacts from criteria pollutant emissions. Despite efforts to be as consistent as possible between the upstream emissions sectors utilized in the CAFE Model with the 2018 EPA source apportionment TSD, the need to use upto-date sources based on newer air quality modeling updates led to the use of multiple papers. In addition to the 2018 EPA source apportionment TSD used in the 2020 final rule, DOT used additional EPA sources and conversations with EPA staff to appropriately map health incidence per ton values to the appropriate CAFE Model emissions source category. We understand that uncertainty exists around the contribution of VOCs to PM2.5 formation in the modeled health impacts from the petroleum extraction sector; however, based on feedback to the 2020 final rule we believe that the updated health incidence values specific to petroleum extraction sector emissions may provide a more appropriate estimate of potential health impacts from that sector’s emissions than the previous approach of applying refinery sector emissions impacts to the petroleum extraction sector. That said, we are aware of work that EPA has been doing to address concerns about the BPT estimates, and NHTSA will work further with EPA to update and synchronize approaches to the BPT estimates. The basis for the health impacts from the petroleum extraction sector was a 2018 oil and natural gas sector paper written by EPA staff (Fann et al.), which estimated health impacts for this sector in the year 2025.326 This paper defined 324 Environmental Protection Agency (EPA). 2018. Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. https://www.epa.gov/ sites/production/files/2018-02/documents/source apportionmentbpttsd_2018.pdf. 325 As the year 2016 is not included in this analysis, the 2016 values were not used. 326 Fann, N., Baker, K. R., Chan, E., Eyth, A., Macpherson, A., Miller, E., & Snyder, J. (2018). Assessing Human Health PM2.5 and Ozone Impacts from U.S. Oil and Natural Gas Sector Emissions in PO 00000 Frm 00118 Fmt 4701 Sfmt 4702 the oil and gas sector’s emissions not only as arising from petroleum extraction but also from transportation to refineries, while the CAFE/GREET component is composed of only petroleum extraction. After consultation with the authors of the EPA paper, it was determined that these were the best available estimates for the petroleum extraction sector, notwithstanding this difference. Specific health incidence per pollutant were not reported in the paper, so EPA staff sent BenMAP health incidence files for the oil and natural gas sector upon request. DOT staff then calculated per ton values based on these files and the tons reported in the Fann et al. paper.327 The only available health impacts corresponded to the year 2025. Rather than trying to extrapolate, these 2025 values were used for all the years in the CAFE Model structure: 2020, 2025, and 2030.328 This simplification implies an overestimate of damages in 2020 and an underestimate in 2030.329 The petroleum transportation sector and fuel TS&D sector did not correspond to any one EPA source sector in the 2018 EPA source apportionment TSD, so a weighted average of multiple different EPA sectors was used to determine the health impact per ton values for those sectors. We used a combination of different EPA mobile source sectors from two different papers, the 2018 EPA source apportionment TSD,330 and a 2019 mobile source sectors paper (Wolfe et al.)331 to generate these values. The health incidence per ton values associated with the refineries sector and 2025. Environmental science & technology, 52(15), 8095–8103 (hereinafter Fann et al.). 327 Nitrate-related health incidents were divided by the total tons of NOX projected to be emitted in 2025, sulfate-related health incidents were divided by the total tons of projected SOX, and EC/OC (elemental carbon and organic carbon) related health incidents were divided by the total tons of projected EC/OC. Both Fann et al. and the 2018 EPA source apportionment TSD define primary PM2.5 as being composed of elemental carbon, organic carbon, and small amounts of crustal material. Thus, the EC/OC BenMAP file was used for the calculation of the incidents per ton attributable to PM2.5. 328 These three years are used in the CAFE Model structure because it was originally based on the estimate provided in the 2018 EPA source apportionment TSD. 329 See EPA. 2018. Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. https://www.epa.gov/sites/production/files/201802/documents/sourceapportionmentbpttsd_ 2018.pdf p.9. 330 Environmental Protection Agency (EPA). 2018. Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. https://www.epa.gov/ sites/production/files/2018-02/documents/source apportionmentbpttsd_2018.pdf. 331 Wolfe et al. 2019. Monetized health benefits attributable to mobile source emissions reductions across the United States in 2025. https:// pubmed.ncbi.nlm.nih.gov/30296769/. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules G. Simulating Economic Impacts of Regulatory Alternatives This section describes the agency’s approach for measuring the economic costs and benefits that will result from establishing alternative CAFE standards for future model years. The benefit and cost measures the agency uses are important considerations, because as Office of Management and Budget (OMB) Circular A–4 states, benefits and costs reported in regulatory analyses must be defined and measured consistently with economic theory, and should also reflect how alternative regulations are anticipated to change the behavior of producers and consumers from a baseline scenario.333 For CAFE standards, those include vehicle manufacturers, buyers of new cars and light trucks, owners of used vehicles, and suppliers of fuel, all of whose behavior is likely to respond in complex ways to the level of CAFE standards that DOT establishes for future model years. It is important to report the benefits and costs of this proposed action in a format that conveys useful information about how those impacts are generated and also distinguishes the impacts of those economic consequences for private businesses and households from the effects on the remainder of the U.S. economy. A reporting format will accomplish this objective to the extent that it clarifies who incurs the benefits and costs of the proposed, and shows how the economy-wide or ‘‘social’’ benefits and costs of the proposed action are composed of its direct effects on vehicle producers, buyers, and users, plus the indirect or ‘‘external’’ benefits 332 Wolfe et al. 2019. Monetized health benefits attributable to mobile source emissions reductions across the United States in 2025. https:// pubmed.ncbi.nlm.nih.gov/30296769/. 333 White House Office of Management and Budget, Circular A–4: Regulatory Analysis, September 17, 2003 (https://obamawhitehouse. archives.gov/omb/circulars_a004_a-4/), Section E. lotter on DSK11XQN23PROD with PROPOSALS2 electricity generation sector were drawn solely from the 2018 EPA source apportionment TSD. The CAFE Model follows a similar process for computing health impacts resulting from tailpipe emissions as it does for calculating health impacts from upstream emissions. Previous rulemakings used the 2018 EPA source apportionment TSD as the source for the health incidence per ton, matching the CAFE Model tailpipe emissions inventory to the ‘‘on-road mobile sources sector’’ in the TSD. However, a more recent EPA paper from 2019 (Wolfe et al.) 332 computes monetized damage costs per ton values at a more disaggregated level, separating on-road mobile sources into multiple categories based on vehicle type and fuel type. Wolfe et al. did not report incidences per ton, but that information was obtained through communications with EPA staff. The methodology for generating values for each emissions category in the CAFE Model is discussed in detail in Chapter 5 of the TSD accompanying this proposal. The Parameters file contains all of the health impact per ton of emissions values used in this proposal. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00119 Fmt 4701 Sfmt 4702 49719 and costs it creates for the general public. Table III–37 and Table III–38 present the incremental economic benefits and costs of the proposed action and the alternatives (described in detail in Section IV) to increase CAFE standards for model years 2024–26 at three percent and seven percent discount rates in a format that is intended to meet these objectives. The tables include costs which are transfers between different economic actors—these will appear as both a cost and a benefit in equal amounts (to separate affected parties). Societal cost and benefit values shown elsewhere in this document do not show costs which are transfers for the sake of simplicity but report the same net societal costs and benefits. The proposed action and the alternatives would increase costs to manufacturers for adding technology necessary to enable new cars and light trucks to comply with fuel economy and emission regulations. It may also increase fine payments by manufacturers who would have achieved compliance with the less demanding baseline standards. Manufacturers are assumed to transfer these costs on to buyers by charging higher prices; although this reduces their revenues, on balance, the increase in compliance costs and higher sales revenue leaves them financially unaffected. Since the analysis assumes that manufacturers are left in the same economic position regardless of the standards, they are excluded from the tables. BILLING CODE 4910–59–P E:\FR\FM\03SEP2.SGM 03SEP2 49720 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-37 - Incremental Benefits and Costs Over the Lifetimes of Total Fleet Produced Through 2029 (2018$ Billions), 3% Percent Discount Rate, by Alternative 1 2 3 34.3 67.6 100.1 - - - 0.1 6.2 0.6 8.2 1.3 11.2 40.6 76.3 112.7 7.3 10.1 13.5 7.5 11.0 25.9 66.5 15.8 18.9 44.7 121.1 23.2 27.0 63.6 176.3 Reduced Fuel Benefits from Additional Driving Less Frequent Refueling Subtotal - Incremental Private Benefits External Benefits Reduction in Petroleum Market Externality Reduced Climate Damages Reduced Health Damages Subtotal - Incremental External Benefits Total Incremental Social Benefits 47.9 12.3 -0.5 59.7 73.0 15.3 -0.8 87.6 103.8 20.8 0.3 124.8 0.9 20.3 1.7 22.8 82.6 1.5 32.0 0.4 33.9 121.4 2.1 45.6 0.3 48.0 172.9 Net Incremental Social Benefits 16.1 0.3 -3.4 Alternative: Private Costs Technology Costs to Increase Fuel Economy Increased Maintenance and Repair Costs Sacrifice in Other Vehicle Attributes Consumer Surplus Loss from Reduced New Vehicle Sales Safety Costs Internalized by Drivers Subtotal - Incremental Private Costs External Costs Congestion and Noise Costs from Rebound-Effect Driving Safety Costs Not Internalized by Drivers Loss in Fuel Tax Revenue Subtotal - Incremental External Costs Total Incremental Social Costs lotter on DSK11XQN23PROD with PROPOSALS2 334 A portion of Reduced Fuel Costs represent the benefit to consumers of not having to pay taxes on avoided gasoline consumption. This amount offsets VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 the Loss in Fuel Tax Revenue in External Costs. For example, the $47.9 billion in Reduced Fuel Costs PO 00000 Frm 00120 Fmt 4701 Sfmt 4702 in alternative 1 represents $11 billion of avoided fuel taxes and $36.9 billion in gasoline savings. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.086</GPH> Private Benefits Costs 334 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49721 Table 111-38 - Incremental Benefits and Costs Over the Lifetimes of Total Fleet Produced Through 2029 (2018$ Billions), 7% Percent Discount Rate, by Alternative 1 2 3 28.1 55.0 81.4 - - - 0.1 3.7 0.5 4.9 1.1 6.8 31.9 60.4 89.3 4.8 6.8 9.3 5.5 7.0 17.3 11.6 11.9 30.3 17.3 17.0 43.5 49.3 90.7 132.8 Reduced Fuel Costs Benefits from Additional Driving Less Frequent Refueling 29.7 7.5 -0.4 44.9 9.3 -0.6 63.7 12.7 0.0 Subtotal - Incremental Private Benefits 36.8 53.6 76.4 Reduction in Petroleum Market Externality Reduced Climate Damages Reduced Health Damages 0.5 13.3 0.9 0.9 21.0 0.1 1.3 29.9 -0.1 Subtotal - Incremental External Benefits 14.8 51.6 22.0 75.6 31.2 107.6 2.3 -15.1 -25.2 Alternative: Private Costs Technology Costs to Increase Fuel Economy Increased Maintenance and Repair Costs Sacrifice in Other Vehicle Attributes Consumer Surplus Loss from Reduced New Vehicle Sales Safety Costs Internalized by Drivers Subtotal - Incremental Private Costs External Costs Congestion and Noise Costs from Rebound-Effect Driving Safety Costs Not Internalized by Drivers Loss in Fuel Tax Revenue Subtotal - Incremental External Costs Total Incremental Social Costs Private Benefits Total Incremental Social Benefits Net Incremental Social Benefits lotter on DSK11XQN23PROD with PROPOSALS2 BILLING CODE 4910–59–C Compared to the baseline standards, if the preferred alternative is finalized, the analysis shows that buyers of new cars and light trucks will incur higher purchasing prices and financing costs, which will lead to some buyers dropping out of the new vehicle market. Drivers of new vehicles will also experience a slight uptick in the risk of being injured in a crash because of mass reduction technologies employed to meet the increased standards. While this effect is not statistically significant, NHTSA provides these results for transparency, and to demonstrate that their inclusion does not affect NHTSA’s proposed policy decision. Because of the increasing price of new vehicles, some owners may delay retiring and replacing their older vehicles with newer models. In effect, this will VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 transfer some driving that would have been done in newer vehicles under the baseline scenario to older models within the legacy fleet, thus increasing costs for injuries (both fatal and less severe) and property damages sustained in motor vehicle crashes. This stems from the fact that cars and light trucks have become progressively more protective in crashes over time (and also slightly less prone to certain types of crashes, such as rollovers). Thus, shifting some travel from newer to older models would increase injuries and damages sustained by drivers and passengers because they are traveling in less safe vehicles and not because it changes the risk profiles of drivers themselves. These costs are largely driven by assumptions regarding consumer valuation of fuel efficiency and an assumption that more fuelefficient vehicles are less preferable to PO 00000 Frm 00121 Fmt 4701 Sfmt 4702 consumers than their total cost to improve fuel economy. These are issues on which we seek comments. In exchange for these costs, consumers will benefit from new cars and light trucks with better fuel economy. Drivers will experience lower costs as a consequence of new vehicles’ decreased fuel consumption, and from fewer refueling stops required because of their increased driving range. They will experience mobility benefits as they use newly purchased cars and light trucks more in response to their lower operating costs. On balance, consumers of new cars and light trucks produced during the model years subject to this proposed action will experience significant economic benefits. Table III–37 and Table III–38 also show that the changes in fuel consumption and vehicle use resulting E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.087</GPH> External Benefits lotter on DSK11XQN23PROD with PROPOSALS2 49722 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules from this proposed action will in turn generate both benefits and costs to society writ large. These impacts are ‘‘external,’’ in the sense that they are byproducts of decisions by private firms and individuals that alter vehicle use and fuel consumption but are experienced broadly throughout society rather than by the firms and individuals who indirectly cause them. In terms of costs, additional driving by consumers of new vehicles in response to their lower operating costs will increase the external costs associated with their contributions to traffic delays and noise levels in urban areas, and these additional costs will be experienced throughout much of the society. While most of the risk of additional driving or delaying purchasing a newer vehicle are internalized by those who make those decisions, a portion of the costs are borne by other road users. Finally, since owners of new vehicles will be consuming less fuel, they will pay less in fuel taxes. Society will also benefit from more stringent standards. Increased fuel efficiency will reduce the amount of petroleum-based fuel consumed and refined domestically, which will decrease the emissions of carbon dioxide and other greenhouse gases that contribute to climate change, and, as a result, the U.S. (and the rest of world) will avoid some of the economic damages from future changes in the global climate. Similarly, reduced fuel production and use will decrease emissions of more localized air pollutants (or their chemical precursors), and the resulting decrease in the U.S. population’s exposure to harmful levels of these pollutants will lead to lower costs from its adverse effects on health. Decreasing consumption and imports of crude petroleum for refining lower volumes of gasoline and diesel will also accrue some benefits throughout to the U.S., in the form of potential gains of energy security as businesses and households that are dependent on fuel are subject to less sudden and sharp changes in energy prices. On balance, Table III–37 and Table III–38 show that both consumers and society as a whole will experience net economic benefits from the proposed action. The following subsections will briefly describe the economic costs and benefits considered by the agency. For a complete discussion of the methodology employed and the results, see TSD Chapter 6 and PRIA Chapter 6, respectively. The safety implications of the proposal—including the monetary impacts—are reserved for Section III.H. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 NHTSA seeks comment on the following discussion. 1. Private Costs and Benefits (a) Costs to Consumers (1) Technology Costs The proposed action and the alternatives would increase costs to manufacturers for adding technology necessary to enable new cars and light trucks to comply with fuel economy and emission regulations. Manufacturers are assumed to transfer these costs on to buyers by charging higher prices. See Section III.C.6 and TSD Chapter 2.5. (2) Consumer Sales Surplus Buyers who would have purchased a new vehicle with the baseline standards in effect but decide not to do so in response to the changes in new vehicles’ prices due to more stringent standards in place will experience a decrease in welfare. The collective welfare loss to those ‘‘potential’’ new vehicle buyers is measured by the foregone consumer surplus they would have received from their purchase of a new vehicle in the baseline. Consumer surplus is a fundamental economic concept and represents the net value (or net benefit) a good or service provides to consumers. It is measured as the difference between what a consumer is willing to pay for a good or service and the market price. OMB Circular A–4 explicitly identifies consumer surplus as a benefit that should be accounted for in cost-benefit analysis. For instance, OMB Circular A– 4 states the ‘‘net reduction in total surplus (consumer plus producer) is a real cost to society,’’ and elsewhere elaborates that consumer surplus values be monetized ‘‘when they are significant.’’ 335 Accounting for the portion of fuel savings that the average new vehicle buyer demands, and holding all else equal, higher average prices should depress new vehicle sales and by extension reduce consumer surplus. The inclusion of consumer surplus is not only consistent with OMB guidance, but with other parts of the regulatory analysis. For instance, we calculate the increase in consumer surplus associated with increased driving that results from the decrease in the cost per mile of operation under more stringent regulatory alternatives, as discussed in Section III.G.1.b)(3). The surpluses associated with sales and additional mobility are inextricably linked as they capture the direct costs and benefits accrued by purchasers of new vehicles. 335 OMB PO 00000 Circular A–4, at 37–38. Frm 00122 Fmt 4701 Sfmt 4702 The sales surplus captures the welfare loss to consumers when they forego a new vehicle purchase in the presence of higher prices and the additional mobility measures the benefit increased mobility under lower operating expenses. The agency estimates the loss of sales surplus based on the change in quantity of vehicles projected to be sold after adjusting for quality improvements attributable to fuel economy. For additional information about consumer sales surplus, see TSD Chapter 6.1.5. (3) Ancillary Costs of Higher Vehicle Prices Some costs of purchasing and owning a new or used vehicle scale with the value of the vehicle. Where fuel economy standards increase the transaction price of vehicles, they will affect both the absolute amount paid in sales tax and the average amount of financing required to purchase the vehicle. Further, where they increase the MSRP, they increase the appraised value upon which both value-related registration fees and a portion of insurance premiums are based. The analysis assumes that the transaction price is a set share of the MSRP, which allows calculation of these factors as shares of MSRP. For a detailed explanation of how the agency estimates these costs, see TSD Chapter 6.1.1. These costs are included in the consumer per-vehicle cost-benefit analysis but are not included in the societal cost-benefit analysis because they are assumed to be transfers from consumers to governments, financial institutions, and insurance companies. (b) Benefits to Consumers (1) Fuel Savings The primary benefit to consumers of increasing CAFE standards are the additional fuel savings that accrue to new vehicle owners. Fuel savings are calculated by multiplying avoided fuel consumption by fuel prices. Each vehicle of a given body style is assumed to be driven the same as all the others of a comparable age and body style in each calendar year. The ratio of that cohort’s VMT to its fuel efficiency produces an estimate of fuel consumption. The difference between fuel consumption in the baseline, and in each alternative, represents the gallons (or energy) saved. Under this assumption, our estimates of fuel consumption from increasing the fuel economy of each individual model depend only on how much its fuel economy is increased, and do not reflect whether its actual use differs from other E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules models of the same body type. Neither do our estimates of fuel consumption account for variation in how much vehicles of the same body type and age are driven each year, which appears to be significant (see TSD Chapter 4.3.1.2). Consumers save money on fuel expenditures at the average retail fuel price (fuel price assumptions are discussed in detail in TSD Chapter 4.1.2), which includes all taxes and represents an average across octane blends. For gasoline and diesel, the included taxes reflect both the Federal tax and a calculated average state fuel tax. Expenditures on alternative fuels (E85 and electricity, primarily) are also included in the calculation of fuel expenditures, on which fuel savings are based. And while the included taxes net out of the social benefit cost analysis (as they are a transfer), consumers value each gallon saved at retail fuel prices including any additional fees such as taxes. See TSD Chapter 6.1.3 for additional details. In the TSD, the agency considers the possibility that several of the assumptions made about vehicle use could lead to misstating the benefits of fuel savings. The agency notes that these assumptions are necessary to model fuel savings and likely have minimal impact to the accuracy of this analysis. Technologies that can be used to improve fuel economy can also be used to increase other vehicle attributes, especially acceleration performance, weight, and energy-using accessories. While this is most obvious for technologies that improve the efficiency of engines and transmissions, it is also true of technologies that reduce mass, aerodynamic drag, rolling resistance or any road or accessory load. The exact nature of the potential to trade-off attributes for fuel economy varies with the technology, but at a minimum, increasing vehicle efficiency or reducing loads allows a more powerful engine to be used while achieving the same level of fuel economy. How consumers value increased fuel economy and how fuel economy regulations affect manufacturers’ decisions about how to use efficiency improving technologies 49723 can have important effects on the estimated costs, benefits, and indirect impacts of fuel economy standards. NHTSA’s preliminary regulatory impact analysis assumes that consumers will purchase, and manufacturers will supply, fuel economy technologies in the absence of fuel economy standards if the technology ‘‘pays for itself’’ in fuel savings over the first 30 months vehicle use. This assumption is based on statements manufacturers have made to us and to NASEM CAFE committees and has been deployed in NHTSA’s prior analyses of fuel economy standards. However, classical economic concepts suggest that deploying this assumption may be problematic when the baseline standards are binding— meaning that they constrain consumers’ behavior to vehicles that are more fuel efficient than they would have chosen in the absence of fuel economy standards. To demonstrate this, we introduce a standard economic model of consumer optimization subject to a budgetary constraint.336 Horsepower HP2 HP1 B2 FS1 FS2 Fuel Savings Figure III–17 models consumer behavior when constrained by a budget. Line B1 represents the consumer’s original budget constraint. Curve I1 is called an indifference curve, which shows each combination of horsepower, which we use here to represent a variety of attributes that could be traded-off for increased fuel economy, and fuel savings between which a consumer is indifferent. The curvature of the indifference curve reflects the principle of diminishing marginal utility—the idea that consumers value consumption of the first unit of any product greater than subsequent units. Curve I1 represents the highest utility achievable when subject to budget constraint B1, as the consumer may select the combination of performance and fuel economy represented by point (HP1, FS1)—which is the point of tangency between I1 and B1. When new technology becomes available that 336 Note that the following section examines whether consumers are rational in their fuel economy consumption patterns. This analysis could represent a scenario where consumers are rational, or one in which the underweight future fuel savings in their car purchasing decisions. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00123 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.088</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure 111-17 -Constrained Optimization Model of Consumer Preferences Between Horsepower and Fuel Economy in the Absence of Fuel Economy Standards 49724 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules makes either fuel economy or performance (or both) more affordable, the consumer’s budget constraint shifts from B1 to B2, and the consumer can now achieve the point of tangency between I2 and B2 (HP2, FS2). In this case, both fuel economy and performance are modeled as normal goods—meaning that as they become more affordable, consumers will elect to consume more of each. Horsepower 82 FS1, Fuel Savings FE Standard Figure 111-18-Constrained Optimization Model of Consumer Preferences Between Horsepower and Fuel Economy in the Presence of Binding Fuel Economy Standards VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 affordable, the consumer’s budget constraint shifts from B1 to B2 again, but the existing fuel economy standard is still binding, so a corner solution remains at FS1. The consumer will choose the corner combination of fuel economy and performance again, where I2 is tangent with B2, at point (FS1, HP2). Note that the consumer has elected to improve performance from HP1 to HP2 but has not elected to improve fuel economy. This model implies that fuel economy standards prevent consumers from achieving their optimal bundle of fuel PO 00000 Frm 00124 Fmt 4701 Sfmt 4702 economy and performance given their current preferences, creating an opportunity cost to consumers in the form of lost performance. The constrained optimization model can be slightly tweaked to show this loss to consumers. In this example, the y-axis uses the composite good M reflecting all other goods and services, including performance. This makes the interpretation of the y axis simpler, as it can be more easily translated into dollars. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.089</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 A different analysis is required when fuel economy standards also bind on consumer decisions. Here, minimum fuel economy standards eliminate some combinations of performance and fuel economy, creating a corner solution in the budget constraint. Figure III–18 shows this effect, as the consumer will elect the point of tangency with budget constraint B1 at the corner solution at (HP1 and FS1), which is also the minimum fuel economy standard. When new technology is introduced (or becomes cheaper) which makes fuel economy and performance more Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules M (Including Horsepower) 49725 11 FS1, - - • FS2, FE Stand 1 FE Stand 2 fuel Savings Figure III–19 shows the effect of new binding fuel economy standards on consumer behavior. The consumer begins at point (M1, FS1) on indifference curve I1. If more stringent fuel economy standards were in place, the consumer would shift to the lower indifference curve I2—reflecting a lower level of utility—and would consume at point (M2, FS2). One concept from the economics literature for valuing the change in welfare from a change in prices or quality (or in this case fuel economy standards) is to look at the compensating variation between the original and final equilibrium. The compensating variation is the amount of money that a consumer would need to return to their original indifference curve.337 It is found by finding the point of tangency with the new indifference curve at the new marginal rate of substitution between the two products and finding the equivalent point on the old indifference curve. Figure III–19 shows this as the distance between points A and B on the Y-axis.338 The above logic appears to explain the trends in fuel economy and vehicle performance (measured by horsepower/ pound) between 1986 and 2004, when gasoline prices fluctuated between $2.00 and $2.50 per gallon and new light duty vehicle fuel economy standards remained nearly constant Figure III–20. Over the same period numerous advanced technologies with the potential to increase fuel economy were adopted. However, the fuel economy of new light duty vehicles did not increase. In fact, increases in the market share of light trucks caused fuel economy to decline somewhat. 337 There is a very similar concept for valuing this opportunity cost known as the equivalent variation. NHTSA presents the compensating variation here for simplicity but acknowledges that the equivalent variation is an equally valid approach. 338 Boardman, Greenberg, Vining, Weimer (2011). Cost-Benefit Analysis; Concepts and Practice. Pgs. 69–73. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00125 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.090</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure 111-19 - Constrained Optimization Model of Consumer Preferences Between Horsepower and Fuel Economy Showing Opportunity Cost of Fuel Economy Standards 49726 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules $4.50 $4.00 $3.50 $3.00 C 0 $2.50 ~ ~ $2.00 ~ 0 $1.50 - -All car ,._ $1.00 "' $0.50 N ••••• All Truck 5 Price of Gasoline - $0.00 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Sources: EPA 2020 Automotive Trends Report; EIA Monthly Energy Review, 5/23; federal Reserve Bank of St. Louis, CPI-U Figure 111-20 - Test Cycle Combined Fuel Economy and Gasoline Price: 19752020 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 required by the car and light truck standards, consistent with the idea the standards were a binding constraint on the fuel economy of new vehicles. The pattern for periods of price shocks and increasing standards is different, however, as can be seen in Figure III– 20. In the early period up to 1986, there is almost no change in performance and vehicle weight decreased. However, in the more recent period post-2004, performance continued to increase although apparently at a slower rate than during the 1986–2004 period and vehicle weight changed very little. The PO 00000 Frm 00126 Fmt 4701 Sfmt 4702 large and rapid price increases appear to have been an important factor. Even before manufacturers can respond to prices and regulations by adding fuel economy technologies to new vehicles, demand can respond by shifting towards smaller, lighter and less powerful makes and models. The period of voluntary increase in fuel economy is consistent with the constrained optimization problem presented above if fuel economy standards no longer constrained consumer behavior after the change in fuel prices. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.091</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 On the other hand, from 1986–2004 the acceleration performance of lightduty vehicles increased by 45% (Figure III–21). Advances in engine technology are reflected in the steadily increasing ratio of power output to engine size, measured by displacement. Without increased fuel economy standards, all the potential of advanced technology appears to have gone into increasing performance and other attributes (for example average weight also increased by 27% from 1986–2004) and none to increasing fuel economy. Fuel economy remained nearly constant at the levels Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 0.07 ... .!P 0.06 i 0.05 ·-u .c .c A ..... CJ ····· ••""' ..... ... ....... ♦ "ti C ::J i. ..... ... CJ 0.03 3 0.02 0 a. CJ t? 0.01 •►••·•...♦• ~ ·- ..... ... 1.6 1.4 8flJ -·aVI. ,.. 1.0 Q .c u 0.8 .5 u ·-..c 0.6 ♦ 1:CJ E 1.2 ..... ••••• a.1..t.•• ttt:.•• ► .....+ti- ...... ~.a• ~ 0.04 ... ..... 49727 HP/Weight (lbs) ,.. 0.4 a ..... ... CJ 3 0 a. 0.2 fCJ A hp/cid 0 :c 0 0 1975 ' ' 1985 1995 2005 0.0 ' ::c 2015 Source: EPA, 2020 Automotive Trends Report. If this constrained optimization model is a reliable predictor of consumer behavior for some substantive portion of the new vehicle market, it would have important implications for how NHTSA models baseline consumer choices. In this case, it would mean that as technology that could improve fuel economy is added absent standards, it would be primarily geared towards enhancing performance rather than fuel economy. Depending on how consumers value future fuel savings, it might be appropriate for NHTSA to change its methods of analysis to reflect consumer preferences for performance, and to develop methods for valuing the opportunity cost to consumers for constraining them to more fuel efficient options. NHTSA seeks comment on the analysis presented in this section and its implications for the assumptions that consumers will add technologies that payback within thirty months. It also seeks comment on possible approaches to valuing the opportunity cost to consumers. lotter on DSK11XQN23PROD with PROPOSALS2 Potential Implications of Behavioral Theories for Fuel Economy Standards In this proposed rule, the costeffectiveness of technology-based fuel economy improvements is used to estimate fuel economy improvements by manufacturers in the No-Policy case and to estimate components of the benefits and costs of alternative increases in fuel economy standards. In the interest of insuring that our theory and methods VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 reflect the best current understanding of how consumers perceive the value of technology-based fuel economy improvements, we are seeking comment on our current, and possible alternative representations of how consumers value fuel economy when purchasing a new vehicle and while owning and operating it, and how manufacturers decide to implement fuel economy technologies.339 We are particularly interested in comments on our assumption that in our Alternative 0 (no change in existing standards) manufacturers will implement technologies to improve fuel economy even if existing standards do not require them to do so, provided that the first 30 months of fuel savings will be greater than or equal to the cost of the technology. We are also interested in comments concerning our use of the difference between the price consumers pay for increased fuel economy and the value of fuel savings over the first 30 month for estimating the impacts of the standards on new and used vehicle markets. Finally, we are interested in comments on when attributes that can be traded-off for increased fuel economy should be considered opportunity costs of increasing fuel economy. 339 We are making a distinction between consumers choices when presented with technology-based fuel economy improvements versus consumers’ choices among various makes and models of vehicles. The latter topic is also of interest and is discussed in (see TSD, Ch. 4.2.1). PO 00000 Frm 00127 Fmt 4701 Sfmt 4702 How manufacturers choose to implement technologies that can increase fuel economy depends on consumers’ willingness to pay (WTP) for fuel economy and the other attributes the technologies can improve. Consumers’ WTP for increasing levels of an attribute defines the consumers’ demand function for that attribute. Here, we consider how consumers’ WTP for increased fuel economy (WTPFE) and for performance (WTPHP), where FE stands for fuel economy and HP stands for ‘‘Horse Power’’/performance, and the cost of technology (C) affect manufacturers’ decisions about how to implement the technologies with and without fuel economy standards. For the purpose of this discussion, it is convenient to think of fuel economy in terms of its inverse, the rate of fuel consumption per mile. While miles per gallon (mpg) delivers decreasing fuel savings per mpg, decreasing fuel consumption delivers constant fuel savings per gallon per mile (gpm) reduced. Thinking in terms of gpm is appropriate because fuel economy standards are in fact defined in terms of the inverse of fuel economy, i.e., gpm. In the CAFE Model we typically assume that for a technology that can improve fuel economy, consumers are willing to pay an amount equal to the first thirty months of fuel savings (WTP30FE). This is an important assumption for several reasons. The market will tend to equilibrate the ratio of consumers’ WTP for fuel economy E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.092</GPH> Figure 111-21 - Trends in Performance and Engine Technology: 1975-2020 49728 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 divided by its cost to the ratio of consumers’ WTP for other attributes divided by their cost. The value of the first thirty months of fuel savings is typically about one-fourth of the value of savings over the expected life of a vehicle, discounted at annual rates between 3% and 7%. Arguably, this represents an important undervaluing of technology-based fuel economy improvement relative to its true economic value. Our use of the 30month payback assumption is based on statements manufacturers have made to us and to NASEM CAFE committees. It is also based on the fact that repeated assessments of the potential for technology to improve fuel economy have consistently found a substantial potential to cost-effectively increase fuel economy. But it is also partly based on the fact that the substantial literature that has endeavored to infer consumers’ WTP for fuel economy is approximately evenly divided between studies that support severe undervaluation and those that support valuation at approximately full lifetime discounted present value (e.g., Greene et al., 2018; Helfand and Wolverton, 2011; Greene, 2010; for a more complete discussion see TSD, Ch. 6.1.6). The most recent studies based on detailed data and advanced methods of statistical inference have not resolved the issue (NASEM, 2021, Ch. 11.3). If consumers value technology-based fuel economy improvements at only a small fraction of their lifetime present value and the market equates WTP30FE/ C to WTPHP/C, the market will tend to oversupply performance relative to fuel economy (Allcott et al., 2014; Heutel, 2015). The WTP30FE assumption also has important consequences when fuel economy standards are in effect. Alternative 0 in this proposed rule VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 assumes not only that the SAFE standards are in effect but that the manufacturers who agreed to the California Framework will be bound by that agreement. If those existing regulations are binding, it is likely that WTPHP > WTP30FE. (For simplicity we assume that over the range of fuel economy and performance achievable by the technology, both WTP values are constant.)340 This outcome would be expected in a market where consumers undervalue fuel savings in their normal car buying decisions and standards require levels of fuel economy beyond what they are willing to pay.341 This is illustrated in Figure III–22. The initial consumer demand function for vehicles (D0) is shifted upward by WTP30FE to represent the consumer demand function for the increased fuel economy the technology could produce (D30FE) and by WTPHP to represent the demand function (DHP) for the potential increase in performance. Because the technology has a cost (C), the manufacturers’ supply function (S0) shifts upward to S1 = S0 + C.342 If the cost of the technology 340 Although there are diminishing returns to increased miles per gallon, in terms of fuel savings in gallons or dollars, there are not diminishing returns to reductions in fuel consumption per mile, except due to decreasing marginal utility of income. WTPHP likely decreases with increasing performance, but if the changes are not too large, the assumption of constant WTP is reasonable. 341 If there are no binding regulatory constraints and fuel economy and other vehicle attributes are normal goods, consumers will elect more of each in the event technological progress makes it possible to afford them. This simplifying assumption is consistent with a scenario where consumers’ baseline vehicle choices are constrained by regulatory standards. See above for more discussion. 342 The supply function for new cars is assumed to be perfectly elastic for the sake of simplicity of exposition. Note that if the cost of the technology exceeds consumers’ WTP for both fuel economy and performance, the technology will not be adopted in the absence of regulations requiring it. PO 00000 Frm 00128 Fmt 4701 Sfmt 4702 exceeds consumers’ WTP for either the fuel economy or the performance it can deliver, the technology will not be adopted in the absence of regulations requiring it. In Figure III–22 we show the case where C < WTP30FE < WTPHP. In this case, using the technology to increase performance provides the greatest increase in sales and revenues: QHP > Q30FE > Q0. Since both WTP values are assumed to be approximately constant over the range of improvement the technology can provide, there is no possible combination of fuel economy and performance improvement that would produce a larger increase in sales than using the technology entirely to increase performance.343 Importantly, as long as C < WTPHP, the actual cost of the technology does not affect the manufacturer’s decision to use 100% of its potential to increase performance and 0% to increase fuel economy. The technology’s payback period for the increase in fuel economy is irrelevant. If we reverse the relative WTP values (i.e., WTP30FE > WTPHP), then the manufacturer will choose to use 100% of the technology’s potential to increase fuel economy and 0% to increase performance, assuming constant WTP values.344 This conclusion may contradict our current method, which assumes that even with increasing fuel economy standards in Alternative 0, manufacturers will adopt fuel economy technologies with WTP30FE < C and use them to increase fuel economy rather than performance. 343 In fact, all that is required is that over the range of increases achievable by the technology, WTPHP > WTPFE. 344 However, as noted above, the market will tend to equate WTPHP/C to WTPFE/C, so if there is sufficient variation in WTPHP over the range of values achievable by the technology, some of each will be provided. E:\FR\FM\03SEP2.SGM 03SEP2 49729 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules p WTPJ -lWTP30F~ C { i--------,.,,.,....___..,.,,,..___.,..________ So+ C So Q Oo~OFE Because the expected present value of fuel savings is several times the 30month value, it is quite possible that the WTP for performance lies between the lifetime present value of fuel savings and the 30-month value: WTPPVFE > WTPHP > WTP30FE. This possibility is illustrated in Figure III–23, in which there are three demand functions in addition to the initial demand function, D0. In Figure III–23, if the consumer were willing to pay for the full present value of fuel savings, the technology would be applied 100% to increasing fuel economy, provided C < WTPPVFE. But if standards were binding and the consumer were willing to pay for only 30 months of fuel savings, the technology would be applied 100% to increasing performance, provided C < WTPHP. Suppose that the cost of the technology is not C, but a much smaller value, say c < C and c < WTP30FE. Assuming consumers value increased fuel economy at WTP30FE, it remains the case that all the technology’s potential will be applied to increasing performance because that gives the greatest increase in sales. The implication is that when there is a binding fuel economy standard, as long as WTPHP > WTP30FE, no technologies would be used to increase fuel economy in the absence of a regulatory requirement to do so. If consumers’ WTP for fuel economy is WTP30FE and regulatory standards are binding, WTPHP > WTPFE seems likely. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 If WTP30FE < WTPHP (recalling that HP can represent attributes in addition to fuel economy), the above analysis of producer behavior contradicts the current operation of the CAFE Model, which assumes that manufacturers will apply technologies whose costs are less than WTP30FE to improving fuel economy in the absence of regulations requiring them to do so. For the final rule, NHTSA is considering changing the assumption that in the absence of standards that require it, manufactures will adopt technologies to improve fuel economy that have a payback period of 30 months or less, in favor of the above analysis. We are interested in receiving comments that specifically address the validity of the current and proposed approach. As discussed in TSD Chapter 4.2.1.1, there is no consensus in the literature about how consumers value fuel economy improvements when making vehicle purchases. In this and past analyses, we have assumed that consumers value only the first 30 months of fuel savings when making vehicle purchase decisions. This value is a small fraction, approximately one fourth of the expected present value of future fuel savings over the typical life of a light-duty vehicle, assuming discount rates in the range of 3% to 7% per year. On the other hand, when estimating the societal value of fuel economy improvements, we use the full present value of discounted fuel savings PO 00000 Frm 00129 Fmt 4701 Sfmt 4702 over the expected life of the vehicle because it represents a real resource savings. However, the possibility that consumers’ perceptions of utility at the time of purchase (decision utility) may differ from the utility consumers experience while consuming a good and that experienced utility may be the preferrable metric for policy evaluation has been raised in the economic literature (Kahneman and Sugden, 2005). In our methods, we use WTP30FE to represent consumers’ decision utility. Gallons saved over the life of a vehicle, valued at the current price of gasoline, and discounted to present value appears to be an appropriate measure of experienced utility. The large difference between our measure of decision utility and lifetime present value fuel savings as a measure of experienced utility has potentially important implications for how we estimate the impacts of fuel economy standards on new vehicle sales and the used vehicle market. It seems plausible that as consumers experience the fuel savings benefits of increased fuel economy, their valuation of the fuel economy increases required by regulation may adjust over time towards the full lifetime discounted present value. In addition, behavioral economic theory accepts that consumers’ willingness to pay for fuel economy may change depending on the context of consumers’ car purchase decisions. The implications of such possibilities are analyzed below. We are interested in E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.093</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure 111-22 - Manufacturers Decision to Adopt a Technology When WTPHP > WTPJOFE > C 49730 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules how they might affect our current methods for estimate the impacts of standards on new vehicle sales and the used vehicle market, and whether any changes to our current methods are appropriate. The existence of fuel economy standards changes manufacturers’ decision making. First, if a standard is set at a level that requires only part of the technological potential to increase fuel economy, if C < WTPHP, and WTPHP > WTP30FE, the remainder of the technology’s potential will be used to provide some increase in performance. This appears to have occurred post 2004 when the rate of improvement in performance slowed while fuel economy improved. Assuming that consumers value fuel economy improvement at time of purchase at WTP30FE, there would be a consumers’ surplus cost of foregone performance equal to the cross-hatched trapezoid in Figure III–23. The foregone performance cost will be less than what it would have been if none of the technology’s potential to increase fuel economy were used to increase performance. Even if the cost of the technology is less than WTP30FE, the technology will be applied to improve fuel economy only up to the required level and the remainder of its potential will be used to increase performance. If the cost of applying enough of the technology to achieve the fuel economy standard is greater than WTPHP, there would be no cost of foregone performance since the cost of applying the technology to increasing fuel economy exceeds its opportunity cost when applied to increase performance.345 In that case, the technology cost represents the full cost of the fuel economy improvement, since that cost exceeds consumers’ WTP for the performance it could produce. On the other hand, if under regulatory standards consumers valued fuel economy at WTPPVFE, there would also be no opportunity cost of performance because WTPPVFE > WTPHP. Price C ·{ Not Zero Quantity Because the CAFE Model estimates the effects of standards on new vehicle sales and scrappage based on the difference between the cost of technology and the perceived value of fuel savings at the time a new vehicle is purchased, whether consumers perceive the value differently in regulated and unregulated markets is an important question. Traditional utility theory of consumer decision making does not allow that consumers’ preference rankings depend on the context of the choices they make. However, in addition to the theory of utility maximizing rational economic behavior, modern economics includes the insights and findings of behavioral economics, which has established many examples of human decision making that differ in important ways from the rational economic model. In particular, the behavioral model allows the possibility that consumers’ preferences and decision-making processes often do change depending on the context or framing of choices. The possibility that behavioral theories of decision making may be useful for understanding how consumers value fuel economy and for evaluating the costs and benefits of fuel economy standards was noted in the most recent NASEM (2021) report. An explanation of the different contexts helps to illustrate this point. If a consumer is thinking about buying a new car and is looking at two models, one that includes fuel economy technology and is more expensive and another that does not, she may buy the cheaper, less fuel efficient version even if the more expensive model will save 345 This is because using the technology to increase performance would not be the second-best use of the cost of increasing fuel economy. The second-best use would instead be to invest the cost at a market rate of return. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00130 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.094</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure 111-23 - Manufacturers' Decision to Adopt Technology with Fuel Economy Standards lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules money in the long run. But if, instead, the consumer is faced with whether to buy a new car at all as opposed to keeping an older one, if all new cars contain technology to meet fuel economy standards then she may view the decision differently. Will, for example, an extra $1,000 for a new car— a $1,000 that the consumer will more than recoup in fuel savings—deter her from buying the new car, especially when most consumers finance cars over a number of years rather than paying the $1,000 cost up front and will therefore partly or entirely offset any increase in monthly payment with lower fuel costs? In addition, the fact that standards generally increase gradually over a period of years allows time for consumers and other information sources to verify that fuel savings are real and of substantial value. The CAFE Model’s representation of consumers’ vehicle choices under regulation reflects the ‘‘Gruenspecht Effect’’, the theory that regulation will inevitably cause new vehicles to be less desirable than they would have been in the absence of regulation, which will inevitably lead to reduced new vehicle sales, higher prices for used vehicles and slower turnover of the vehicle stock. However, if consumers severely undervalue fuel savings at the time of vehicle purchase, not only is that itself a market failure (a large discrepancy between decision and experienced utility) but it raises important questions about what causes such undervaluation and whether consumers’ perceptions may change as the benefits of increased fuel economy are realized or whether the different framing of new vehicle choices in a regulated market might partially or entirely mitigate that undervaluation. The 2021 NASEM report asserts that if the behavioral model is correct, consumers might value fuel savings at or near their full lifetime discounted present value, potentially reversing the Gruenspecht Effect. ‘‘On the other hand, the Gruenspecht effect is not predicted by the behavioral model, under which it is not only possible but likely that if the fuel savings from increased fuel economy exceed its cost, consumers will find the more fuel-efficient vehicles required by regulation to be preferable to those that would otherwise have been produced.’’ ‘‘It is possible that sales would increase rather than decrease and likewise manufacturers’ profits. In that case, increased new vehicle sales would reduce used vehicle prices, benefiting buyers of used vehicles and accelerating the turnover of the vehicle stock.’’ 346 346 NASEM, VerDate Sep<11>2014 2021, p. 11–357. 21:48 Sep 02, 2021 Jkt 253001 NHTSA is interested in comments that can help contribute to resolving or improving our understanding of this issue and its implications for how the costs and benefits of fuel economy standards should be estimated. (2) Refueling Benefit Increasing CAFE standards, all else being equal, affect the amount of time drivers spend refueling their vehicles in several ways. First, they increase the fuel economy of ICE vehicles produced in the future, which increases vehicle range and decreases the number of refueling events for those vehicles. Conversely, to the extent that more stringent standards increase the purchase price of new vehicles, they may reduce sales of new vehicles and scrappage of existing ones, causing more VMT to be driven by older and less efficient vehicles which require more refueling events for the same amount of VMT driven. Finally, sufficiently stringent standards may also change the number of electric vehicles that are produced, and shift refueling to occur at a charging station, rather than at the pump—changing per-vehicle lifetime expected refueling costs. The agency estimates these savings by calculating the amount of refueling time avoided—including the time it takes to find, refuel, and pay—and multiplying it by DOT’s value of time of travel savings estimate. For a full description of the methodology, refer to TSD Chapter 6.1.4. (3) Additional Mobility Any increase in travel demand provides benefits that reflect the value to drivers and other vehicle occupants of the added—or more desirable—social and economic opportunities that become accessible with additional travel. Under the alternatives in this analysis, the fuel cost per mile of driving would decrease as a consequence of the higher fuel economy levels they require, thus increasing the number of miles that buyers of new cars and light trucks would drive as a consequence of the well-documented fuel economy rebound effect. The fact that drivers and their passengers elect to make more frequent or longer trips to gain access to these opportunities when the cost of driving declines demonstrates that the benefits they gain by doing so exceed the costs they incur. At a minimum, the benefits must equal the cost of the fuel consumed to travel the additional miles (or they would not have occurred). The cost of that energy is subsumed in the simulated fuel expenditures, so it is necessary to account for the benefits PO 00000 Frm 00131 Fmt 4701 Sfmt 4702 49731 associated with those miles traveled here. But the benefits must also offset the economic value of their (and their passengers’) travel time, other vehicle operating costs, and the economic cost of safety risks due to the increase in exposure that occurs with additional travel. The amount by which the benefits of this additional travel exceeds its economic costs measures the net benefits drivers and their passengers experience, usually referred to as increased consumer surplus. TSD Chapter 6.1.5 explains the agency’s methodology for calculating additional mobility. 2. External Costs and Benefits (a) Costs (1) Congestion and Noise Increased vehicle use associated with the rebound effect also contributes to increased traffic congestion and highway noise. Although drivers obviously experience these impacts, they do not fully value their impacts on other system users, just as they do not fully value the emissions impacts of their own driving. Congestion and noise costs are ‘‘external’’ to the vehicle owners whose decisions about how much, where, and when to drive more— or less—in response to changes in fuel economy result in these costs. Therefore, unlike changes in the costs incurred by drivers for fuel consumption or safety risks they willingly assume, changes in congestion and noise costs are not offset by corresponding changes in the travel benefits drivers experience. Congestion costs are limited to road users; however, since road users include a significant fraction of the U.S. population, changes in congestion costs are treated as part of the rule’s economic impact on the broader society instead of as a cost or benefit to private parties. Costs resulting from road and highway noise are even more widely dispersed, because they are borne partly by surrounding residents, pedestrians, and other non-road users, and for this reason are also considered as a cost to the society as a whole. To estimate the economic costs associated with changes in congestion and noise caused by differences in miles driven, the agency updated the underlying components of the cost estimates of per-mile congestion and noise costs from increased automobile and light truck use provided in FHWA’s 1997 Highway Cost Allocation Study. The agencies previously relied on this study in the 2010, 2011, and 2012 final rules, and updating the individual underlying components for congestion E:\FR\FM\03SEP2.SGM 03SEP2 49732 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules costs in this analysis improves currency and internal consistency with the rest of the analysis. See TSD Chapter 6.2 for details on how the agency calculated estimate the economic costs associated with changes in congestion and noise caused by differences in miles driven. NHTSA specifically seeks comment on the congestion costs employed in this analysis, and whether and how to change them for the analysis for the final rule. (2) Fuel Tax Revenue As mentioned in III.G.1.b)(1), a portion of the fuel savings experienced by consumers includes avoided fuel taxes. While fuel taxes are treated as a transfer within the analysis and do not affect net benefits, the agency provides an estimate here to show the potential impact to state and local governments. (b) Benefits lotter on DSK11XQN23PROD with PROPOSALS2 (1) Reduced Climate Damages Extracting and transporting crude petroleum, refining it to produce transportation fuels, and distributing fuel generate additional emissions of GHGs and criteria air pollutants beyond those from cars’ and light trucks’ use of fuel. By reducing the volume of petroleum-based fuel produced and consumed, adopting higher CAFE standards will thus mitigate global climate-related economic damages caused by accumulation of GHGs in the atmosphere, as well as the more immediate and localized health damages caused by exposure to criteria pollutants. Because they fall broadly on the U.S.—and global, in the case of climate damages—population, reducing them represents an external benefit from requiring higher fuel economy. NHTSA estimates the global social benefits of CO2, CH4, and N2O emission reductions expected from this proposed rule using the social cost of greenhouse gases (SC–GHG) estimates presented in the Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (‘‘February 2021 TSD’’). These SC–GHG estimates are interim values developed under Executive Order (E.O.) 13990 for use in benefit-cost analyses until updated estimates of the impacts of climate change can be developed based on the best available science and economics. NHTSA uses the SC–GHG interim values to estimate the benefits of decreased fuel consumption stemming from the proposal. The SC–GHG estimates used in our analysis were developed over many years, using transparent process, peer- VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 reviewed methodologies, the best science available at the time of that process, and with input from the public. Specifically, in 2009, an interagency working group (IWG) that included the DOT and other executive branch agencies and offices was established to ensure that agencies were using the best available science and to promote consistency in the social cost of carbon dioxide (SC–CO2) values used across agencies. The IWG published SC–CO2 estimates in 2010. These estimates were updated in 2013 based on new versions of each IAM. In August 2016 the IWG published estimates of the social cost of methane (SC–CH4) and nitrous oxide (SC–N2O) using methodologies that are consistent with the methodology underlying the SC–CO2 estimates. Executive Order 13990 (issued on January 20, 2021) re-established the IWG and directed it to publish interim SC–GHG values for CO2, CH4, and N2O within thirty days. Furthermore, the E.O. tasked the IWG with devising longterm recommendations to update the methodologies used in calculating these SC–GHG values, based on ‘‘the best available economics and science,’’ and incorporating principles of ‘‘climate risk, environmental justice, and intergenerational equity’’.347 The E.O. also instructed the IWG to take into account the recommendations from the NAS committee convened on this topic, published in 2017.348 The February 2021 TSD provides a complete discussion of the IWG’s initial review conducted under E.O. 13990. NHTSA is using the IWG’s interim values, published in February 2021 in a technical support document, for the CAFE analysis in this NPRM.349 This approach is the same as that taken in DOT regulatory analyses over 2009 through 2016. If the IWG issues new estimates before the final rule, the agency will consider revising the estimates within the CAFE Model time permitting. We request comment on this 347 Executive Order on Protecting Public Health and the Environment and Restoring Science to Tackle the Climate Crisis. (2021). Available at https://www.whitehouse.gov/briefing-room/ presidential-actions/2021/01/20/executive-orderprotecting-public-health-and-environment-andrestoring-science-to-tackle-climate-crisis/. 348 National Academies of Science (NAS). (2017). Valuing Climate Damage: Updating Estimation of the Social Cost of Carbon Dioxide. Available at https://www.nap.edu/catalog/24651/valuingclimate-damages-updating-estimation-of-the-socialcost-of. 349 Interagency Working Group on Social Cost of Greenhouse Gases, United States Government. (2021). Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990, available at https://www.whitehouse.gov/wp-content/uploads/ 2021/02/TechnicalSupportDocument_SocialCostof CarbonMethaneNitrousOxide.pdf?source=email. PO 00000 Frm 00132 Fmt 4701 Sfmt 4702 approach to estimating social benefits of reducing GHG emissions in this rulemaking in light of the ongoing interagency process. NHTSA notes that the primary analysis for this proposal estimates benefits from reducing emissions of CO2 and other GHGs that incorporate a 2.5% discount rate for distant future climate damages, while discounting costs and non-climate related benefits using a 3% rate. NHTSA also presents cost and benefits estimates in the primary analysis that reflect a 3% discount rate for reductions in climate-related damages while discounting costs and non-climate related benefits at 7%. NHTSA believes this approach represents an appropriate treatment of the intergenerational issues presented by emissions that result in climaterelated damages over a very-long time horizon, and is within scope of the IWG’s Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide that recommends discounting future climate damages at rates of 2.5%, 3%, and 5%.350 In addition, NHTSA emphasize the importance and value of considering the benefits calculated using all four SC– GHG estimates for each of three greenhouse gases. NHTSA includes the social costs of CO2, CH4, and N2O calculated using the four different estimates recommended in the February 2021 TSD (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent discount rate) in the PRIA. The February 2021 TSD does not specify how agencies should combine its estimates of benefits from reducing GHG emissions that reflect these alternative discount rates with the discount rates for nearer-term benefits and costs prescribed in OMB Circular A–4. Instead, it provides agencies with broad flexibility in implementing the February 2021 TSD. However, the February 2021 TSD does identify 2.5% as the ‘‘average certainty-equivalent rate using the mean-reverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate of 3 percent.’’ 351 As such, NHTSA believes using a 2.5% discount rate for climaterelated damages is consistent with the IWG guidance. This section provides further discussion of the discount rates that NHTSA uses in its regulatory analysis 350 Interagency Working Group on Social Cost of Greenhouse Gases, United States Government, Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide, Interim Estimates under Executive Order 13990, February 2021. 351 Ibid. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 and presents results of a sensitivity analysis using a 3% discount rate for reductions in climate-related damages. NHTSA welcomes public comment on its selection of 2.5% for climate-related damages and will consider other discount rates for the final rule. For a full discussion of the agency’s quantification of GHGs, see TSD Chapter 6.2.1 and the PRIA. (a) Discount Rates Accounting for Intergenerational Impacts A standard function of regulatory analysis is to evaluate tradeoffs between impacts that occur at different points in time. Many, if not most, Federal regulations involve costly upfront investments that generate future benefits in the form of reductions in health, safety, or environmental damages. To evaluate these tradeoffs, the analysis must account for the social rate of time preference—the broadly observed social preference for benefits that occur sooner versus those that occur further in the future.352 This is accomplished by discounting impacts that occur further in the future more than impacts that occur sooner. OMB Circular A–4 affirmed the appropriateness of accounting for the social rate of time preference in regulatory analyses and prescribed discount rates of 3% and 7% for doing so. The 3% discount rate was chosen to represent the ‘‘consumption rate of interest’’ approach, which discounts future costs and benefits to their present values using the rate at which consumers appear to make tradeoffs between current consumption and equal consumption opportunities deferred to the future. OMB Circular A–4 reports a real rate of return on 10-year Treasury notes of 3.1% between 1973 and its 2003 publication date and interprets this as approximating the rate at which society is indifferent between consumption today and in the future. The 7% rate reflects the opportunity cost of capital approach to discounting, where the discount rate approximates the foregone return on private investment if the regulation were to divert resources from capital formation. OMB Circular A–4 cites pre-tax rates of return on capital as part of its selection of the 7% rate.353 The IWG rejected the use of the opportunity cost of capital approach to discounting reductions in climate-related damages because 352 This preference is observed in many market transactions, including by savers that expect a return on their investments in stocks, bonds, and other equities; firms that expect positive rates of return on major capital investments; and banks that demand positive interest rates in lending markets. 353 OMB Circular A–4. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 ‘‘consumption rate of interest is the correct discounting concept to use when future damages from elevated temperatures are estimated in consumption-equivalent units as is done in the IAMs used to estimate the SC– GHG (National Academies 2017).’’ 354 As the IWG states, ‘‘GHG emissions are stock pollutants, where damages are associated with what has accumulated in the atmosphere over time, and they are long lived such that subsequent damages resulting from emissions today occur over many decades or centuries depending on the specific greenhouse gas under consideration.’’355 OMB Circular A–4 states that impacts occurring over such intergenerational time horizons require special treatment: Special ethical considerations arise when comparing benefits and costs across generations. Although most people demonstrate time preference in their own consumption behavior, it may not be appropriate for society to demonstrate a similar preference when deciding between the well-being of current and future generations. Future citizens who are affected by such choices cannot take part in making them, and today’s society must act with some consideration of their interest.356 In addition to the ethical considerations, Circular A–4 also identifies uncertainty in long-run interest rates as a potential justification for using lower rates to discount intergenerational impacts. As Circular A–4 states, ‘‘Private market rates provide a reliable reference for determining how society values time within a generation, but for extremely long time periods no comparable private rates exist.’’357 The social costs of distant future climate damages—and by implication, the value of reducing them by lowering emissions of GHGs—are highly sensitive to the discount rate, and the present value of reducing climate damages grows at an increasing rate as the discount rate used in the analysis declines. This ‘‘non-linearity’’ means that even if uncertainty about the exact value of the long-run interest rate is equally distributed between values above and below the 3% consumption rate of interest, the probability-weighted (or ‘‘expected’’) present value of a unit reduction in climate damages will be higher than the value calculated using a 3% discount rate. The effect of such 354 Interagency Working Group on Social Cost of Greenhouse Gases, United States Government, Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide, Interim Estimates under Executive Order 13990, February 2021. 355 Ibid. 356 OMB Circular A–4. 357 Ibid. PO 00000 Frm 00133 Fmt 4701 Sfmt 4702 49733 uncertainty about the correct discount rate can thus be accounted for by using a lower ‘‘certainty-equivalent’’ rate to discount distant future damages. The IWG identifies ‘‘a plausible range of certainty-equivalent constant consumption discount rates: 2.5, 3, and 5 percent per year.’’ The IWG’s justification for its selection of these rates is summarized in this excerpt from its 2021 guidance: The 3 percent value was included as consistent with estimates provided in OMB’s Circular A–4 (OMB 2003) guidance for the consumption rate of interest. . . .The upper value of 5 percent was included to represent the possibility that climate-related damages are positively correlated with market returns, which would imply a certainty equivalent value higher than the consumption rate of interest. The low value, 2.5 percent, was included to incorporate the concern that interest rates are highly uncertain over time. It represents the average certaintyequivalent rate using the mean-reverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random walk model and 2.8 percent using the mean reverting approach. Without giving preference to a particular model, the average of the two rates is 2.5 percent. Additionally, a rate below the consumption rate of interest would also be justified if the return to investments in climate mitigation are negatively correlated with the overall market rate of return. Use of this lower value was also deemed responsive to certain judgments based on the prescriptive or normative approach for selecting a discount rate and to related ethical objections that have been raised about rates of 3 percent or higher. Because the certainty-equivalent discount rate will lie progressively farther below the best estimate of the current rate as the time horizon when future impacts occur is extended, the IWG’s recent guidance also suggest that it may be appropriate to use a discount rate that declines over time to account for interest rate uncertainty, as has been recommended by the National Academies and EPA’s Science Advisory Board.358 The IWG mentioned that it will consider these recommendations and the relevant academic literature on declining rates in developing its final 358 Interagency Working Group on Social Cost of Greenhouse Gases, United States Government, Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide, Interim Estimates under Executive Order 13990, February 2021. E:\FR\FM\03SEP2.SGM 03SEP2 49734 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules guidance on the social cost of greenhouse gases. The IWG 2021 interim guidance also presented new evidence on the consumption-based discount rate suggesting that a rate lower than 3% may be appropriate. For example, the IWG replicated OMB Circular A–4’s original 2003 methodology for estimating the consumption rate using the average return on 10-year Treasury notes over the last 30 years and found a discount rate close to 2%. They also presented rates over a longer time horizon, finding an average rate of 2.3% from 1962 to the present. Finally, they summarized results from surveys of experts on the topic and found a ‘‘surprising degree of consensus’’ for using a 2% consumption rate of interest to discount future climate-related impacts.359 NHTSA expects that the Interagency Working Group will continue to develop its final guidance on the appropriate discount rates to use for reductions in climate damages as NHTSA develops its final rule. If new guidance is issued in time for NHTSA’s final rule, NHTSA will incorporate the IWG’s updated guidance in the final regulatory analysis. (b) Discount Rates Used in This Proposal for Climate-Related Benefits As indicated above, NHTSA’s primary analysis presents cost and benefit estimates using a 2.5% discount rate for reductions in climate-related damages and 3% for non-climate related impacts. NHTSA also presents cost and benefits estimates using a 3% discount rate for reductions in climate-related damages alongside estimates of non-climate related impacts discounted at 7%. This latter pairing of a 3% rate for discounting benefits from reducing climate-related damages with a 7% discount rate for non-climate related impacts is consistent with NHTSA’s past practice.360 However, NHTSA’s pairing of 2.5% for climate-related damage reductions with 3% for nonclimate related impacts is novel in this proposal. As discussed above, the IWG’s guidance indicates that uncertainty in long-run interest rates suggests that a lower ‘‘certainty-equivalent’’ discount rate is appropriate for intergenerational impacts, and identifies 2.5%, 3%, and 5% as ‘‘certainty-equivalent’’ discount rates. NHTSA emphasizes the importance and value of considering the benefits calculated using all four SC– GHG estimates for each of three greenhouse gases. NHTSA includes the social costs of CO2, CH4, and N2O calculated using the four different estimates recommended in the February 2021 TSD (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent discount rate) in the PRIA. For presentation purposes in this rule, NHTSA shows two primary estimates. NHTSA believes that pairing OMB’s 3% estimate of the consumption discount rate for near-term costs and benefits with the IWG’s lower certainty-equivalent rate of 2.5% is consistent with current interim guidance in the February 2021 TSD. NHTSA also believe that its pairing of the 3% certainty-equivalent rate for climate-related benefits with OMB’s 7% discount rate is consistent with guidance from the February 2021 TSD for GHGs and OMB Circular A–4 for other costs and benefits. In addition, NHTSA presents a sensitivity analysis where both distant future and nearer-term GHG impacts are discounted using the 3% rate combined with all other costs and benefits discounted at 3%. Table 111-39- Comparison of Results Using a 3% Discount Rate for All Impacts Except GHGs with Impacts Using Either 2.5% or 3% for Climate-Related Benefits, Model Years 1981 through 2029 Totals Costs Benefits Net Benefits 3%/2.5% SC-GHG Discount Rate 121.1 3%/3% SC-GHG Discount Rate 121.1 121.4 0.3 110.5 -10.6 Table 111-40- Comparison of Results Using a 3% Discount Rate for All Impacts Except GHGs with Impacts Using Either 2.5% or 3% for Climate-Related Benefits, Calendar Years 2021 through 2050 359 Ibid. VerDate Sep<11>2014 360 See, 21:48 Sep 02, 2021 Jkt 253001 PO 00000 3%/3% SC-GHG Discount Rate 333.6 391.7 58.1 e.g., the 2012 and 2020 final CAFE rules. Frm 00134 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.096</GPH> Costs Benefits Net Benefits 3%/2.5% SC-GHG Discount Rate 333.6 433.6 100 EP03SE21.095</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Totals Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules NHTSA seeks comment on the above discussion. lotter on DSK11XQN23PROD with PROPOSALS2 (2) Reduced Health Damages The CAFE Model estimates monetized health effects associated with emissions from three criteria pollutants: NOX, SOx, and PM2.5. As discussed in Section III.F above, although other criteria pollutants are currently regulated, only impacts from these three pollutants are calculated since they are known to be emitted regularly from mobile sources, have the most adverse effects to human health, and there exist several papers from the EPA estimating the benefits per ton of reducing these pollutants. Other pollutants, especially those that are precursors to ozone, are more difficult to model due to the complexity of their formation in the atmosphere, and EPA does not calculate benefit-per-ton estimates for these. The CAFE Model computes the monetized impacts associated with health damages from each pollutant by multiplying monetized health impact per ton values by the total tons of these pollutants, which are emitted from both upstream and tailpipe sources. Chapter 5 of the TSD accompanying this proposal includes a detailed description of the emission factors that inform the CAFE Model’s calculation of the total tons of each pollutant associated with upstream and tailpipe emissions. These monetized health impacts per ton values are closely related to the health incidence per ton values described above in Section III.F and in detail in Chapter 5.4 of the TSD. We use the same EPA sources that provided health incidence values to determine which monetized health impacts per ton values to use as inputs in the CAFE Model. Like the estimates associated with health incidences per ton of criteria pollutant emissions, we used multiple EPA papers and conversations with EPA staff to appropriately account for monetized damages for each pollutant associated with the source sectors included in the CAFE Model, based on which papers contained the most up-to-date data.361 The various emission source sectors included in the EPA papers do not always correspond exactly to the emission source categories 361 Environmental Protection Agency (EPA). 2018. Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. https://www.epa.gov/ sites/production/files/2018–02/documents/source apportionmentbpttsd_2018.pdf; Wolfe et al. 2019. Monetized health benefits attributable to mobile source emissions reductions across the United States in 2025. https://pubmed.ncbi.nlm.nih.gov/ 30296769/; Fann et al. 2018. Assessing Human Health PM2.5 and Ozone Impacts from U.S. Oil and Natural Gas Sector Emissions in 2025. https:// www.ncbi.nlm.nih.gov/pmc/articles/PMC6718951/. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 used in the CAFE Model.362 In those cases, we mapped multiple EPA sectors to a single CAFE source category and computed a weighted average of the health impact per ton values. The EPA uses the value of a statistical life (VSL) to estimate premature mortality impacts, and a combination of willingness to pay estimates and costs of treating the health impact for estimating the morbidity impacts.363 EPA’s 2018 technical support document, ‘‘Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors,’’ 364 (referred to here as the 2018 EPA source apportionment TSD) contains a more detailed account of how health incidences are monetized. It is important to note that the EPA sources cited frequently refer to these monetized health impacts per ton as ‘‘benefits per ton,’’ since they describe these estimates in terms of emissions avoided. In the CAFE Model input structure, these are generally referred to as monetized health impacts or damage costs associated with pollutants emitted, not avoided, unless the context states otherwise. The CAFE Model health impacts inputs are based partially on the structure the 2018 EPA source apportionment TSD, which reported benefits per ton values for the years 2020, 2025, and 2030. For the years in between the source years used in the input structure, the CAFE Model applies values from the closest source year. For instance, the model applies 2020 monetized health impact per ton values for calendar years 2020–2022 and applies 2025 values for calendar years 2023–2027. For some of the monetized health damage values, in order to match the structure of other impacts costs, DOT staff developed proxies for 7% discounted values for specific source sectors by using the ratio between a comparable sector’s 3% and 7% discounted values. In addition, we used implicit price deflators from the Bureau of Economic Analysis (BEA) to convert different monetized estimates to 2018 dollars, in order to be consistent with the rest of the CAFE Model inputs. 362 The CAFE Model’s emission source sectors follow a similar structure to the inputs from GREET. See Chapter 5.2 of the TSD accompanying this proposal for further information. 363 Although EPA and DOT’s VSL values differ, DOT staff determined that using EPA’s VSL was appropriate here, since it was already included in these monetized health impact values, which were best suited for the purposes of the CAFE Model. 364 See Environmental Protection Agency (EPA). 2018. Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. https:// www.epa.gov/sites/production/files/2018–02/ documents/sourceapportionmentbpttsd_2018.pdf. PO 00000 Frm 00135 Fmt 4701 Sfmt 4702 49735 This process is described in more detail in Chapter 6.2.2 of the TSD accompanying this proposal. In addition, the CAFE Model documentation contains more details of the model’s computation of monetized health impacts. All resulting emissions damage costs for criteria pollutants are located in the Criteria Emissions Cost worksheet of the Parameters file. (3) Reduction in Petroleum Market Externality By amending existing standards, the proposal would decrease domestic consumption of gasoline, producing a correspondingly decrease in the Nation’s demand for crude petroleum, a commodity that is traded actively in a worldwide market. Although the U.S. accounts for a sufficient (albeit diminishing) share of global oil consumption that the resulting decrease in global petroleum demand will exert some downward pressure on worldwide prices. U.S. consumption and imports of petroleum products have three potential effects on the domestic economy that are often referred to collectively as ‘‘energy security externalities,’’ and increases in their magnitude are sometimes cited as possible social costs of increased U.S. demand for petroleum. First, any increase in global petroleum prices that results from higher U.S. gasoline demand will cause a transfer of revenue to oil producers worldwide from consumers of petroleum, because consumers throughout the world are ultimately subject to the higher global price that results. Although this transfer is simply a shift of resources that produces no change in global economic welfare, the financial drain it produces on the U.S. economy is sometimes cited as an external cost of increased U.S. petroleum consumption because consumers of petroleum products are unlikely to consider it. As the U.S. approaches selfsufficiency in petroleum production (the Nation became a net exporter of petroleum in 2020), this transfer is increasingly from U.S. consumers of refined petroleum products to U.S. petroleum producers, so it not only leaves welfare unaffected, but even ceases to be a financial burden on the U.S. economy. In fact, as the U.S. becomes a larger net petroleum exporter, any transfer from global consumers to petroleum producers would become a financial benefit to the U.S. economy. Nevertheless, uncertainty in the Nation’s long-term import-export balance makes it difficult to project precisely how these effects might E:\FR\FM\03SEP2.SGM 03SEP2 49736 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 change in response to increased consumption. Higher U.S. petroleum consumption can also increase domestic consumers’ exposure to oil price shocks and thus increase potential costs to all U.S. petroleum users (including those outside the light duty vehicle sector, whose consumption would be unaffected by this proposed rule) from possible interruptions in the global supply of petroleum or rapid increases in global oil prices. Because users of petroleum products are unlikely to consider the effect of their increased purchases on these risks, their economic value is often cited as an external cost of increased U.S. consumption. Finally, some analysts argue that domestic demand for imported petroleum may also influence U.S. military spending; because the increased cost of military activities would not be reflected in the price paid at the gas pump, this is often suggested to represent a third category of external costs form increased U.S. petroleum consumption. For example, NHTSA has received extensive comments to past actions from the group Securing America’s Energy Future on this topic. Each of these three factors would be expected to decrease—albeit by a limited magnitude—as a consequence of decrease in U.S. petroleum consumption resulting from the proposed standards. TSD Chapter 6.2.4 provides a comprehensive explanation of the agency’s analysis of these three impacts. (4) Changes in Labor As vehicle prices rise, we expect consumers to purchase fewer vehicles than they would have at lower prices. If manufacturers produce fewer vehicles as a consequence of lower demand, manufacturers may need less labor to produce their fleet and dealers may need less labor to sell the vehicles. Conversely, as manufacturers add equipment to each new vehicle, the industry will require labor resources to develop, sell, and produce additional fuel-saving technologies.365 We also account for the possibility that new standards could shift the relative shares of passenger cars and light trucks in the overall fleet. Since the production of different vehicles involves different amounts of labor, this shift impacts the quantity of estimated labor. The analysis considers the direct labor effects that the CAFE standards have across the automotive sector. The 365 For the purposes of this analysis, DOT assumes a linear relationship between labor and production volumes. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 facets include (1) dealership labor related to new light-duty vehicle unit sales; (2) assembly labor for vehicles, engines, and transmissions related to new vehicle unit sales; and (3) labor related to mandated additional fuel savings technologies, accounting for new vehicle unit sales. The labor utilization analysis is intentionally narrow in its focus and does not represent an attempt to quantify the overall labor or economic effects of this rulemaking because adjacent employment factors and consumer spending factors for other goods and services are uncertain and difficult to predict. We do not consider how direct labor changes may affect the macro economy and potentially change employment in adjacent industries. For instance, we do not consider possible labor changes in vehicle maintenance and repair, nor changes in labor at retail gas stations. We also do not consider possible labor changes due to raw material production, such as production of aluminum, steel, copper, and lithium, nor does the agency consider possible labor impacts due to changes in production of oil and gas, ethanol, and electricity. All labor effects are estimated and reported at a national level, in personyears, assuming 2,000 hours of labor per person-year.366 These labor hours are not converted into monetized values because we assume that the labor costs are included into a new vehicle’s purchasing price. The analysis estimates labor effects from the forecasted CAFE Model technology costs and from review of automotive labor for the MY 2020 fleet. The agency uses information about the locations of vehicle assembly, engine assembly, and transmission assembly, and the percent of U.S. content of vehicles collected from American Automotive Labeling Act (AALA) submissions for each vehicle in the reference fleet.367 The analysis assumes the portion of parts that are made in the U.S. will remain constant for each vehicle as manufacturers add fuel-savings technologies. This should not be misconstrued as a prediction that the percentage of U.S.-made parts—and by extension U.S. labor—will remain constant, but rather that the agency does not have a clear basis to project where future productions may shift. The analysis also uses data from the National Automotive Dealers 366 The agencies recognize a few local production facilities may contribute meaningfully to local economies, but the analysis reports only on national effects. 367 49 CFR part 583. PO 00000 Frm 00136 Fmt 4701 Sfmt 4702 Association (NADA) annual report to derive dealership labor estimates. In sum, the analysis shows that the increased labor from production of new technologies used to meet the preferred alternative will outweigh any decreases attributable to the change in new vehicle sales. For a full description of the process the agency uses to estimate labor impacts, see TSD Chapter 6.2.5. 3. Costs and Benefits Not Quantified In addition to the costs and benefits described above, Table III–37 and Table III–38 each include two line-items without values. The first is maintenance and repair costs. Many of the technologies manufacturers apply to vehicles to meet CAFE standards are sophisticated and costly. The technology costs capture only the initial or ‘‘upfront’’ costs to incorporate this equipment into new vehicles; however, if the equipment is costlier to maintain or repair—which is likely either because the materials used to produce the equipment are more expensive or the equipment is significantly more complex than less fuel efficient alternatives and requires more time and labor—then consumers will also experience increased costs throughout the lifetime of the vehicle to keep it operational. The agency does not calculate the additional cost of repair and maintenance currently because it lacks a basis for estimating the incremental change attributable to the standards. The agency seeks comment on methods for estimating these costs. The second item is the potential sacrifice in other vehicle attributes. In addition to fuel economy, potential buyers of new cars and light trucks value other features such as their seating and cargo-carrying capacity, ride comfort, safety, and performance. Changing some of these other features, however, can affect vehicles’ fuel economy, so manufacturers will carefully consider tradeoffs among them when deciding how to comply with stricter CAFE standards. Currently the analysis assumes that these vehicle attributes will not change as a result of these rules,368 but in practice manufacturers may need to make practical design changes to meet the standards. Even if manufacturers are able to hold vehicles’ other attributes at today’s levels while meeting higher fuel economy targets, manufacturers may have to dedicate additional resources to comply with stricter CAFE targets and forego improvements in other vehicle attributes. The potential loss of other 368 See E:\FR\FM\03SEP2.SGM TSD Chapter 2.4.5. 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 vehicle attributes is an opportunity cost to consumers. The agency has previously attempted to model the potential sacrifice in other vehicle attributes in sensitivity analyses. In those other rulemakings, the agency acknowledged that it is extremely difficult to quantify the potential loss of other vehicle attributes. To accurately do so requires extensive projections about which and how much of other attributes will be sacrificed and a detailed accounting of how much value consumers assigned to those attributes. The agency modeled the loss in other vehicle attributes using published empirical estimates of tradeoffs between higher fuel economy and improvements to other attributes, together with estimates of the values buyers attach to those attributes. The agency is unsure whether this is an appropriate methodology since there is uncertainty about how much fuel economy consumers are willing to pay for and how consumers value other vehicle attributes. The agency seeks comment on alternative methods for estimating the potential sacrifice in other vehicle attributes. H. Simulating Safety Effects of Regulatory Alternatives The primary objective of CAFE standards is to achieve maximum feasible fuel economy, thereby reducing fuel consumption. In setting standards to achieve this intended effect, the potential of the standards to affect vehicle safety is also considered. As a safety agency, the agency has long considered the potential for adverse safety consequences when establishing CAFE standards. This safety analysis includes the comprehensive measure of safety impacts from three factors: 1. Changes in Vehicle Mass. Similar to previous analyses, the agency calculates the safety impact of changes in vehicle mass made to reduce fuel consumption and comply with the standards. Statistical analysis of historical crash data indicates reducing mass in heavier vehicles generally improves safety, while reducing mass in lighter vehicles generally reduces safety. The agency’s crash simulation modeling of vehicle design concepts for reducing mass revealed similar effects. These observations align with the role of mass disparity in crashes; when vehicles of different masses collide, the smaller vehicle will experience a larger change in velocity (and, by extension, force) which increases the risk to its occupants. 2. Impacts of Vehicle Prices on Fleet Turnover. Vehicles have become safer VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 49737 over time through a combination of new safety regulations and voluntary safety improvements. The agency expects this trend to continue as emerging technologies, such as advanced driver assistance systems, are incorporated into new vehicles. Safety improvements will likely continue regardless of changes to CAFE standards. As discussed in Section III.E.2, technologies added to comply with fuel economy standards have an impact on vehicle prices, therefore slowing the acquisition of newer vehicles and retirement of older ones. The delay in fleet turnover caused by the effect of new vehicle prices affect safety by slowing the penetration of new safety technologies into the fleet. The standards also influence the composition of the light-duty fleet. As the safety provided by light trucks, SUVs and passenger cars responds differently to technology that manufacturers employ to meet the standards—particularly mass reduction—fleets with different compositions of body styles will have varying numbers of fatalities, so changing the share of each type of lightduty vehicle in the projected future fleet impacts safety outcomes. 3. Increased driving because of better fuel economy. The ‘‘rebound effect’’ predicts consumers will drive more when the cost of driving declines. More stringent standards reduce vehicle operating costs, and in response, some consumers may choose to drive more. Additional driving increases exposure to risks associated with motor vehicle travel, and this added exposure translates into higher fatalities and injuries. The contributions of the three factors described above generate the differences in safety outcomes among regulatory alternatives.369 The agency’s analysis makes extensive efforts to allocate the differences in safety outcomes between the three factors. Fatalities expected during future years under each alternative are projected by deriving a fleet-wide fatality rate (fatalities per vehicle mile of travel) that incorporates the effects of differences in each of the three factors from baseline conditions and multiplying it by that alternative’s expected VMT. Fatalities are converted into a societal cost by multiplying fatalities with the DOT-recommended value of a statistical life (VSL) supplemented by economic impacts that are external to VSL measurements. Traffic injuries and property damage are also modeled directly using the same process and valued using costs that are specific to each injury severity level. All three factors influence predicted fatalities, but only two of them— changes in vehicle mass and in the composition of the light-duty fleet in response to changes in vehicle prices— impose increased risks on drivers and passengers that are not compensated for by accompanying benefits. In contrast, increased driving associated with the rebound effect is a consumer choice that reveals the benefit of additional travel. Consumers who choose to drive more have apparently concluded that the utility of additional driving exceeds the additional costs for doing so, including the crash risk that they perceive additional driving involves. As discussed in Chapter 7 of the accompanying Technical Support Document, the benefits of rebound driving are accounted for by offsetting a portion of the added safety costs. The agency categorizes safety outcome through three measures of light-duty vehicle safety: Fatalities to occupants occurring in crashes, serious injuries sustained by occupants, and the number of vehicles involved in crashes that cause property damage but no injuries. Counts of fatalities to occupants of automobiles and light trucks are obtained from the agency’s Fatal Accident Reporting System (FARS). Estimates of the number of serious injuries to drivers and passengers of light-duty vehicles are tabulated from the agency’s General Estimates System (GES), an annual sampling of motor vehicle crashes occurring throughout the U.S. Weights for different types of crashes were used to expand the samples of each type to estimates of the total number of crashes occurring during each year. Finally, estimates of the number of automobiles and light trucks involved in property damage-only (PDO) crashes each year were also developed using GES. NHTSA seeks comment on the following discussion. 369 The terms safety performance and safety outcome are related but represent different concepts. When we use the term safety performance, we are discussing the intrinsic safety of a vehicle based on its design and features, while safety outcome is used to describe whether a vehicle has been involved in an accident and the severity of the accident. While safety performance influences safety outcomes, other factors such as environmental and behavioral characteristics also play a significant role. 1. Mass Reduction Impacts Vehicle mass reduction can be one of the more cost-effective means of improving fuel economy, particularly for makes and models not already built with much high-strength steel or aluminum closures or low-mass components. Manufacturers have stated that they will continue to reduce vehicle PO 00000 Frm 00137 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 49738 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules mass to meet more stringent standards, and therefore, this expectation is incorporated into the modeling analysis supporting the standards. Safety tradeoffs associated with mass-reduction have occurred in the past, particularly before CAFE standards were attributebased; past safety trade-offs may have occurred because manufacturers chose at the time, in response to CAFE standards, to build smaller and lighter vehicles. In cases where fuel economy improvements were achieved through reductions in vehicle size and mass, the smaller, lighter vehicles did not fare as well in crashes as larger, heavier vehicles, on average. Although The agency now uses attribute-based standards, in part to reduce or eliminate the incentive to downsize vehicles to comply with CAFE standards, the agency must be mindful of the possibility of related safety trade-offs. For this proposed rule, the agency employed the modeling technique developed in the 2016 Puckett and Kindelberger report to analyze the updated crash and exposure data by examining the cross sections of the societal fatality rate per billion vehicle miles of travel (VMT) by mass and footprint, while controlling for driver age, gender, and other factors, in separate logistic regressions for five vehicle groups and nine crash types.370 The agency utilized the relationships between weight and safety from this analysis, expressed as percentage increases in fatalities per 100-pound weight reduction (which is how mass reduction is applied in the technology analysis; see Section III.D.4), to examine the weight impacts applied in this CAFE analysis. The effects of mass reduction on safety were estimated relative to (incremental to) the regulatory baseline in the CAFE analysis, across all vehicles for MY 2021 and beyond. In computing the impact of changes in mass on safety, the agency is faced with competing challenges. Research has consistently shown that mass reduction affects ‘‘lighter’’ and ‘‘heavier’’ vehicles differently across crash types. The 2016 Puckett and Kindelberger report found mass reduction concentrated among the heaviest vehicles is likely to have a beneficial effect on overall societal fatalities, while mass reduction concentrated among the lightest vehicles is likely to have a detrimental effect on fatalities. This represents a relationship between the dispersion of 370 Puckett, S.M. and Kindelberger, J.C. (2016, June). Relationships between Fatality Risk, Mass, and Footprint in Model Year 2003–2010 Passenger Cars and LTVs—Preliminary Report. (Docket No. 2016–0068). Washington, DC: National Highway Traffic Safety Administration. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 mass across vehicles in the fleet and societal fatalities: Decreasing dispersion is associated with a decrease in fatalities. Mass reduction in heavier vehicles is more beneficial to the occupants of lighter vehicles than it is harmful to the occupants of the heavier vehicles. Mass reduction in lighter vehicles is more harmful to the occupants of lighter vehicles than it is beneficial to the occupants of the heavier vehicles. To accurately capture the differing effect on lighter and heavier vehicles, the agency splits vehicles into lighter and heavier vehicle classifications in the analysis. However, this poses a challenge of creating statistically meaningful results. There is limited relevant crash data to use for the analysis. Each partition of the data reduces the number of observations per vehicle classification and crash type, and thus reduces the statistical robustness of the results. The methodology employed by the agency was designed to balance these competing forces as an optimal trade-off to accurately capture the impact of mass-reduction across vehicle curb weights and crash types while preserving the potential to identify robust estimates. Comments on the NPRM (83 FR 42986, August 24, 2018) for the 2020 CAFE rule included suggestions that the sample of LTVs in the analysis should not include the medium- or heavy-duty (i.e., truck-based vehicles with GVWR above 8,500 pounds) equivalents of light-duty vehicles in the sample (e.g., Ford F–250 versus F–150, RAM 2500 versus RAM 1500, Chevrolet Suburban 2500 versus Chevrolet Suburban 1500), or Class 2b and 3 vehicles. For the proposal, NHTSA explored revising the analysis consistent with such comments. The process involved two key analytical steps: (1) Removing all case vehicles from the analysis whose GVWR exceeded 8,500 pounds; and (2) re-classifying all crash partners with GVWR above 8,500 pounds as heavy vehicles. The direct effects of these changes are: (1) The range of curb weights in the LTV sample is reduced, lowering the median curb weight from 5,014 pounds to 4,808 pounds; (2) the sample size of LTVs is reduced (the number of case LTVs under this alternative specification is approximately 18 percent lower than in the central analysis); and (3) the relative impact of crashes with LTVs on overall impacts on societal fatality rates decreases, while the corresponding impact of crashes with heavy vehicles increases. PO 00000 Frm 00138 Fmt 4701 Sfmt 4702 The results from the exploratory analysis of this alternative approach are provided in Table III–41. The agency seeks comment on this alternative approach; public comment will inform the decision whether to incorporate the results into the CAFE Model. The primary functional change offered by the alternative approach is that the sample of vehicles classified as LTVs would be restricted to vehicles that would be subject to CAFE regulations. At the statistical level, the concerns raised in the agency’s response to comment on the 2018 CAFE NPRM remain. In particular, including Class 2b and 3 vehicles in the analysis to determine the relationship of vehicle mass on safety has the added benefit of improving correlation constraints. Notably, curb weight increases faster than footprint for large light trucks and Class 2b and 3 pickup trucks and SUVs, in part because the widths of vehicles are constrained more tightly (i.e., due to lane widths) than their curb weights. Including data from Class 2b and 3 pickup truck and SUV fatal crashes provides data over a wider range of vehicle weights, which improves the ability to estimate the mass-crash fatality relationship. That is, by extending the footprint-curb weight-fatality data to include Class 2b and 3 trucks that are functionally and structurally similar to corresponding 1⁄2-ton models that are subject to CAFE regulation, the sample size and ranges of curb weights and footprint are improved. Sample size is a challenge for estimating relationships between curb weight and fatality risk for individual crash types in the main analysis; dividing the sample further or removing observations makes it increasingly difficult to identify meaningful estimates and the relationships that are present in the data, as shown in the sensitivity analysis below. For the proposal, the agency has determined that the benefit of the additional data points outweighs the concern that some of the vehicles used to determine the mass-safety coefficients are not regulated by CAFE vehicles. The agency also explored three other alternative model specifications that are presented in Table III–41. The first alternative centers on aligning CUVs and minivans with the rest of the sample, by splitting these vehicles into two weight classes. The key factor restricting this change historically has been a low sample size for these vehicles; the exploratory analysis examined whether the current database (which, due to the range of CYs covered, contains a smaller share of CUVs and E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules minivans than the current fleet) contains a sufficient sample size to evaluate two weight classes for CUVs and minivans. A complicating factor in this analysis is that minivans tend to have higher curb weights than other CUVs, adding statistical burden in identifying meaningful effects of mass on societal fatality rates after accounting for body type in the weight class with the fewest minivans (i.e., lighter CUVs and minivans). The second alternative centers on aligning passenger cars with the rest of the sample by including cars that are equipped with all-wheel drive (AWD). In previous analyses, passenger cars with AWD were excluded from the analysis because they represented a sufficiently low share of the vehicle fleet that statistical relationships between AWD status and societal fatality risk were highly prone to being conflated with other factors associated with AWD status (e.g., location, luxury vehicle status). However, the share of AWD passenger cars in the fleet has grown. Approximately one-quarter of the passenger cars in the database have AWD, compared to an approximately five-percent share in the MY 2000–2007 database. Furthermore, all other vehicle types in the analysis include AWD as an explanatory variable. Thus, the agency finds the inclusion of a considerable portion of the real-world fleet (i.e., passenger cars with AWD) to be a meaningful consideration. The third alternative is a minor procedural question: Whether to expand the CYs and MYs used to identify the distribution of fatalities across crash types. The timing of the safety databases places the years of the analysis used to 49739 establish the distribution of fatalities by crash type firmly within the central years of the economic downturn of the late 2000s and early 2010s. During these years, travel demand was below longterm trends, resulting in fewer crashes. In turn, applying the same window of CYs and MYs to the identification of the distribution of fatalities across crash types results in notably fewer crashes to incorporate into the analysis. The agency conducted exploratory analysis on the question of whether to add CYs and MYs to the range of crashes used to identify the distribution of fatalities across crash types; this analysis was conducted in concert with the two alternatives discussed directly above. Results incorporating these three alternatives are presented in Table III– 41. Vehicle Class Point Estimates, Fatalities Weighted Across MY 2008-2011 in CY 2008-2012 (Original Weights) Point Estimates, Fatalities Weighted Across MY 2007-2011 in CY 2007-2012 Point Estimates, Fatalities Weighted Across MY 2006-2011 in CY 2006-2012 Point Estimates, Fatalities Weighted Across MY 2004-2011 in CY 2006-2012 (Full Sample) 1.12% 1.12% l.ll% 1.12% 0.89% 0.87% 0.84% 0.86% 0.26% 0.26% 0.26% 0.29% -0.16% -0.17% -0.16% -0.17% 0.20% 0.19% 0.18% 0.18% -0.52% -0.52% -0.53% -0.51% lotter on DSK11XQN23PROD with PROPOSALS2 Cars< 3,201 Pounds (including AWD) Cars 3,201+ Pounds (including AWD) LTVs < 4,808 Pounds (No Class 2b/3) LTVs 4,808+ Pounds (No Class 2b/3) CUVs and Minivans < 3,955 Pounds CUVs and Minivans 3,955+ Pounds Under the alternative specification excluding Class 2b and Class 3 truckbased vehicles as case vehicles, the median curb weight for LTVs is 4,808 pounds, or 206 pounds lighter than in the central analysis. When splitting CUVs and minivans into two weight classes, the median curb weight for the vehicles is 3,955 pounds. Under this alternative specification, where Class 2b and Class 3 truck-based crash partners are shifted from truck-based LTVs to heavy-duty vehicles, the median curb weight for LTV crash partners is 4,216 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 pounds, or 144 pounds lighter than in the central analysis. Re-classifying Class 2b and Class 3 truck-based vehicles has a strong effect on the point estimate for heavier LTVs. Critically, removing the heaviest trucks as case vehicles yields a much smaller point estimate (reduction in societal fatality rates of between 0.16% and 0.17% per 100-pound mass reduction, versus 0.61% in the central analysis). This result is consistent with a relationship where a key share of the sensitivity of fatality risk is attributed to the mass of the heaviest vehicles in the PO 00000 Frm 00139 Fmt 4701 Sfmt 4702 fleet (i.e., supporting the role of mass dispersion in societal fatality rates). Importantly, the point estimate for lighter LTVs is not meaningfully different from the corresponding estimate in the central analysis (increase in societal fatality rates of between 0.26% and 0.29% per 100-pound mass reduction, versus 0.3% in the central analysis). Considered in concert, these results indicate that the most effective reductions in societal fatality rates via mass reduction in truck-based vehicles would arise not from lightweighting the heaviest vehicles subject to CAFE E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.097</GPH> Table 111-41-Fatality Increase(%) per 100-Pound Mass Reduction While Holding Footprint Constant with Alternative Model Specifications - MY 2004-2011, CY 2006-2012 lotter on DSK11XQN23PROD with PROPOSALS2 49740 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules regulation, but rather from lightweighting similar, medium- and heavy-duty vehicles. Including passenger cars with AWD in the analysis has little effect on the point estimate for lighter passenger cars (increase in societal fatality rates of approximately 1.1% per 100-pound mass reduction, versus 1.2% in the central analysis). However, this revision has a strong effect on the point estimate for heavier passenger cars (increase in societal fatality rates of between 0.84% and 0.89% per 100-pound mass reduction, versus 0.42% in the central analysis). This result supports a hypothesis that, after taking AWD status into account, mass reduction in heavier passenger cars is a more important driver of societal fatality rates than previously estimated. Although this result could be spurious, estimated confidence bounds (presented below) indicate that accounting for AWD status reduces uncertainty in the point estimate. The agency seeks comment on the inclusion of passenger cars with AWD when estimating the effects of mass reduction on societal fatality rates. Splitting CUVs and minivans into two vehicle classes yields point estimates that are consistent with the point estimate for the consolidated CUVminivan vehicle class (an average decrease in societal fatality rates of approximately 0.16% to 0.18% per 100pound mass reduction across the two vehicle classes, versus a decrease of 0.25% in the central analysis). However, sample sizes half as large in the two vehicle classes relative to the consolidated vehicle class lead to very large estimated confidence bounds, as shown below. Due to this uncertainty, The agency does not feel that the current databases contain a large enough sample of CUVs and minivans to split these vehicles into two classes in the analysis; however, this issue will be reexamined when the next iteration of the databases is complete. Extending the range of CYs and MYs used to establish the distribution of fatalities across crash types has a negligible effect on the point estimates. Based on the narrow ranges of results in Table III–41, The agency finds evidence supporting a flexible approach in the choice of CYs and MYs used in this manner. All else being equal, extending the range helps to mitigate the potential for individual crash types with large estimated effects to drive spurious effects on overall estimates through unrepresentatively high estimated shares of overall fatalities. As a hedge in this direction, the agency applied the estimates from the alternative specification with two additional CYs VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 and MYs (i.e., the second column from the right in Table III–41) when evaluating 95-percent confidence bounds for the alternative models considered here. The agency seeks comment on this approach to representing the distribution of fatalities across crash types. A more detailed description of the mass-safety analysis can be found in Chapter 7 of the accompanying TSD. 2. Sales/Scrappage Impacts The sales and scrappage responses to higher vehicle prices discussed in Section III.E.2 have important safety consequences and influence safety through the same basic mechanism, fleet turnover. In the case of the scrappage response, delaying fleet turnover keeps drivers in older vehicles which tend to be less safe than newer vehicles.371 Similarly, the sales response slows the rate at which newer vehicles, and their associated safety improvements, enter the on-road population. The sales response also influences the mix of vehicles on the road—with more stringent CAFE standards leading to a higher share of light trucks sold in the new vehicle market, assuming all else is equal. This occurs because there is diminishing value to marginal improvements in fuel economy (there are fewer gallons to be saved), and as the difference in consumption between light trucks and passenger cars diminishes, the other attributes of the trucks will likely lead to increases in their market share—especially under lower gas prices. Light trucks have higher rates of fatal crashes when interacting with passenger cars and, as earlier discussed, different directional responses to mass reduction technology based on the existing mass and body style of the vehicle. Any effects on fleet turnover (either from delayed vehicle retirement or deferred sales of new vehicles) will affect the distribution of both ages and model years present in the on-road fleet. Because each of these vintages carries with it inherent rates of fatal crashes, and newer vintages are generally safer than older ones, changing that distribution will change the total number of on-road fatalities under each regulatory alternative. Similarly, the dynamic fleet share model captures the 371 See Passenger Vehicle Occupant Injury Severity by Vehicle Age and Model Year in Fatal Crashes, Traffic Safety Facts Research Note, DOT– HS–812–528, National Highway Traffic Safety Administration, April, 2018, and The Relationship Between Passenger Vehicle Occupant Injury Outcomes and Vehicle Age or Model Year in PoliceReported Crashes, Traffic Safety Facts Research Note, DOT–HS–812–937, National Highway Traffic Safety Administration, March, 2020. PO 00000 Frm 00140 Fmt 4701 Sfmt 4702 changes in the fleet’s composition of cars and trucks. As cars and trucks have different fatality rates, differences in fleet composition across the alternatives will affect fatalities. At the highest level, the agency calculates the impact of the sales and scrappage effects by multiplying the VMT of a vehicle by the fatality risk of that vehicle. For this analysis, calculating VMT is rather simple: The agency uses the distribution of miles calculated in TSD Chapter 4.3. The trickier aspect of the analysis is creating fatality rate coefficients. The fatality risk measures the likelihood that a vehicle will be involved in a fatal accident per mile driven. The agency calculates the fatality risk of a vehicle based on the vehicle’s model year, age, and style, while controlling for factors which are independent of the intrinsic nature of the vehicle, such as behavioral characteristics. Using this same approach, the agency designed separate models for fatalities, non-fatal injuries, and property damaged vehicles. The fatality risk projections described above capture the historical evolution of safety. Given that modern technologies are proliferating faster than ever and offer greater safety benefits than traditional safety improvements, the agency augmented the fatality risk projections with knowledge about forthcoming safety improvements. The agency applied detailed empirical estimates of the market uptake and improving effectiveness of crash avoidance technologies to estimate their effect on the fleet-wide fatality rate, including explicitly incorporating both the direct effect of those technologies on the crash involvement rates of new vehicles equipped with them, as well as the ‘‘spillover’’ effect of those technologies on improving the safety of occupants of vehicles that are not equipped with these technologies.372 The agency’s approach to measuring these impacts is to derive effectiveness rates for these advanced crashavoidance technologies from safety technology literature. The agency then applies these effectiveness rates to specific crash target populations for 372 These technologies included Forward Collision Warning (FCW), Crash Imminent Braking (CIB), Dynamic Brake Support (DBS), Pedestrian AEB (PAEB), Rear Automatic Braking, Semiautomatic Headlamp Beam Switching, Lane Departure Warning (LDW), Lane Keep Assist (LKA), and Blind Spot Detection (BSD). While Autonomous vehicles offer the possibility of significantly reducing or eventually even eliminating the effect of human error in crash causation, a contributing factor in roughly 94% of all crashes, there is insufficient information and certainty regarding autonomous vehicles eventual impact to include them in this analysis. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules which the crash avoidance technology is designed to mitigate and adjusted to reflect the current pace of adoption of the technology, including the public commitment by manufactures to install these technologies. The products of these factors, combined across all 6 advanced technologies, produce a fatality rate reduction percentage that is applied to the fatality rate trend model discussed above, which projects both vehicle and non-vehicle safety trends. The combined model produces a projection of impacts of changes in vehicle safety technology as well as behavioral and infrastructural trends. A much more detailed discussion of the methods and inputs used to make these projections of safety impacts from advanced technologies is included in Chapter 7 of the accompanying TSD. 3. Rebound Effect Impacts lotter on DSK11XQN23PROD with PROPOSALS2 The additional VMT demanded due to the rebound effect is accompanied by more exposure to risk, however, rebound miles are not imposed on consumers by regulation. They are a freely chosen activity resulting from reduced vehicle operational costs. As such, the agencies believe a large portion of the safety risks associated with additional driving are offset by the benefits drivers gain from added driving. The level of risk internalized by drivers is uncertain. This analysis assumes that consumers internalize 90 percent of this risk, which mostly offsets the societal impact of any added fatalities from this voluntary consumer VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 choice. Additional discussion of internalized risk is contained in TSD Chapter 7.4. 4. Value of Safety Impacts Fatalities, nonfatal injuries, and property damage crashes are valued as a societal cost within the CAFE Model’s cost and benefit accounting. Their value is based on the comprehensive value of a fatality, which includes lost quality of life and is quantified in the value of a statistical life (VSL) as well as economic consequences such as medical and emergency care, insurance administrative costs, legal costs, and other economic impacts not captured in the VSL alone. These values were derived from data in Blincoe et al. (2015), adjusted to 2018 dollars, and updated to reflect the official DOT guidance on the value of a statistical life. Nonfatal injury costs, which differ by severity, were weighted according to the relative incidence of injuries across the Abbreviated Injury Scale (AIS). To determine this incidence, the agency applied a KABCO 373/maximum abbreviated injury scale (MAIS) translator to GES KABCO based injury counts from 2010 through 2015. This produced the MAIS based injury profile. This profile was used to weight nonfatal 373 The ‘‘KABCO’’ injury scale also can be used for establishing crash costs. This scale was developed by the National Safety Council (NSC) and is frequently used by law enforcement for classifying injuries: K—Fatal; A—Incapacitating injury; B—Non-incapacitating injury; C—Possible injury; and O—No injury. PO 00000 Frm 00141 Fmt 4701 Sfmt 4702 49741 injury unit costs derived from Blincoe et al., adjusted to 2018 economics and updated to reflect the official DOT guidance on the value of a statistical life. Property-damaged vehicle costs were also taken from Blincoe et al. and adjusted to 2018 economics. VSL does not affect property damage. This gives societal values of $10.8 million for each fatality, $132,000 for each nonfatal injury, and $7,100 for each property damaged vehicle. 5. Impacts of the Proposal on Safety Table III–42 through Table III–44 summarize the safety impacts of the proposed standards on safety broken down by factor. These impacts are summarized over the lifetimes of model year 1981 through 2029 vehicles for all light passenger vehicles (including passenger cars and light trucks). Economic impacts are shown separately under both 3% and 7% discount rates. Model years 1981 through 2029 were examined because they represent the model years that might be affected by shifts in fleet composition due to the impact of higher new vehicle prices on sales of new vehicles and retention of older vehicles. Earlier years will be affected by slower scrappage rates and we expect the impacts of these standards will be fully realized in vehicle designs by MY 2029. BILLING CODE 4910–59–P E:\FR\FM\03SEP2.SGM 03SEP2 49742 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-42- Change in Safety Parameters from Alternative O(Baseline) for MY 19812029 for Total Fleet, 3% Percent Discount Rate, by Alternative 1 Alternative: 2 3 Fatalities 64 449 506 1,019 Fatalities from Mass Changes Fatalities from Rebound Effect Fatalities from Sales/Scrappage Total Changes in Fatalities 115 142 584 801 1,123 1,681 1,822 2,624 Fatality Costs ($b) Fatality Costs from Mass Changes Fatality Costs from Rebound Effect Fatality Costs from Sales/Scrappage Total - Fatality Costs ($b) 0.4 3.0 4.4 7.8 0.8 3.9 9.8 14.5 1.0 5.4 14.8 21.1 0.5 3.2 1.2 4.9 0.9 4.3 2.8 8.0 1.1 5.9 4.1 11.1 0.1 0.7 0.2 1.0 0.2 0.9 0.5 1.6 0.2 1.2 0.7 2.2 1.0 6.9 5.8 13.7 1.9 9.1 13.0 24.0 2.3 12.5 19.6 34.4 Non-Fatal Crash Costs ($b) Non-Fatal Crash Costs from Mass Changes Non-Fatal Crash Costs from Rebound Effect Non-Fatal Crash Costs from Sales/Scrappage Total - Non-Fatal Crash Costs ($b) Property Damage Costs ($b) Property Damage Costs from Mass Changes Property Damage Costs from Rebound Effect Property Damage Costs from Sales/Scrappage Total - Property Damage Costs ($b) Crash Costs from Mass Changes Crash Costs from Rebound Effect Crash Costs from Sales/Scrappage lotter on DSK11XQN23PROD with PROPOSALS2 Total - Societal Crash Costs ($b) VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00142 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.098</GPH> Total Crash Costs ($b) 49743 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-43- Change in Safety Parameters from Alternative O(Baseline) for MY 19812029 for Total Fleet, 7% Percent Discount Rate, by Alternative 1 Alternative: 2 3 Fatalities 64 449 506 1,019 115 584 1,123 1,822 142 801 1,681 2,624 Fatality Costs from Mass Changes 0.3 Fatality Costs from Rebound Effect 1.7 Fatality Costs from Sales/Scrappage 3.3 Total - Fatality Costs ($b) 5.2 Non-Fatal Crash Costs ($b) Non-Fatal Crash Costs from Mass Changes 0.3 Non-Fatal Crash Costs from Rebound Effect 2.0 Non-Fatal Crash Costs from Sales/Scrappage 1.0 Total - Non-Fatal Crash Costs ($b) 3.3 Property Damage Costs ($b) 0.1 Property Damage Costs from Mass Changes Property Damage Costs from Rebound Effect 0.4 Property Damage Costs from Sales/Scrappage 0.2 Total - Property Damage Costs ($b) 0.7 Total Crash Costs ($b) 0.6 Crash Costs from Mass Changes Crash Costs from Rebound Effect 4.1 Crash Costs from Sales/Scrappage 4.5 Total - Societal Crash Costs ($b) 9.2 0.5 2.2 7.2 9.9 0.6 3.1 11.0 14.7 0.6 2.7 2.3 5.6 0.7 3.7 3.5 7.9 0.1 0.6 0.4 1.1 0.1 0.8 0.6 1.5 1.2 5.5 9.9 16.6 1.4 7.5 15.1 24.0 Fatalities from Mass Changes Fatalities from Rebound Effect Fatalities from Sales/Scrappage Total Changes in Fatalities VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00143 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.099</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Fatality Costs ($b) 49744 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table 111-44- Change in Non-Fatal Safety Parameters from Alternative O(Baseline) for MY 1981-2029 for Total Fleet, by Alternative Alternative: 1 2 3 Non-Fatal Injuries Non-Fatal Injuries from Mass Changes 5,537 10,048 12,377 Non-Fatal Injuries from Rebound Effect 36,587 48,618 66,522 Non-Fatal Injuries from Sales/Scrappage 9,723 22,269 32,249 51,847 80,936 111,147 21,195 38,471 47,389 Property Damaged Vehicles from Rebound Effect 139,798 185,800 254,194 Property Damaged Vehicles from Sales/Scrappage 29,900 69,638 99,711 190,892 293,909 401,294 Total Changes in Non-Fatal Injuries Property Damaged Vehicles Total Changes in Property Damaged Vehicles BILLING CODE 4910–59–C As seen in the tables, all three safety factors—changes in mass, fleet turnover, and rebound—increase as the standards become more stringent. As expected, rebound fatalities grow at a constant rate as vehicles become more fuel efficient and are used more frequently. Mass reduction has a relatively minimal impact on safety and diminishes as stringency increases. This may point to either the fleet becoming more homogeneous and hence less mass disparate in crashes. Alternatively, the model may be capturing that there’s little room for more mass reductions in particular models. The slowing of fleet turnover due to higher vehicle prices has the largest impact of the three factors and accelerates with higher alternatives. Of course, if the agency’s assumptions overstate the rebound effect and/or slower fleet turnover, fatalities, injuries and property damage would be lower, and vice versa. PRIA Chapter 5.5 discusses the results of the analysis in more detail and PRIA Chapter 5.6—Safety Impacts provides an overview of sensitivity analyses performed to isolate the uncertainty parameters of each of the three safety impacts. IV. Regulatory Alternatives Considered in this NPRM lotter on DSK11XQN23PROD with PROPOSALS2 A. Basis for Alternatives Considered Agencies typically consider regulatory alternatives in proposals as a way of evaluating the comparative effects of VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 different potential ways of accomplishing their desired goal. NEPA requires agencies to compare the potential environmental impacts of their proposed actions to those of a reasonable range of alternatives. Executive Orders 12866 and 13563, as well as 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 proposal includes a no-action alternative, described below, and three ‘‘action alternatives.’’ The proposed standards may, in places, be referred to as the ‘‘preferred alternative,’’ which is NEPA parlance, but NHTSA intends ‘‘proposal’’ and ‘‘preferred alternative’’ to be used interchangeably for purposes of this rulemaking. Regulations regarding implementation of NEPA require 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.’’ This does not amount to a requirement that agencies evaluate the widest conceivable spectrum of alternatives. Rather, the range of alternatives must be reasonable and consistent with the purpose and need of the action. PO 00000 Frm 00144 Fmt 4701 Sfmt 4702 The different regulatory alternatives are defined in terms of percent-increases in CAFE stringency from year to year. Readers should recognize that those year-over-year changes in stringency are not measured in terms of mile per gallon 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 rather in terms of shifts in the footprint functions that form the basis for the actual CAFE standards (as in, on a gallon per mile basis, the CAFE standards change by a given percentage from one model year to the next). Under some alternatives, the rate of change is the same from year to year, while under others, it differs, and under some alternatives, the rate of change is different for cars and for trucks. One action alternative is more stringent than the proposal, while one is less stringent than the proposal. The alternatives considered in this proposal represent a reasonable range of possible final agency actions. B. Regulatory Alternatives and Proposed CAFE Standards for MYs 2024–2026 The regulatory alternatives for this proposal are presented here as the percent-increases-per-year that they represent. The sections that follow will present the alternatives as the literal coefficients which define standards curves increasing at the given percentage rates and will also further explain the basis for the alternatives selected. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.100</GPH> Property Damaged Vehicles from Mass Changes 49745 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table IV-1- Regulatory Alternatives Considered in this Proposal Year-Over-Year Stringency Increases (Passenger Cars) Year-Over-Year Stringency Increases (Light Trucks) 2024 2025 2026 2024 2025 2026 1.5% 1.5% 1.5% 1.5% 1.5% 1.5% 9.14% 3.26% 3.26% 11.02% 3.26% 3.26% Alternative 2 (Preferred) 8% 8% 8% 8% 8% 8% Alternative 3 10% 10% 10% 10% 10% 10% Regulatory Alternative Alternative 0 (No Action) Alternative 1 As for past rulemaking analyses, NHTSA has analyzed each of the regulatory alternatives in a manner that estimates manufacturers’ potential application of technology in response to the corresponding CAFE requirements and the estimated market demand for fuel economy, considering estimated fuel prices, estimated product development cadence, and the estimated availability, applicability, cost, and effectiveness of fuel-saving technologies. The analysis sometimes shows that specific manufacturers could increase CAFE levels beyond requirements in ways estimated to ‘‘pay buyers back’’ very quickly (i.e., within 30 months) for the corresponding additional costs to purchase new vehicles through avoided fuel outlays. Consistent with the analysis published with the 2020 final rule, this analysis shows that if battery costs decline as projected while fuel prices increase as projected, BEVs should become increasingly attractive on this basis, such that the modeled application of BEVs (and some other technologies) clearly outstrips regulatory requirements after the mid-2030s. The analysis accompanying the 2020 final rule presented such results for CAFE standards as well as— separately—CO2 standards. New in this proposal, DOT has modified the CAFE Model to account for the combined effect of both CAFE and CO2 standards, simulating technology application decisions each manufacturer could possibly make when faced with both CAFE standards and CO2 standards (and also estimated market demand for fuel economy). This capacity was exercised for purposes of creating the baseline against which alternatives were analyzed, but not for purposes of modeling compliance with both agencies’ proposals. Also, new for this proposal, DOT has further modified the CAFE Model to account for the ‘‘Framework’’ agreements California has reached with BMW, Ford, Honda, Volkswagen, and Volvo, and for the ZEV mandate that California and the ‘‘Section 177’’ states have adopted. The TSD elaborates on these new model capabilities. Generally speaking, the model treats each manufacturer as applying the following logic when making technology decisions: 1. What do I need to carry over from last year? 2. What should I apply more widely in order to continue sharing (of, e.g., engines) across different vehicle models? 3. What new PHEVs or BEVs do I need to build in order to satisfy the ZEV mandates? 4. What further technology, if any, could I apply that would enable buyers to recoup additional costs within 30 months after buying new vehicles? 5. What additional technology, if any, should I apply in order to respond to CAFE and CO2 standards? All of the regulatory alternatives considered here include, for passenger cars, the following coefficients defining the combination of baseline Federal CO2 standards and the California Framework agreement. b (g/mi) c (g/mi per s.f.) d (g/mi) e (s.f.) f(s.f.) .£ (g/mi) h (g/mi) i fo/mi per s.f.) i (g/mi) Coefficients a, b, c, d, e, and f define the current Federal CO2 standards for passenger cars. Analogous to VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 2022 2023 2024 2025 2026 159 217 3.88 -0.1 41 56 151 207 3.70 -0.4 156 214 3.82 -0.4 41 56 146 199 3.56 -0.4 154 210 3.77 -0.6 41 56 140 192 3.43 -0.4 151 207 3.71 -0.9 41 56 135 185 3.30 -0.3 149 203 3.65 -1.2 41 56 130 178 3.18 -0.3 coefficients defining CAFE standards, coefficients a and b specify minimum and maximum passenger car CO2 targets PO 00000 Frm 00145 Fmt 4701 Sfmt 4702 in each model year. Coefficients c and d specify the slope and intercept of the linear portion of the CO2 target function, E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.101</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 a (g/mi) EP03SE21.102</GPH> Table IV-2- Passenger Car CO2 Target Function Coefficients 49746 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules and coefficients e and f bound the region within which CO2 targets are defined by this linear form. Coefficients g, h, i, and j define the CO2 targets applicable to BMW, Ford, Honda, Volkswagen, and Volvo, pursuant to the agreement these manufacturers have reached with California. Beyond 2026, the MY 2026 Federal standards apply to all manufacturers, including these five manufacturers. The coefficients shown in Table IV–3 define the corresponding CO2 standards for light trucks. Table IV-3 - Light Truck CO2 Target Function Coefficients a (g/mi) b fo/mi) c (.g/mi per s.f.) dfo/mi) e(s.f.) f(s.f.) ~ (g/mi) h fo/mi) i (.g/mi per s.f.) j (g/mi) All of the regulatory alternatives considered here also include NHTSA’s estimates of ways each manufacturer could introduce new PHEVs and BEVs in response to ZEV mandates. As discussed in greater detail below, these 2022 203 324 4.44 20.6 41 74 188 322 4.12 19.1 2023 200 319 4.37 20.2 41 74 181 310 3.97 18.4 2024 196 314 4.31 19.6 41 74 175 299 3.82 17.7 2025 193 309 4.23 19.6 41 74 168 288 3.68 17.0 estimates force the model to convert specific vehicle model/configurations to either a BEV200, BEV300, or BEV400 at the earliest estimated redesign. These ‘‘ZEV Candidates’’ define an incremental response to ZEV mandates 2026 190 304 4.17 19.0 41 74 162 277 3.54 16.4 (i.e., beyond PHEV and BEV production through MY 2020) comprise the following shares of manufacturers’ MY 2020 production for the U.S. market as shown in Table IV–4. Table IV-4-ZEV "Candidates" as Share of MY 2020 Production BMW Daimler FCA Ford GM Honda Hyundai Kia Jaguar - Land Rover Mazda Mitsubishi Nissan Subaru Tesla Toyota Volvo VWA For example, while Tesla obviously need not introduce additional BEVs to comply with ZEV mandates, our VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 I BEV200 I BEV300 I BEV400 I 1.9% 2.6% 0.1% 1.7% 0.2% 3.1% 0.6% 1.2% 2.3% 0.8% 1.1% 1.1% 1.0% 1.8% 1.3% 0.5% 1.4% 1.2% 0.5% 2.2% 0.7% 0.7% 1.5% analysis indicates Nissan could need to increase BEV offerings modestly to do so, and Mazda and some other PO 00000 Frm 00146 Fmt 4701 Sfmt 4702 manufacturers may need to do considerably more than Nissan to introduce new BEV offerings. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.104</GPH> Manufacturer EP03SE21.103</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 I Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules This representation of CO2 standards and ZEV mandates applies equally to all regulatory alternatives, and NHTSA’s analysis applies the CAFE Model to examine each alternative treating each manufacturer as responding jointly to the entire set of requirements. This is distinct from model application of BEVs for compliance purposes under the compliance simulations of the different action alternatives which inform decision-makers regarding potential effects of the standards. Chapter 1 of the TSD contains extensive discussion of the development of the No-Action Alternative, and explains the reasons for and effect of apparent ‘‘over-compliance’’ with the No-Action Alternative, which reduces costs and benefits attributable to the proposed CAFE standards and other action alternatives. NHTSA seeks comment broadly on that discussion and whether and how to change its approach to developing the No-Action Alternative for the final rule. NHTSA also specifically seeks comment on whether and how to add to the NoAction Alternative for the final rule an estimation of GHG standards that 49747 California and the Section 177 states might separately enforce if California’s waiver of CAA preemption was reestablished. 1. No-Action Alternative The No-Action Alternative (also sometimes referred to as ‘‘Alternative 0’’) applies the CAFE target curves set in 2020 for MYs 2024–2026, which raised stringency by 1.5 percent per year for both passenger cars and light trucks. BILLING CODE 4910–59–P Table IV-5 - Characteristics of No-Action Alternative - Passenger Cars a (mv<;!) b (mmz) c (~pm per sf) d (<;!vm) 2024 2025 2026 51.78 38.74 0.000433 0.00155 52.57 39.33 0.000427 0.00152 53.37 39.93 0.000420 0.00150 Table IV-6 - Characteristics of No-Action Alternative - Light Trucks C d 2025 2026 41.55 26.82 0.000484 0.00423 42.18 27.23 0.000477 0.00417 42.82 27.64 0.000469 0.00410 fuel economy for smaller footprint vehicles and lower for larger footprint vehicles. EP03SE21.106</GPH> vehicle footprint and the y-axis represents fuel economy, showing that in ‘‘CAFE space,’’ targets are higher in VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00147 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.105</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 These equations are presented graphically in Figure IV–1 and Figure IV–2, where the x-axis represents 2024 49748 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 60 55 _---. ~ ........................... ~ OJ &: 40 - - - -..--..--.. . -.. ------·---·-.. ---.. . ----~-.. --.......--.. --.. M---M-----..- - - 35 30 25 35 40 45 50 55 60 65 70 75 80 Footprint (sf) ............. 2020 ·······2021 ----2022 -2023 ·······2024 ----2025 - -2026 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00148 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.107</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure IV-1 - No-Action Alternative, Passenger Car Fuel Economy Target Curves 49749 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 60 55 35 30 25 35 40 45 50 55 60 65 70 75 80 Footprint (sf) """""""" 2020 ··----- 2021 · --- 2022 -2023 ....... 2024 ----2025 - -2026 Figure IV-2-No-Action Alternative, Light Truck Fuel Economy Target Curves NHTSA must also set a minimum standard for domestically manufactured passenger cars, which is often referred to as the ‘‘MDPCS.’’ Any time NHTSA establishes or changes a passenger car standard for a model year, the MDPCS must also be evaluated or re-evaluated and established accordingly, but for purposes of the No-Action alternative, the MDPCS is as it was established in the 2020 final rule, as shown in Table IV–7. Table IV-7 - No-Action Alternative - Minimum Domestic Passenger Car Standard As the baseline against which the Action Alternatives are measured, the No-Action Alternative also includes several other actions that NHTSA believes will occur in the absence of further regulatory action. First, NHTSA has included California’s ZEV mandate as part of the No-Action Alternative. NHTSA has already proposed to rescind the 2019 ‘‘SAFE I’’ rule,374 and EPA has reopened consideration of whether to grant California a waiver to consider its ZEV mandate,375 although California does not currently possess a waiver of preemption under the CAA and NHTSA regulations currently purport to preempt the California ZEV program. Although 374 86 375 86 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 FR 25980 (May 12, 2021). FR 22421 (Apr. 28, 2021). Frm 00149 Fmt 4701 Sfmt 4702 neither of these actions has yet been finalized, it is reasonably foreseeable that manufacturers selling vehicles in California and in the Section 177 states could be required to comply with the ZEV mandate during the timeframe of this rulemaking. Second, NHTSA has included the agreements made between California and BMW, Ford, Honda, VWA, and Volvo, because these agreements by their terms are contracts, E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.108</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 BILLING CODE 4910–59–C EP03SE21.109</GPH> 41.8 m 49750 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules even though they were entered into voluntarily.376 NHTSA did so by including EPA’s baseline (i.e., 2020) GHG standards in its analysis, and introducing more stringent GHG target functions during MYs 2022–2026, but treating only these five manufacturers as subject to these more stringent target functions. Because a significant portion of the market voluntarily adopted the California framework, presumably because the manufacturers who joined believed it could be met, and because that adoption is contractually binding once entered into, it is reasonable to assume that it will occur as expected during the rulemaking timeframe, and thus, reasonable to include in the NoAction Alternative. As in past analyses, NHTSA’s analysis further assumes that, beyond any technology applied in response to CAFE standards, EPA GHG standards, California/OEM agreements, and ZEV mandates applicable in California and the Section 177 states, manufacturers could also make any additional fuel economy improvements estimated to reduce owners’ estimated average fuel outlays during the first 30 months of vehicle operation by more than the estimated increase in new vehicle price. NHTSA accomplished much of this through expansion of the CAFE Model after the prior rulemaking. The previous version of the model had been extended to apply to GHG standards as well as CAFE standards but had not been published in a form that simulated simultaneous compliance with both sets of standards. As discussed at greater length in the current CAFE Model documentation, the updated version of the model simulates all the following simultaneously: 1. Compliance with CAFE standards 2. Compliance with GHG standards applicable to all manufacturers 3. Compliance with alternative GHG standards applicable to a subset of manufacturers 4. Compliance with ZEV mandates 5. Further fuel economy improvements applied if sufficiently cost-effective for buyers Inclusion of these actions in the NoAction Alternative means that they are necessarily included in each of the Action Alternatives. That is, the impacts of all the alternatives evaluated in this proposal are against the backdrop of these State and voluntary actions by automakers. This is important to remember, because it means that automakers will be taking actions to improve fuel economy even in the absence of new CAFE standards, and that costs and benefits attributable to those actions are therefore not attributable to possible future CAFE standards. 2. Alternative 1 Alternative 1 would increase CAFE stringency for MY 2024 by 9.14% for passenger cars and 11.02% for light trucks and increase stringency in MYs 2025 and 2026 by 3.26% per year for both passenger cars and light trucks. NHTSA calculates that the stringency of Alternative 1 in each of MYs 2024–2026 is equivalent to the average stringency of the California framework agreement applied to all manufacturers in those model years. NHTSA calculated the stringency values using a spreadsheet, shown in TSD Chapter 1, assuming manufacturers would achieve a one percent reduction in stringency each model year under the California framework through the application of ZEV vehicle multipliers. The spreadsheet applies a normalized stringency value of 100 percent in MY 2021 for both CO2 standards and CAFE standards. Informed by these calculations, NHTSA defined Alternative 1 by applying the CAFE equivalent stringency increases in MYs 2024–2026, resulting in the coefficients listed in Table IV–8 and Table IV–9. BILLING CODE 4910–59–P Table IV-8 - Characteristics of Alternative 1 - Passenger Cars a(mp~ b (mv<;!) c (f!pm per s.f) d (f!Dm) 2024 2025 2026 56.15 42.00 0.000400 0.00141 58.04 43.41 0.000387 0.00136 60.00 44.88 0.000374 0.00132 Table IV-9 - Characteristics of Alternative 1 - Light Trucks377 2025 2026 46.17 27.73 0.000436 0.00377 47.73 28.67 0.000422 0.00365 49.34 29.63 0.000408 0.00353 376 See https://ww2.arb.ca.gov/news/frameworkagreements-clean-cars. 377 For this and other action alternatives, readers may note that the cutpoint for large trucks is further to the right than in the 2020 final rule. The 2020 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 final rule (and its preceding NPRM) did not contain an adjustment to the right cutpoint that had been finalized in 2012. Because comments were not received to the NPRM, the lack of adjustment was finalized. Considering the question again for this PO 00000 Frm 00150 Fmt 4701 Sfmt 4702 proposal, NHTSA believes that moving the cutpoint to the right for large trucks (consistent with the intent and requirements in 2012) is reasonable, given the rate of increase in stringency for this proposal. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.111</GPH> These equations are represented graphically in Figure IV–4 and Figure IV–4. EP03SE21.110</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 a (mpg) b(mp~ c (gpm per sf) d (gpm) 2024 49751 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 60 55 35 30 25 35 40 45 50 55 60 Footprint (sf) 65 70 75 ...... 2020 -·-···-2021 ---- 2022 -2023 ....... 2024 ----2025 - 80 -2026 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00151 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.112</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure IV-3-Alternative 1, Passenger Car Fuel Economy, Target Curves 49752 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 35 3() 25 j5 40 50 45 65 60 55 70 75 80 Footprmt(sf) ........... 2020 ------2021 --- .2022 -....-.2023 ;;;;;.; 2024 ;,;;.;;.; .. zozs . . .; ;,;;2026 Figure IV-4-Alternative 1, Light Truck Fuel Economy, Target Curves Under this alternative, the MDPCS is as shown in Table IV–10. 378 CAFE standards defining this alternative reflect the fact that EPCA does not provide a basis for CAFE standards to include ‘‘multipliers’’ applicable to PHEV and/or BEV production volumes, as well as the fact that EPCA’s treatment VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 2025 2026 44.9mpg 46.5 mpg 48.0mpg manufacturers voluntarily bound themselves to the framework levels, not just for MYs 2024–2026 but for MYs 2021–2026, is a relevant data point in terms of their technological feasibility and economic practicability for the fleet as a whole. NHTSA seeks comment on whether Alternative 1 (as defined by the rate of increase and the curve of BEV energy consumption is different from the ‘‘0 grams/mile’’ treatment for purposes of determining compliance with GHG emissions standards. PO 00000 Frm 00152 Fmt 4701 Sfmt 4702 coefficients) appropriately captures its stated goal of approximating the fuel savings that would occur under an industry-wide application of fuel economy standards harmonized with the California framework, or whether changes might be appropriate for the final rule. NHTSA asks that commenters explain the specific technical basis for any requested changes, as well as the basis for determining that the resultant CAFE standards could meet EPCA’s E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.114</GPH> NHTSA considered this alternative as a way to evaluate the effects of industrywide CAFE standards approximately harmonized with the California framework agreement applied to signatory OEMs’ production for the U.S. market.378 The fact that five major 2024 EP03SE21.113</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table IV-10 -Alternative 1 - Minimum Domestic Passenger Car Standard Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules requirement that NHTSA select the maximum feasible standard for each fleet in each model year. 3. Alternative 2 Alternative 2 would increase CAFE stringency at 8 percent per year, which NHTSA calculates would result in total lifetime fuel savings from vehicles 49753 produced during MYs 2021–2029 similar to total lifetime fuel savings that would occur if the fuel economy standards harmonized with California framework agreement had applied to all manufacturers during MYs 2021–2026. Table IV-11 - Characteristics of Alternative 2 - Passenger Cars a (mpg) b (mpg) c (gpm per s.f.) d (gpm) 2024 2025 2026 55.44 41.48 0.000405 0.00144 60.26 45.08 0.000372 0.00133 65.50 49.00 0.000343 0.00122 Table IV-12 - Characteristics of Alternative 2 - Light Trucks a (mpg) b (mpf?) c (gpm per sf) d (gpm) 2024 2025 2026 44.48 26.74 0.000452 0.00395 48.35 29.07 0.000416 0.00364 52.56 31.60 0.000382 0.00334 Under this alternative, the MDPCS is as shown in Table IV–13. 2024 2025 2026 44.4 mpg 48.2mpg 52.4 mpg practicability for the fleet as a whole.379 NHTSA seeks comment on whether Alternative 2 (as defined by the rate of increase and the curve coefficients) appropriately captures its stated goal of representing the fuel savings achievement that would be achieved if fuel economy standards harmonized with the California framework agreement were applied to all companies at a national level over MYs 2021–2026, or whether changes might be appropriate for the final rule. NHTSA asks that commenters explain the specific technical basis for any requested changes, as well as the basis for determining that the resultant CAFE standards could meet EPCA’s requirement that NHTSA select the maximum feasible standard for each fleet in each model year. As another possibility, NHTSA could modify Alternative 2 by increasing the stringency of CAFE standards by 10 percent between model years 2025 and 2026, rather than by 8 percent. Shown graphically, this possibility would look as shown in Figure IV–5. 379 Section VI discusses economic practicability in more detail, including NHTSA’s long-standing interpretation that economic practicability need not mean that the standards are comfortably achievable for every single manufacturer individually, as long as they appear economically practicable for the fleet as a whole. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00153 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.116</GPH> EP03SE21.117</GPH> NHTSA considered this alternative as a way to evaluate the effects of CAFE standards that sought to achieve the fuel savings that would be achieved if fuel economy standards harmonized with the California framework agreement had been applied to all vehicle manufacturers from its beginning the time the framework was agreed. As for Alternative 1, the fact that five major manufacturers voluntarily bound themselves to these levels, not just for MYs 2024–2026 but for MYs 2021–2026, is a relevant data point in terms of their technological feasibility and economic EP03SE21.115</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table IV-13 -Alternative 2 - Minimum Domestic Passenger Car Standard 49754 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 55 NHTSA is proposing Alternative 2, and also seeks comment on a further 2% stringency increase in 2026 NHTSAseeks comment on these four regulatory alternatives. ·······.. •·•·8············· a······ .......o 30 2018 2022 2020 2024 2028 2026 ....... 2012 Rule ···D·· Alt. 0 {SAFE Rule) ···O·· Alt. 1 - Alt. 2 2030 ··+·· Alt. 3 ••••• Alt 2 + 2% Figure IV-5 - Graphic Representation of Possible Other Alternative NHTSA seeks comment on this option as well as on Alternative 2. 4. Alternative 3 Alternative 3 would increase CAFE stringency at 10 percent per year, which NHTSA calculates would result in total lifetime fuel savings from vehicles produced during MYs 2021–2029 similar to total lifetime fuel savings that would have occurred if NHTSA had promulgated final CAFE standards for MYs 2021–2025 at the augural levels announced in 2012 and, in addition, if NHTSA had also promulgated MY 2026 standards that reflected a continuation of that average rate of stringency increase (4.48% for passenger cars and 4.54% for light trucks). 2024 2025 2026 56.67 42.40 0.000396 0.00141 62.97 47.11 0.000356 0.00127 69.96 52.34 0.000321 0.00114 a (mpg) b (mog) c (gpm per s.f.) d foom) VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00154 2024 2025 2026 45.47 27.34 0.000442 0.00387 50.53 30.38 0.000398 0.00348 56.14 33.75 0.000358 0.00313 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM EP03SE21.119</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table IV-15- Characteristics of Alternative 3- Light Trucks 03SEP2 EP03SE21.118</GPH> a (mog) b (mpg) c (gpm per s.f.) d (gpm) EP03SE21.120</GPH> Table IV-14- Characteristics of Alternative 3 - Passenger Cars 49755 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules These equations are represented graphically in Figure IV–6 and Figure IV–7. 70 '' 65 '' 60 '' '' 55 '' ' ' , ______________ _ 35 30 25 35 40 45 50 55 60 Footprint (sf) 65 70 75 80 2020 ....... 2021 ---- 2022 -2023 ....... 2024 ----2025 - -2026 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00155 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.121</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure IV-6-Alternative 3, Passenger Car Fuel Economy, Target Curves 49756 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 70 65 60 '' 55 i '' ---------,...... ......... ''' ..........., ............ '' ...... ..... ...... , '' ........... ' ' ' ........ ......... 35 ....... ....... ............ ...... ....... ............ ....... ....... ........ .......... ....._ ............. ___ _ .........,,~,, __________ _ 30 25 35 40 50 45 55 60 Footprint (sf) 70 65 75 80 2020 ------·2021 ----2022 -2023 ....... 2024 ----2025 - -2026 Figure IV-7 -Alternative 3, Light Truck Fuel Economy, Target Curves Under this alternative, the MDPCS is as follows in Table IV–16. Table IV-16-Alternative 3-Minimum Domestic Passenger Car Standard lotter on DSK11XQN23PROD with PROPOSALS2 BILLING CODE 4910–59–C NHTSA considered this alternative as a way to evaluate the effects of CAFE standards that would return to a fuel consumption trajectory exemplified by the standards announced in 2012. NHTSA seeks comment on whether Alternative 3 (as defined by the rate of increase and the curve coefficients) appropriately captures this goal, or whether changes might be appropriate for the final rule. NHTSA asks that commenters explain the specific VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 2026 50.4 mpg 56.0 mpg technical basis for any requested changes, as well as the basis for determining that the resultant CAFE standards could meet EPCA’s requirement that NHTSA select the maximum feasible standard for each fleet in each model year. While NHTSA believes that this alternative may be beyond maximum feasible based on the information currently before us, as discussed in more detail in Section VI, all alternatives remain under PO 00000 Frm 00156 Fmt 4701 Sfmt 4702 11 I consideration for the final rule. Moreover, because Alternative 3 produces significant social benefits, NHTSA seeks comment on whether to adopt a more stringent increase from MY 2025 to MY 2026, as described above, that would parallel the year over year increase Alternative 3 analyzes. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.123</GPH> 1 45.4 mpg 2025 EP03SE21.122</GPH> 2024 11 49757 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules V. Effects of the Regulatory Alternatives A. Effects on Vehicle Manufacturers Each of the regulatory alternatives NHTSA has considered would increase the stringency of both passenger car and light truck CAFE standards in each of model years 2024–2026. To estimate the potential impacts of each of these alternatives, NHTSA has, as for all recent rulemakings, assumed that standards would continue unchanged after the last model year (in this case, 2026) to be covered by newly issued standards. It is possible that the size and composition of the fleet (i.e., in terms of distribution across the range of vehicle footprints) could change over time, affecting the average fuel economy requirements under both the passenger car and light truck standards, and for the overall fleet. If fleet changes differ from NHTSA’s projections, average requirements could, therefore, also differ from NHTSA’s projections. At this time, NHTSA estimates that, under each of the regulatory alternatives, average fuel economy requirements could increase as summarized in the following three tables. BILLING CODE 4910–59–P Table V-1-Estimated Required Average Fuel Economy (mpg), Passenger Car Fleet for Manufacturer (Total) Model Year Alternative Alternative Alternative Alternative 0 (Baseline) 1 2 3 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 43.3 43.3 43.3 43.3 43.9 43.9 43.9 43.9 44.6 44.6 44.6 44.6 45.2 45.2 45.2 45.2 45.9 49.8 49.2 50.2 46.6 51.5 53.4 55.8 47.3 53.2 58.1 62.0 47.3 53.2 58.1 62.0 47.3 53.2 58.1 62.0 47.3 53.2 58.1 62.0 Table V-2-Estimated Required Average Fuel Economy (mpg), Light Truck Fleet for Manufacturer (Total) Model Year Alternative Alternative Alternative Alternative 0 (Baseline) 1 2 3 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 31.0 31.0 31.0 31.0 31.5 31.5 31.5 31.5 31.9 31.9 31.9 31.9 32.4 32.4 32.4 32.4 32.9 36.4 35.1 35.9 33.5 37.7 38.2 39.9 33.9 39.0 41.5 44.3 33.9 39.0 41.5 44.3 33.9 39.0 41.5 44.3 33.9 39.0 41.5 44.3 Table V-3-Estimated Required Average Fuel Economy (mpg), Total Fleet for Manufacturer (Total) 35.4 35.4 35.4 35.4 Manufacturers do not always comply exactly with each CAFE standard in each model year. To date, some manufacturers have tended to regularly exceed one or both requirements. Many manufacturers make use of EPCA’s provisions allowing CAFE compliance credits to be applied when a fleet’s CAFE level falls short of the corresponding requirement in a given model year. Some manufacturers have paid civil penalties (i.e., fines) required under EPCA when a fleet falls short of a standard in a given model year and the VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 36.0 36.0 36.0 36.0 36.8 36.8 36.8 36.8 37.4 37.4 37.4 37.4 38.1 41.8 40.7 41.5 38.7 43.2 44.2 46.2 manufacturer cannot provide compliance credits sufficient to address the compliance shortfall. As discussed in the accompanying PRIA and TSD, NHTSA simulates manufacturers’ responses to each alternative given a wide range of input estimates (e.g., technology cost and efficacy, fuel prices), and, per EPCA, setting aside the potential that any manufacturer would respond to CAFE standards in model years 2024–2026 by applying CAFE compliance credits or introducing new models of alternative fuel vehicles. PO 00000 Frm 00157 Fmt 4701 Sfmt 4702 39.4 44.7 48.1 51.3 39.4 44.8 48.1 51.3 39.5 44.8 48.2 51.3 39.5 44.9 48.2 51.4 Many of these inputs are subject to uncertainty and, in any event, as in all CAFE rulemakings, NHTSA’s analysis merely illustrates one set of ways manufacturers could potentially respond to each regulatory alternative. At this time, NHTSA estimates that manufacturers’ responses to standards defining each alternative could lead average fuel economy levels to increase through model year 2029 as summarized in the following three tables. Changes are shown to occur in MY 2023 even though NHTSA is not explicitly E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.126</GPH> 0 (Baseline) 1 2 3 EP03SE21.125</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Alternative Alternative Alternative Alternative I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029 I EP03SE21.124</GPH> Model Year 49758 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules proposing to regulate that model year because NHTSA anticipates that manufacturers could make changes as early as that model year to affect future compliance positions (i.e., multi-year planning). Table V-4-Estimated Achieved Average Fuel Economy (mpg), Passenger Car Fleet for Manufacturer (Total) Model Year Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 2020 41.7 41.7 41.7 41.7 2021 43.6 43.6 43.6 43.6 2022 46.6 46.6 46.6 46.6 2023 48.3 49.3 49.7 50.1 2024 50.4 52.6 53.9 55.3 2025 51.5 54.6 57.1 59.4 2026 52.4 55.8 59.6 62.9 2027 52.8 56.3 60.5 64.1 2028 53.0 56.7 61.3 65.3 2029 53.4 57.0 61.4 65.5 Table V-5-Estimated Achieved Average Fuel Economy (mpg), Light Truck Fleet for Manufacturer (Total) Model Year Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 2020 30.2 30.2 30.2 30.2 2021 31.5 31.5 31.5 31.5 2022 33.1 33.1 33.1 33.1 2023 34.4 34.6 34.8 34.9 2024 35.5 36.6 36.5 37.4 2025 36.0 37.5 37.9 39.1 2026 37.0 38.7 40.2 41.8 2027 37.2 39.2 40.7 42.5 2028 37.4 39.5 41.1 43.0 2029 37.7 39.8 41.4 43.2 Table V-6-Estimated Achieved Average Fuel Economy (mpg), Total Fleet for Manufacturer (Total) I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029 I 39.8 40.3 40.5 40.7 41.3 42.8 43.2 44.2 42.1 44.1 45.1 46.6 potentially respond to each regulatory alternative. Manufacturers’ actual responses will almost assuredly differ from NHTSA’s current estimates. At this time, NHTSA estimates that manufacturers’ application of advanced gasoline engines (i.e., gasoline engines with cylinder deactivation, turbocharging, high or variable compression ratios) could increase 43.2 45.5 47.6 49.7 43.5 46.0 48.3 50.6 43.8 46.4 48.9 51.4 44.2 46.8 49.2 51.7 through MY 2029 under the no-action alternative and through at least MY 2024 under each of the action alternatives. However, NHTSA also estimates that in MY 2024, reliance on advanced gasoline engines could begin to decline under the more stringent action alternatives, as manufacturers shift toward electrification. lotter on DSK11XQN23PROD with PROPOSALS2 Table V-7 -Estimated Advanced Gasoline Engine Penetration Rate, Passenger Car Fleet for Manufacturer (Total) Model Year Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 2020 53% 53% 53% 53% PO 00000 2021 56% 56% 56% 56% 2022 61% 61% 61% 61% Frm 00158 2023 59% 59% 59% 58% Fmt 4701 2024 64% 63% 66% 65% Sfmt 4725 2025 62% 62% 63% 58% 2026 61% 64% 62% 55% E:\FR\FM\03SEP2.SGM 2027 62% 64% 62% 52% 03SEP2 2028 61% 65% 62% 52% 2029 65% 69% 62% 52% EP03SE21.130</GPH> 38.2 38.2 38.2 38.2 EP03SE21.129</GPH> While these increases in average fuel economy account for estimated changes in the composition of the fleet (i.e., the relative shares of passenger cars and light trucks), they result almost wholly from the projected application of fuelsaving technology. As mentioned above, NHTSA’s analysis merely illustrates one set of ways manufacturers could 35.9 35.9 35.9 35.9 EP03SE21.128</GPH> 34.3 34.3 34.3 34.3 EP03SE21.127</GPH> Model Year Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 49759 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-8 - Estimated Advanced Gasoline Engine Penetration Rate, Light Truck Fleet for Manufacturer (Total) Model Year 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 55% 55% 55% 55% 55% 55% 55% 55% 56% 56% 56% 56% 56% 57% 56% 56% 57% 57% 56% 55% 59% 57% 54% 53% 61% 58% 53% 48% 61% 57% 52% 46% 64% 56% 52% 45% 63% 57% 52% 45% Table V-9-Estimated Advanced Gasoline Engine Penetration Rate, Total Fleet for Manufacturer (Total) Model Year 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 54% 54% 54% 54% 55% 55% 55% 55% 58% 58% 58% 58% 58% 58% 58% 57% 60% 60% 61% 60% 60% 59% 58% 55% 61% 61% 57% 51% 62% 60% 57% 49% 62% 61% 57% 48% 65% 62% 57% 48% The aforementioned estimated shift to electrification under the more stringent regulatory alternatives is the most pronounced for hybrid-electric vehicles (i.e., ‘‘mild’’ ISG HEVs and ‘‘strong’’ P2 and Power-Split HEVs). Table V-10- Estimated Hybrid Electric Vehicle (HEV) Penetration Rate, Passenger Car Fleet for Manufacturer (Total) Model Year Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 2020 2021 4% 4% 4% 4% 4% 4% 4% 4% 2022 ..:.u..:.., 4% 4% 4% 4% 4% 4% 4% 5% .. . 2025 2026 2027 2028 2029 7% 7% 8% 11% 7% 9% 10% 17% 8% 9% 11% 20% 8% 10% 12% 21% 8% 11% 13% 23% 8% 11% 13% 23% I 1 ~u~ Model Year 2021 2022 2023 2024 2025 2026 2027 2028 2029 6% 6% 6% 6% 9% 9% 9% 9% 10% 10% 10% 10% 12% 11% 12% 13% 15% 20% 16% 19% 15% 22% 19% 21% 17% 26% 27% 29% 17% 26% 27% 30% 17% 28% 30% 32% EP03SE21.132</GPH> EP03SE21.133</GPH> 17% 28% 29% 32% VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00159 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.131</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 2020 EP03SE21.134</GPH> Table V-11-Estimated Hybrid Electric Vehicle (HEV) Penetration Rate, Light Truck Fleet for Manufacturer (Total) 49760 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-12-Estimated Hybrid Electric Vehicle (HEV) Penetration Rate, Total Fleet for Manufacturer (Total) Model Year 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 5% 5% 5% 5% 7% 7% 7% 7% 7% 7% 7% 7% 8% 8% 8% 9% 11% 14% 12% 15% 11% 16% 15% 19% 13% 18% 19% 24% 13% 18% 20% 26% 13% 20% 21% 28% Alternative O(Baseline) Alternative 1 Alternative 2 Alternative 3 Under the more stringent action alternatives, NHTSA estimates that manufacturers could increase production of plug-in hybrid electric 13% 20% 21% 28% vehicles (PHEVs) well over current rates. Table V-13-Estimated Plug-In Hybrid Electric Vehicle (PHEV) Penetration Rate, Passenger Car Fleet for Manufacturer (Total) Model Year 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 2% 2% 2% 1% 2% 5% 7% 2% 3% 8% 10% 2% 3% 8% 10% 1% 3% 8% 10% Alternative O(Baseline) Alternative 1 Alternative 2 Alternative 3 2% 3% 8% 10% Table V-14- Estimated Plug-In Hybrid Electric Vehicle (PHEV) Penetration Rate, Light Truck Fleet for Manufacturer (Total) Model Year 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 2% 2% 4% 1% 2% 4% 8% 1% 2% 7% 12% 1% 2% 7% 12% 1% 2% 7% 12% 1% 2% 7% 11% Alternative O(Baseline) Alternative 1 Alternative 2 Alternative 3 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 2% 2% 3% 1% 2% 4% 8% 1% 3% 7% 11% 1% 3% 7% 11% 1% 2% 7% 11% analysis does consider the potential that manufacturers might respond to CAFE standards by introducing new BEV models outside of MYs 2024–2026, and NHTSA’s analysis does account for the potential that ZEV mandates could lead manufacturers to introduce new BEV models even during MYs 2024–2026. Also accounting for shifts in fleet mix, NHTSA projects increased production of BEVs through MY 2029. EP03SE21.136</GPH> For this NPRM and accompanying PRIA, NHTSA’s analysis excludes the introduction of new alternative fuel vehicle (AFV) models during MY 2024– 2026 as a response to CAFE standards.380 However, NHTSA’s 1% 3% 7% 11% 380 The SEIS does not make this analytical exclusion. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00160 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.135</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Alternative O(Baseline) Alternative 1 Alternative 2 Alternative 3 EP03SE21.137</GPH> Model Year EP03SE21.138</GPH> Table V-15-Estimated Plug-In Hybrid Electric Vehicle (PHEV) Penetration Rate, Total Fleet for Manufacturer (Total) 49761 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-16- Estimated Battery Electric Vehicle (BEV) Penetration Rate, Passenger Car Fleet for Manufacturer (Total) Model Year 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 4% 4% 4% 4% 5% 5% 5% 5% 6% 6% 6% 6% 7% 8% 9% 9% 7% 9% 9% 10% 8% 9% 10% 10% 8% 9% 10% 10% 8% 10% 10% 11% 9% 10% 11% 12% Alternative O(Baseline) Alternative 1 Alternative 2 Alternative 3 8% 10% 11% 12% Table V-17 - Estimated Battery Electric Vehicle (BEV) Penetration Rate, Light Truck Fleet for Manufacturer (Total) Model Year 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 0% 0% 0% 0% 0% 0% 0% 0% 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 3% 2% 2% 3% 3% 2% 2% 3% 3% 2% 2% 3% 3% 3% 3% 3% 3% Alternative O(Baseline) Alternative 1 Alternative 2 Alternative 3 Table V-18-Estimated Battery Electric Vehicle (BEV) Penetration Rate, Total Fleet for Manufacturer (Total) Model Year 2020 2021 2022 2023 2024 2025 2026 2% 2% 2% 2% 2% 2% 2% 2% 3% 3% 3% 3% 4% 5% 5% 6% 4% 5% 6% 6% 5% 6% 6% 6% 5% 6% 6% 6% Alternative O(Baseline) Alternative 1 Alternative 2 Alternative 3 5% 6% 7% 7% 6% 6% 7% 8% manufacturers’ cumulative costs during MYs 2023–2029 could total $121b under the no-action alternative, and $166b, $208b, and $251b under alternatives 1, 2, and 3, respectively. The table below shows how these costs are estimated to vary among manufacturers, accounting for differences in the quantities of vehicles produced for sale in the U.S. Appendices I and II of the accompanying PRIA present results separately for each manufacturer’s passenger car and light truck fleets in each model year under each regulatory alternative, and the underlying CAFE Model output files also show results specific to manufacturers’ domestic and imported car fleets. EP03SE21.140</GPH> EP03SE21.141</GPH> NHTSA’s analysis shows manufacturers’ regulatory costs for CAFE standards, CO2 standards, and ZEV mandates increasing through MY 2029, and (logically) increasing more under the more stringent alternatives. Accounting for fuel-saving technologies estimated to be added under each regulatory alternative (including air conditioning improvements and other off-cycle technologies), and also accounting for CAFE fines that NHTSA estimates some manufacturers could elect to pay rather than achieving full compliance with CAFE standards in some model years, NHTSA estimates that relative to the continued application of MY 2020 technologies, 5% 6% 6% 7% 2029 381 See Appendices I and II of the accompanying PRIA and the CAFE Model output files. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00161 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.139</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 The PRIA provides a wider-ranging summary of NHTSA’s estimates of manufacturers’ potential application of fuel-saving technologies (including other types of technologies, such as advanced transmissions, aerodynamic improvements, and reduced vehicle mass) in response to each regulatory alternative. Appendices I and II of the accompanying PRIA provide much more detailed and comprehensive results, and the underlying CAFE Model output files provide all information, including the specific combination of technologies estimated to be applied to every specific vehicle model/configuration in each of model years 2020–2050.381 2027 2028 49762 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-19-Cumulative Costs ($b) During MYs 2023-2029 Manufacturer Alternative 0 Alternative 1 Alternative 2 Alternative 3 4 5 18 18 18 10 5 4 1 3 1 6 6 0 12 2 9 121 4 6 21 22 34 10 8 6 2 4 1 9 9 0 19 2 8 166 5 6 23 27 39 15 11 9 2 5 1 22 10 0 22 2 9 208 6 7 25 33 48 22 14 11 2 5 2 24 10 0 29 3 10 251 BMW Daimler Stellantis (FCA) Ford General Motors Honda Hyundai Kia Jaguar - Land Rover Mazda Mitsubishi Nissan Subaru Tesla Toyota Volvo Volkswagen Industry Total As discussed in the TSD, these estimates reflect technology cost inputs that, in turn, reflect a ‘‘markup’’ factor that includes manufacturers’ profits. In other words, if costs to manufacturers’ are reflected in vehicle price increases as in the past, NHTSA estimates that the average costs to new vehicle purchasers could increase through MY 2029 as summarized in Table V–20 through Table V–22. Table V-20-Estimated Average Per Vehicle Regulatory Costs($), Passenger Car Fleet for Manufacturer (Total) Model Year Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 265 369 586 694 873 1,008 1,076 1,058 1,028 1,001 265 265 265 369 369 369 586 586 586 896 1,055 1,147 1,242 1,521 1,748 1,455 1,968 2,327 1,550 2,264 2,733 1,507 2,198 2,649 1,473 2,157 2,607 1,426 2,073 2,506 VerDate Sep<11>2014 21:48 Sep 02, 2021 2021 2022 2023 2024 2025 2026 2027 2028 2029 155 365 633 833 1,056 1,153 1,257 1,260 1,251 1,240 155 155 155 365 365 365 633 633 633 888 933 980 1,456 1,413 1,760 1,616 1,795 2,255 1,748 2,210 2,810 1,715 2,159 2,730 1,717 2,134 2,687 1,684 2,086 2,619 Frm 00162 Fmt 4701 Sfmt 4725 Jkt 253001 PO 00000 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.143</GPH> Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 2020 EP03SE21.142</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Model Year EP03SE21.144</GPH> Table V-21- Estimated Average Per Vehicle Regulatory Costs($), Light Truck Fleet for Manufacturer (Total) 49763 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-22- Estimated Average Per Vehicle Regulatory Costs($), Total Fleet for Manufacturer (Total) 2020 2021 Model Year Alternative 0 (Baseline) Alternative 1 Alternative 2 Alternative 3 2022 2023 2024 2025 2026 2027 2028 2029 203 367 611 768 969 1,083 1,169 1,160 1,140 1,120 203 203 203 367 367 367 611 611 611 892 991 1,058 1,354 1,464 1,754 1,539 1,877 2,289 1,653 2,236 2,773 1,614 2,177 2,692 1,598 2,145 2,649 1,557 2,080 2,565 Table V–23 shows how these costs could vary among manufacturers, suggesting that disparities could decrease as the stringency of standards increases. Table V-23-Average Manufacturer Per-Vehicle Costs by Alternative Manufacturer Alternative 0 Alternative 1 Alternative 2 Alternative 3 1,604 1,583 1,527 1,331 1,056 965 846 850 1,168 1,523 587 737 1,058 47 859 1,867 2,459 1,120 1,644 2,062 1,887 1,488 2,014 972 1,516 1,295 1,829 1,819 1,115 1,134 1,568 47 1,394 2,578 2,408 1,557 2,126 2,412 2,185 2,021 2,591 1,515 2,320 2,006 2,137 2,416 1,720 2,679 1,699 47 1,583 2,855 2,547 2,080 2,607 2,741 2,484 2,609 3,160 2,107 2,859 2,595 2,479 2,829 2,124 3,147 1,802 47 2,181 3,201 2,937 2,565 BMW Daimler Stellantis (FCA) Ford General Motors Honda Hyundai Kia Jaguar- Land Rover Mazda Mitsubishi Nissan Subaru Tesla Toyota Volvo Volkswagen Industry Average lower prices and/or higher fuel economy improvements, vehicle sales effects could differ. For example, in the case of the ‘‘unconstrained’’ SEIS results, manufacturer costs across alternatives are lower. EP03SE21.146</GPH> decline slightly under the more stringent alternatives. The magnitude of these fuel savings and vehicle price increases depends on manufacturer compliance decisions, especially technology application. In the event that manufacturers select technologies with VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00163 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.145</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 NHTSA estimates that although projected fuel savings under the more stringent regulatory alternatives could tend to increase new vehicles sales, this tendency could be outweighed by the opposing response to higher prices, such that new vehicle sales could 49764 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 20 18 16 14 8 2 2020 2021 2023 2022 -· · · - Alterantive O 2024 2025 - - Altemative 1 2026 2028 2027 -0- Alterantive2 ··+· 2029 2030 Alternative 3 Figure V-1- Estimated Annual New Vehicles Sales (Millions) VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 While these slight reductions in new vehicles sales tend to slightly reduce projected automobile industry labor, NHTSA estimates that the cost increases could reflect an underlying increase in PO 00000 Frm 00164 Fmt 4701 Sfmt 4702 employment to produce additional fuelsaving technology, such that automobile industry labor could about the same under each of the four regulatory alternatives. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.147</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 The TSD discusses NHTSA’s approach to estimating new vehicle sales, including NHTSA’s estimate that new vehicle sales could recover from 2020’s aberrantly low levels. 49765 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 1.4 1.2 1.0 0.8 0.6 0.4 2020 2021 2022 2023 ·-·· - Alterantive 0 - - 2024 2025 Altemative 1 2026 2027 2028 ··+· ---0- Alterantive 2 2029 2030 Alfomative 3 Figure V-2 - Estimated Automobile Industry Labor (as Millions of Full-Time-Equivalent Jobs) The accompanying TSD discusses NHTSA’s approach to estimating automobile industry employment, and the accompanying RIA (and its Appendices I and II) and CAFE Model output files provide more detailed results of NHTSA’s analysis. B. Effects on New Car and Truck Buyers As discussed above, NHTSA estimates that the average fuel economy and purchase cost of new vehicles could increase between 2020 and 2029 and increase more quickly under each of the action alternatives than under the baseline No-Action Alternative. On one hand, buyers could realize the benefits of increase fuel economy: Spending less on fuel. On the other, buyers could pay more for new vehicles, for some costs tied directly to vehicle value (e.g., sales taxes and collision insurance). Table V– 24 reports sales-weighted MSRP values for the No-Action Alternative and relative increases in MSRP for the three regulatory alternatives. Table V-24- Sales-Weighted MSRP and Incremental Costs Under the Regulatory Alternatives by Regulatory Class, Undiscounted 2018$ VerDate Sep<11>2014 42,300 42,400 42,500 42,500 42,490 42,480 21:48 Sep 02, 2021 Jkt 253001 400 460 490 460 470 450 PO 00000 350 640 950 900 890 850 Frm 00165 700 1,100 1,550 1,470 1,440 1,380 Fmt 4701 Alt. 0 31,220 31,360 31,440 31,430 31,410 31,390 Sfmt 4725 Passenger Car Relative to Alt. 0 Alt.1 Alt. 2 Alt. 3 360 440 460 440 430 410 E:\FR\FM\03SEP2.SGM 640 950 1,170 1,120 1,100 1,040 03SEP2 870 1,300 1,630 1,550 1,540 1,460 EP03SE21.149</GPH> 2024 2025 2026 2027 2028 2029 Alt. 0 Light Truck Relative to Alt. 0 Alt.1 Alt. 2 Alt. 3 EP03SE21.148</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Model Year lotter on DSK11XQN23PROD with PROPOSALS2 Alt. 2 PO 00000 73 157 232 5,128 60 116 166 4,153 59 125 186 4,103 48 93 132 Ta~cs and fees Regulatory cost Foregone consumer sales surplus Maintenance and repair cost 2,016 28 61 90 1,992 23 45 64 1,120 437 960 1,444 924 324 645 934 0 1 7 17 0 0 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 598 1,310 1,970 12,147 456 899 1,299 Sfmt 4725 5,190 Fmt 4701 Insurance cost Financing cost Frm 00166 Consumer Costs Retail fuel outlay 19,703 -738 -1,186 -1,688 19,727 -818 -1,622 -2,351 Refueling time cost 1,046 -1 -2 -15 1,191 15 89 181 219 779 137 162 204 1,347 1,922 21,696 940 1,694 2,373 Net benefits 8,964 266 37 -48 9,550 484 795 1,074 CAFE Model output files. For all of the action alternatives, avoided outlays for fuel purchases account for most of the projected benefits to consumers, and increases in the cost to purchase new vehicles account for most of the projected costs. 160 864 03SEP2 125 21,442 E:\FR\FM\03SEP2.SGM Consumer Benefits discounted at annual rates of 3% and 7%. The TSD and PRIA accompanying this NPRM discuss underlying methods, inputs, and results in greater detail, and more detailed tables and underlying results are contained in the accompanying CAFE Data Book and Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Jkt 253001 MY 2029 Alt.3 Alt.1 Alt. 3 Alt. 2 Alt.1 49766 21:48 Sep 02, 2021 693 Total consumer benefits Drive value 0 12,478 Implicit opportunity cost Total consumer costs Relative to Alt. 0 Alt. 0 Relative to Alt. 0 Alt. 0 MY 2039 Table V–25 through Table V–27 presents projected consumer costs and benefits along with net benefits for model year 2029 and 2039 vehicles under the proposed alternatives. Results are shown in 2018 dollars, without discounting and with benefits and costs VerDate Sep<11>2014 EP03SE21.150</GPH> Table V-25-Average Per-Vehicle Consumer Benefits and Costs - Passenger Cars and Light Trucks, Undiscounted 2018$ lotter on DSK11XQN23PROD with PROPOSALS2 VerDate Sep<11>2014 Jkt 253001 PO 00000 Alt. 0 MY2029 Relative to Alt. 0 Alt. I Alt. 2 Alt. 3 Alt. 0 MY2039 Relative to Alt. 0 Alt.1 Alt. 2 Alt. 3 Frm 00167 Consumer Costs Insurance cost 4,353 61 131 195 4,301 50 97 139 124 Fmt 4701 Financing cost 3,874 55 117 173 3,828 45 86 Taxes and fees 2,016 28 61 90 1,992 23 45 64 Regulatory cost 1,120 437 960 1,444 924 324 645 934 3 Sfmt 4725 E:\FR\FM\03SEP2.SGM Foregone consumer sales surplus 0 l 7 17 0 0 l Maintenance and repair cost 0 0 0 0 0 0 0 0 implicit opportunity cost 0 0 0 0 0 0 0 0 11,362 582 1,276 1,920 11,044 443 874 1,263 -648 -1,287 -1,866 Total consumer costs Consumer Benefits Retail fuel outlay 15,510 -581 03SEP2 Refueling time cost 834 0 -937 -1 -1,332 -12 15,652 951 13 72 145 Drive value 546 97 125 171 622 108 128 161 Total consumer benefits 16,890 679 1,063 1,516 17,226 743 1,343 1,882 Net benefits 5,527 96 -213 -404 6,182 300 469 619 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 21:48 Sep 02, 2021 Table V-26-Average Per-Vehicle Consumer Benefits and Costs - Passenger Cars and Light Trucks, Discounted at 3% 2018$ 49767 EP03SE21.151</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 49768 Jkt 253001 81 79 45 645 1 0 0 851 115 113 64 934 3 0 0 1,230 -1,032 -9 132 1,173 -700 12,217 747 489 13,453 -503 10 84 578 -1,001 56 100 1,045 -1,453 115 126 1,464 3,449 147 194 234 PO 00000 Frm 00168 Fmt 4701 Sfmt 4702 2,767 Consumer Benefits E:\FR\FM\03SEP2.SGM 03SEP2 to all U.S. citizens, who then benefit from the additional Federal revenue. While they are calculated in the analysis, and do influence consumer decisions in the marketplace, they do not contribute to the calculation of net benefits (and are omitted from the tables below). While incremental maintenance and repair costs would accrue to buyers of new cars and trucks affected by more stringent CAFE standards, we do not carry these costs in the analysis. They are difficult to estimate for emerging Retail fuel outlay Refueling time cost Drive value Total consumer benefits Net benefits 12,001 654 422 13,077 -449 0 75 524 -44 823 -421 42 41 23 324 0 0 0 431 96 3,576 3,512 1,992 924 0 0 0 10,004 -726 -1 162 159 90 1,444 17 0 0 1,873 109 107 61 960 7 0 0 1,244 truck buyers, in the form of higher prices. Other assumptions are possible, but we do not currently have data to support attempting to model crosssubsidization. We also assume that any civil penalties—paid by manufacturers for failing to comply with their CAFE standards—are passed through to new car and truck buyers and are included in the sales price. However, those civil penalties are paid to the U.S. Treasury, where they currently fund the general business of Government. As such, they are a transfer from new vehicle buyers Consumer Costs Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules BILLING CODE 4910–59–C C. Effects on Society 21:48 Sep 02, 2021 Alt.0 51 50 28 437 1 0 0 568 3,619 3,555 2,016 1,120 0 0 0 10,310 Insurance cost Financing cost Taxes and fees Regulatory cost Foregone consumer sales surplus Maintenance and repair cost implicit opportunity cost Total consumer costs Alt.0 MY2039 Relative to Alt. 0 Alt. I Alt. 2 Alt. 3 MY2029 Relative to Alt. 0 Alt.1 Alt. 2 Alt. 3 Table V–28 and Table V–29 describe the costs and benefits of increasing CAFE standards in each alternative, as well as the party to which they accrue. Manufacturers are directly regulated under the program and incur additional production costs when they apply technology to their vehicle offerings in order to improve their fuel economy. In this analysis, we assume that those costs are fully passed through to new car and VerDate Sep<11>2014 EP03SE21.152</GPH> Table V-27 -Average Per-Vehicle Consumer Benefits and Costs - Passenger Cars and Light Trucks, Discounted at 7% 2018$ Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 technologies but represent real costs (and benefits in the case of alternative fuel vehicles that may require less frequent maintenance events). They may be included in future analyses as data become available to evaluate lifetime maintenance costs. This analysis assumes that drivers of new vehicles internalize 90 percent of the risk associated with increased exposure to crashes when they engage in additional travel (as a consequence of the rebound effect). Private benefits are dominated by the value of fuel savings, which accrue to new car and truck buyers at retail fuel prices (inclusive of Federal and state taxes). In addition to saving money on fuel purchases, new vehicle buyers also benefit from the increased mobility that results from the lower cost of driving their vehicle (higher fuel economy reduces the per-mile cost of travel) and fewer refueling events. The additional travel occurs as drivers take advantage of lower operating costs to increase mobility, and this generates benefits to those drivers—equivalent to the cost of operating their vehicles to travel those miles, the consumer surplus, and the offsetting benefit that represents 90 percent of the additional safety risk from travel. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 In addition to private benefits and costs, there are purely external benefits and costs that can be attributed to increases in CAFE standards. These are benefits and costs that accrue to society more generally, rather than to the specific individuals who purchase a new vehicle that was produced under more stringent CAFE standards. Of the external costs, the largest is the loss in fuel tax revenue that occurs as a result of falling fuel consumption. While drivers of new vehicles (purchased in years where CAFE stringency is increasing) save fuel costs at retail prices, the rest of U.S. road users experience a welfare loss, in two ways. First, the revenue generated by fuel taxes helps to maintain roads and bridges, and improve infrastructure more generally, and that loss in fuel tax revenue is a social cost. And second, the additional driving that occurs as new vehicle buyers take advantage of lower per-mile fuel costs is a benefit to those drivers, but the congestion (and road noise) created by the additional travel impose a social cost to all road users. Among the purely external benefits created when CAFE standards are increased, the largest is the reduction in damages resulting from greenhouse gas emissions. The estimates in Table V–28 PO 00000 Frm 00169 Fmt 4701 Sfmt 4702 49769 assume a social cost of GHG emissions based on a 2.5% discount rate, and those in Table V–29 assume a social cost of GHG emissions based on a 3% discount rate. The associated benefits related to reduced health damages from conventional pollutants and the benefit of improved energy security are both significantly smaller than the associated change in GHG damages across alternatives. As the tables also illustrate, the overwhelming majority of both costs and benefits are private costs and benefits that accrue to buyers of new cars and trucks, rather than external welfare changes that affect society more generally. This has been consistently true in CAFE rulemakings. The choice of discount rate also affects the resulting benefits and costs. As the tables show, net social benefits are positive for Alternative 1 and 2 at a 3% discount rate, but only for Alternative 1 when applying a 7% discount rate to benefits and costs. Alternative 3 has negative net benefits under both discount rates. As mentioned above, the benefits of the regulatory alternatives, but especially Alternative 3, are concentrated in later years where a higher discount rate has a greater contracting effect. BILLING CODE 4910–59–P E:\FR\FM\03SEP2.SGM 03SEP2 49770 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-28 - Incremental Benefits and Costs Over the Lifetimes of Total Fleet Produced Through 2029 (2018$ Billions), 3% Percent Discount Rate, by Alternative Alternative: 1 2 3 Private Costs Technology Costs to Increase Fuel Economy 34.3 67.6 100.1 Increased Maintenance and Repair Costs Sacrifice in Other Vehicle Attributes 0.0 0.0 0.0 0.0 0.0 0.0 Consumer Surplus Loss from Reduced New Vehicle Sales Safety Costs Internalized by Drivers 0.1 6.2 0.6 8.2 1.3 11.2 40.6 76.4 112.6 7.3 10.1 13.5 Safety Costs Not Internalized by Drivers Loss in Fuel Tax Revenue for the Highway Trust Fund 7.5 11.0 15.8 18.9 23.2 27.0 Subtotal - External Costs 25.8 44.8 63.7 66.4 121.2 176.3 Reduced Fuel Costs Benefits from Additional Driving 47.9 12.3 73.0 15.3 103.8 20.8 Less Frequent Refueling -0.5 -0.8 0.3 Subtotal - Private Benefits 59.7 87.5 124.9 0.9 20.3 1.5 32.0 2.1 45.6 1.7 0.4 0.3 22.9 33.9 48.0 Total Social Benefits 82.6 121.4 172.9 Net Social Benefits 16.1 0.3 -3.4 Subtotal - Private Costs External Costs Congestion and Noise Costs from Rebound-Effect Driving Total Social Costs Private Benefits Reduction in Petroleum Market Externality Reduced Climate Damages Reduced Health Damages lotter on DSK11XQN23PROD with PROPOSALS2 Subtotal - External Benefits VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00170 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.153</GPH> External Benefits 49771 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-29 - Incremental Benefits and Costs Over the Lifetimes of Total Fleet Produced Through 2029 (2018$ Billions), 7% Percent Discount Rate, by Alternative Private Costs Technology Costs to Increase Fuel Economy Increased Maintenance and Repair Costs Sacrifice in Other Vehicle Attributes Consumer Surplus Loss from Reduced New Vehicle Sales Safety Costs Internalized by Drivers Subtotal - Private Costs External Costs Congestion and Noise Costs from Rebound-Effect Driving Safety Costs Not Internalized by Drivers Loss in Fuel Tax Revenue Subtotal - External Costs Total Social Costs Private Benefits Reduced Fuel Costs Benefits from Additional Driving Less Frequent Refueling Subtotal - Private Benefits External Benefits Reduction in Petroleum Market Externality Reduced Climate Damages Reduced Health Damages Subtotal - External Benefits Total Social Benefits Net Social Benefits lotter on DSK11XQN23PROD with PROPOSALS2 The following tables show the costs and benefits associated with external effects to society. As seen in Table V– 28 and Table V–29, the external benefits are composed of reduced climate damages (Table V–30 and Table V–31), reduced health damages (Table V–32 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 and Table V–33), and reduced petroleum market externalities (Table V–36). The external costs to society include congestion and noise costs (Table V–34 and Table V–35) and safety costs (Table V–37). We show the costs and benefits by model year (1981–2029), PO 00000 Frm 00171 Fmt 4701 Sfmt 4702 1 2 3 28.1 55.0 81.4 0.0 0.0 0.0 0.0 0.0 0.0 0.1 3.7 0.5 4.9 1.1 6.8 31.9 60.4 89.3 4.8 6.8 9.3 5.5 7.0 11.6 11.9 17.3 17.0 17.3 30.3 43.6 34.6 60.6 87.2 29.7 7.5 44.9 9.3 63.7 12.7 -0.4 36.8 -0.6 53.6 0.0 76.4 0.5 13.3 0.9 21.0 1.3 29.9 0.9 14.8 0.1 22.0 -0.1 31.2 51.6 75.6 107.6 2.3 -15.1 -25.2 II in contrast to the tables above, which present incremental and net costs and benefits over the lifetimes of the entire fleet produced through 2029, beginning with model year 1981. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.154</GPH> Alternative: 49772 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-30 -Total and Incremental Costs of GHGs (2018$, billions), MY 1981-2029, 2.5% Discount Rate, by Alternative Model Year 1981 2023 2024 CO2 CIL N2O 1,202.4 40.4 15.5 91.6 3.2 1.0 CO2 CIL N2O 1.8 0.1 0.0 -3.0 -0.1 0.0 CO2 CHi N2O 4.5 0.2 0.1 -3.4 -0.1 0.0 CO2 CHi N2O 7.3 0.3 0.1 -5.2 -0.2 -0.1 Table V–30 and Table V–31 present the total costs of GHGs in the baseline scenario and the incremental costs relative to the baseline in the other three alternatives. Negative incremental values indicate a decrease in social costs 2025 2026 2027 Alternative 0/Baseline (Totals) 87.7 83.0 80.0 3.1 2.9 2.9 1.0 0.9 0.9 Alternative 1 (Relative to Baseline) -3.6 -3.7 -3.7 -0.1 -0.1 -0.1 0.0 0.0 0.0 Alternative 2 (Relative to Baseline) -5.2 -6.8 -6.7 -0.2 -0.2 -0.2 -0.1 -0.1 -0.1 Alternative 3 (Relative to Baseline) -7.6 -9.8 -9.7 -0.2 -0.3 -0.3 -0.1 -0.1 -0.1 of GHGs, while positive incremental values indicate an increase in costs relative to the baseline for the given model year. The GHG costs follow a similar pattern in all three alternatives, decreasing across all model years, with 2028 2029 Total 77.4 2.8 0.9 75.2 2.7 0.9 1,697.2 58.0 21.1 -3.7 -0.1 0.0 -3.5 -0.1 0.0 -19.4 -0.6 -0.2 -6.7 -0.2 -0.1 -6.3 -0.2 -0.1 -30.7 -1.0 -0.3 -9.7 -0.3 -0.1 -9.0 -0.3 -0.1 -43.8 -1.4 -0.4 the largest reductions associated with 2025–2028 model years. The magnitude of CO2 emissions is much higher than the magnitudes of CH4 and N2O emissions, which is why the total costs are so much larger for CO2. 1981 2023 2024 CO2 CIL N2O 796.4 30.3 10.4 60.2 2.4 0.7 CO2 CIL N2O 1.2 0.0 0.0 -2.0 -0.1 0.0 CO2 CHi N2O 3.0 0.1 0.0 -2.2 -0.1 0.0 CO2 CHi N2O 4.8 0.2 0.1 -3.4 -0.1 0.0 The CAFE Model calculates health costs attributed to criteria pollutant VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 2025 2026 2027 Alternative 0/Baseline (Totals) 57.6 54.4 52.4 2.3 2.2 2.1 0.7 0.6 0.6 Alternative 1 (Relative to Baseline) -2.4 -2.4 -2.4 -0.1 -0.1 -0.1 0.0 0.0 0.0 Alternative 2 (Relative to Baseline) -3.4 -4.5 -4.4 -0.1 -0.2 -0.2 0.0 0.0 0.0 Alternative 3 (Relative to Baseline -5.0 -6.5 -6.3 -0.2 -0.2 -0.2 -0.1 -0.1 -0.1 emissions of NOX, SOX, and PM2.5, shown in Table V–32 and Table V–33. PO 00000 Frm 00172 Fmt 4701 Sfmt 4702 2028 2029 Total 50.6 2.1 0.6 49.0 2.0 0.6 1,120.5 43.3 14.0 -2.4 -0.1 0.0 -2.3 -0.1 0.0 -12.7 -0.5 -0.1 -4.4 -0.2 0.0 -4.1 -0.2 0.0 -20.1 -0.7 -0.2 -6.3 -0.2 -0.1 -5.9 -0.2 -0.1 -28.6 -1.0 -0.3 These costs are directly related to the tons of each pollutant emitted from E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.156</GPH> Model Year: EP03SE21.155</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table V-31-Total and Incremental Costs of GHGs (2018$, billions), MY 1981-2029, 3% Discount Rate, by Alternative 49773 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules various upstream and downstream sources, including on-road vehicles, electricity generation, fuel refining, and fuel transportation and distribution. See Chapter 4 of the SEIS and Chapter 5.4 of the TSD for further information regarding the calculations used to estimate health impacts, and more details about the types of health effects. The following section of the preamble, V.D, discusses the changes in tons of emissions themselves across rulemaking alternatives, while the current section focuses on the changes in social costs associated with those emissions. Criteria pollutant health costs (presented in Table V–32 and Table V– 35) increase slightly in earlier model years (1981–2023), but those cost increases are offset by the decrease in health costs in later model years. In Table V–32 and Table V–33, the costs in alternatives 1–3 are shown in terms of percent of the baseline. For instance, the total decrease in SOX costs in Alternative 2 is equivalent to 0.2% of the total baseline SOX costs. The changes across alternatives relative to the baseline are relatively minor, although some impacts in later model years are more significant (e.g., 7.5% decrease in PM2.5 in 2028, Alternative 3). Since the health cost value per ton of emissions differs by pollutant, the pollutants that incur the highest costs are not necessarily those with the largest amount of emissions. Model Year: 1981 2023 2024 NOx SOx PM2.s 119.0 168.7 330.6 1.7 11.6 9.9 NOx SOx PM2.s 0.2% 0.2% 0.2% -1.0% -1.7% -2.1% NOx SOx PM2.s 0.5% 0.4% 0.5% -0.3% -1.3% -2.3% NOx SOx PM2.s 0.8% 0.7% 0.8% -0.5% -2.0% -3.5% VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 2025 2026 2027 Alternative 0/Baseline (Totals) 1.5 1.4 1.4 11.0 10.3 9.8 9.4 8.8 8.4 Alternative 1 (Relative to Baseline) -1.6% -1.7% -1.6% -2.5% -2.6% -2.6% -2.6% -2.8% -2.8% Alternative 2 (Relative to Baseline -0.4% 0.1% 0.3% -2.1% -2.2% -2.0% -3.7% -5.0% -4.9% Alternative 3 (Relative to Baseline -0.2% 0.0% 0.4% -2.6% -3.2% -2.9% -5.5% -7.4% -7.3% PO 00000 Frm 00173 Fmt 4701 Sfmt 4725 2028 2029 Total 1.3 9.3 8.1 1.3 8.9 7.8 127.6 229.7 383.0 -1.9% -2.9% -2.9% -1.9% -2.9% -2.8% 0.1% -0.5% -0.2% 0.2% -2.2% -5.1% 0.2% -2.1% -4.9% 0.5% -0.2% -0.1% 0.3% -3.0% -7.5% 0.1% -3.0% -7.3% 0.7% -0.2% -0.2% E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.157</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table V-32 -Totals and Percent Changes in Health Costs of Criteria Pollutants (2018$, billions), MY 1981-2029, 3% Discount Rate, by Alternative 49774 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-33 -Totals and Percent Changes in Health Costs of Criteria Pollutants (2018$, billions), MY 1981-2029, 7% Discount Rate, by Alternative Model Year: 1981 2023 2024 2025 2026 2027 2028 2029 Total 0.7 5.2 4.3 0.7 4.8 3.9 96.2 161.9 276.0 -2.0% -2.9% -2.9% -2.0% -2.9% -2.9% 0.1% -0.4% -0.1% -0.1% -2.2% -5.0% -0.1% -2.1% -4.8% 0.4% -0.2% -0.1% -0.1% -3.0% -7.4% -0.3% -3.1% -7.2% 0.6% -0.2% -0.1% Alternative 0/Baseline (Totals) NOx SOx PM2.s 91.1 125.8 246.6 1.1 7.5 6.1 NOx SOx PM2s 0.2% 0.2% 0.2% -1.0% -1.8% -2.2% NOx SOx PM2.s 0.4% 0.4% 0.4% -0.4% -1.4% -2.3% NOx SOx PM2.s 0.6% 0.6% 0.7% -0.6% -2.1% -3.6% 1.0 6.8 5.5 0.9 6.2 5.0 0.8 5.6 4.6 Alternative 1 (Relative to Baseline) -1.6% -2.5% -2.7% -1.7% -2.7% -2.9% -1.7% -2.7% -2.8% Alternative 2 (Relative to Baseline) -0.6% -2.2% -3.7% -0.1% -2.3% -5.0% 0.1% -2.1% -4.9% Alternative 3 (Relative to Baseline) NHTSA estimates social costs of congestion and noise across regulatory alternatives, throughout the lifetimes of model years 1981–2029. Congestion and noise are functions of VMT and fleet mix, and the differences between alternatives are due mainly to differences in VMT (see Section V.D). -0.4% -2.8% -5.5% -0.3% -3.3% -7.4% 0.0% -3.0% -7.3% Overall, congestion and noise costs increase relative to the baseline across all alternatives, but viewed from a model year perspective, the congestion and noise costs associated with later model years are negative relative to the baseline. It is important to note that the overall increases in congestion and noise costs are relatively small when compared to the total congestion and noise costs in the baseline (No-Action Alternative). For further details regarding congestion and noise costs, see Chapter 6.2.3 of the TSD and Chapter 6.5 of the PRIA. Table V-34 -Total and Incremental Congestion and Noise Costs (2018$, billions), MY 1981-2029, 3% Discount Rate, by Alternative Model Year: 19812023 2024 Congestion Noise 4,003.4 28.5 347.5 2.5 Congestion Noise 8.07 0.06 -0.83 -0.01 Congestion Noise 17.61 0.13 -0.39 0.00 Congestion Noise 27.43 0.20 -0.92 -0.01 2025 2026 2027 2028 2029 Total 285.9 2.0 274.8 1.9 5,856.1 41.6 0.38 0.00 0.59 0.00 7.28 0.05 -0.91 -0.01 -0.44 0.00 9.98 0.07 -1.88 -0.01 -1.10 -0.01 13.35 0.10 Alternative 0/Baseline (Totals) 331.3 2.3 314.3 2.2 298.9 2.1 Alternative 1 (Relative to the Baseline) -0.62 0.00 -0.42 0.00 0.10 0.00 Alternative 2 (Relative to the Baseline) -1.61 -0.01 -2.66 -0.02 -1.61 -0.01 -4.42 -0.03 -2.90 -0.02 EP03SE21.159</GPH> -2.85 -0.02 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00174 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.158</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Alternative 3 (Relative to the Baseline) 49775 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-35 -Total and Incremental Congestion and Noise Costs (2018$, billions), MY 2020-2029, 7% Discount Rate, by Alternative Model Year: 19812023 2024 Congestion Noise 3.276.3 23.3 242.6 1.7 Congestion Noise 5.62 0.04 -0.63 0.00 2025 2026 2027 2028 2029 Total 171.7 1.2 158.9 1.1 4 462.3 31.7 0.21 0.00 0.33 0.00 4.77 0.04 -0.58 0.00 -0.27 0.00 6.75 0.05 -1.17 -0.01 -0.65 0.00 9.20 0.07 Alternative 0/Baseline (Totals) 222.8 1.6 203.5 1.4 186.4 1.3 Alternative 1 (Relative to the Baseline) -0.47 0.00 -0.32 0.00 0.03 0.00 Alternative 2 (Relative to the Baseline) Congestion Noise 12.06 0.09 -0.39 0.00 -1.19 -0.01 -1.81 -0.01 -1.07 -0.01 Alternative 3 (Relative to the Baseline) Congestion Noise 18.80 0.13 -0.83 -0.01 The CAFE Model accounts for benefits of increased energy security by computing changes in social costs of petroleum market externalities. These social costs represent the risk to the U.S. economy incurred by exposure to price shocks in the global petroleum market that are not accounted for by oil prices and are a direct function of gallons of -2.07 -0.01 -2.98 -0.02 -1.89 -0.01 fuel consumed. Chapter 6.2.4 of the accompanying TSD describes the inputs involved in calculating these petroleum market externality costs. Petroleum market externality costs decrease relative to the baseline under all alternatives, regardless of the discount rate used. This pattern occurs due to the decrease in gallons of fuel consumed (see Section V.D) as the stringency of alternatives increases. Only the earlier model year cohorts (1981–2023) contribute to slight increases in petroleum market externality costs, but these are offset by the decreases from later model years. Table V-36 -Total and Incremental Petroleum Market Externalities Costs (2018$, billions), MY 1981-2029, by Alternative I Model Year: I 1981-2020 Discount rate Alternative 35.31 28.89 Alternative 0.08 0.06 Alternative 0.18 0.13 Alternative 0.28 0.19 3% 7% 3% 7% 3% 7% I 2021-2029 0/Baseline (Totals) I 10.9 I 10.3 I 7.9 I 6.7 1 (Relative to Baseline) I -0.02 I -0.45 I -0.02 I -0.29 2 (Relative to Baseline) I -0.02 I -o.n I -0.02 I -o.47 3 (Relative to Baseline) I -0.01 I -1.06 I -0.01 I -0.69 damage costs. Table V–37 presents these social costs across alternatives and discount rates. Safety effects are discussed at length in the PRIA I I 9.3 15.4 I -0.48 I -0.28 I -0.94 I -o.55 I -1.36 I -0.80 accompanying this NPRM (see Chapter 5 of the PRIA). EP03SE21.161</GPH> NHTSA estimates various monetized safety impacts across regulatory alternatives, including costs of fatalities, non-fatal crash costs, and property I 2024-2026 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00175 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.160</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 3% 7% I 2021-2023 49776 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table V-37 -Total Social Costs of Safety Impacts (2018$, billions), MY 1981-2029, All Alternatives Alternative 1 Alternative 2 Alternative 3 3% 7% 3% 7% 3% 7% Fatality Costs 7.8 5.2 14.5 9.9 21.1 14.7 Non-Fatal Crash Costs 4.9 3.3 8.0 5.6 11.1 7.9 Property Damage Crash Costs 1.0 0.7 1.6 1.1 2.2 1.5 BILLING CODE 4910–59–C D. Physical and Environmental Effects NHTSA calculates estimates for the various physical and environmental effects associated with the proposed standards. These include quantities of fuel and electricity consumption, tons of greenhouse gas (GHG) emissions and criteria pollutants, and health and safety impacts. In terms of fuel and electricity usage, NHTSA estimates that the proposal would save about 50 billion gallons of gasoline and increase electricity consumption by about 275 TWh over the lives of vehicles produced prior to MY 2030, relative to the baseline standards (i.e., the No-Action Alternative). From a calendar year perspective, NHTSA’s analysis also estimates total annual consumption of fuel by the entire on-road fleet from calendar year 2020 through calendar year 2050. On this basis, gasoline and electricity consumption by the U.S. light-duty vehicle fleet evolves as shown in the following two graphs, each of which shows projections for the NoAction Alternative (Alternative 0, i.e., the baseline), Alternative 1, Alternative 2 (the proposal), and Alternative 3. BILLING CODE 4910–59–P 140 ~120 ·0.Qo ·······•9.oo .............0 oooo ...................... Ooo ................ ooo ............... Oo 0 ·······•....... ooo ,g -; C, ] 100 - i$ ... '-' 0 80 .....= ··········· t i;; u= 60 0 .s 000 40 c:!S C, c:!S J 20 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00176 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.163</GPH> Figure V-3-Estimated Annual Gasoline Consumption by Light-Duty On-Road Fleet EP03SE21.162</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 0 Alt. 0 ········· Alt. 1 -Alt. 2 --+- Alt. 3 49777 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 300 § ."[ § 150 "' § u 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 0 Alt. 0 ......... Alt. 1 -Alt. 2 -+-- Alt. 3 Figure V-4-Estimated Electricity Consumption by Light-Duty On-Road Fleet VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 reduce greenhouse gases by about 465 million metric tons of carbon dioxide (CO2), about 500 thousand metric tons of methane (CH4), and about 12 thousand tons of nitrous oxide (N2O). The following three graphs (Figure V–5, Figure V–6, and Figure V–7) present NHTSA’s estimate of how emissions PO 00000 Frm 00177 Fmt 4701 Sfmt 4702 from these three GHGs could evolve over the years. Note that these graphs include emissions from both vehicle and upstream processes. All three GHG emissions follow similar trends in the years between 2020–2050. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.164</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 NHTSA estimates the greenhouse gas emissions (GHGs) attributable to the light-duty on-road fleet, from both vehicles and upstream energy sector processes (e.g., petroleum refining, fuel transportation and distribution, electricity generation). Overall, NHTSA estimates that the proposed rule would 49778 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 1,600 l,400 9..Q.9..9.000 ';;' 1,200 ············· Oooo ...······· Ooo 0 ............. Ooo C r3 ~ i::: ;.§ l,000 ::; "'i::: 800 0 '._;,,., ~u; ·a"' U,l -;j 600 § i::: < 400 200 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 0 Alt. 0 ········· Alt. 1 -Alt. 2 -+-Alt. 3 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00178 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.165</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure V-5- Estimated Annual CO2 Emissions Attributable to Light-Duty On-Road Fleet 49779 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 1,800,000 ·°-oo ··· ·•99..oo 0 1,600,000 ............. ............ 00000 0o ...... l,400,000 0 .................... 000000 •••• ••••••••"••h•Ho._ i' 1,200,000 ~ .....,, ....§ 1,000,000 00 00 ·e ~ ,..., 800,000 Cl$ j 600,000 400,000 200,000 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 0 Alt. 0 ......... Alt. 1 -Alt. 2 -+- Alt. 3 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00179 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.166</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure V-6-Estimated Annual CH4 Emissions Attributable to Light-Duty On-Road Fleet 49780 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 60,000 50,000 ·O.Q . ··0.Q ,;;-.40000 § ' ••Q.0.Q.ooo ... Oo c ·····················900 ·············Oo 0 "' .§ ··············•2? ·~ 30,000 i:.:t-1 ~ ;:s ~ 20,000 10,000 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 0 Alt. 0 ········· Alt. 1 -Alt. 2 -+- Alt. 3 The figures presented here are not the only estimates NHTSA has calculated regarding projected GHG emissions in future years. As discussed in Section II, the accompanying SEIS uses an ‘‘unconstrained’’ analysis as opposed to the ‘‘standard setting’’ analysis presented in this NPRM and PRIA. For more information regarding projected GHG emissions, as well as model-based estimates of corresponding impacts on several measures of global climate change, see the SEIS. NHTSA also estimates criteria pollutant emissions resulting from vehicle and upstream processes attributable to the light-duty on-road fleet. NHTSA includes estimates for all of the criteria pollutants for which EPA has issued National Ambient Air Quality Standards. Under each regulatory alternative, NHTSA projects a dramatic decline in annual emissions of carbon monoxide (CO), volatile organic compounds (VOC), nitrogen oxide (NOX), and fine particulate matter (PM2.5) attributable to the light-duty onroad fleet between 2020 and 2050. As exemplified in Figure V–8, emissions in any given year could be very nearly the same under each regulatory alternative. On the other hand, as discussed in the PRIA and SEIS accompanying this NPRM, NHTSA projects that annual SO2 emissions attributable to the light-duty on-road fleet could increase modestly under the action alternatives, because, as discussed above, NHTSA projects that each of the action alternatives could lead to greater use of electricity (for PHEVs and BEVs). The adoption of actions—such as actions prompted by President Biden’s Executive order directing agencies to develop a Federal Clean Electricity and Vehicle Procurement Strategy—to reduce electricity generation emission rates beyond projections underlying NHTSA’s analysis (discussed in the TSD) could dramatically reduce SO2 emissions under all regulatory alternatives considered here.382 382 E.O. 14008, 86 FR 7619 (Feb. 1, 2021), https:// www.whitehouse.gov/briefing-room/presidential- actions/2021/01/27/executive-order-on-tackling- the-climate-crisis-at-home-and-abroad/, accessed June 17, 2021. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00180 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.167</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure V-7 -Estimated Annual N20 Emissions Attributable to Light-Duty On-Road Fleet 49781 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 1,200,000 1,000,000 -a ~ '-" 800,000 f./) t:: ...... 0 ti) ·s,:.a 1'll 600,000 ...... (,:I J 400,000 200,000 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 0 Alt. 0 ......... Alt. 1 -Alt. 2 -+- Alt. 3 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00181 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.168</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure V-8-Estimated Annual NOx Emissions Attributable to Light-Duty On-Road Fleet 49782 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 120,000 100,000 ......... O= 0 +-+±-+ I I I I I I I I +--+-t+-+ ·O ooOO 00-0-G-000-00GG000 ~' 80,000 ~ '-'' cf) .....§ ~ 60,000 '§ r.:t:1 1 <e 40,000 20,000 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 0 Alt. 0 ......... Alt. 1 -Alt. 2 -+-Alt. 3 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00182 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.169</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure V-9- Estimated Annual S02 Emissions Attributable to Light-Duty On-Road Fleet 49783 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 35,000 30,000 25,000 i ~ ~ 20,000 0 ·~ '§ ~ 15 000 ,,._.'' ell s ~ 10,000 5,000 0 2015 2025 2020 2030 2035 2045 2040 2050 2055 0 Alt. 0 ......... Alt. I -Alt. 2 --+- Alt. 3 Figure V-10 - Estimated Annual PM2.s Emissions Attributable to Light-Duty On-Road Fleet VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Figure V–11 shows the differences in select health impacts relative to the baseline, across alternatives 1–3. These changes are split between calendar year decades, with the largest differences between the baseline and alternatives occurring between 2041–2050. The PO 00000 Frm 00183 Fmt 4701 Sfmt 4702 magnitude of the differences relates directly to the changes in tons of criteria pollutants emitted. See Chapter 5.4 of the TSD for information regarding how the CAFE Model calculates these health impacts. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.170</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Health impacts quantified by the CAFE Model include various instances of hospital visits due to respiratory problems, minor restricted activity days, non-fatal heart attacks, acute bronchitis, premature mortality, and other effects of criteria pollutant emissions on health. 49784 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules □ 2021- Iii 2031- 2040 2030 ■ 2041- 2050 Asthma Exacerbation Work loss Days Minor Resmcted ActMty Days Upper Respiratory Symptoms . . . J ............... - --•-- l _ Lower Respiratory Symptoms -- ,-«c-,~- .--•~ .....-----------··•·------------------- .... , . .. . '. ....,.... ___ .________ ...... L ..........-.l .............- .. •.. Asthma Exacerbation l •- WorklossDays ' -~'>-_a.Ch-> ---~-----·--- Minor Resmcted Activity Days . , ~ ¾ .. i --·-------~~- ~ Upper Respiratory Symptoms . . lower Respiratory Symptoms - ,_ - - - · - , _____ , __ , ~ -'<'' -<, ~ ~ .---<-«,,~~- Asthma Exacerbation Work loss Days Minor Restricted Activity Days ~ ! ' : , -,•.>---· .-----,-- Upper Respiratory Symptoms lower Respiratory Symptoms '. < . --~~- ---~----~ -- .. ·-·- ---- --'------- - -800 -700 -600 -500 -400 -300 -200 -100 0 fnddents (Thousands) Lastly, NHTSA also quantifies safety impacts in its analysis. These include estimated counts of fatalities, non-fatal injuries, and property damage crashes occurring over the lifetimes of the lightduty on-road vehicles considered in the analysis. Chapter 5 in the PRIA accompanying this NPRM contains an in-depth discussion on the effects of the various alternatives on these safety measures, and TSD Chapter 7 contains information regarding the construction of the safety estimates. lotter on DSK11XQN23PROD with PROPOSALS2 E. Sensitivity Analysis The analysis conducted to support this proposal consists of data, estimates, and assumptions, all applied within an analytical framework, the CAFE Model. Just like in all past CAFE rulemakings, NHTSA recognizes that many analytical inputs are uncertain, and some inputs are very uncertain. Of those uncertain inputs, some are likely to exert considerable influence over specific types of estimated impacts, and some are likely to do so for the bulk of the 383 In contrast to an uncertainty analysis, where many assumptions are varied simultaneously, the sensitivity analyses included here vary a single VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 analysis. Yet making assumptions in the face of that uncertainty is necessary, if we are going to try to analyze meaningfully the effects of something that will happen in the future—i.e., the regulatory alternatives being considered, that represent different possible CAFE standards for MYs 2024–2026. To get a sense of the effect that these assumptions have on the analytical findings, we conducted additional model runs with alternative assumptions, which explored a range of potential inputs and the sensitivity of estimated impacts to changes in model inputs. Sensitivity cases in this analysis span assumptions related to technology applicability and cost, economic conditions, consumer preferences, externality values, and safety assumptions, among others.383 A sensitivity analysis can identify two critical pieces of information: How big an influence does each parameter exert on the analysis, and how sensitive are the model results to that assumption? That said, influence is different from likelihood. NHTSA does not mean to suggest that any one of the sensitivity cases presented here is inherently more likely than the collection of assumptions that represent the reference case in the figures and tables that follow. Nor is this sensitivity analysis intended to suggest that only one of the many assumptions made is likely to prove off-base with the passage of time or new observations. It is more likely that, when assumptions are eventually contradicted by future observation (e.g., deviations in observed and predicted fuel prices are nearly a given), there will be collections of assumptions, rather than individual parameters, that simultaneously require updating. For this reason, we do not interpret the sensitivity analysis as necessarily providing justification for alternative regulatory scenarios to be preferred. Rather, the analysis simply provides an indication of which assumptions are most critical, and the extent to which future deviations from central analysis assumption and provide information about the influence of each individual factor, rather than suggesting that an alternative assumption would have justified a different preferred alternative. PO 00000 Frm 00184 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.171</GPH> Figure V-11-Changes in Cumulative Emission Health Impacts Relative to the Baseline Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules assumptions could affect costs and benefits of this proposal. 49785 Table V–38 lists and briefly descries the cases that we examined in the sensitivity analysis. Table V-38- Cases Included in Sensitivity Analysis Sensitivity Case Description Reduced MDPCS stringency 60-month payback period Battery direct costs (-20%) Battery direct costs (+20%) Battery learning costs (-20%) lotter on DSK11XQN23PROD with PROPOSALS2 Battery learning costs (+20%) Rebound (10%) Rebound (20%) Mass-size-safety (low) Mass-size-safety (high) Crash avoidance (low effectiveness) Crash avoidance (high effectiveness) Sales-scrappage response (-20%) Sales-scrappage response (+20%) Low GDP High GDP Oil price (EIA low) Oil price (Global Insight) Oil price (EIA high) VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Reference case with 2.5% SCC discount rate Reference case with 3% SCC discount rate (DR) (for 7% social discount rate) Reference case with 5% SCC discount rate Reference case with 95th percentile SCC discount rate Social cost of carbon values at 2020 Final Rule levels Vehicles redesigned every year MR5 and MR6 skipped for platforms with 100k or more units MR5 and MR6 skipped for platforms with 2k or more units No MR5 or MR6 application applied without SKIP restriction Cost values for MR5 and MR6 at levels from 2020 Final Rule HCR engine applicable for all OEMs and technology classes No additional AC or OC credit accumulation after MY 2021 levels Minimum domestic passenger car standard reduced as described in Section VI of the preamble 60-month payback period Battery direct manufacturing cost decreased by 20%, reference battery learning cost Battery direct manufacturing cost increased by 20%, reference battery learning cost Battery learning cost decreased by 20%, reference direct manufacturing cost Battery learning cost increased by 20%, reference direct manufacturing cost Ten percent rebound effect Twenty percent rebound effect The lower bound of the 95% CI for all model coefficients The upper bound of the 95% CI for all model coefficients Lower-bound estimate of effectiveness for 6 current crash avoidance technologies at avoiding fatal, injury, and property damage Upper-bound estimate of effectiveness for 6 current crash avoidance technologies at avoiding fatal, injury, and property damage Sales-scrappage elasticity decreased by 20% Sales-scrappage elasticity increased by 20% Low economic growth (AEO202 l) High economic growth (AEO202 l) Input oil price series based on EIA low forecast Input oil price series based on Global Insight forecast Input oil price series based on EIA high forecast Frm 00185 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.172</GPH> Reference case (RC) RC w/ 7% social DR, 3% SC-GHG DR RC w/ 7% social DR, 5% SC-GHG DR RC w/ 95th pctile SC-GHG DR 2020 sec One-year redesign cadence MR5/6 skip (> 100k) MR5/6 skip (>2k) No MR5/6 skip 2020 Final Rule MR5/6 costs NoHCRskip FlatAC/OC 49786 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Complete results for the sensitivity cases are summarized in Chapter 7 of the accompanying PRIA, and detailed model inputs and outputs for curious readers are available on NHTSA’s website.384 For purposes of this preamble, Figure V–12 below illustrates the relative change of the sensitivity Reference case (RC) RC w/ 7% social DR, 3% SC-GHG DR RC w/ 7% social DR, 5% SC-GHG DR RC w/ 95th pctile SC-GHG DR 2020 sec One-year redesign cadence MRS/6 skip(> 100k) MRS/6 skip (>2k) No MRS/6 skip 2020 Final Rule MRS/6 costs NoHCRskip FlatAC/OC Reduced MDPCS stringency 60-month payback period Battery direct costs (-20%) Battery direct costs (+20%) Battery learning costs (-20%) Battery learning costs (+20%) Rebound ( 10%) Rebound (20%) Mass-size-safety (low) Mass-size-safety (high) Crash avoidance (low effectiveness) Crash avoidance (high effectiveness) Sales-scrappage response (-20%) Sales-scrappage response (+20%) Low GDP High GDP Oil price (EIA low) Oil price (Global Insight) Oil price (EIA high) '---------+---------' -30% 0% 30% effect of selected inputs on the costs and benefits that we estimate for the proposal. -30% 0% 30% Percent Deviation from Reference Case While Figure V–12 does not show precise values, it gives us a sense of which inputs are ones for which a different assumption would have a much different effect on analytical findings, and which ones would not have much effect. Assuming a morediscounted or lower social cost of carbon would have a relatively large effect, as would assuming a different oil price, or doubling the assumed ‘‘payback period.’’ Making very high levels of mass reduction unavailable in the modeling appears to have a (relatively) very large effect on costs, but this is to some extent an artifact of the ‘‘standard setting’’ runs used for the preamble and PRIA analysis, where electrification is limited due to statutory restrictions. On the other hand, assumptions about which there has been significant disagreement in the past, like the rebound effect or the sales-scrappage response, appear to cause only relatively small changes in net benefits. Chapter 7 of the PRIA provides a much fuller discussion of these findings, and presents net benefits estimated under each of the cases included in the sensitivity analysis, including the subset for which impacts are summarized in Figure V–13. 384 https://www.nhtsa.gov/laws-regulations/ corporate-average-fuel-economy. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00186 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.173</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure V-12- Relative Change in Total Costs and Total Benefits from Reference Case 49787 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Oil price (EIA) --32.0 1 I I I I I .. - ~ Battery direct costs (lt• +200/4; L• ·20%) --•15;6 • Mas$-size..safety --~3• 47.6- 019.9 to 10.0 .•.9T7 ...,_ Base ~4 Battery teaming costs(H • +200/4; L--200/4) ..... High Salcs-sctappagnesponse (11 • +20%; L • -20%) ---+------3.9T4.4 ODP "'°"" -3.ST3.1 Rcbound(ll•20%;L• JO%) Low -----2.,T4.S --.,;.;,.,.,...-i----•0.J -30 I 0 30 60 Net Social Benefits ($ billions) BILLING CODE 4910–59–C The results presented in the earlier subsections of Section V and discussed in Section VI reflect the agency’s best judgments regarding many different factors, and the sensitivity analysis discussed here is simply to illustrate the obvious, that differences in assumptions can lead to differences in analytical outcomes, some of which can be large and some of which may be smaller than expected. Policy-making in the face of future uncertainty is inherently complex. Section VI explains how NHTSA proposes to balance the statutory factors in light of the analytical findings, the uncertainty that we know exists, and our Nation’s policy goals, to determine the CAFE standards that NHTSA tentatively concludes are maximum feasible for MYs 2024–2026. lotter on DSK11XQN23PROD with PROPOSALS2 VI. Basis for NHTSA’s Tentative Conclusion That the Proposed Standards Are Maximum Feasible In this section, NHTSA discusses the factors, data, and analysis that the agency has considered in the tentative selection of the proposed CAFE standards for MYs 2024–2026. The primary purpose of EPCA, as amended by EISA, and codified at 49 U.S.C. chapter 329, is energy conservation, and fuel economy standards help to conserve energy by requiring automakers to make new vehicles travel a certain distance on a gallon of fuel.385 385 While individual vehicles need not meet any particular mpg level, as discussed elsewhere in this preamble, fuel economy standards do require vehicle manufacturers’ fleets to meet certain compliance obligations based on fuel economy VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 The goal of the CAFE standards is to conserve energy, while taking into account the statutory factors set forth at 49 U.S.C. 32902(f), as discussed below. The provision at 49 U.S.C. 32902(f) states that when setting maximum feasible CAFE standards for new passenger cars and light trucks, the Secretary of Transportation386 ‘‘shall consider 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.’’ In previous rulemakings, including the 2012 final rule issued during the Obama Administration and the recent 2020 final rule, NHTSA considered technological feasibility, including the availability of various fuel-economy-improving technologies to be applied to new vehicles in the timeframe of the standards depending on the ultimate stringency levels, and also considered economic practicability, including the differences between a range of regulatory alternatives in terms of effects on per-vehicle costs, the ability of both the industry and individual manufacturers to comply with standards at various levels, as well as effects on vehicle sales, industry employment, and consumer demand. NHTSA also considered how compliance with other motor vehicle standards of the Government might affect manufacturers’ ability to meet CAFE standards represented by a range levels target curves set forth by NHTSA in regulation. 386 By delegation, the NHTSA Administrator. PO 00000 Frm 00187 Fmt 4701 Sfmt 4702 of regulatory alternatives, and how the need of the U.S. to conserve energy could be more or less addressed under a range of regulatory alternatives, in terms of considerations like costs to consumers, the national balance of payments, environmental implications like climate and smog effects, and foreign policy effects such as the likelihood that U.S. military and other expenditures could change as a result of more or less oil consumed by the U.S. vehicle fleet. These elements are discussed in detail throughout this analysis. As will be explained in greater detail below, while NHTSA is considering all of the same factors in proposing revised CAFE standards for MYs 2024–2026 that it considered in previous rulemakings, the agency’s balancing of those factors has shifted, and NHTSA is therefore choosing to set CAFE standards at a different level from what both the 2012 final rule and the 2020 final rule set forth. Besides the factors specified in 32902(f), NHTSA has also historically considered the safety effects of potential CAFE standards, and additionally considers relevant case law. NHTSA and EPA have coordinated in setting standards, and many of the factors that NHTSA considers to set maximum feasible standards complement factors that EPA considers under the Clean Air Act. The balancing of competing factors by both EPA and NHTSA are consistent with each agency’s statutory authority and recognize the statutory obligations the Supreme Court pointed to in Massachusetts v. EPA. NHTSA also E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.174</GPH> Figure V-13- Relative Magnitude of Sensitivity Effect on Net Benefits 49788 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 considers the Ninth Circuit’s decision in Center for Biological Diversity v. NHTSA, which remanded NHTSA’s 2006 final rule establishing standards for MYs 2008–2011 light trucks and underscored that ‘‘the overarching purpose of EPCA is energy conservation.’’387 This proposal contains a range of regulatory alternatives for MYs 2024– 2026, from retaining the 1.5 percent annual increases set in 2020, up to a stringency increase of 10 percent annually. The analysis supported this range of alternatives based on factors relevant to NHTSA’s exercise of its 32902(f) authority, such as fuel saved and emissions reduced, the technologies available to meet the standards, the costs of compliance for automakers and their abilities to comply by applying technologies, the impact on consumers with respect to cost, fuel savings, and vehicle choice, and effects on safety, among other things. NHTSA’s tentative conclusion, after consideration of the factors described below and information in the administrative record for this action, is that 8 percent increases in stringency for MYs 2024–2026 (Alternative 2 of this analysis) are maximum feasible. The Biden Administration is deeply committed to working aggressively to improve energy conservation, and higher standards appear increasingly likely to be economically practicable given almost-daily announcements by major automakers about forthcoming new high-fuel-economy vehicle models, as described below. Despite only one year having passed since the 2020 final rule, enough has changed in the U.S. and the world that revisiting the CAFE standards for MYs 2024–2026, and raising their stringency considerably, is both appropriate and reasonable. The following sections discuss in more detail the statutory requirements and considerations involved in NHTSA’s tentative determination of maximum feasible CAFE standards, and NHTSA’s explanation of its balancing of factors for this tentative determination. A. EPCA, as Amended by EISA EPCA, as amended by EISA, contains a number of provisions regarding how NHTSA must set CAFE standards. DOT (by delegation, NHTSA) 388 must establish separate CAFE standards for passenger cars and light trucks 389 for each model year,390 and each standard must be the maximum feasible that the Secretary (again, by delegation, NHTSA) believes the manufacturers can achieve in that model year.391 In determining the maximum feasible levels of CAFE standards, EPCA requires that NHTSA consider 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.392 In addition, NHTSA has the authority to consider (and typically does consider) other relevant factors, such as the effect of CAFE standards on motor vehicle safety and consumer preferences. The ultimate determination of what standards can be considered maximum feasible involves a weighing and balancing of factors, and the balance may shift depending on the information before NHTSA about the expected circumstances in the model years covered by the rulemaking. The agency’s decision must also be guided by the overarching purpose of EPCA, energy conservation, while balancing these factors.393 Besides the requirement that the standards be maximum feasible for the fleet in question and the model year in question, EPCA/EISA also contain several other requirements, as follow. 1. Lead Time EPCA requires that NHTSA prescribe new CAFE standards at least 18 months before the beginning of each model year.394 For amendments to existing standards (as this NPRM proposes), EPCA requires that if the amendments make an average fuel economy standard more stringent, at least 18 months of lead time must be provided.395 Thus, if the first year for which NHTSA is proposing to amend standards in this NPRM is MY 2024, NHTSA interprets this provision as requiring the agency to issue a final rule covering MY 2024 standards no later than April 2022. 2. Separate Standards for Cars and Trucks, and Minimum Standards for Domestic Passenger Cars As mentioned above, EPCA requires NHTSA to set separate standards for passenger cars and light trucks for each 390 49 U.S.C. 32902(a) (2007). 391 Id. 392 49 387 538 F.3d 1172 (9th Cir. 2008). 388 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 1.95(a). 389 49 U.S.C. 32902(b)(1) (2007). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 U.S.C. 32902(f) (2007). for Biological Diversity v. NHTSA, 538 F.3d 1172, 1197 (9th Cir. 2008) (‘‘Whatever method it uses, NHTSA cannot set fuel economy standards that are contrary to Congress’s purpose in enacting the EPCA—energy conservation.’’). 394 49 U.S.C. 32902(a) (2007). 395 49 U.S.C. 32902(g)(2) (2007). 393 Center PO 00000 Frm 00188 Fmt 4701 Sfmt 4702 model year.396 NHTSA has long interpreted this requirement as preventing the agency from setting a single combined CAFE standard for cars and trucks together, based on the plain language of the statute. Congress originally required separate CAFE standards for cars and trucks to reflect the different fuel economy capabilities of those different types of vehicles, and over the history of the CAFE program, has never revised this requirement. Even as many cars and trucks have come to resemble each other more closely over time—many crossover and sport-utility models, for example, come in versions today that may be subject to either the car standards or the truck standards depending on their characteristics—it is still accurate to say that vehicles with truck-like characteristics such as 4-wheel drive, cargo-carrying capability, etc., currently consume more fuel per mile than vehicles without these characteristics. EPCA, as amended by EISA, also requires another separate standard to be set for domestically-manufactured 397 passenger cars. Unlike the generallyapplicable standards for passenger cars and light trucks described above, the compliance obligation of the minimum domestic passenger car standard (MDPCS for brevity) is identical for all manufacturers. The statute clearly states that any manufacturer’s domestically manufactured 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 manufactured for sale in the United States 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).398 Since that requirement was promulgated, the ‘‘92 percent’’ has always been greater than 27.5 mpg, and foreseeably will continue to be so in the future. While NHTSA published 92 percent MDPCSs for MYs 2024–2026 at 49 CFR 531.5(d) as part of the 2020 final rule, the statutory language is clear that 396 49 U.S.C. 32902(b)(1) (2007). the CAFE program, ‘‘domesticallymanufactured’’ is defined by Congress in 49 U.S.C. 32904(b). The definition roughly provides that a passenger car is ‘‘domestically manufactured’’ as long as at least 75 percent of the cost to the manufacturer is attributable to value added in the United States, Canada, or Mexico, unless the assembly of the vehicle is completed in Canada or Mexico and the vehicle is imported into the United States more than 30 days after the end of the model year. 398 49 U.S.C. 32902(b)(4) (2007). 397 In E:\FR\FM\03SEP2.SGM 03SEP2 49789 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules the MDPCS must be determined at the time an overall passenger car standards is promulgated and published in the Federal Register. Thus, any time NHTSA establishes or changes a passenger car standard for a model year, the MDPCS must also be evaluated or re-evaluated and established accordingly. As in the 2020 final rule, NHTSA recognizes industry concerns that actual total passenger car fleet standards have differed significantly from past projections, perhaps more so when the agency has projected significantly into the future. In that final rule, because the compliance data showed that the standards projected in 2012 were consistently more stringent than the actual standards, by an average of 1.9 percent. NHTSA stated that this difference indicated that in rulemakings conducted in 2009 through 2012, NHTSA’s and EPA’s projections of passenger car vehicle footprints and production volumes, in retrospect, underestimated the production of larger passenger cars over the MYs 2011 to 2018 period.399 Unlike the passenger car standards and light truck standards which are vehicle-attribute-based and automatically adjust with changes in consumer demand, the MDPCS are not attribute-based, and therefore do not adjust with changes in consumer demand and production. They are instead fixed standards that are established at the time of the rulemaking. As a result, by assuming a smaller-footprint fleet, on average, than what ended up being produced, the MYs 2011–2018 MDPCS ended up being more stringent and placing a greater burden on manufacturers of domestic passenger cars than was projected and expected at the time of the rulemakings that established those standards. In the 2020 final rule, therefore, NHTSA agreed with industry concerns over the impact of changes in consumer demand (as compared to what was assumed in 2012 about future consumer demand for greater fuel economy) on manufacturers’ ability to comply with the MDPCS and in particular, manufacturers that produce larger passenger cars domestically. Some of the largest civil penalties for noncompliance in the history of the CAFE program have been paid for noncompliance with the MDPCS. NHTSA also expressed concern that consumer demand may shift even more in the direction of larger passenger cars if fuel prices continue to remain low. Sustained low oil prices can be expected to have real effects on consumer demand for additional fuel economy, and consumers may foreseeably be even more interested in 2WD crossovers and passenger-car-fleet SUVs (and less interested in smaller passenger cars) than they are at present. Therefore, in the 2020 final rule, to help avoid similar outcomes in the 2021–2026 timeframe to what had happened with the MDPCS over the preceding model years, NHTSA determined that it was reasonable and appropriate to consider the recent projection errors as part of estimating the total passenger car fleet fuel economy for MYs 2021–2026. NHTSA therefore projected the total passenger car fleet fuel economy using the central analysis value in each model year, and applied an offset based on the historical 1.9 percent difference identified for MYs 2011–2018. For this proposal, recognizing that we are proposing to increase stringency considerably over the baseline standards and that civil penalties have also recently increased, NHTSA remains concerned that the MDPCS may pose a significant challenge to certain manufacturers. To that end, NHTSA is proposing to retain the 1.9 percent offset for the MDPCS for MYs 2024–2026, which we have appropriately recalculated based on the current projections for passenger cars based on the current analysis fleet. Table VI–1 shows the calculation values used to determine the total passenger car fleet fuel economy value for each model year for the preferred alternative. BILLING CODE 4910–59–P 2024 2025 2026 Projected Total PC Fleet Standard - Central Analysis (mpg) 49.2 53.4 58.1 Offset: Average Historical Difference Between Regulatory Analyses and Actual Total PC Fleet Standard (percent) -1.9 -1.9 -1.9 Offset: Average Historical Difference Between Regulatory Analyses and Actual Total PC Fleet Standard (mpg) -0.92 -1.00 -1.08 Projected Total PC Standard Accounting for Historical Offset (mpg) 48.2 52.4 57.0 Minimum Domestic Passenger Car Standard= 92% of Projected Total PC Standard Accounting for Historical Offset (mpg) 44.4 48.2 52.4 Using this approach, the MDPCS under each regulatory alternative would thus be as shown in Table VI–2. 399 See 85 FR at 25127 (Apr. 30, 2020). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00189 Fmt 4701 Sfmt 4702 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.175</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Table VI-1- Calculation of the Projected Total Passenger Car Fleet Standard and the Minimum Domestic Passenger Car Standard (92 Percent of the Total Passenger Car Standard) for the Preferred Alternative 49790 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table VI-2 - Proposed MDPCS for Each Regulatory Alternative, Calculated per 1.9 Percent Offset Alternative MY2024 MY2025 MY2026 41.4 44.9 44.4 45.4 42.1 46.5 48.2 50.4 42.7 48.0 52.4 56.0 No Action Alternative 1 Alternative 2 (Preferred) Alternative 3 U.S., NHTSA could instead attempt to make such a projection explicitly. Examination of the average footprints of passenger cars sold in the U.S. from 2008, when EPA began reporting footprint data, to 2020 indicates a clear and statistically significant trend of gradually increasing average footprint (Figure VI–1). The average annual increase in passenger car footprint, NHTSA is also seeking comment on another approach to offsetting the MDPCS. Recognizing that the analysis supporting this proposal does not attempt to project how vehicle footprints may change in the future, nor how that might affect the average fuel economy of passenger cars sold in the estimated by ordinary least squares, indicates that the passenger car footprints increased by an average of 0.1206 square feet annually over the 2008–2020 period. The estimated average increase is statistically significant at the 0.000001 level, with a 95 percent confidence interval of (0.0929, 0.1483). 47.0 46.8 ........-. 46.6 46.4 ~ i 46.2 ,., ""'46.0 i II ._,,,,,.. _J' 45.8 45.6 .......... .......... • 45.4 45.2 45.0 ~ ~ 44.8 2006 2008 2010 2012 2014 2016 2018 2020 2022 Figure VI-1 - Trend in Passenger Car Footprint, 2008-2020 (Source: EPA 2020 Automotive Trends Report) The alternate method for calculating an offset to the MDPCS would be three steps, as follows: 1. Starting from the average footprint of passenger cars in 2020 as reported by EPA, add 0.1206 square feet per year through 2026. 2. Calculate the estimated fuel economy of passenger cars using the average projected footprint numbers calculated in step 1 and the footprint functions that are the passenger car standards for the corresponding model year, which then become ‘‘the Secretary’s projected passenger car fuel economy numbers.’’ 3. Apply the 92 percent factor to calculate the MDPCS for 2024, 2025, and 2026. The results of this approach are shown in Table VI–3. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00190 Fmt 4701 Sfmt 4725 MY2025 42.2 46.5 48.3 50.5 MY2026 42.7 48.0 52.4 56.0 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.177</GPH> MY2024 41.6 45.1 44.6 45.5 EP03SE21.176</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Alternative No Action Alternative 1 Alternative 2 (Preferred) Alternative 3 EP03SE21.178</GPH> Table VI-3 - Alternate Approach to Offsetting MDPCS, on Which NHTSA Seeks Comment Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Comparing all of these, Table VI–4 shows (1) the unadjusted 92 percent MDPCS for MYs 2024–2026, (2) the proposed 1.9 percent-offset MDPCS for MYs 2024–2026, and (3) the alternate 49791 approach offset MDPCS for MYs 2024– 2026. Table VI-4-Comparing the Required mpg Levels for the MDPCS by Regulatory Alternative and Offset Approach Alternative MY2024 MY2025 MY2026 Unadjusted 92% 42.2 42.9 43.5 1.9% offset 41.4 42.1 42.7 Alternate approach offset 41.6 42.2 42.7 Unadjusted 92% 45.8 47.3 48.9 1.9% offset 44.9 46.5 48.0 Alternate approach offset 45.1 46.5 48.0 Unadjusted 92% 45.2 49.2 53.4 1.9% offset 44.4 48.2 52.4 Alternate approach offset 44.6 48.3 52.4 Unadjusted 92% 50.2 55.8 62.0 1.9% offset 45.4 50.4 56.0 Alternate approach offset 45.5 50.5 56.0 No Action Alternative 1 Alternative 2 (Preferred) Alternative 3 While the CAFE Model analysis underlying this proposal, the PRIA, and the Draft SEIS does not reflect an offset to the unadjusted 92 percent MDPCS, separate analysis that does reflect the change demonstrates that doing so does not change estimated impacts of any of the regulatory alternatives under consideration, despite the mpg values being slightly different as shown in Table VI–4. NHTSA seeks comment on the discussion above. To be clear, the agency also seeks comment on whether to apply the MDPCS without any modifier. Section III.B of this preamble and Chapter 1 of the accompanying TSD. As in previous rulemakings, NHTSA is proposing to define the standards in the form of a constrained linear function that generally sets higher (more stringent) targets for smaller-footprint vehicles and lower (less stringent) targets for larger-footprint vehicles. These footprint curves are discussed in more detail in Section III.B and TSD Chapter 1. NHTSA seeks comment in Section III.B both on the continued use of footprint as the relevant attribute and on the continued use of the constrained linear curve shapes. 3. Attribute-Based and Defined by a Mathematical Function 4. Number of Model Years for Which Standards May Be Set at a Time EISA requires NHTSA to set CAFE standards that are ‘‘based on 1 or more attributes related to fuel economy and express[ed] . . . in the form of a mathematical function.’’ 400 Historically, NHTSA has based standards on vehicle footprint, and proposes to continue to do so for the reasons described in EISA also states that NHTSA shall ‘‘issue regulations under this title prescribing average fuel economy standards for at least 1, but not more than 5, model years.’’ 401 In this NPRM, NHTSA is proposing to set CAFE standards for three model years, MYs 400 49 U.S.C. 32902(b)(3)(A) (2007). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 401 49 PO 00000 U.S.C. 32902(b)(3)(B) (2007). Frm 00191 Fmt 4701 Sfmt 4702 2024–2026. This proposal fits squarely within the plain language of the statute. 5. Maximum Feasible Standards As discussed above, EPCA requires NHTSA to consider four factors in determining what levels of CAFE standards would be maximum feasible. NHTSA presents in the sections below its understanding of the meanings of those four factors. (a) Technological Feasibility ‘‘Technological feasibility’’ refers to whether a particular method of improving fuel economy is available for deployment in commercial application in the model year for which a standard is being established. Thus, NHTSA is not limited in determining the level of new standards to technology that is already being applied commercially at the time of the rulemaking. For this proposal, NHTSA has considered a wide range of technologies that improve fuel economy, while considering the need to account for which technologies have already been applied to which vehicle model/configuration, as well as the need to estimate realistically the cost and fuel E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.179</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 BILLING CODE 4910–59–C 49792 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules economy impacts of each technology as applied to different vehicle models/ configurations. NHTSA has not, however, attempted to account for every technology that might conceivably be applied to improve fuel economy, nor does NHTSA believe it is necessary to do so given that many technologies address fuel economy in similar ways.402 NHTSA notes that the technological feasibility factor allows NHTSA to set standards that force the development and application of new fuel-efficient technologies, but this factor does not require NHTSA to do so.403 In the 2012 final rule, NHTSA stated that ‘‘[i]t is important to remember that technological feasibility must also be balanced with the other of the four statutory factors. Thus, while ‘technological feasibility’ can drive standards higher by assuming the use of technologies that are not yet commercial, ‘maximum feasible’ is also defined in terms of economic practicability, for example, which might caution the agency against basing standards (even fairly distant standards) entirely on such technologies.’’ 404 NHTSA further stated that ‘‘. . . as the ‘maximum feasible’ balancing may vary depending on the circumstances at hand for the model year in which the standards are set, the extent to which technological feasibility is simply met or plays a more dynamic role may also shift.’’ 405 For purposes of this proposal covering standards for MYs 2024–2026, NHTSA is certain that sufficient technology exists to meet the standards—even for the most stringent regulatory alternative. As will be discussed further below, for this proposal, the question is more likely rather, given that the technology exists, how much of it should be required to be added to new cars and trucks in order to conserve more energy, and how to balance that objective against the additional cost of adding that technology. lotter on DSK11XQN23PROD with PROPOSALS2 (b) Economic Practicability ‘‘Economic practicability’’ has consistently referred to whether a standard is one ‘‘within the financial capability of the industry, but not so 402 For example, NHTSA has not considered highspeed flywheels as potential energy storage devices for hybrid vehicles; while such flywheels have been demonstrated in the laboratory and even tested in concept vehicles, commercially-available hybrid vehicles currently known to NHTSA use chemical batteries as energy storage devices, and the agency has considered a range of hybrid vehicle technologies that do so. 403 See 77 FR at 63015 (Oct. 12, 2012). 404 Id. 405 Id. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 stringent as to’’ lead to ‘‘adverse economic consequences, such as a significant loss of jobs or unreasonable elimination of consumer choice.’’ 406 In evaluating economic practicability, NHTSA considers the uncertainty surrounding future market conditions and consumer demand for fuel economy alongside consumer demand for other vehicle attributes. There is not necessarily a bright-line test for whether a regulatory alternative is economically practicable, but there are several metrics that we discuss below that we find can be useful for making this assessment. In determining whether standards may or may not be economically practicable, NHTSA considers: Application rate of technologies— whether it appears that a regulatory alternative would impose undue burden on manufacturers in either or both the near and long term in terms of how much and which technologies might be required. This metric connects to the next two metrics, as well. Other technology-related considerations—related to the application rate of technologies, whether it appears that the burden on several or more manufacturers might cause them to respond to the standards in ways that compromise, for example, vehicle safety, or other aspects of performance that may be important to consumer acceptance of new products. Cost of meeting the standards—even if the technology exists and it appears that manufacturers can apply it consistent with their product cadence, if meeting the standards will raise pervehicle cost more than we believe consumers are likely to accept, which could negatively impact sales and employment in this sector, the standards may not be economically practicable. While consumer acceptance of additional new vehicle cost associated with more stringent CAFE standards is uncertain, NHTSA still finds this metric useful for evaluating economic practicability. Elsewhere in this preamble, we seek comment specifically on consumer valuation of fuel economy. Sales and employment responses—as discussed above, sales and employment responses have historically been key to NHTSA’s understanding of economic practicability. Uncertainty and consumer acceptance 407 of technologies— considerations not accounted for 406 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). 407 See, PO 00000 Frm 00192 Fmt 4701 Sfmt 4702 expressly in our modeling analysis, but important to an assessment of economic practicability given the timeframe of this rulemaking. Consumer acceptance can involve consideration of anticipated consumer responses not just to increased vehicle cost and consumer valuation of fuel economy, but also the way manufacturers may change vehicle models and vehicle sales mix in response to CAFE standards. Over time, NHTSA has tried different methods to account for economic practicability. Many years ago, prior to the MYs 2005–2007 rulemaking under the non-attribute-based (fixed value) CAFE standards, NHTSA sought to ensure the economic practicability of standards in part by setting them at or near the capability of the ‘‘least capable manufacturer’’ with a significant share of the market, i.e., typically the manufacturer whose fleet mix was, on average, the largest and heaviest, generally having the highest capacity and capability so as not to limit the availability of those types of vehicles to consumers. NHTSA rejected the ‘‘least capable manufacturer’’ approach several rulemakings ago and no longer believes that it is consistent with our root interpretation of economic practicability. Economic practicability focuses on the capability of the industry and seeks to avoid adverse consequences such as (inter alia) a significant loss of jobs or unreasonable elimination of consumer choice. If the overarching purpose of EPCA is energy conservation, it seems reasonable to expect that maximum feasible standards may be harder for some automakers than for others, and that they need not be keyed to the capabilities of the least capable manufacturer. NHTSA has also sought to account for economic practicability by applying marginal cost-benefit analysis since the first rulemakings establishing attributebased standards, considering both overall societal impacts and overall consumer impacts. Whether the standards maximize net benefits has thus been a significant, but not dispositive, factor in the past for NHTSA’s consideration of economic practicability. Executive Order 12866, as amended by Executive Order 13563, states that agencies should ‘‘select, in choosing among alternative regulatory approaches, those approaches that maximize net benefits . . .’’ In practice, however, agencies, including NHTSA, must consider that the modeling of net benefits does not capture all considerations relevant to economic practicability. Therefore, as in past rulemakings, NHTSA is considering net societal impacts, net consumer impacts, E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules and other related elements in the consideration of economic practicability. That said, it is well within the agency’s discretion to deviate from the level at which modeled net benefits are maximized if the agency concludes that the level would not represent the maximum feasible level for future CAFE standards. Economic practicability is complex, and like the other factors must be considered in the context of the overall balancing and EPCA’s overarching purpose of energy conservation. (c) The Effect of Other Motor Vehicle Standards of the Government on Fuel Economy lotter on DSK11XQN23PROD with PROPOSALS2 ‘‘The effect of other motor vehicle standards of the Government on fuel economy’’ involves 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 408 until recently, the effects of such compliance on fuel economy capability over the history of the CAFE program have been negative ones. For example, safety standards that have the effect of increasing vehicle weight thereby lower fuel economy capability, thus decreasing the level of average fuel economy that NHTSA can determine to be feasible. NHTSA has also accounted for EPA’s ‘‘Tier 3’’ standards for criteria pollutants in its estimates of technology effectiveness in this proposal, and State emissions standards (like California’s) that address the tailpipe NOX, NMOG, and CO emissions that occur during cold start.409 408 43 FR 63184, 63188 (Dec. 15, 1977). See also 42 FR 33534, 33537 (Jun. 30, 1977). 409 For most ICE vehicles on the road today, the majority of tailpipe NOX, NMOG, and CO emissions occur during ‘‘cold start,’’ before the three-way catalyst has reached the very high temperature (e.g., 900–1000 °F) at which point it is able to convert (through oxidation and reduction reactions) those emissions into less harmful derivatives. By limiting the amount of those emissions, tailpipe smog standards require the catalyst to be brought to temperature extremely quickly, so modern vehicles employ cold start strategies that intentionally release fuel energy into the engine exhaust to heat the catalyst to the right temperature as quickly as possible. The additional fuel that must be used to heat the catalyst is typically referred to as a ‘‘coldstart penalty,’’ meaning that the vehicle’s fuel economy (over a test cycle) is reduced because the fuel consumed to heat the catalyst did not go toward the goal of moving the vehicle forward. The Autonomie work employed to develop technology effectiveness estimates for this proposal accounts for cold-start penalties, as discussed in the Autonomie model documentation. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 In other cases, the effect of other motor vehicle standards of the Government may be neutral, or positive. Since the Obama administration, NHTSA has considered the GHG standards set by EPA as ‘‘other motor vehicle standards of the Government.’’ In the 2012 final rule, NHTSA stated that ‘‘To the extent the GHG standards result in increases in fuel economy, they would do so almost exclusively as a result of inducing manufacturers to install the same types of technologies used by manufacturers in complying with the CAFE standards.’’ 410 NHTSA concluded in 2012 that ‘‘no further action was needed’’ because ‘‘the agency had already considered EPA’s [action] and the harmonization benefits of the National Program in developing its own [action].’’ 411 In the 2020 final rule, NHTSA reinforced that conclusion by explaining that a textual analysis of the statutory language made it clear that EPA’s CO2 standards applicable to lightduty vehicles are literally ‘‘other motor vehicle standards of the Government,’’ because they are standards set by a Federal agency that apply to motor vehicles. NHTSA and EPA are obligated by Congress to exercise their own independent judgment in fulfilling their statutory missions, even though both agencies’ regulations affect both fuel economy and CO2 emissions. There are differences between the two agencies’ programs that make NHTSA’s CAFE standards and EPA’s GHG standards not perfectly one-to-one (even besides the fact that EPA regulates other GHGs besides CO2, EPA’s CO2 standards also differ from NHTSA’s in a variety of ways, often because NHTSA is bound by statute to a certain aspect of CAFE regulation). NHTSA endeavors to create standards that meet our statutory obligations and still avoid requiring manufacturers to build multiple fleets of vehicles for the U.S. market.412 As in 2020, NHTSA has continued to do all of these things with this proposal. Similarly, NHTSA has considered and accounted for California’s ZEV mandate (and its adoption by the other Section 177 states) in developing the baseline for this proposal. As discussed above, NHTSA has not expressly accounted for California’s GHG standards for the model years subject to this rulemaking in the baseline analysis for this proposal,413 but seeks comment on this 410 77 FR 62624, 62669 (Oct. 15, 2012). 411 Id. 412 Massachusetts v. EPA, 549 U.S. 497, 532 (2007) (‘‘[T]here is no reason to think that the two agencies cannot both administer their obligations and yet avoid inconsistency.’’). 413 As discussed elsewhere, however, NHTSA has sought to account in the baseline for the California PO 00000 Frm 00193 Fmt 4701 Sfmt 4702 49793 approach for the final rule. NHTSA notes again that no final decision has yet been made on the CAA waiver for California. (d) The Need of the U.S. To Conserve Energy NHTSA has consistently interpreted ‘‘the need of the United States to conserve energy’’ to mean ‘‘the consumer cost, national balance of payments, environmental, and foreign policy implications of our need for large quantities of petroleum, especially imported petroleum.’’ 414 (1) Consumer Costs and Fuel Prices Fuel for vehicles costs money for vehicle owners and operators, so all else equal, 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 regulatory action; and they inform NHTSA about the ‘‘consumer cost . . . of our need for large quantities of petroleum.’’ For this proposal, NHTSA relied on fuel price projections from the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) for 2021. Federal government agencies generally use EIA’s price projections in their assessment of future energy-related policies. In previous CAFE rulemakings, discussions of fuel prices have always been intended to reflect the price of motor gasoline. However, a growing set of vehicle offerings that rely in part, or entirely, on electricity suggests that gasoline prices are no longer the only fuel prices relevant to evaluations of proposed CAFE standards. In the analysis supporting this proposal, NHTSA considers the energy consumption and resulting emissions from the entire on-road fleet, which already contains a number of plug-in hybrid and fully electric vehicles. Higher CAFE standards encourage manufacturers to improve fuel economy; concurrently, manufacturers will foreseeably seek to continue to maximize profit (or minimize compliance cost), and some reliance on electrification is a viable strategy for some manufacturers, even though NHTSA does not consider it in determining maximum feasible CAFE Framework Agreement with BMW, Ford, Honda, VWA, and Volvo. 414 42 FR 63184, 63188 (Dec. 15, 1977). E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 49794 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules stringency. Under the more stringent CAFE alternatives in this proposal, we see a greater reliance on electrification technologies in the analysis in the years following the explicitly-regulated model years, even though internal combustion engines continue to be the most common powertrain across the industry in the action years of this proposal. While the current national average electricity price is significantly higher than that of gasoline, on an energy equivalent basis ($/MMBtu),415 electric motors convert energy into propulsion much more efficiently than internal combustion engines. This means that, even though the energy-equivalent prices of electricity are higher, electric vehicles still produce fuel savings for their owners. EIA also projects rising real gasoline prices over the next three decades, while projecting real electricity prices to remain relatively flat. As the reliance on electricity grows in the lightduty fleet, NHTSA will continue to monitor the trends in electricity prices and their implications for CAFE standards. Even if NHTSA is prohibited from considering electrification as a technology during the model years covered by the rulemaking, the consumer (and social) cost implications of manufacturers otherwise switching to electrification may remain relevant to the agency’s considerations. For now, gasoline is still the dominant fuel used in light-duty transportation. As such, consumers, and the economy more broadly, are subject to fluctuations in price that impact the cost of travel and, consequently, the demand for mobility. Over the last decade, the U.S. has become a stabilizing force in the global oil market and our reliance on imported petroleum has decreased steadily. The most recent Annual Energy Outlook, AEO 2021, projects the U.S. to be a net exporter of petroleum and other liquids through 2050 in the Reference Case. Over the last decade, EIA projections of real fuel prices have generally flattened in recognition of the changing dynamics of the oil market and slower demand growth, both in the U.S. and in developing markets. For example, the International Energy Agency projects that global demand for gasoline is unlikely to ever return to its 2019 level (before the pandemic).416 However, vehicles are long-lived assets and the long-term price uncertainty of petroleum still represents a risk to consumers, albeit one that has 415 Source: AEO 2021, Table 3. Energy Agency, Oil 2021, (p. 30), https://iea.blob.core.windows.net/assets/1fa45234bac5–4d89-a532-768960f99d07/Oil_2021-PDF.pdf. 416 International VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 decreased in the last decade. Continuing to reduce the amount of money consumers spend on vehicle fuel thus remains an important consideration for the need of the U.S. to conserve energy. (2) National Balance of Payments NHTSA has consistently included consideration of the ‘‘national balance of payments’’ as part of the need of the U.S. to conserve energy because of concerns that importing large amounts of oil created a significant wealth transfer to oil-exporting countries and left the U.S. economically vulnerable.417 As recently as 2009, nearly half the U.S. trade deficit was driven by petroleum,418 yet this concern has been less critical in more recent CAFE actions, in part because other factors besides petroleum consumption have been playing a bigger role in the U.S. trade deficit.419 While transportation demand is expected to increase as the economy recovers from the pandemic, it is foreseeable that the trend of trade in consumer goods and services continuing to dominate the national balance of payments, as compared to petroleum, will continue during the rulemaking timeframe. That said, the U.S. continues to rely on oil imports, and NHTSA continues to recognize that reducing the vulnerability of the U.S. to possible oil price shocks remains important. This proposal aims to improve fleet-wide fuel efficiency and to help reduce the amount of petroleum consumed in the U.S., and therefore aims to improve this part of the U.S. balance of payments. 417 For the earliest discussion of this topic, see 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.’’). 418 See, Today in Energy: Recent improvements in petroleum trade balance mitigate U.S. trade deficit, U.S. Energy Information Administration (July 21, 2014). Available at https://www.eia.gov/today inenergy/detail.php?id=17191 and in the docket for this rulemaking, NHTSA–2021–0053. 419 Consumer products are the primary drivers of the trade deficit. In 2020, the U.S. imported $2.4 trillion in consumer goods, versus $116.4 billion of petroleum, which is the lowest amount since 2002. The 2020 goods deficit of $904.9 billion was the highest on record, while the 2020 petroleum surplus of $18.1 billion was the first annual surplus on record. See U.S. Census Bureau, ‘‘Annual 2020 Press Highlights,’’ at census.gov/foreign-trade/ statistics/highlights/AnnualPressHighlights.pdf, and available in the docket for this rulemaking. While 2020 was an unusual year for U.S. transportation demand, given the global pandemic, this is consistent with existing trends in which consumer products imports significantly outweigh oil imports. PO 00000 Frm 00194 Fmt 4701 Sfmt 4702 (3) Environmental Implications Higher fleet fuel economy reduces U.S. emissions of CO2 as well as various other pollutants by reducing the amount of oil that is produced and refined for the U.S. vehicle fleet, but can also potentially increase emissions by reducing the cost of driving, which can result in increased 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 magnitudes of its reduced emissions in fuel refining and distribution and increases in its emissions from vehicle use. Fuel savings from CAFE standards also necessarily result in lower emissions of CO2, the main greenhouse gas emitted as a result of refining, distribution, and use of transportation fuels. 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,420 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 NPRMs and prepared its first environmental assessment addressing that subject.421 It cited concerns about climate change as one of the reasons for limiting the extent of its reduction of the CAFE standard for MY 1989 passenger cars.422 NHTSA also considers environmental justice issues as part of the environmental considerations under the need of the U.S. to conserve energy, per Executive Order 12898, ‘‘Federal Actions to Address Environmental Justice in Minority Populations’’ 423 and DOT Order 5610.2(c), ‘‘U.S. Department of Transportation Actions to Address Environmental Justice in Minority Populations and Low-Income Populations.’’ 424 The affected environment for environmental justice is nationwide, with a focus on areas that 420 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). 421 53 FR 33080, 33096 (Aug. 29, 1988). 422 53 FR 39275, 39302 (Oct. 6, 1988). 423 59 FR 629 (Feb. 16, 1994). 424 Department of Transportation Updated Environmental Justice Order 5610.2(c) (May 14, 2021). E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules could contain minority and low-income communities who would most likely be exposed to the environmental and health effects of oil production, distribution, and consumption, or the impacts of climate change. This includes areas where oil production and refining occur, areas near roadways, coastal flood-prone areas, and urban areas that are subject to the heat island effect. Numerous studies have found that some environmental hazards are more prevalent in areas where minority and low-income populations represent a higher proportion of the population compared with the general population. In terms of effects due to criteria pollutants and air toxics emissions, the body of scientific literature points to disproportionate representation of minority and low-income populations in proximity to a range of industrial, manufacturing, and hazardous waste facilities that are stationary sources of air pollution, although results of individual studies may vary. While the scientific literature specific to oil refineries is limited, disproportionate exposure of minority and low-income populations to air pollution from oil refineries is suggested by other broader studies of racial and socioeconomic disparities in proximity to industrial facilities generally. Studies have also consistently demonstrated a disproportionate prevalence of minority and low-income populations that are living near mobile sources of pollutants (such as roadways) and therefore are exposed to higher concentrations of criteria air pollutants in multiple locations across the United States. Lower-positioned socioeconomic groups are also differentially exposed to air pollution and differentially vulnerable to effects of exposure. In terms of exposure to climate change risks, the literature suggests that across all climate risks, low-income communities, some communities of color, and those facing discrimination are disproportionately affected by climate events. Communities overburdened by poor environmental quality experience increased climate risk due to a combination of sensitivity and exposure. Urban populations experiencing inequities and health issues have greater susceptibility to climate change, including substantial temperature increases. Some communities of color facing cumulative exposure to multiple pollutants also live in areas prone to climate risk. Indigenous peoples in the United States face increased health disparities that cause increased sensitivity to extreme heat and air pollution. Together, this VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 information indicates that climate impacts disproportionately affect minority and low-income populations because of socioeconomic circumstances, histories of discrimination, and inequity. Furthermore, high temperatures can exacerbate poor air quality, further compounding the risk to overburdened communities. Finally, health-related sensitivities in low-income and minority populations increase risk of damaging impacts from poor air quality under climate change, underscoring the potential benefits of improving air quality to communities overburdened by poor environmental quality. In the SEIS, Chapters 3, 4, 5, and 8 discuss the connections between oil production, distribution, and consumption, and their health and environmental impacts. All of the action alternatives considered in this proposal reduce carbon dioxide emissions and, thus, the effects of climate change, as compared to the baseline. Effects on criteria pollutants and air toxics emissions are somewhat more complicated, for a variety of reasons, as discussed in Section VI.C, although over time and certainly over the lifetimes of the vehicles that would be subject to this proposal, these emissions are currently forecast to fall significantly. As discussed above, while the majority of light-duty vehicles will continue to be powered by internal combustion engines in the near- to midterm under all regulatory alternatives, the more stringent alternatives do appear in the analysis to lead to greater electrification in the mid- to longerterm. While NHTSA is prohibited from considering electric vehicles in determining maximum feasible CAFE levels, electric vehicles (which appear both in the agency’s baseline and which may be produced in model years following the period of regulation as an indirect effect of more stringent standards, or in response to other standards or to market demand) produce few to zero tailpipe emissions, and thus contribute meaningfully to the decarbonization of the transportation sector, in addition to having environmental, health, and economic development benefits, although these benefits may not yet be equally distributed across society. They also present new environmental (and social) questions, like those associated with reduced tailpipe emissions, upstream electricity production, minerals extraction for battery components, and ability to charge an electric vehicle. The upstream environmental effects of extraction and refining for petroleum PO 00000 Frm 00195 Fmt 4701 Sfmt 4702 49795 are well-recognized; minerals extraction and refining can also have significant downsides. As one example of documentation of these effects, the United Nations Conference on Trade and Development issued a report in July 2020 describing acid mine drainage and uranium-laced dust associated with cobalt mines in the DRC, along with child labor concerns; considerable groundwater consumption and dust issues that harm miners and indigenous communities in the Andes; issues with fine particulate matter causing human health effects and soil contamination in regions near graphite mines; and so forth.425 NHTSA’s SEIS discusses these and other effects (such as production and end-of-life issues) in more detail, and NHTSA will continue to monitor these issues going forward insofar as CAFE standards may increase electrification levels even if NHTSA does not expressly consider electrification in setting those standards, because NHTSA does not control what technologies manufacturers use to meet those standards, and because NHTSA is required to consider the environmental effects of its standards under NEPA. NHTSA carefully considered the environmental effects of this proposal, both quantitative and qualitative, as discussed in the SEIS and in Sections VI.C and VI.D. (4) Foreign Policy Implications U.S. consumption and imports of petroleum products impose costs 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. These costs 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 of 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. Reducing U.S. consumption of crude oil or refined petroleum products (by reducing motor 425 UNCTAD, ‘‘Commodities at a Glance: Special issue on strategic battery raw materials,’’ No. 13, Geneva, 2020, at 46. Available at https:// unctad.org/system/files/official-document/ ditccom2019d5_en.pdf and in the docket for this rulemaking, NHTSA–2021–0053. E:\FR\FM\03SEP2.SGM 03SEP2 49796 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules fuel use) can reduce these external costs.426 Stephen Brown, who has published extensively on price shock and foreign policy risks associated with U.S. oil consumption, stated in a recent paper that: Over the past few years, world oil market conditions have changed considerably (with the United States importing much less oil), new estimates of the probabilities of world oil supply disruptions have become available, and new estimates of the response of U.S. real GDP to oil supply shocks and the short-run elasticity of oil demand have become available. These developments suggest that it is time to update the estimates of the security costs of U.S. oil consumption. The new estimates of the oil security premiums suggest that U.S. oil security may have become less of an issue than it was in the past, mostly as a result of new estimates of the short-run elasticity of demand and the response of U.S. real GDP to oil price shocks.427 lotter on DSK11XQN23PROD with PROPOSALS2 426 A 2006 report by the Council on Foreign Relations identified six foreign policy costs that it said arose from U.S. consumption of imported oil. These costs include (1) the adverse effect that significant disruptions in oil supply will have for political and economic conditions in the U.S. and other importing countries; (2) the fears that the current international system is unable to ensure secure oil supplies when oil is seemingly scarce and oil prices are high; (3) political realignment from dependence on imported oil that limits U.S. alliances and partnerships; (4) the flexibility that oil revenues give oil-exporting countries to adopt policies that are contrary to U.S. interests and values; (5) an undermining of sound governance by the revenues from oil and gas exports in oilexporting countries; and (6) an increased U.S. military presence in the Middle East that results from the strategic interest associated with oil consumption. Council on Foreign Relations, National Security Consequences of U.S. Oil Dependency, Independent Task Force Report No. 58, October 2006. Available at https://cdn.cfr.org/ sites/default/files/report_pdf/0876093659.pdf and in the docket for this rulemaking, NHTSA–2021– 0053. Brown and Huntington (2015) find that these six costs are either implicitly incorporated in the welfare-theoretic analysis, are not externalities, or cannot be quantified. Brown, Stephen and Hillard Huntington, Evaluating U.S. oil security and import reliance, Energy Policy 108, 2015, at 512–523. Available at https://www.sciencedirect.com/ science/article/abs/pii/S0301421515000026 and for hard copy review at DOT headquarters. To the extent that these costs are externalities that cannot be quantified, the measured security costs of U.S. reliance on imported oil will be understated. 427 Brown, Stephen. ‘‘New Estimates of the security costs of U.S. oil consumption,’’ Energy Policy, Vol. 113, Feb. 2018, at 172. Available at https://www.sciencedirect.com/science/article/abs/ VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 Brown notes that ‘‘Because we have not observed a modern economy with large oil supply disruptions, we have no reliable method to quantify the effects of these disruptions,’’ and ‘‘The result could be an average of old and new results or estimation problems and a poor fit.’’ 428 Geopolitical risk can still affect global oil prices, of course, because oil is a global market, and thus can affect U.S. oil prices, although possibly by less than in the past.429 The U.S. still maintains a military presence in certain parts of the world to help secure global access to petroleum supplies. Chapter 6.2.4 of the TSD discusses this topic in more detail. Brown concludes that: Nonetheless, only the highest estimates of the oil security premiums suggest that U.S. oil security is nearly an equally important issue to the environmental costs of oil use. The mid-estimates from the model that may best represent how the world oil market and the U.S. economy will respond to world oil supply disruptions of various sizes . . . find U.S. consumption of imported or domestic oil does yield important security costs, but those costs are much lower than the estimated environmental costs of oil use. Consistent with Brown and Huntington (2013), the substitution of domestic oil for imported oil only slightly improves U.S. oil security. Oil conservation is more effective pii/S0301421517307413 and for hard copy review at DOT headquarters. 428 Id. at 181. 429 Also in 2018, Beccue, Huntington, Leiby, and Vincent reported on their findings of an expert panel on oil market disruption risks and likelihoods, and stated that based on these findings, during the period of 2016–2025, ‘‘It is very likely that a disruption greater than 2 MMBD will occur (81%). However, it is unlikely that disruptions greater than 15 MMBD will occur (1%).’’ They further state that ‘‘. . . experts in the current study expect that both gross shocks and excess capacity will be lower than before, resulting in similar net disruptions [to what was estimated in 2005]. Although turmoil remains high in these countries with the ongoing Iraq war, tensions between Iran and its Arab neighbors, and concern over the ability of terrorists to cut oil supply facilities, these conditions do not produce larger oil market disruptions.’’ They conclude that ‘‘In general, this panel of energy security experts has concluded that current world events and energy markets have increased the likelihood of oil disruptions since 1996 but demonstrated a similar risk profile compared to the 2005 period. Moreover, their assessments indicate that lower oil price paths make net disruptions of any given size more likely.’’ Beccue et al., ‘‘An updated assessment of oil market disruption risks,’’ Energy Policy, Vol. 115, Apr. 2018, at 456. Available at https:// www.sciencedirect.com/science/article/abs/pii/ S0301421517308285 and for hard copy review at DOT headquarters. PO 00000 Frm 00196 Fmt 4701 Sfmt 4702 than increased domestic oil production at improving U.S. oil security.430 NHTSA agrees both that oil conservation improves U.S. oil security, and that the environmental costs of oil use are intertwined with the security costs of oil use in some ways as climate change destabilizes traditional geopolitical power structures over time. The effect of climate change on natural resources inevitably has security implications—population changes and shifts have already been forced in some countries, which can create social and security effects at all geopolitical levels—local, national, regional, and global. CAFE standards over the last few decades have conserved significant quantities of oil, and the petroleum intensity of the U.S. fleet has decreased significantly. Continuing to improve energy conservation and reduce U.S. oil consumption by raising CAFE standards further has the potential to continue to help with all of these considerations. As standards and market demand move the U.S. light-duty vehicle fleet toward electrification, different potential foreign policy implications arise. Most vehicle electrification is enabled by lithium-ion batteries. Lithium-ion battery global value chains have several phases: Sourcing (mining/ extraction); processing/refining; cell manufacturing; battery manufacturing; installation in an EV; and recycling.431 Because lithium-ion battery materials have a wide global diversity of origin, accessing them can pose varying geopolitical challenges.432 The U.S. International Trade Commission (USITC) recently summarized 2018 data from the U.S. Geological Survey on the production/sourcing of the four key lithium-ion battery materials, as shown in Table VI–5. 430 Brown, 2018, at 182. Sarah, and Robert Ireland, ‘‘Lithium-Ion Battery Materials for Electric Vehicles and their Global Value Chains,’’ Office of Industries Working Paper ID–068, U.S. International Trade Commission, June 2020, at 7. Available at https:// www.usitc.gov/publications/332/working_papers/ gvc_overview_scott_ireland_508_final_061120.pdf and in the docket for this rulemaking, NHTSA– 2021–0053. 432 Id. at 8. 431 Scott, E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49797 Table VI-5 - Lithium-ion Battery Materials Mining Production, 2018433 Lithium-ion Battery Material Ores and Concentrates Countries with Largest Mining Production (Share of Global Total) U.S. Mining Production (Share of Global Total) Lithium Australia (60 percent), Chile (19 percent), China (9 percent), Argentina (7 percent) USITC staff estimates less than 1 percent Cobalt Democratic Republic of Congo (64 percent), Cuba (4 percent), Russia (4 percent), Australia (3 percent) Less than O.5 percent Graphite (natural) China (68 percent), Brazil (10 percent), India (4 percent) 0 percent Nickel Indonesia (24 percent), Philippines (15 percent), Russia (9 percent) Less than 1 percent 433 Id., citing U.S. Geological Survey, Mineral Commodity Summaries, Feb. 2019. 434 Id. at 8, 9. 435 Id at 9. 436 Id. 437 Id. 438 Id. at 10. 439 Id. 440 Id. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 material extraction, mining and refining, battery materials, cell production, battery systems, reuse, and recycling. The United States is lagging in upstream capacity; although the U.S. has some domestic lithium deposits, it has very little capacity in mining and refining any of the key raw materials. As mentioned elsewhere, however, there can be benefits and drawbacks in terms of environmental consequences associated with increased mining, refining, and battery production. China and the European Union (EU) are also major consumers of lithium-ion batteries, along with Japan, Korea, and others. Lithium-ion batteries are used not only in light-duty vehicles, but in many ubiquitous consumer goods, and are likely to be used eventually in other forms of transportation as well. Thus, securing sufficient batteries to enable large-scale shifts to electrification in the U.S. light-duty vehicle fleet may face new issues as vehicle companies compete with other new sectors. NHTSA will continue to monitor these issues going forward. President Biden has already issued an Executive Order on ‘‘America’s Supply Chains,’’ aiming to strengthen the resilience of America’s supply chains, including those for automotive batteries.441 Reports are to be developed within one year of issuance of the Executive Order, and NHTSA will monitor these findings as they develop. (e) Factors That NHTSA Is Prohibited From Considering EPCA also provides that in determining the level at which it should set CAFE standards for a particular 441 Executive Order 14017, ‘‘America’s Supply Chains,’’ Feb. 24, 2021. 86 FR 11849 (Mar. 1, 2021). PO 00000 Frm 00197 Fmt 4701 Sfmt 4702 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 reduce the costs of compliance.442 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 automobiles, nor the fuel economy (i.e., the availability) of dedicated alternative fueled automobiles—including battery-electric 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 equivalent fuel economy level than they actually achieve. The effect of the prohibitions against considering these statutory flexibilities in setting the CAFE standards is that the flexibilities remain voluntarilyemployed measures. If NHTSA were instead to assume manufacturer use of those flexibilities in setting new standards (as NHTSA does in the ‘‘EIS analysis,’’ but not the ‘‘standard setting analysis’’), compliance with higher standards would appear more costeffective and, potentially, more feasible, which would thus effectively require manufacturers to use those flexibilities if NHTSA determined that standards should be more stringent. By keeping NHTSA from including them in our stringency determination, the provision ensures that those statutory credits 442 49 E:\FR\FM\03SEP2.SGM U.S.C. 32902(h). 03SEP2 EP03SE21.180</MATH> lotter on DSK11XQN23PROD with PROPOSALS2 Of these sources, the USITC notes that while ‘‘lithium has generally not faced political instability risks,’’ ‘‘Because of the [Democratic Republic of Congo’s] ongoing political instability, as well as poor labor conditions, sourcing cobalt faces significant geopolitical challenges.’’ 434 Nickel is also used extensively in stainless steel production, and much of what is produced in Indonesia and the Philippines is exported to China for stainless steel manufacturing.435 Obtaining graphite for batteries does not currently pose geopolitical obstacles, but the USITC notes that Turkey has great potential to become a large graphite producer, which would make stability there a larger concern.436 For materials processing and refining, China is the largest importer of unprocessed lithium, which it then transforms into processed or refined lithium,437 the leading producer of refined cobalt (with Finland a distant second),438 one of the leading producers of primary nickel products (along with Indonesia, Japan, Russia, and Canada) and one of the leading refiners of nickel into nickel sulfate, the chemical compound used for cathodes in lithiumion batteries,439 and one of the leading processors of graphite intended for use in lithium-ion batteries as well.440 In all regions, increasing attention is being given to vertical integration in the lithium-ion battery industry from lotter on DSK11XQN23PROD with PROPOSALS2 49798 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules remain true compliance flexibilities. However, the flip side of the effect described above is that preventing NHTSA from assuming use of dedicated alternative fuel vehicles for compliance makes it more difficult for the CAFE program to facilitate a complete transition of the U.S. light-duty fleet to full electrification. In contrast, for the non-statutory fuel economy improvement value program that NHTSA developed by regulation, NHTSA does not consider these fuel economy adjustments subject to the 32902(h) prohibition on considering flexibilities. The statute is very clear as to which flexibilities are not to be considered. When the agency has introduced additional flexibilities such as A/C efficiency and ‘‘off-cycle’’ technology fuel improvement values, NHTSA has considered those technologies as available in the analysis. Thus, this analysis includes assumptions about manufacturers’ use of those technologies, as detailed in Chapter 3.8 of the accompanying TSD. NHTSA notes that one of the recommendations in the 2021 NAS Report was for Congress to ‘‘amend the statute to delete the [32902(h)] prohibition on considering the fuel economy of dedicated alternative fueled vehicles in setting CAFE standards.’’ 443 Recognizing that changing statutory text is Congress’ affair and not NHTSA’s, the committee further recommended that if Congress does not change the statute, NHTSA should consider adding another attribute to the fuel economy standard function, like ‘‘the expected market share of ZEVs in the total U.S. fleet of new light-duty vehicles—such that the standards increase as the share of ZEVs in the total U.S. fleet increases.’’ 444 NHTSA discusses this recommendation further in Section III.B. While NHTSA does not consider the prohibited items in its standard-setting analysis or for making its tentative decision about what levels of standards would be maximum feasible, NHTSA notes that it is informed by the ‘‘EIS’’ analysis presented in the PRIA. The EIS analysis does not contain these restrictions, and therefore accounts for credit availability and usage, and manufacturers’ ability to employ alternative fueled vehicles, for purpose of conformance with E.O. 12866 and NEPA regulations. Under the EIS analysis, compliance generally appears less costly. For example, this EIS analysis shows manufacturers’ costs averaging about $1,070 in MY 2029 443 2021 NAS Report, Summary Recommendation 5. 444 Id. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 under the proposed standards, as compared to the $1,175 shown by the standard setting analysis. Again, however, for purposes of tentatively determining maximum feasible CAFE levels, NHTSA considers only the standard setting analysis shown in the NPRM, consistent with Congress’ direction. (f) Other Considerations in Determining Maximum Feasible CAFE Standards NHTSA has historically considered the potential for adverse safety effects in setting CAFE standards. This practice has been upheld in case law.445 In this proposal, NHTSA has considered the safety effects discussed in Section V of this preamble and in Chapter 5 of the accompanying PRIA. NHTSA discusses its consideration of these effects in Section VI.D. B. Administrative Procedure Act The Administrative Procedure Act governs agency rulemaking generally and provides the standard of judicial review for agency actions. To be upheld under the ‘‘arbitrary and capricious’’ standard of judicial review under the APA, an agency rule must be rational, based on consideration of the relevant factors, and within the scope of the authority delegated to the agency by statute. The agency must examine the relevant data and articulate a satisfactory explanation for its action including a ‘‘rational connection between the facts found and the choice made.’’ 446 Statutory interpretations included in an agency’s rule are subject to the twostep analysis of Chevron, U.S.A. v. Natural Resources Defense Council.447 Under step one, where a statute ‘‘has directly spoken to the precise question at issue,’’ id. at 842, the court and the agency ‘‘must give effect to the 445 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.’’ Competitive 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). Courts have consistently upheld NHTSA’s implementation of EPCA in this manner. See, e.g., Competitive Enterprise Institute v. NHTSA, 956 F. 2d 321, 322 (D.C. Cir. 1992) (‘‘CEI– II’’) (in determining the maximum feasible standard, ‘‘NHTSA has always taken passenger safety into account) (citing CEI–I, 901 F.2d at 120 n. 11); Competitive Enterprise Institute v. NHTSA, 45 F.3d 481, 482–83 (D.C. Cir. 1995) (CEI–III) (same); Center for Biological Diversity v. NHTSA, 538 F.3d 1172, 1203–04 (9th Cir. 2008) (upholding NHTSA’s analysis of vehicle safety issues associated with weight in connection with the MYs 2008–2011 light truck CAFE rulemaking). 446 Burlington Truck Lines, Inc. v. United States, 371 U.S. 156, 168 (1962). 447 467 U.S. 837 (1984). PO 00000 Frm 00198 Fmt 4701 Sfmt 4702 unambiguously expressed intent of Congress.’’ 448 If the statute is silent or ambiguous regarding the specific question, the court proceeds to step two and asks ‘‘whether the agency’s answer is based on a permissible construction of the statute.’’ 449 The APA also requires that agencies provide notice and comment to the public when proposing regulations,450 as NHTSA is doing in this proposal. NHTSA recognizes that this proposal, like the 2020 final rule, is reconsidering standards previously promulgated. NHTSA, like any other Federal agency, is afforded an opportunity to reconsider prior views and, when warranted, to adopt new positions. Indeed, as a matter of good governance, agencies should revisit their positions when appropriate, especially to ensure that their actions and regulations reflect legally sound interpretations of the agency’s authority and remain consistent with the agency’s views and practices. As a matter of law, ‘‘an Agency is entitled to change its interpretation of a statute.’’ 451 Nonetheless, ‘‘[w]hen an Agency adopts a materially changed interpretation of a statute, it must in addition provide a ‘reasoned analysis’ supporting its decision to revise its interpretation.’’ 452 ‘‘Changing policy does not, on its own, trigger an especially ‘demanding burden of justification.’ ’’ 453 Providing a reasoned explanation ‘‘would ordinarily demand that [the Agency] display awareness that it is changing position.’’ 454 Beyond that, however, ‘‘[w]hen 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.’ ’’ 455 While the agency ‘‘must show that there are good reasons for the new policy,’’ the agency ‘‘need not demonstrate to a court’s satisfaction that the reasons for the new policy are 448 Id. at 843. 449 Id. 450 5 U.S.C. 553. Hydro Corp. v. FERC, 775 F.2d 1187, 1191 (D.C. Cir. 1985). 452 Alabama Educ. Ass’n v. Chao, 455 F.3d 386, 392 (D.C. Cir. 2006) (quoting Motor Vehicle Mfrs. Ass’n of U.S., Inc. v. State Farm Mut. Auto. Ins. Co., 463 U.S. 29, 57 (1983)); see also 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.’’) (citations omitted). 453 See Mingo Logan Coal Co. v. EPA, 829 F.3d 710, 718 (D.C. Cir. 2016) (quoting Ark Initiative v. Tidwell, 816 F.3d 119, 127 (D.C. Cir. 2016)). 454 FCC v. Fox Television Stations, Inc. 556 U.S. 502, 515 (2009) (emphasis in original) (‘‘An agency may not, for example, depart from a prior policy sub silentio or simply disregard rules that are still on the books.’’). 455 Encino Motorcars, LLC, 136 S Ct. at 2125–26 (quoting Fox Television Stations, Inc. 556 U.S. at 515). 451 Phoenix E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules better than the reasons for the old one.’’ 456 ‘‘[I]t suffices that the new policy is permissible under the statute, that there are good reasons for it, and that the Agency believes it to be better, which the conscious change of course adequately indicates.’’ 457 For instance, ‘‘evolving notions’’ about the appropriate balance of varying policy considerations constitute sufficiently good reasons for a change in position.458 Moreover, it is ‘‘well within an Agency’s discretion’’ to change policy course even when no new facts have arisen: Agencies are permitted to conduct a ‘‘reevaluation of which policy would be better in light of the facts,’’ without ‘‘rely[ing] on new facts.’’ 459 To be sure, providing ‘‘a more detailed justification’’ is appropriate in some cases. ‘‘Sometimes [the agency] must [provide a more detailed justification than what would suffice for a new policy created on a blank slate]— when, for example, its new policy rests upon 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.’’ 460 This preamble, and the accompanying TSD and PRIA, all provide extensive detail on the agency’s updated analysis, and Section VI.D contains the agency’s explanation of how the agency has considered that analysis and other relevant information in tentatively determining that the proposed CAFE standards are maximum feasible for MYs 2024–2026 passenger cars and light trucks. lotter on DSK11XQN23PROD with PROPOSALS2 C. National Environmental Policy Act As discussed above, EPCA requires NHTSA to determine the level at which to set CAFE standards for each model year by considering the four factors of 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. The National Environmental Policy Act (NEPA) directs that environmental considerations be integrated into that process.461 To explore the potential environmental consequences of this 456 Fox Television Stations, Inc., 556 U.S. at 515 (emphasis in original). 457 Id. (emphasis in original). 458 N. Am.’s Bldg. Trades Unions v. Occupational Safety & Health Admin., 878 F.3d 271, 303 (D.C. Cir. 2017) (quoting the agency’s rule). 459 Nat’l Ass’n of Home Builders v. EPA, 682 F.3d 1032, 1037–38 (D.C. Cir. 2012). 460 See Fox Television Stations, Inc., 556 U.S. at 515 (2009). 461 NEPA is codified at 42 U.S.C. 4321–47. The Council on Environmental Quality (CEQ) NEPA implementing regulations are codified at 40 CFR parts 1500–08. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 rulemaking action, NHTSA has prepared a Supplemental Environmental Impact Statement (‘‘SEIS’’) for this proposal.462 The purpose of an EIS is to ‘‘provide full and fair discussion of significant environmental impacts and [to] inform decisionmakers and the public of the reasonable alternatives which would avoid or minimize adverse impacts or enhance the quality of the human environment.’’ 463 When preparing an EIS, NEPA requires an agency to compare the potential environmental impacts of its proposed action and a reasonable range of alternatives. In the SEIS, NHTSA analyzed a No Action Alternative and three action alternatives. The alternatives represent a range of potential actions the agency could take, and they are described more fully in Section IV of this preamble, Chapter 1 of the TSD, and Chapter 2 of the PRIA. The environmental impacts of these alternatives, in turn, represent a range of potential environmental impacts that could result from NHTSA’s setting maximum feasible fuel economy standards for passenger cars and light trucks. To derive the direct and indirect impacts of the action alternatives, NHTSA compared each action alternative to the No Action Alternative, which reflects baseline trends that would be expected in the absence of any further regulatory action. More specifically, the No Action Alternative in the SEIS assumed that the CAFE standards set in the 2020 final rule for MYs 2021–2026 passenger cars and light trucks would remain in effect. In addition, the No Action Alternative also includes several other actions that NHTSA believes will occur in the absence of further regulatory action, as discussed in more detail in Section IV above: (1) California’s ZEV mandate; (2) the ‘‘Framework Agreements’’ between California and BMW, Ford, Honda, VWA, and Volvo, which NHTSA implemented by including EPA’s baseline GHG standards (i.e., those set in the 2020 final rule) and introducing more stringent GHG target functions for those manufacturers; and (3) the assumption that manufacturers will also make any additional fuel economy improvements estimated to reduce owners’ estimated average fuel outlays during the first 30 months of vehicle operation by more than the estimated 462 Because this proposal revises CAFE standards established in the 2020 final rule, NHTSA chose to prepare a SEIS to inform that amendment of the MYs 2024–2026 standards. See the SEIS for more details. 463 40 CFR 1502.1. PO 00000 Frm 00199 Fmt 4701 Sfmt 4702 49799 increase in new vehicle price. The No Action Alternative provides a baseline against which to compare the environmental impacts of other alternatives presented in the SEIS.464 For the SEIS, NHTSA analyzed three action alternatives, Alternatives 1 through 3, which ranged from increasing CAFE stringency for MY 2024 by 9.14 percent for passenger cars and 11.02 percent for light trucks, and increase stringency in MYs 2025 and 2026 by 3.26 percent per year for both passenger cars and light trucks (Alternative 1) to increasing CAFE stringency for each year, for each fleet, at 10 percent per year (Alternative 3). The range of action alternatives, as well as the No Action Alternative, encompass a spectrum of possible standards NHTSA could determine was maximum feasible based on the different ways the agency could weigh EPCA’s four statutory factors. Throughout the SEIS, estimated impacts were shown for all of these action alternatives, as well as for the No Action Alternative. For a more detailed discussion of the environmental impacts associated with the alternatives, see Chapters 3–6 of the SEIS, as well as Section V of this preamble. NHTSA’s SEIS describes potential environmental impacts to a variety of resources, including fuel and energy use, air quality, climate, land use and development, hazardous materials and regulated wastes, historical and cultural resources, noise, and environmental justice. The SEIS also describes how climate change resulting from global greenhouse gas emissions (including CO2 emissions attributable to the U.S. light-duty transportation sector under the alternatives considered) could affect certain key natural and human resources. Resource areas are assessed qualitatively and quantitatively, as appropriate, in the SEIS, and the findings of that analysis are summarized here.465 464 See 40 CFR 1502.2(e), 1502.14(d). CEQ has explained that ‘‘[T]he regulations require the analysis of the no action alternative even if the agency is under a court order or legislative command to act. This analysis provides a benchmark, enabling decision makers to compare the magnitude of environmental effects of the action alternatives [See 40 CFR 1502.14(c).] . . . Inclusion of such an analysis in the EIS is necessary to inform Congress, the public, and the President as intended by NEPA. [See 40 CFR 1500.1(a).]’’ Forty Most Asked Questions Concerning CEQ’s National Environmental Policy Act Regulations, 46 FR 18026 (Mar. 23, 1981). 465 The impacts described in this section come from NHTSA’s SEIS, which is being publicly issued simultaneously with this NPRM. As described above, the SEIS is based on ‘‘unconstrained’’ modeling rather than ‘‘standard setting’’ modeling. E:\FR\FM\03SEP2.SGM Continued 03SEP2 49800 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules lotter on DSK11XQN23PROD with PROPOSALS2 As the stringency of the alternatives increases, total U.S. passenger car and light truck fuel consumption for the period of 2020 to 2050 decreases. Total light-duty vehicle fuel consumption from 2020 to 2050 under the No Action Alternative is projected to be 3,510 billion gasoline gallon equivalents (GGE). Light-duty vehicle fuel consumption from 2020 to 2050 under the action alternatives is projected to range from 3,409 billion GGE under Alternative 1 to 3,282 billion GGE under Alternative 3. Under Alternative 2, light-duty vehicle fuel consumption from 2020 to 2050 is projected to be 3,344 billion GGE. All of the action alternatives would decrease fuel consumption compared to the NoAction Alternative, with fuel consumption decreases that range from 100 billion GGE under Alternative 1 to 227 billion GGE under Alternative 3. The relationship between stringency and criteria and air toxics pollutant emissions is less straightforward, reflecting the complex interactions among the tailpipe emissions rates of the various vehicle types (passenger cars and light trucks, ICE vehicles and EVs, older and newer vehicles, etc.), the technologies assumed to be incorporated by manufacturers in response to CAFE standards, upstream emissions rates, the relative proportions of gasoline, diesel, and electricity in total fuel consumption, and changes in VMT from the rebound effect. In general, emissions of criteria and toxic air pollutants increase very slightly in the short term, and then decrease dramatically in the longer term, across all action alternatives, with some exceptions. In addition, the action alternatives would result in decreased incidence of PM2.5-related health impacts in most years and alternatives due to the emissions decreases. Decreases in adverse health outcomes include decreased incidences of premature mortality, acute bronchitis, respiratory emergency room visits, and work-loss days. The air quality analysis in the SEIS identified the following impacts on criteria air pollutants. NHTSA conducts modeling both ways in order to reflect the various statutory requirements of EPCA/ EISA and NEPA. The preamble employs the ‘‘standard setting’’ modeling in order to aid the decision-maker in avoiding consideration of the prohibited items in 49 U.S.C. 32902(h) in determining maximum feasible standards, but as a result, the impacts reported here may differ from those reported elsewhere in this preamble. However, NHTSA considers the impacts reported in the SEIS, in addition to the other information presented in this preamble, the TSD, and the PRIA, as part of its decision-making process. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 For all criteria pollutants in 2025, emissions increase slightly under the action alternatives compared to the NoAction Alternative. The emission increases generally get larger (although they are still small) from Alternative 1 through Alternative 3 (the most stringent alternative in terms of required miles per gallon). This temporary increase is largely due to new vehicle prices increasing in the short-term, which slightly slows new-vehicle sales and encourages consumers to buy used vehicles instead or retain existing vehicles for longer. As the analysis timeframe progresses, the new, higher fuel-economy vehicles become used vehicles, and the impacts of the standards change direction. In 2025, across all criteria pollutants and action alternatives, the smallest increase in emissions is 0.01 percent for VOCs under Alternative 2; the largest increase is 0.6 percent and occurs for SO2 under Alternative 3. We underscore that these are fractions of a single percent. In 2035 and 2050, emissions of CO, NOX, PM2.5, and VOCs generally decrease under the action alternatives compared to the No-Action Alternative, except for CO in 2035 under Alternative 1 (0.07 percent increase) and NOX in 2035 under Alternative 3 (0.5 percent increase) (again, these are fractions of a single percent), with the more stringent alternatives having the largest decreases, except for NOX and PM2.5 in 2035 (emissions decrease less or increase with more stringent alternatives) and NOX in 2050 (emissions increase under Alternative 3 relative to Alternative 2, due primarily to slightly higher upstream emissions associated with greater electrification rates). SO2 emissions generally increase under the action alternatives compared to the NoAction Alternative (except in 2035 under Alternative 1), with the more stringent alternatives having the largest increases. SO2 increases are largely due to higher upstream emissions associated with electricity use by greater numbers of electrified vehicles being produced in response to the standards. In 2035 and 2050, across all criteria pollutants and action alternatives, the smallest decrease in emissions is 0.03 percent and occurs for NOX under Alternative 2; the largest decrease is 11.9 percent and occurs for VOCs under Alternative 3. The smallest increase in emissions is 0.07 percent and occurs for CO under Alternative 1; the largest increase is 4.8 percent and occurs for SO2 under Alternative 3. The air quality analysis identified the following impacts on toxic air pollutants. PO 00000 Frm 00200 Fmt 4701 Sfmt 4702 Under each action alternative in 2025 compared to the No-Action Alternative, increases in emissions would occur for all toxic air pollutants by as much as 0.5 (half of 1) percent, except for DPM, for which emissions would decrease by as much as 0.5 percent. For 2025, the largest relative increases in emissions would occur for benzene and 1,3butadiene, for which emissions would increase by as much as 0.5 percent. Percentage increases in emissions of acetaldehyde, acrolein, and formaldehyde would be even smaller. Under each action alternative in 2035 and 2050 compared to the No-Action Alternative, decreases in emissions would occur for all toxic air pollutants, except for acetaldehyde, acrolein, and 1,3-butadiene in 2035 under Alternative 1 where emissions would increase by 0.2 (one-fifth of 1), 0.01, and 0.1 percent, respectively, with the more stringent alternatives having the largest decreases, except for benzene (emissions increase in 2035 under Alternative 3 relative to Alternative 2). The largest relative decreases in emissions would occur for formaldehyde, for which emissions would decrease by as much as 10.3 percent. Percentage decreases in emissions of acetaldehyde, acrolein, benzene, 1,3-butadiene, and DPM would be less. The air quality analysis identified the following health impacts. In 2025, Alternative 3 would result in slightly increased adverse health impacts (mortality, acute bronchitis, respiratory emergency room visits, and other health effects) nationwide compared to the No-Action Alternative as a result of increases in emissions of NOX, PM2.5, and SO2. Alternative 2 would also result in slightly increased adverse health impacts from mortality and non-fatal heart attacks due to increases in NOX, PM2.5, and SO2 emissions, while Alternative 1 would result in decreased adverse health impacts. The more stringent alternatives are associated with the largest increases in adverse health impacts, or the smallest decreases in impacts, relative to the No-Action Alternative. Again, in the short-term, these slight changes in health impacts are projected under the action alternatives as the result of increases in the prices of new vehicles slightly delaying sales of new vehicles and encouraging more VMT in older vehicles instead, but this trend shifts over time as higher fuel-economy new vehicles become used vehicles and older vehicles are removed from the fleet. In 2035 and 2050, all action alternatives would result in decreased E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules adverse health impacts nationwide compared to the No-Action Alternative as a result of general decreases in emissions of NOX, PM2.5, and DPM. The decreases in adverse health impacts get larger from Alternative 1 to Alternative 3. In terms of climate effects, all action alternatives would decrease U.S. passenger car and light truck fuel consumption compared with the NoAction Alternative, resulting in reductions in the anticipated increases in global CO2 concentrations, temperature, precipitation, and sea level, and increases in ocean pH that would otherwise occur. The impacts of the action alternatives on global mean surface temperature, precipitation, sea level, and ocean pH would be small in relation to global emissions trajectories. Although these effects are small, they occur on a global scale and are long lasting; therefore, in aggregate, they can have large consequences for health and welfare and can make an important contribution to reducing the risks associated with climate change. The alternatives would have the following impacts related to GHG emissions. Passenger cars and light trucks are projected to emit 89,600 million metric tons of carbon dioxide (MMTCO2) from 2021 through 2100 under the No-Action Alternative. Alternative 1 would decrease these emissions by 5 percent through 2100. Alternative 3 would decrease these emissions by 10 percent through 2100. Emissions would be highest under the No-Action Alternative, and emission reductions would increase from Alternative 1 to Alternative 3. Compared with total projected CO2 emissions of 984 MMTCO2 from all passenger cars and light trucks under the No-Action Alternative in the year 2100, the action alternatives are expected to decrease CO2 emissions from passenger cars and light trucks in the year 2100 from 6 percent under Alternative 1 to 12 percent under Alternative 3. The emission reductions in 2025 compared with emissions under the NoAction Alternative are approximately equivalent to the annual emissions from 1,284,000 vehicles under Alternative 1 to 2,248,000 vehicles under Alternative 3. For scale, a total of 253,949,000 passenger cars and light trucks are projected to be on the road in 2025 under the No-Action Alternative. CO2 emissions affect the concentration of CO2 in the atmosphere, which in turn affects global temperature, sea level, precipitation, and ocean pH. For the analysis of direct VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 and indirect impacts, NHTSA used the Global Change Assessment Model Reference Scenario to represent the Reference Case emissions scenario (i.e., future global emissions assuming no comprehensive global actions to mitigate GHG emissions). Estimated CO2 concentrations in the atmosphere for 2100 would range from 788.33 pollutant per million parts (ppm) under Alternative 3 to approximately 789.11 ppm under the No-Action Alternative, indicating a maximum atmospheric CO2 decrease of approximately 0.77 ppm compared to the No-Action Alternative. Atmospheric CO2 concentration under Alternative 1 would decrease by 0.37 ppm compared with the No-Action Alternative. Global mean surface temperature is projected to increase by approximately 3.48 °C (6.27 °F) under the No-Action Alternative by 2100. Implementing the most stringent alternative (Alternative 3) would decrease this projected temperature rise by 0.003 °C (0.006 °F), while implementing Alternative 1 would decrease projected temperature rise by 0.002 °C (0.003 °F). Projected sea-level rise in 2100 ranges from a high of 76.28 centimeters (30.03 inches under the No-Action Alternative to a low of 76.22 centimeters (30.01 inches) under Alternative 3. Alternative 3 would result in a decrease in sea-level rise equal to 0.06 centimeter (0.03 inch) by 2100 compared with the level projected under the No-Action Alternative compared to a decrease under Alternative 1 of 0.03 centimeter (0.01 inch) compared with the NoAction Alternative. Global mean precipitation is anticipated to increase by 5.85 percent by 2100 under the No-Action Alternative. Under the action alternatives, this increase in precipitation would be reduced by 0.00 to 0.01 percent. Ocean pH is anticipated to be 8.2180 under Alternative 3, about 0.0004 more than the No-Action Alternative. Under Alternative 1, ocean pH in 2100 would be 8.2178, or 0.0002 more than the NoAction Alternative. The action alternatives would reduce the impacts of climate change that would otherwise occur under the NoAction Alternative. Although the projected reductions in CO2 and climate effects are small compared with total projected future climate change, they are quantifiable and directionally consistent and would represent an important contribution to reducing the risks associated with climate change. Although NHTSA does quantify the changes in monetized damages that can be attributable to each action PO 00000 Frm 00201 Fmt 4701 Sfmt 4702 49801 alternative, many specific impacts of climate change on health, society, and the environment cannot be estimated quantitatively. Therefore, NHTSA provides a qualitative discussion of these impacts by presenting the findings of peer-reviewed panel reports including those from the Intergovernmental Panel on Climate Change (IPCC), U.S. Global Change Research Program (GCRP), the U.S. Climate Change Science Program (CCSP), the National Research Council, and the Arctic Council, among others. While the action alternatives would decrease growth in GHG emissions and reduce the impact of climate change across resources relative to the NoAction Alternative, they would not themselves prevent climate change and associated impacts. Long-term climate change impacts identified in the scientific literature are briefly summarized below, and vary regionally, including in scope, intensity, and directionality (particularly for precipitation). While it is difficult to attribute any particular impact to emissions that could result from this proposal, the following impacts are likely to be beneficially affected to some degree by reduced emissions from the action alternatives: • Impacts on freshwater resources could include changes in rainfall and streamflow patterns, warming temperatures and reduced snowpack, changes in water availability paired with increasing water demand for irrigation and other needs, and decreased water quality from increased algal blooms. Inland flood risk could increase in response to increasing intensity of precipitation events, drought, changes in sediment transport, and changes in snowpack and the timing of snowmelt. • Impacts on terrestrial and freshwater ecosystems could include shifts in the range and seasonal migration patterns of species, relative timing of species’ life-cycle events, potential extinction of sensitive species that are unable to adapt to changing conditions, increases in the occurrence of forest fires and pest infestations, and changes in habitat productivity due to increased atmospheric concentrations of CO2. • Impacts on ocean systems, coastal regions, and low-lying areas could include the loss of coastal areas due to inundation, submersion, or erosion from sea-level rise and storm surge, with increased vulnerability of the built environment and associated economies. Changes in key habitats (e.g., increased temperatures, decreased oxygen, decreased ocean pH, increased E:\FR\FM\03SEP2.SGM 03SEP2 lotter on DSK11XQN23PROD with PROPOSALS2 49802 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules salinization) and reductions in key habitats (e.g., coral reefs) may affect the distribution, abundance, and productivity of many marine species. • Impacts on food, fiber, and forestry could include increasing tree mortality, forest ecosystem vulnerability, productivity losses in crops and livestock, and changes in the nutritional quality of pastures and grazing lands in response to fire, insect infestations, increases in weeds, drought, disease outbreaks, or extreme weather events. Increased concentrations of CO2 in the atmosphere can also stimulate plant growth to some degree, a phenomenon known as the CO2 fertilization effect, but the impact varies by species and location. Many marine fish species could migrate to deeper or colder water in response to rising ocean temperatures, and global potential fish catches could decrease. Impacts on food and agriculture, including yields, food processing, storage, and transportation, could affect food prices, socioeconomic conditions, and food security globally. • Impacts on rural and urban areas could affect water and energy supplies, wastewater and stormwater systems, transportation, telecommunications, provision of social services, incomes (especially agricultural), air quality, and safety. The impacts could be greater for vulnerable populations such as lowerincome populations, historically underserved populations, some communities of color and tribal and Indigenous communities, the elderly, those with existing health conditions, and young children. • Impacts on human health could include increases in mortality and morbidity due to excessive heat and other extreme weather events, increases in respiratory conditions due to poor air quality and aeroallergens, increases in water and food-borne diseases, increases in mental health issues, and changes in the seasonal patterns and range of vector-borne diseases. The most disadvantaged groups such as children, the elderly, the sick, those experiencing discrimination, historically underserved populations, some communities of color and tribal and Indigenous communities, and low-income populations are especially vulnerable and may experience disproportionate health impacts. • Impacts on human security could include increased threats in response to adversely affected livelihoods, compromised cultures, increased or restricted migration, increased risk of armed conflicts, reduction in adequate essential services such as water and energy, and increased geopolitical rivalry. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 In addition to the individual impacts of climate change on various sectors, compound events may occur more frequently. Compound events consist of two or more extreme weather events occurring simultaneously or in sequence when underlying conditions associated with an initial event amplify subsequent events and, in turn, lead to more extreme impacts. To the extent the action alternatives would result in reductions in projected increases in global CO2 concentrations, this rulemaking would contribute to reducing the risk of compound events. NHTSA has considered the SEIS carefully in arriving at its tentative conclusion that Alternative 2 is maximum feasible, as discussed below. We seek comment on the SEIS associated with this NPRM. D. Evaluating the EPCA Factors and Other Considerations To Arrive at the Proposed Standards Despite only one year having passed since the 2020 final rule, enough has changed in the United States and in the world that revisiting the CAFE standards for MYs 2024–2026 is reasonable and appropriate. The global coronavirus pandemic, with all of its tragedy, also demonstrated what happens to U.S. and global oil consumption (and CO2 and other pollutant emissions) when driving demand plummets. The Biden Administration committed itself in its earliest moments to improving energy conservation and tackling climate change. Nearly all auto manufacturers have announced forthcoming new advanced technology, high-fueleconomy vehicle models, making strong public commitments that mirror those of the Administration. Five major manufacturers voluntarily bound themselves to stricter GHG nationallevel requirements as part of the California Framework agreement. While some facts on the ground remain similar to what was before NHTSA in the prior analysis—gas prices remain relatively low in the U.S., for example, and while light-duty vehicle sales fell sharply in MY 2020, the vehicles that did sell tended to be, on average, larger, heavier, and more powerful, all factors which increase fuel consumption—again, enough has changed that a rebalancing of the EPCA factors is appropriate for model years 2024–2026. In the 2020 final rule, NHTSA interpreted the need of the U.S. to conserve energy as less important than in previous rulemakings. This was in part because of structural changes in global oil markets as a result of shale oil drilling in the U.S., but also because in PO 00000 Frm 00202 Fmt 4701 Sfmt 4702 the context of environmental effects, NHTSA interpreted the word ‘‘conserve’’ as ‘‘to avoid waste.’’ NHTSA concluded then that the ultimate difference to the climate (among the regulatory alternatives) of thousandths of a degree Celsius in 2100 did not represent a ‘‘wasteful’’ use of energy, given the other considerations involved in the balancing of factors. One of those factors was consumer demand for vehicles with higher fuel economy levels. In the 2020 final rule, NHTSA expressed concern that low gasoline prices and apparent consumer preferences for larger, heavier, more powerful vehicles would make it exceedingly difficult for manufacturers to achieve higher standards without negative consequences to sales and jobs, and would cause consumer welfare losses. Since then, however, more and more manufacturers are announcing more and more vehicle models with advanced engines and varying levels of electrification. It is reasonable to conclude that manufacturers (who are all for-profit companies) would not be announcing plans to offer these types of vehicles if they did not expect to be able to sell them,466 and thus that manufacturers are more sanguine about consumer demand for fuel efficiency and the market for fully electric vehicles going forward than they have been previously. Additionally, NHTSA no longer believes that it is reasonable or appropriate to focus only on ‘‘avoiding waste’’ in evaluating the need of the U.S. to conserve energy. EPCA’s overarching purpose is energy conservation. The need of the U.S. to conserve energy may be reasonably interpreted as continuing to push the balancing toward greater stringency. The following sections will walk through the four statutory factors in more detail and discuss NHTSA’s decision-making process more thoroughly. To be clear at the outset, however, the fundamental balancing of factors for this proposal is different from the 2020 final rule because the evidence suggests that manufacturers believe there is a market for advanced technology vehicles with higher fuel economy, and CAFE standards are likely to be maximum feasible if they are set at levels that reflect that evidence. 466 To the extent that manufacturers are offering these vehicles in response to expected regulations, NHTSA still believes that they would not do so if they believed the vehicles were unsaleable or unmanageably detrimental to profits. Vehicle manufacturers are sophisticated corporate entities well able to communicate their views to regulatory agencies. E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules We may begin with the need of the U.S to conserve energy, which as stated is being considered more holistically in this proposal as compared to in the 2020 final rule. According to the analysis presented in Section V and in the accompanying PRIA and SEIS, Alternative 3 would save consumers the most in fuel costs, and would achieve the greatest reductions in climate change-causing CO2 emissions. Alternative 3 would also maximize fuel consumption reductions, better protecting consumers from international oil market instability and price spikes. As discussed above, for now, gasoline is still the dominant fuel used in lightduty transportation. As such, consumers, and the economy more broadly, are subject to fluctuations in price that impact the cost of travel and, consequently, the demand for mobility. Vehicles are long-lived assets and the long-term price uncertainty of petroleum still represents a risk to consumers. By increasing the fuel economy of vehicles in the marketplace, more stringent CAFE standards better insulate consumers against these risks over longer periods of time. Fuel economy improvements that reduce demand for oil are a more certain hedging strategy against price volatility than increasing U.S. energy production. Continuing to reduce the amount of money consumers spend on vehicle fuel thus remains an important consideration for the need of the U.S. to conserve energy. Additionally, the SEIS finds that overall, projected changes in both upstream and downstream emissions of criteria and toxic air pollutants are mixed, with emissions of some pollutants remaining constant or increasing and emissions of some pollutants decreasing. These increases are associated with both upstream and downstream sources, and therefore, may disproportionately affect minority and low-income populations that reside in proximity to these sources. However, the magnitude of the change in emissions relative to the No-Action alternative is minor for all action alternatives, and would not be characterized as high or adverse; over time, adverse health impacts are projected to decrease nationwide under each of the action alternatives. For the other considerations that contribute to the need of the U.S. to conserve energy, it follows reasonably that reducing fuel consumption more would improve our national balance of payments more, and our energy security, as discussed above. It is therefore likely that Alternative 3 best meets the need of the U.S. to conserve energy. During interagency review, the Department of Energy urged NHTSA to propose Alternative 3, on the basis that ‘‘a faster transition to battery electric vehicles (BEVs) is feasible,’’ because a variety of market analysts and the National Academies of Sciences, Engineering, and Medicine find that BEVs will reach cost parity with ICE vehicles by or before 2025. DOE further commented that new BEV prices would drop over time because ‘‘DOE has set aggressive technology targets for battery costs and electric drive technologies, . . . And DOE has a consistent track 49803 record in meeting its technology targets: DOE met or exceeded its technology cost and performance goals for battery and electric drive technologies every year between 2012 and 2018.’’ [citation omitted] While NHTSA appreciates this comment from DOE, as stated repeatedly throughout this proposal, NHTSA is statutorily prohibited from considering the fuel economy of dedicated alternative fuel vehicles during the rulemaking time frame when determining what levels of standards would be maximum feasible. NHTSA believes that Alternative 3 could potentially end up being maximum feasible in the final rule depending on a variety of factors, but NHTSA would be prohibited from basing such a finding exclusively on the date by which DOE estimates that BEVs will achieve cost parity with ICEs. We next evaluate how the regulatory alternatives fare in terms of economic practicability. NHTSA recognizes that the amount of lead time available before MY 2024 is less than what was provided in the 2012 rule. As will be discussed further below, NHTSA believes that the evidence suggests that the proposed standards are still economically practicable, and not out of reach for a significant portion of the industry. CAFE standards can help support industry by requiring ongoing improvements even if demand for more fuel economy flags unexpectedly. For the proposed standards, the annual rates of increase in the passenger car and light truck standards represent increases over the required levels in MY 2023 and are as shown in Table VI–6. Model year Passenger Car (percent) Light Truck (percent) 8 8 8 8 8 8 lotter on DSK11XQN23PROD with PROPOSALS2 2024 2025 2026 Part of the way that we try to evaluate economic practicability, and thus where the tipping point in the balancing of factors might be, is through a variety of metrics, examined in more detail below. If the amounts of technology or pervehicle cost increases required to meet the standards appear to be beyond what we believe the market could bear; or sales and employment appear to be VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 unduly impacted, the agency may decide that the standards under consideration may not be economically practicable. We underscore again, as throughout this preamble, that the modeling analysis does not dictate the ‘‘answer,’’ it is merely one source of information among others that aids the agency’s balancing of the standards. We similarly underscore that there is no PO 00000 Frm 00203 Fmt 4701 Sfmt 4702 single bright line beyond which standards might be economically practicable, and that these metrics are not intended to suggest one; they are simply ways to think about the information before us. Economic practicability may be evaluated in terms of how much technology manufacturers would have to apply to meet a given regulatory E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.181</GPH> Table VI-6 - Annual Rate of Increase in Proposed CAFE Stringency for Each Model Year from 2024 to 2026 49804 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules alternative. Technology application can be considered as ‘‘which technologies, and when’’—both the technologies that NHTSA’s analysis suggests would be used, and how that application occurs given manufacturers’ product redesign cadence. While the need of the U.S. to conserve energy may encourage the agency to be more technology-forcing in its balancing, and while technological feasibility is not limiting in this rulemaking given the state of technology in the industry, regulatory alternatives that require extensive application of very advanced technologies (that may have known or unknown consumer acceptance issues) or that require manufacturers to apply additional technology in earlier model years, in which meeting the standards is already challenging, may not be economically practicable, and may thus be beyond maximum feasible. The first issue is timing of technology application. While the MY 2024 standards provide less lead time for an increase in stringency than was provided by the standards set in 2012, NHTSA believes that the standards for MYs 2021–2023 should provide a relative ‘‘break’’ for compliance purposes. NHTSA does not believe that significant additional technology application would be required by the CAFE standards in the years immediately preceding the rulemaking time frame. That said, NHTSA is aware of, and has accounted for, several manufacturers voluntarily agreeing with CARB to increase their fuel economy during those model years. Manufacturers would have to apply more technology than would be required by the MYs 2021–2023 CAFE standards alone to meet those higher fuel economy levels. Again, NHTSA interprets these agreements as evidence that the participating companies believe that applying that additional technology is practicable, because for-profit companies can likely be relied upon to make decisions that maximize their profit. Companies who did not agree with CARB to meet higher targets may not increase their fuel economy levels by as much over MYs 2021–2023, but they, too, will get the relative ‘‘break’’ in CAFE obligations mentioned above, and have additional time to plan for the higher stringency increases in subsequent years. Those manufacturers can opt to employ more modest technologies to improve fuel economy (beyond their standard) to generate credits to carry forward into more challenging years, or concentrate limited research and development resources on the next generation of higher fuel economy vehicles that will be needed to meet the proposed standards in MYs 2024–2026 (and beyond), rather investing in more modest improvements in the near-term. NHTSA’s analysis estimates manufacturers’ product ‘‘cadence,’’ representing them in terms of estimated schedules for redesigning and ‘‘freshening’’ vehicles, and assuming that significant technology changes will be implemented during vehicle redesigns—as they historically have been. Once applied, a technology will be carried forward to future model years until superseded by a more advanced technology. NHTSA does not consider model years in isolation in the analysis, because that is not consistent with how industry responds to standards, and thus would not accurately reflect practicability. If manufacturers are already applying technology widely and intensively to meet standards in earlier years, requiring them to add yet more technology in the model years subject to the rulemaking may be less economically practicable; conversely, if the preceding model years require less technology, more technology during the rulemaking time frame may be more economically practicable. The tables below illustrate how the agency has modeled that process of manufacturers applying technologies in order to comply with different alternative standards. The technologies themselves are described in detail in Chapters 2 and 3 of the accompanying TSD. Table VI- 7 - Estimated Market Share (%) of Selected Technologies, Passenger Cars, Alternative 2 and Alternative 3, Standard Setting Analysis Tech PHEV (all types) BEV (all ranges) Advanced AERO 1 Strong Hybrid (all types) MR42 Advanced Engine3 PHEV (all types) BEV (all ranges) Advanced AERO MR4 Advanced Engine 2020 2023 2024 2025 2026 2 2 2 2 2 2 3 3 3 3 3 3 <l <l 5 8 4 8 3 5 10 10 13 9 48 3 12 29 <l 82 5 36 50 7 87 6 44 50 <l 2 9 71 5 28 46 2 4 8 3 5 13 9 48 4 12 29 10 10 10 76 7 30 46 87 8 38 51 92 8 46 52 10 Combined penetration of 15% and 20% aerodynamic improvement Reduce glider weight by 15% 3 Combined penetration of advanced cylinder deactivation, advanced turbo, variable compression ratio, high compression ratio and diesel engines 1 2 VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00204 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.182</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Strong Hybrid (all types) Alt 49805 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table VI-8 - Estimated Market Share(%) of Selected Technologies, Light Trucks, Alternative 2 and Alternative 3, Standard Setting Analysis Tech PHEV (all types) BEV (all ranges) Advanced AERO 1 Strong Hybrid (all types) MR4 2 Advanced Engine 3 PHEV (all types) BEV (all ranges) Advanced AERO Strong Hybrid (all types) MR4 Advanced Engine Alt 2020 2023 2024 2025 2026 2 2 2 2 2 2 <1 <1 <1 2 16 2 38 15 4 12 32 2 2 55 7 16 37 4 2 64 9 21 42 7 3 75 9 28 50 3 3 3 3 3 3 <1 <1 <1 2 16 2 38 4 2 55 9 16 36 8 3 64 9 21 40 12 3 74 9 29 51 11 11 15 5 12 32 Combined penetration of 15% and 20% aerodynamic improvement Reduce glider weight by 15% 3 Combined penetration of advanced cylinder deactivation, advanced turbo, variable compression ratio, high compression ratio and diesel engines 1 Although NHTSA’s analysis is intended to estimate ways manufacturers could respond to new standards, not to predict how manufacturers will respond to new standards, manufacturers have indicated in meetings with the agency and in public announcements (including the CARB Framework Agreements) that they do intend to increase technology application over the coming years, and specifically electrification technology which NHTSA does not model as part of its standard-setting analysis, considered for decision-making, due to the 49 U.S.C. 32902(h) restrictions for MYs 2024–2026. As the tables illustrate, both Alternative 2 and Alternative 3 appear to require rapid deployment of fuel efficiency technology across a variety of vehicle systems—body improvements due to weight reduction and improved aerodynamic drag, engine advancements, and electrification.467 The aggressive application that is simulated to occur between MY 2020 (which NHTSA observed and is the starting point of this analysis) and MY 2023 occurs in all of the alternatives, for both cars and light trucks. This reflects 467 While these technology pathways reflect NHTSA’s statutory restrictions under EPCA/EISA, it is worth noting that they represent only one possible solution. In the simulations that support the SEIS, PHEV market share grows by less, and is mostly offset by an increase in BEV market share. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 both the task presented to signatories by the California Framework and existing compliance positions (in some fleets) across the industry to improve fuel economy in the near-term. In general, technology market shares for Alternative 3 look similar to those for Alternative 2, with the notable exception of plug-in hybrids which differ by only a couple of percent for cars and about 5 percent for light trucks. While still relatively small differences on their own, the market share of plug-in hybrids is currently less than one percent in total. While manufacturers could certainly choose to produce fully electric vehicles instead of PHEVs, fully electric vehicles are projected to grow by multiples of their current market share as well. The market for high levels of electrification is likely to continue growing but NHTSA acknowledges that consumer demand, especially in the near-term, remains somewhat unclear. If policy decisions are made to extend or expand incentives for electric vehicle purchases, NHTSA could potentially consider the greater reliance on electrification in Alternative 3 to be a smaller risk. NHTSA’s analysis seeks to account for manufacturers’ capital and resource constraints in several ways—through the restriction of technology application to refreshes and redesigns, through the phase-in caps applied to certain technologies, and through the explicit PO 00000 Frm 00205 Fmt 4701 Sfmt 4702 consideration of vehicle components (like powertrains) and technologies (like platforms based on advanced materials) that are shared by models throughout a manufacturer’s portfolio. NHTSA is aware that there is a significant difference in the level of capital and resources required to implement one or more new technologies on a single vehicle model, and the level of capital and resources required to implement those same technologies across the entire vehicle fleet. NHTSA realizes that it would not be economically practicable to expand some of the most advanced technologies to every vehicle in the fleet within the rulemaking time frame, although it should be possible to increase the application of advanced technologies across the fleet in a progression that accounts for those resource constraints. That is what NHTSA’s analysis tries to do. Another consideration for economic practicability is the extent to which new standards could increase the average cost to acquire new vehicles, because even insofar as the underlying application of technology leads to reduced outlays for fuel over the useful lives of the affected vehicles, these pervehicle cost increases provide both a measure of the degree of effort faced by manufacturers, and also the degree of adjustment, in the form of potential vehicle price increases, that will ultimately be required of vehicle E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.183</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 2 49806 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules purchasers. Table VI–9 and Table VI–10 show the agency’s estimates of average cost increase under the Preferred Alternative for passenger cars and light trucks, respectively. Because our analysis includes estimates of manufacturers’ indirect costs and profits, as well as civil penalties that some manufacturers (as allowed under EPCA/EISA) might elect to pay in lieu of achieving compliance with CAFE standards, we report cost increases as estimated average increases in vehicle price (as MSRP). These are average values, and the agency does not expect that the prices of every vehicle would increase by the same amount; rather, the agency’s underlying analysis shows unit costs varying widely between different vehicle models. For example, a small SUV that replaces an advanced internal combustion engine with a plug-in hybrid system may incur additional production costs in excess of $10,000, while a comparable SUV that replaces a basic engine with an advanced internal combustion engine incurs a cost closer to $2,000. While we recognize that manufacturers will distribute regulatory costs throughout their fleet to maximize profit, we have not attempted to estimate strategic pricing, having insufficient data (which would likely be confidential business information (CBI)) on which to base such an attempt. To provide an indication of potential price increases relative to today’s vehicles, we report increases relative to the market forecast using technology in the MY 2020 fleet—the most recent actual fleet for which we have information sufficient for use in our analysis. We provide results starting in MY 2023 in part to illustrate the cost impacts in the first model year that we believe manufacturers might actually be able to change their products in preparation for compliance with standards in MYs 2024–2026. Manufacturer BMW Daimler FCA (Stellantis) Ford GM Honda Hyundai Kia-H Hyundai Kia-K JLR lotter on DSK11XQN23PROD with PROPOSALS2 Mazda Mitsubishi Nissan Subaru Tesla Toyota VWA Volvo Total, Average VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 2023 2024 2025 2026 1,133 1,180 2,697 3,699 848 685 623 411 609 2,288 822 1,349 909 48 364 1,102 943 1,468 2,422 3,031 3,402 1,339 829 978 997 1,532 2,427 1,342 2,054 2,055 47 934 1,397 2,761 2,125 2,789 3,404 3,421 2,065 1,332 1,661 1,371 1,837 3,285 1,815 2,871 2,265 49 1,075 1,743 2,829 2,769 3,204 3,740 3,310 2,474 1,757 2,357 1,880 2,256 3,401 1,785 2,856 2,748 49 1,179 4,523 3,006 1,055 1,521 1,968 2,264 Frm 00206 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.184</GPH> Table VI-9-Estimated Total (vs. MY 2020 Technology) Average MSRP Increases During MYs 2023-2026 Under Preferred Alternative, Passenger Cars Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49807 Table VI-to-Estimated Total (vs. MY 2020 Technology) Average MSRP Increases During MYs 2023-2026 Under Preferred Alternative, Light Trucks Manufacturer 2023 2024 2025 2026 BMW Daimler FCA (Stellantis) Ford GM Honda Hyundai Kia-H Hyundai Kia-K JLR Mazda Mitsubishi Nissan Subaru Tesla Toyota VWA Volvo 1,282 634 1,114 938 738 527 638 599 822 492 363 1,133 1,121 82 1,239 2,210 901 1,379 657 1,325 1,187 1,311 1,183 764 2,416 1,311 594 841 2,249 1,267 81 1,921 2,222 2,010 1,404 1,358 1,643 1,219 2,309 1,705 883 2,414 1,850 1,370 1,862 2,327 1,441 79 1,925 2,467 2,392 1,431 1,935 1,973 1,912 2,935 1,674 3,117 2,421 2,247 1,664 1,832 2,824 1,434 78 2,331 2,482 2,628 933 1,413 1,795 2,210 lotter on DSK11XQN23PROD with PROPOSALS2 Relative to current vehicles (again, as represented here by technology in the MY 2020 fleet, the most recent for which NHTSA has adequate data), NHTSA judges these cost increases to be significant, but not impossible for the market to bear. Cost increases will be partially offset by fuel savings, which consumers will experience eventually, if not concurrent with the upfront increase in purchase price. And as discussed VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 previously, nearly every manufacturer has already indicated their intent to continue introducing advanced technology vehicles between now and MY 2026. Again, NHTSA believes that manufacturers introduce new vehicles (and technologies) expecting that there is a market for them—if not immediately, then in the near future. For-profit companies cannot afford to lose money indefinitely. This trend PO 00000 Frm 00207 Fmt 4701 Sfmt 4702 suggests that manufacturers believe that at least some cost increases should be manageable for consumers. Relative to the Preferred Alternative, however, NHTSA notes significant further cost increases for several major manufacturers under Alternative 3. Table VI–11 and Table VI–12 show additional technology costs estimated to be incurred under Alternative 3 as compared to the Preferred Alternative. E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.185</GPH> Total, Average 49808 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table VI-11- Estimated Difference Between Estimated Average MSRP Increase under Preferred Alternative and Alternative 3 for Passenger Cars Manufacturer BMW Daimler FCA (Stellantis) Ford GM Honda Hyundai Kia-H Hyundai Kia-K JLR Mazda Mitsubishi Nissan Subaru Tesla Toyota VWA Volvo Total, Average 2023 48 45 (0) (0) 115 498 4 (2) (0) 16 (0) 2024 207 292 122 11 139 555 206 111 125 266 119 308 (0) 2025 631 407 265 (239) 367 516 462 696 292 542 602 427 147 2026 693 546 379 78 428 534 617 670 463 534 576 573 468 - - - - 56 (0) (12) 92 326 47 (216) 227 383 129 (131) 360 441 160 337 469 Table VI-12 - Estimated Difference Between Estimated Average MSRP Increase under Preferred Alternative and Alternative 3 for Light Trucks VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 312 0 16 0 0 0 2024 23 43 83 521 283 1,036 17 719 122 17 128 27 0 2025 44 168 187 605 622 1,046 29 693 214 96 355 58 47 - - - - 53 653 10 46 652 624 369 347 622 599 490 461 798 597 573 600 light truck fleets appear to be pressed harder to comply with Alternative 3 than passenger car fleets across the industry. For example, Ford’s passenger car compliance costs are estimated to PO 00000 2026 143 331 318 847 798 1,037 671 672 363 387 340 181 (0) Frm 00208 Fmt 4701 Sfmt 4702 increase minimally between Alternative 2 and Alternative 3, but light truck compliance costs increase by over 40 percent (in most years). A number of other manufacturers are pushed in both E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.187</GPH> For example, Honda’s light truck fleet appears to hit an inflection point in cost where much more aggressive technology application is required in order to comply with Alternative 3. In general, 2023 24 (8) 0 66 EP03SE21.186</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Manufacturer BMW Daimler FCA (Stellantis) Ford GM Honda Hyundai Kia-H Hyundai Kia-K JLR Mazda Mitsubishi Nissan Subaru Tesla Tovota VWA Volvo Total, Average Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules fleets (Honda, Toyota, and Kia, for example), and make significant additional investments in fuel economy technology to reach compliance with the standards in Alternative 3. Changes in costs for new vehicles are not the only costs that NHTSA considers in balancing the statutory factors—fuel costs for consumers are relevant to the need of the U.S. to conserve energy, and NHTSA believes that consumers themselves weigh expected fuel savings against increases in purchase price for vehicles with higher fuel economy. Fuel costs (or savings) continue to be the largest source of benefits for CAFE standards, and GHG reduction benefits, which are also part of the need of the U.S. to conserve energy, are also increasing. E.O. 12866 and Circular A–4 also direct agencies to consider maximizing net 49809 benefits in rulemakings whenever possible and consistent with applicable law. Thus, because it can be relevant to balancing the statutory factors and because it is directed by E.O. 12866 and OMB guidance, NHTSA also considers the net benefits attributable to the different regulatory alternatives, as shown in Table VI–13. 3% Rate lotter on DSK11XQN23PROD with PROPOSALS2 7% Rate 21:48 Sep 02, 2021 Alternative 2 Alternative 3 82.6 121.4 172.9 66.5 121.1 176.3 16.1 0.3 -3.4 51.6 75.6 107.6 49.3 90.7 132.8 2.3 -15.1 -25.2 Total Benefits Total Costs Net Benefits Total Benefits Total Costs Net Benefits While maximizing net benefits is a valid decision criterion for choosing among alternatives, it is not the only reasonable decision perspective. When NHTSA recognizes that the need of the U.S. to conserve fuel weighs importantly in the overall balancing of factors, it is reasonable to consider choosing the regulatory alternative that produces the largest reduction in fuel consumption, while remaining net beneficial. The benefit-cost analysis is not the sole factor that NHTSA considers in determining the maximum feasible stringency, though it supports NHTSA’s tentative conclusion that Alternative 2 is the maximum feasible stringency. While Alternative 1 produces higher net benefits, it also continues to allow fuel consumption that could have been avoided in a costbeneficial manner. And while Alternative 3 achieves greater reductions in fuel consumption than Alternative 2, it shows relatively high negative net benefits under both discount rates. While NHTSA estimates that new vehicle sales will be slightly lower under Alternative 2 than under the NoAction Alternative, as a consequence of the higher retail prices that result from additional technology application, the difference is only about 1 percent over the entire period covered by MYs 2020– VerDate Sep<11>2014 Alternative 1 Jkt 253001 2026. NHTSA does not believe that this estimated change in new vehicle sales over the period covered by the rule is a persuasive reason to choose another regulatory alternative. Similarly, the estimated labor impacts within the automotive industry provide no evidence that another alternative should be preferred. While the change in sales is estimated to decrease industry employment over the period, the decrease is even smaller than the impact on new vehicle sales (about 0.1 percent). As NHTSA explained earlier in defining economic practicability, standards simply should avoid a significant loss of jobs, and may still be economically practicable even though they appear to show a negative impact (here, a very slight impact) on sales and employment. As with any analysis of sufficient complexity, there are a number of critical assumptions here that introduce uncertainty about manufacturer compliance pathways, consumer responses to fuel economy improvements and higher vehicle prices, and future valuations of the consequences from higher CAFE standards. While NHTSA considers dozens of sensitivity cases to measure the influence of specific parametric assumptions and model relationships, only a small number of them PO 00000 Frm 00209 Fmt 4701 Sfmt 4702 demonstrate meaningful impacts to net benefits under the proposed standards. Looking at these cases more closely, the majority of both costs and benefits that occur under the proposed standards accrue to buyers of new cars and trucks, rather than society in general. It then follows that the assumptions that exert the greatest influence over private costs and benefits also exert the greatest influence over net benefits—chief among these is the assumed trajectory of future fuel prices, specifically gasoline. NHTSA considers the ‘‘High Oil Price’’ and ‘‘Low Oil Price’’ cases from AEO 2021 as bounding cases, though they are asymmetrical (while the low case is only about 25 percent lower than the Reference case on average, the high case is almost 50 percent higher on average). The sensitivity cases suggest that fuel prices exert considerable influence on net benefits—where higher and lower prices not only determine the dollar value of each gallon saved, but also how market demand responds to higher levels of fuel economy in vehicle offerings. Under the low case, net benefits become negative and exceed $30 billion, but increase to almost (positive) $50 billion in the high case (the largest increase among any sensitivity cases run for this proposal). This suggests that the net benefits resulting from this proposal are E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.188</GPH> Table VI-13 - Summary of Cumulative Benefits and Costs for Model Years through MY 2029, by Alternative and Discount Rate lotter on DSK11XQN23PROD with PROPOSALS2 49810 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules dependent upon the future price of gasoline being at least as high as the AEO 2021 Reference Case projects. Another critical uncertainty that affects private benefits is the future cost of advanced electrification technologies, specifically batteries. These emerging technologies provide both the greatest fuel savings to new car buyers and impose the highest technology costs (at the moment). While the cost to produce large vehicle batteries has been rapidly declining for years, they are still expensive relative to advancements in internal combustion engines and transmissions. However, the analysis projects continued cost learning over time and shows battery electric vehicles reaching price parity with conventional vehicles in the 2030s for most market segments—after which market adoption of BEVs accelerates—although other estimates show price parity occurring sooner and we seek comment on whether and how to use those estimates in our analysis for the final rule. Electrification is also a viable compliance strategy, as partially or fully electric vehicles benefit from generous compliance incentives that improve their estimated fuel economy relative to measured energy consumption. As such, the assumption about future battery costs has the ability to influence compliance costs to manufacturers and prices to consumers, the rate of electric vehicle adoption in the market, and thus the emissions associated with their operation. NHTSA considered two different mechanisms to affect battery costs: Higher/lower direct costs, and faster/slower cost learning rates. The two mechanisms that reduce cost (whether by faster cost learning or lower direct costs) both increase net benefits relative to the central case, though lowering initial direct costs by 20 percent had a greater effect than increasing the learning rate by 20 percent. Increasing cost (though either mechanism) by 20 percent produced a similar effect, but in the opposite direction (reducing net benefits). However, none of those cases exerted a level of influence that compares to alternative fuel price assumptions. There is one assumption that affects the analysis without influencing the benefits and costs that accrue to new car buyers: The social cost of damages attributable to greenhouse gas emissions. While there is no feedback in either the analysis or the policy between the assumed social cost of GHGs and metric tons of GHGs emitted (or gallons of fuel consumed), it directly controls the valuation of each metric ton saved over time. The central analysis assumes a SC–GHG cost based on the 2.5 percent VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 discount rate for the 3 percent social discount rate, and a SC–GHG cost based on the 3 percent discount rate in the 7 percent social discount rate case. However, this assumption directly scales total benefits by increasing (or decreasing) the value of each ton saved. Using the highest SCC–GHG, based on the 95th percentile estimate, pushes net benefits above $30 billion under Alternative 2. NHTSA does not independently develop the SC–GHG assumptions used in this proposal but takes them from the interagency working group on the social cost of GHGs. If future analyses by that group determine that the SC–GHG should be different from what it currently is, NHTSA will consider those values and whether to include them in subsequent analyses. As the sensitivity cases illustrate, their inclusion could exert enough influence on net benefits to suggest that a different alternative could represent the maximum feasible stringency—at least based on the decision criteria described in this section. As mentioned above, NHTSA is seeking comment on the methodology employed by that group for determining the SC–GHG. Based on all of the above, NHTSA tentatively concludes that while all of the action alternatives are technologically feasible, Alternative 3 may be too costly to be economically practicable in the rulemaking timeframe, even if choosing it could result in greater fuel savings. NHTSA interprets the need of the U.S. to conserve energy as pushing the balancing toward greater stringency— consumer savings on fuel costs are estimated to be higher under Alternative 3 than under Alternative 2, but the additional technology cost required to meet Alternative 3 (as evidenced by the negative net benefits at both discount rates) may yet make Alternative 3 too stringent for these model years. Changes in criteria pollutants, health effects, and vehicle safety effects are relatively minor under all action alternatives, and thus not dispositive. NHTSA has considered the effect of other motor vehicle standards of the Government by incorporating the fuel economy effects of California’s ZEV program into its baseline, and calculating the costs and benefits of CAFE standards as above and beyond those baseline costs and benefits. The additional costs of the proposed standards are, on average, not far from what NHTSA estimated in the 2012 final rule for standards in a similar timeframe; the additional benefits are lower, but this is due to a variety of factors, including significant addition of PO 00000 Frm 00210 Fmt 4701 Sfmt 4702 fuel-economy-improving technology to new vehicles between then and now (including the growing market for electric vehicles), and lower fuel price projections from EIA. To the extent that higher prices for new vehicles as a result of the technology required by the standards could translate to decreases in new vehicle sales, we note that those effects appear small, as discussed above. Moreover, improving the fuel efficiency of new vehicles has effects over time, not just at point of first sale, on consumer fuel savings. Somewhat-moreexpensive-but-more-efficient new vehicles eventually become moreefficient used vehicles, which may be purchased by consumers who may be put off by higher new vehicle prices. The benefits have the potential to continue across the fleet and over time, for all consumers regardless of their current purchasing power. NHTSA recognizes, again, that lead time for this proposal is less than past rulemakings have provided, and that the economy and the country are in the process of recovering from a global pandemic. NHTSA also recognizes that at least parts of the industry are nonetheless making announcement after announcement of new forthcoming advanced technology, high-fueleconomy vehicle models, and does not believe that they would be doing so if they thought there was no market at all for them. Perhaps some of the introductions are driven by industry perceptions of future regulation, but the fact remains that the introductions are happening. CAFE standards can help to buttress this momentum by continuing to require the fleets as a whole to improve their fuel economy levels steadily over the coming years, so that a handful of advanced technology vehicles do not inadvertently allow backsliding in the majority of the fleet that will continue to be powered by internal combustion for likely the next 5–10 years. CAFE standards that increase steadily may help industry make this transition more smoothly. And finally, if the purpose of EPCA is energy conservation, and NHTSA is interpreting the need to conserve energy to be largely driven by fuel savings, energy security, and environmental concerns, then it makes sense to interpret EPCA’s factors as asking the agency to push stringency as far as possible before benefits become negative. The energy conservation benefits of Alternative 3 appear, under the current analysis, to be highest, as discussed in the SEIS and in Section VI.C above, and better protect consumers from international oil market instability and price spikes. By E:\FR\FM\03SEP2.SGM 03SEP2 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules increasing the fuel economy of vehicles in the marketplace, more stringent CAFE standards better insulate consumers against these risks over longer periods of time. Fuel economy improvements that reduce demand for oil are a more certain hedging strategy against price volatility than increasing U.S. energy production. However, with negative net benefits for Alternative 3 under both discount rates, it may be that for the moment, the costs of achieving those benefits are more than the market is willing to bear. NHTSA thus aims to help bolster the industry’s trajectory toward higher future standards, by keeping stringency high in the midterm, but not so high as to be economically impracticable. NHTSA therefore proposes that Alternative 2 is maximum feasible for MYs 2024–2026. We seek comment on this tentative conclusion. VII. Compliance and Enforcement 2. How Manufacturers’ Target and Achieved Performances Are Calculated A. Introduction lotter on DSK11XQN23PROD with PROPOSALS2 1. Overview of the NHTSA Compliance Program A manufacturer’s fleet is divided into three compliance categories of automobiles: Passenger vehicles manufactured domestically, passenger vehicles not manufactured domestically; and non-passenger automobiles.468 Each category has its own CAFE fleet mpg standard that a manufacturer is required to meet. The CAFE standard is determined for each model year by a combination of the production volume of vehicles produced for sale, the footprint of those vehicles, and the requisite CAFE footprint-based fuel economy target curves. For each compliance category, manufacturers self-report data at the end of each MY in the form of a Final Model Year Report, and once these data are verified by EPA, NHTSA determines final compliance. Using EPA’s final verified data, a manufacturer fleet is determined to be compliant if the 2cycle CAFE performance of their fleet with the addition of the Alternative Motor Fuels Act (AMFA) and AC/OC incentives are equal to or greater than the CAFE fleet mpg standard. The manufacturer fleet is out of compliance if its fleet mpg falls below the CAFE mpg standard, in which case the manufacturer may resolve the shortfall through civil penalties or the use of flexibilities. Resolving a shortfall through flexibilities may include the 468 See 49 U.S. Code 32903.6. Passenger vehicles not manufactured domestically are referenced as import passenger cars and non-passenger automobiles as light trucks. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 application of CAFE credits through trade, carry-forward, carry-back, or transfer from within the manufacturer’s fleet accounts or from another manufacturer’s fleet accounts. The following sections provide a brief overview how CAFE standards and compliance values are derived, what compliance flexibilities and incentives are available to manufacturers, and the revisions to the CAFE program NHTSA is proposing in this rulemaking. In summary, NHTSA is proposing to: (1) Increase and clarify flexibilities for its off-cycle program; (2) revive incentives for hybrid and electric full-size pickup trucks through MY 2025; (3) modify its standardized templates for CAFE reporting and credit transactions; and (4) add a new template for manufacturers to report information on the monetary and non-monetary costs associated with credit trades. Compliance begins each model year with manufacturers testing vehicles on a dynamometer in a laboratory over predefined test cycles and controlled conditions.469 EPA and manufacturers use two different dynamometer test procedures—the Federal Test Procedure (FTP) and the Highway Fuel Economy Test (HFET) to determine fuel economy. These procedures originated in the early 1970s and were intended to generally represent city and highway driving conditions, respectively. These two tests are commonly referred to as the ‘‘2cycle’’ test procedures for CAFE. A machine is connected to the vehicle’s tailpipe while it performs the test cycle, which collects and analyzes exhaust 469 For readers unfamiliar with this process, the test is similar to running a car on a treadmill following a program—or more specifically, two programs. 49 U.S.C. 32904(c) states that, in testing for fuel economy, EPA must ‘‘use the same procedures for passenger automobiles [that EPA] used for model year 1975 (weighted 55 percent urban cycle and 45 percent highway cycle), or procedures that give comparable results.’’ Thus, 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 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. The 2cycle dynamometer test results differ somewhat from what consumers will experience in the realworld driving environment because of the lack of high speeds, rapid accelerations, and hot and cold temperatures evaluations with the A/C operation. These added conditions are more so reflected in the EPA 5-cycle test results listed on each vehicle’s fuel economy label and on the fueleconomy.gov website. PO 00000 Frm 00211 Fmt 4701 Sfmt 4702 49811 gases, such as CO2 quantities.470 Fuel economy is determined from relating a derived emissions factor to the amount of observed CO2 using a reference test fuel.471 Manufacturers continue to test vehicles over the course of the model year and will test enough vehicles to cover approximately 90 percent of the subconfigurations within each model type. Manufacturers self-report this information to EPA as part of their endof-the-model year reports, which are due 90 days after the model year is completed. After manufacturers submit their reports, EPA confirms and validates those results by testing a random sample of vehicles at the National Vehicle and Fuel Emissions Laboratory (NVFEL) in Ann Arbor, Michigan. A manufacturer’s fleet fuel economy performance (hereafter referenced as Base CAFE) for a given model year is calculated through the following steps: • Each vehicle model’s mile per gallon (mpg) performance in the city and highway test cycles are calculated based off the carbon emitted during dynamometer testing. The vehicle’s mpg performance is combined at 55 percent city and 45 percent highway. Measurement incentives for alternative fuel vehicles (such as for electricity, counting 15 percent of the actual energy used to determine the gasoline equivalent mpg) are applied as part of these procedures; • Performance improvements not fully captured through 2-cycle dynamometer testing, such as eligible A/C and off-cycle technologies are then added to the vehicle’s mpg performance. Incentives for full-size pickup trucks with mild or strong HEV technology or other technologies that perform significantly better than the vehicle’s target value are also applied. • The quantity of vehicles produced of each model type within a manufacturer’s fleet is divided by its respective fuel economy performance (mpg) including any flexibility/ incentive increases; The resulting numbers for each model type are summed; • The manufacturer’s total production volume is then divided by the summed value calculated in the previous step; and 470 Vehicles without tailpipe emissions, such as battery electric vehicles, have their performance measured differently, as discussed below. 471 Technically, for the CAFE program, carbonbased tailpipe emissions (including CO2, CH4, and CO) are measured, and fuel economy is calculated using a carbon balance equation. EPA uses carbonbased emissions (CO2, CH4, and CO, the same as for CAFE) to calculate the tailpipe CO2 equivalent for the tailpipe portion of its standards. CO2 is by far the largest carbon-based exhaust constituent. E:\FR\FM\03SEP2.SGM 03SEP2 49812 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules • That number, which is the harmonic average of the fleet’s fuel economy, is rounded to the nearest tenth of an mpg and represents the manufacturer’s achieved fuel economy. The Base CAFE of each fleet is compared to the manufacturer’s unique fleet compliance obligation, which is calculated using the same approach as the Base CAFE performance, except that the fuel economy target value (based on the unique footprint of each vehicle within a model type) is used instead of the measured fuel economy performance values. The fuel economy target values of the model types within each fleet and production volumes are used to derive the manufacturer’s fleet standard (also known as the obligation) which is the harmonic average of these values. To further illustrate how Base CAFE and fuel economy targets are calculated, assume that a manufacturer produces two models of cars—a hatchback and a sedan. Figure VII–1 shows the two vehicle models imposed onto a fuel economy target function. From Figure VII–1, we can see that the target function extends from about 30 mpg for the largest cars to about 41 mpg for the smallest cars. Hatchback 39SF 48mpg 50 · 45 Sedan 51 SF 25 mpg 20 .. 15 I f.··········---40 35 45 55 50 60 Footprint (sf) The manufacturer’s required CAFE obligation would be determined by calculating the production-weighted harmonic average of the fuel economy target values applicable at the hatchback and sedan footprints (from the curve, about 41 mpg for the hatchback and about 33 mpg for the sedan). The manufacturer’s achieved Base CAFE level is determined by calculating the production-weighted harmonic average of the hatchback and sedan fuel economy levels (in this example the values shown in the boxes in Figure VII–1, 48 mpg for the hatchback and 25 mpg for the sedan). Depending on the relative mix of hatchbacks and sedans produced, the manufacturer’s fleet Base CAFE may be equal to the standard, perform better than the standard (if the required fleet CAFE is less than the achieved fleet Base CAFE) and thereby VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 earn credits, or perform worse than the standard (if the required fleet CAFE is greater than achieved fleet Base CAFE) and thereby earn a credit shortfall which would need to be made up using CAFE credits, otherwise the manufacturer would be subject to civil penalties. As illustrated by the example, the CAFE program’s use of sales-weighted harmonic averages makes compliance more intricate than comparing a model to its target as not every model type needs to precisely meet its target for a manufacturer to achieve compliance. Consequently, if a manufacturer finds itself producing large numbers of vehicles that fall well-short of its targets, a manufacturer can attempt to equally balance its compliance by producing vehicles that are excessively overcompliant. However, NHTSA PO 00000 Frm 00212 Fmt 4701 Sfmt 4702 understands that several factors determine the ability of manufacturers to change their fleet-mix mid-year. In response, the CAFE program is structured to provide relief to manufacturers in offsetting any shortfalls by offering several compliance flexibilities. Many manufacturers use these flexibilities to avoid civil penalties. 3. The Use for CAFE Compliance Flexibilities and Incentives The CAFE program offers several compliance flexibilities which expand options for compliance, and incentives which encourage manufacturers to build vehicles with certain technologies to achieve longer range policy objectives. For example, since MY 2017, manufacturers have had the flexibility to earn credits for air conditioning E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.189</GPH> lotter on DSK11XQN23PROD with PROPOSALS2 Figure VII-1- Illustration of Vehicle Models vs. Fuel Economy Targets Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules (A/C) systems with improved efficiency. These fuel economy improvements are added to the 2-cycle performance results of the vehicle and increases the calculation of a manufacturer’s fleet Base CAFE in determining compliance relative to standards.472 Some CAFE flexibilities and incentives are codified by statute in EPCA or EISA, while others have been implemented by the NHTSA through regulations, consistent with the statutory scheme. Compliance flexibilities and incentives have a great deal of theoretical attractiveness: If designed properly, they can help reduce the overall regulatory costs, while maintaining or improving programmatic benefits. If designed poorly, they may create significant potential for market distortion. Consequently, creating or lotter on DSK11XQN23PROD with PROPOSALS2 472 NHTSA characterizes any programmatic benefit manufacturers can use to comply with CAFE standards that fully accounts for fuel use as a ‘‘flexibility’’ (e.g., credit trading) and any benefit that counts less than the full fuel use as an ‘‘incentive’’ (e.g., adjustment of alternative fuel vehicle fuel economy). NHTSA flexibilities and incentives are discussed further in Section VII.B.3.a). VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 revising compliance flexibilities and incentives requires proper governmental and industry collaboration for understanding upcoming technological developments and for determining whether a technology is economically feasible for compliance. When designing these programmatic elements, the agency must be mindful to ensure flexibilities and incentives are provided with long term benefits to the CAFE program while avoiding unintended windfalls for only certain manufacturers or technologies. Compliance incentives and flexibilities are structured to encourage implementation of technology that will further increase fuel savings. Some incentives are designed to encourage the development of technologies that may have high initial costs but offer promising fuel efficiency benefits in the long-term. Others are designed to bring low cost technologies uniformly into the market that improve fuel economy in the real-world but may be missed by the 2-cycle test, such as the cost-effective off-cycle menu technologies included by EPA for CAFE compliance. PO 00000 Frm 00213 Fmt 4701 Sfmt 4702 49813 Below is a summary of all the current and proposed changes to the flexibilities and incentives for the CAFE and CO2 programs in Table VII–1 through Table VII–4. Note that this proposal only covers the CAFE program; the EPA program is listed here to demonstrate the congruencies between the two programs. NHTSA is proposing to maintain the bulk of its current program with a few modifications. One of the changes raised in this proposal is to increase the off-cycle flexibility technology benefit cap along with new technology definitions as shown in the table. NHTSA is also proposing to reinstate incentives for full-size hybrid and game changing advanced technology pickup trucks for model years 2022 through 2026. NHTSA believes that these incentives will increase the production of environmentally beneficial technologies and help achieve economies of scale to reduce costs that will enable more stringent CAFE standards in the future. These proposals are explained in further detail in Section VII.B. BILLING CODE 4910–59–P E:\FR\FM\03SEP2.SGM 03SEP2 49814 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table VII-1- Statutory Flexibilities for Over-compliance with Standards lotter on DSK11XQN23PROD with PROPOSALS2 Credit Earning NHTSA EPA Authority Current Program Authority 49 U.S.C. 32903(a) Denominated in tenths of a mpg Current and Proposed Pro2ram CAA 202(a) Denominated in g/mi Credit "Carryforward" 49 U.S.C. 32903(a)(2) 5 MY s into the future CAA 202(a) 5 MY s into the future (except for MYs 2010-2015 = credits may be carried forward through MY 2021) EPA proposes to extend credit expiration for MY 2016 by 2 years, and/or MYs 2017-2020 bv 1 vear Credit "Carryback" (AKA "deficit carry-forward") 49 U.S.C. 32903(a)(l) 3 MY s into the past CAA 202(a) 3 MY s into the past Credit Transfer 49 U.S.C. 32903(g) Up to 2 mpg per fleet; transferred credits may not be used to meet MDPCS CAA 202(a) Unlimited Credit Trade 49 U.S.C. 32903(±) Unlimited quantity; traded credits may not be used to meet MDPCS CAA 202(a) Unlimited VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 PO 00000 Frm 00214 Fmt 4701 Sfmt 4725 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.190</GPH> Regulatory Item Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules 49815 Table VII-2- Current and Proposed Flexibilities that Address Gaps in Compliance Test Procedures Regulatory Item A/C efficiency Off-cycle NHTSA EPA Authority Current and Proposed Program 49 U.S.C. 32904 Allows mfrs to earn "fuel consumption improvement values" (FCIVs) equivalent to EPA credits starting in MY 2017 49 U.S.C. 32904 Authority Allows mfrs to earn "fuel consumption improvement values" (FCIVs) equivalent to EPA credits starting in MY 2017 For MY 2020 and beyond, NHTSA proposes to implement CAFE provisions equivalent to the EPA proposed changes Current and Proposed Program CAA 202(a) '.'Credits" for A/C efficiency improvements up to caps of 5. O g/mi for cars and 7 .2 g/mi for trucks CAA 202(a) "Menu" of pre-approved credits (~10), up to cap of 10 g/mi for MY 201 ~ and beyond; other pathways reqmre EPA approval through either 5-cycle testing or through public notice and comment EPA proposes to revise the definitions for passive cabin ventilation and active engine and transmission warm-up beginning in MY 2023; for MY 2020-2022, the cap is 15 glmi if the revised definitions are met (if these technologies are used). In MY 2023 and later, the cap is increased to 15 l!lmile Table VII-3 - Incentives that Encourage Application of Technologies lotter on DSK11XQN23PROD with PROPOSALS2 Full-size pickup trucks with HEV or overperforming target VerDate Sep<11>2014 21:48 Sep 02, 2021 Authority Proposed Program Authority 49 U.S.C. 32904 Allows mfrs to earn FCIV s equivalent to EPA credits for MYs 2017-2021 NHTSA proposes to reinstate incentives for strong hybrid OR overperforming target by 20% for MYs 2022-2025 CAA 202(a) Jkt 253001 PO 00000 Frm 00215 Fmt 4701 Sfmt 4725 Current and Proposed Pro2:ram 10 g/mi for full-size pickups with mild hybrids OR overperforming target by 15% (MYs 2017-2021); 20 g/mi for full-size pickups with strong hybrids OR overperforming target by 20% (MY s 20172021); requires 10% or more of full-size pickup production volume EPA proposes to reinstate incentives for strong hybrid OR overperforming by 20% for MYs 2022-2025 E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.192</GPH> EPA EP03SE21.191</GPH> NHTSA Regulatory item 49816 Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules Table VII-4 - Incentives that Encourage Alternative Fuel Vehicles Dedicated alternative fuel vehicle Dual-fueled vehicles NHTSA Authority Current Program Authority 49 U.S.C. 32905(a) and (c) Fuel economy calculated assuming gallon of liquid or gallon equivalent gaseous alt fuel = 0.15 gallons of gasoline; for EVs petroleum equivalency factor CAA 202(a) 49 U.S.C. 32905(b), (d), and (e); 32906(a) FE calc using 50% operation on alt fuel and 50% on gasoline through MY 2019. Starting with MY 2020, NHTSA uses the SAE defined "Utility Factor" methodology to account for actual potential use, and "Ffactor" for FFV; NHTSAwill continue to incorporate the 0 .15 incentive factor BILLING CODE 4910–59–C lotter on DSK11XQN23PROD with PROPOSALS2 4. Light Duty CAFE Compliance Data for MYs 2011–2020 NHTSA uses compliance data in part to identify industry trends. For this proposal, NHTSA examined CAFE compliance data for model years 2011 through 2020 using final compliance data for MYs 2011 through 2017,473 projections from end-of-the-model year reports submitted by manufacturers for 473 Final compliance data have been verified by EPA and are published on the NHTSA’s Public Information Center (PIC) site. MY 2017 is currently the most-recent model year verified by EPA. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 CAA 202(a) EPA Current and Proposed Proe:ram Multiplier incentives for EVs and FCVs (each vehicle counts as 2.0/1. 75/1.5 vehicles in 20172021), NGVs (1.6/1.45/1.3 vehicles for MYs 2017-2021, then 2.0 for MY s 2022-2026); each EV = 0 g/mi upstream emissions through MY 2021 (then phases out based on permfr production cap of 200k vehicles) 2026 EPA proposes to add vehicle multiplier incentive for EVs and FCVs; each vehicle counts as 2.0 for MYs 2022-2024, and 1. 75 for MY 2025, subject to a cap on all vehicle multipliers Multiplier incentives for PHEVs and NGVs (each vehicle counts as 1.6/1.45/1.3 vehicles in 20172021 NGVs count as 2.0 vehicles in 2022-2026); electric operation = 0 g/mi through MY 2026; the SAE defined "Utility Factor" method for use, and "Ffactor" for FFV EPA proposes to add vehicle multiplier incentive for PHEVs; each vehicle counts as 1. 6 for MYs 2022-2024, and 1.45 for MY 2025, subject to a cap on all vehicle multipliers MYs 2018 and 2019,474 and projections from manufacturers’ mid model year reports for MY 2020.475 Projections from the mid-year and end-of-the-model year reports may differ from EPA-verified final CAFE values either because of differing test results or final salesvolume figures. MY 2011 was selected as the start of the data because it represents the first compliance model year for which manufacturers were 474 MY 2018 data come from information received in manufacturers’ final reports submitted to EPA according to 40 CFR 600.512–12. 475 Manufacturers’ mid-model year CAFE reports are submitted to NHTSA in accordance with 49 CFR part 537. At the time of the analysis, end of the model year data had not yet been submitted for MY 2020. PO 00000 Frm 00216 Fmt 4701 Sfmt 4702 permitted to trade and transfer credits.476 The data go up to MY 2020, because this was the most recent year compliance reports were available. Figure VII–2 through Figure VII–5 provide a graphical overview of the actual and projected compliance data for MYs 2011 to 2020.477 In the figures, an overview is provided for the total fuel economy performance of the industry (the combination of all passenger cars and light trucks produced for sale during the 476 49 CFR 535.6(c). mentioned previously, the figures include estimated values for certain model years based on the most up to date information provided to NHTSA from manufacturers. 477 As E:\FR\FM\03SEP2.SGM 03SEP2 EP03SE21.193</GPH> Regulatory item Federal Register / Vol. 86, No. 169 / Friday, September 3, 2021 / Proposed Rules model year) as a single fleet, and for each of the three CAFE compliance fleets: Domestic passenger car, import passenger car, and light truck fleets. For each of the graphs, a sale-production weighting is applied to determine the average total or fleet Base CAFE performances.478 479 480 The graphs do not include adjustments for full-size pickup trucks because manufactures have yet to bring qualifying products into production. The figures also show how many credits remain in the market each model year. One complicating factor for presenting credits is that the mpg-value of a credit is contingent where it was earned and applied. Therefore, the actual use of the credits for MYs 2018 and beyond will be uncertain until compliance for those model years is completed. Also, since credits can be lotter on DSK11XQN23PROD with PROPOSALS2 478 In the figures, the label ‘‘2-Cycle CAFE’’ represents the maximum increase each year in the average fuel economy set to the limitation ‘‘cap’’ for manufacturers attributable to dual-fueled automobiles as prescribed in 49 U.S.C. 32906. The label ‘‘AC/OC contribution’’ represents the increase in the average fuel economy adjusted for A/C and off-cycle fuel consumption improvement values as prescribed by 40 CFR 600.510–12. 479 Consistent with applicable law, NHTSA established provisions starting in MY 2017 allowing manufacturers to increase compliance performance based on fuel consumption benefits gained by technologies not accounted for during normal 2cycle EPA compliance testing (called ‘‘off-cycle technologies’’ for technologies such as stop-start systems) as well as for A/C systems with improved efficiencies and for hybrid or electric full-size pickup trucks. 480 Adjustments for earned credits include those that have been adjusted for fuel saving using the manufacturers CAFE values for the model years in which they were earned and adjusted to the average CAFE values for the fleets they exist within. VerDate Sep<11>2014 21:48 Sep 02, 2021 Jkt 253001 retained for up