Equipment Price Forecasting in Energy Conservation Standards Analysis, 9696-9700 [2011-3873]
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[FR Doc. 2011–3934 Filed 2–18–11; 8:45 am]
BILLING CODE P
DEPARTMENT OF ENERGY
10 CFR Parts 430 and 431
[Docket No. EE–2008–BT–STD–0012]
Equipment Price Forecasting in Energy
Conservation Standards Analysis
Office of Energy Efficiency and
Renewable Energy, Department of
Energy.
ACTION: Notice of data availability;
request for comment.
AGENCY:
The U.S. Department of
Energy (DOE) seeks information related
to potential technical improvements its
energy conservation standards
rulemaking analysis, and requests
comment on corresponding revisions to
the analysis for energy conservation
standards for refrigerators, refrigeratorfreezers and freezers.
DATES: Written comments and
information are requested on or before
March 24, 2011.
ADDRESSES: Interested persons are
encouraged to submit comments using
the Federal eRulemaking Portal at
https://www.regulations.gov. Follow the
instructions for submitting comments.
Alternatively, interested persons may
submit comments, identified by docket
number EE–2008–BT–STD–0012, by any
of the following methods:
• E-mail: to ResRefFreez–2008–STD–
0012@hq.doe.gov. Include EE–2008–
BT–STD–0012 in the subject line of the
message.
• Mail: Ms. Brenda Edwards, U.S.
Department of Energy, Building
Technologies Program, Mailstop EE–2J,
Equipment Price Forecasting in Energy
Conservation Standards Analysis, EE–
2008–BT–STD–0012, 1000
Independence Avenue, SW.,
Washington, DC 20585–0121. Phone:
(202) 586–2945. Please submit one
signed paper original.
• Hand Delivery/Courier: Ms. Brenda
Edwards, U.S. Department of Energy,
Building Technologies Program, 6th
Floor, 950 L’Enfant Plaza, SW.,
Washington, DC 20024. Phone: (202)
586–2945. Please submit one signed
paper original.
Instructions: All submissions received
must include the agency name and
docket number for this rulemaking.
Docket: For access to the docket to
read background documents, or
comments received, go to the Federal
eRulemaking Portal at https://
www.regulations.gov.
SUMMARY:
FOR FURTHER INFORMATION CONTACT:
Requests for additional information may
be sent to Mr. John Cymbalsky, U.S.
Department of Energy, Office of Energy
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Efficiency and Renewable Energy,
Building Technologies Program, EE–2J,
1000 Independence Avenue, SW.,
Washington, DC 20585–0121.
Telephone: 202–586–4617. E-mail:
Lucas.Adin@ee.doe.gov.
In the office of the General Counsel,
contact Ms. Elizabeth Kohl, U.S.
Department of Energy, Office of the
General Counsel, GC–71,1000
Independence Ave., SW., Room 6A–179,
Washington, DC 20585. Telephone:
202–586–7796; E-mail:
Elizabeth.Kohl@hq.doe.gov.
On
January 18, 2011, the President issued
Executive Order (the Order) 13563,
meant to ensure that regulations seek
more affordable, less intrusive means to
achieve policy goals, and that agencies
give careful consideration to the benefits
and costs of those regulations. Among
other things, the Order requires agencies
propose or adopt a regulation only upon
a reasoned determination that its
benefits justify its costs, the regulation
imposes the least burden on society
consistent with obtaining the regulatory
objectives, and that in choosing among
alternative regulatory approaches,
agencies choose those approaches that
maximize net benefits.
The Order also contains provisions
that bear on the analysis of benefits and
costs. It provides that agencies must
‘‘use the best available techniques to
quantify anticipated present and future
benefits and costs as accurately as
possible.’’ In subsequent guidance on
February 2, 2011, the Office of
Information and Regulatory Affairs
explained that such techniques include
‘‘identifying changing future compliance
costs that might result from
technological innovation or anticipated
behavioral changes.’’
In light of the Order, DOE has
examined its processes for establishing
energy efficiency standards for
consumer products and commercial
equipment. In examining its analytical
approaches for developing these
regulations, DOE has developed a
supplemental approach to help quantify
the impacts flowing from the setting of
efficiency levels for a given product or
equipment. This approach is intended
to improve accuracy in the assessment
of future compliance costs. As part of
this notice, DOE is soliciting comment
on the potential inclusion of this
approach for its future rulemaking
activities. Additionally, DOE is seeking
comment on the merits of adopting this
approach within the context of its
ongoing rulemaking to set standards for
refrigerators, refrigerator-freezers, and
SUPPLEMENTARY INFORMATION:
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freezers (collectively, ‘‘refrigeration
products’’).
Price Forecast Methodology
One of the key estimates that DOE
currently makes during the analysis of
energy conservation standards is the
impact of efficiency regulations on
equipment price. DOE uses its
engineering analysis—which determines
a given appliance’s cost as a function of
its efficiency (through the development
of cost-efficiency curves)—as the basis
for estimating these equipment price
impacts. The technology costs derived
in the engineering analyses form the
basis for product prices used in the
national impact analysis that estimates
regulatory impacts for products sold
over the 30-year analysis period.
Consequently, the price projections
affect the economic impacts calculated
for any potential energy conservation
standard levels.
Currently, DOE’s analyses assume that
the manufacturer costs and retail prices
of products meeting various efficiency
levels remain fixed, in real terms, after
the compliance date and throughout the
period of the analysis. This assumption
is conservative. Examination of
historical price data for certain
appliances and equipment that have
been subject to energy conservation
standards indicates that the assumption
of constant real prices and costs may, in
many cases, over-estimate long-term
appliance and equipment price trends.
Economic literature and historical data
suggest that the real costs of covered
products and equipment may in fact
trend downward over time according to
‘‘learning’’ or ‘‘experience’’ curves. A
draft paper, ‘‘Using the Experience
Curve Approach for Appliance Price
Forecasting,’’ posted on the DOE Web
site along with this notice at https://
www.eere.energy.gov/buildings/
appliance_standards, provides a
summary of the data and literature
currently available to DOE that is
relevant to price forecasts for selected
appliances and equipment.
In light of these data and DOE’s aim
to improve the accuracy and robustness
of its analyses, DOE is considering
assessing future costs by incorporating
learning over time, consistent with the
analysis in the currently available
literature, in its analysis of regulatory
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options in the energy conservation
standards for refrigeration products, in
an attempt to create a more accurate and
robust forecast of the pricing effects that
accompany amended energy efficiency
standards for these products. The
consequences of this approach are
outlined below. DOE is also considering
applying this approach generally to its
energy conservation standards-related
analyses for appliance and commercial
equipment.
DOE seeks comment on the merits of
this approach, particularly with respect
to its application to an analysis of
potential energy efficiency standards for
refrigeration products and the data
presented in this notice.
In addition, DOE requests information
regarding the potential for improving
the methodology for projecting the cost
of efficiency improvements over the
analysis period in general. DOE
provides additional background in the
following paragraphs and seeks input on
three broad categories: (1) Data sources;
(2) potential methodologies; and
(3) procedural issues.
Background
Forecast Method. An extensive
economic literature discusses the
‘‘learning’’ or ‘‘experience’’ curve
phenomenon, typically based on
observations in the manufacturing
sector.1 In the experience curve method,
the real cost of production is related to
the cumulative production or
‘‘experience’’ with a product. To explain
the empirical relationship, the theory of
technology learning is used to
substantiate a decline in the cost of
producing a given product as firms
accumulate experience with the
technology. A common functional
relationship used to model the
evolution of production costs in this
case is:
Y = aX¥b,
where a is an initial price (or cost), b is a
positive constant known as the learning
rate parameter, X is cumulative
production, and Y is the price as a
function of cumulative production.
1 See, for example, the review paper: Weiss, M.,
Junginger, H.M., Patel, M.K., Blok, K., (2010a). A
Review of Experience Curve Analyses for Energy
Demand Technologies. Technological Forecasting &
Social Change. 77:411–428, which provides an
extensive list of studies that have performed
experience curve analyses.
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9697
Thus, as experience (production)
accumulates, the cost of producing the
next unit decreases. The percentage
reduction in cost that occurs with each
doubling of cumulative production is
known as the learning rate (LR), given
by:
LR = 1¥2¥b
DOE’s current price forecast
methodology is a special case of the
forecast equations specified above, but
to date, DOE has assumed that the
learning rate parameter is 0 in its energy
conservation standards analysis. This
notice describes an approach for
improving this assumption and
estimating non-zero learning rate
parameters consistent with historical
cost data.
Data. In typical learning curve
formulations, the learning rate
parameter is derived using two
historical data series: Cumulative
production and price (or cost). On the
basis of previous rulemakings, DOE is
aware of several relevant data sets.
Annual shipments (for calculating
cumulative production) of several
appliances can be found in industry
publications (e.g., Appliance Magazine)
and industry association (e.g., the AirConditioning, Heating, and Refrigeration
Institute (AHRI), the Association of
Home Appliance Manufacturers
(AHAM) Fact Book, etc.) data sets.
Historical shipment-weighted efficiency
data could be gathered from these
sources, as well as from the Energy
Information Administration (EIA).
Historical price or cost data for several
products could be derived from the
Bureau of Labor Statistics’ (BLS)
Producer Price Index (PPI) and/or
Consumer Price Index (CPI).
Table 1 provides these data for
refrigerators, refrigerator-freezers, and
freezers (including compacts). The
inflation-adjusted price index is derived
from CPI data for 1947 to 1997 and PPI
data from 1998 to 2009. The inflationadjusted price is derived from a current
price estimate for refrigerator-freezers
that is then scaled over time by the
inflation-adjusted price index. DOE
estimates that cumulative refrigerator,
refrigerator-freezer, and freezer
shipments are 22.22 million in 1946 and
then they increase each year with the
current year shipments.
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TABLE 1—HISTORICAL DATA REGARDING REFRIGERATOR, REFRIGERATOR-FREEZER, AND FREEZER PRICES AND
SHIPMENTS
Inflation-adjusted price
index
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Year
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
.................................................
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3.95
4.03
3.96
3.83
3.73
3.52
3.37
3.12
2.94
2.50
2.22
2.09
2.07
1.99
1.94
1.88
1.81
1.75
1.67
1.56
1.51
1.47
1.42
1.38
1.35
1.31
1.23
1.17
1.21
1.20
1.16
1.15
1.09
1.02
0.99
1.01
1.01
0.98
0.94
0.92
0.88
0.86
0.83
0.79
0.75
0.72
0.72
0.73
0.71
0.70
0.68
0.63
0.60
0.57
0.54
0.52
0.49
0.48
0.47
0.46
0.45
0.45
0.47
Application to Standards. Given the
information currently available to DOE,
DOE believes (and invites comments on
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Inflation-adjusted price
(2009$)
$4,132
4,218
4,144
4,001
3,906
3,686
3,522
3,258
3,071
2,611
2,326
2,186
2,164
2,081
2,032
1,967
1,890
1,829
1,747
1,633
1,581
1,536
1,482
1,439
1,410
1,366
1,289
1,226
1,262
1,250
1,217
1,200
1,137
1,062
1,031
1,055
1,055
1,028
984
957
923
895
872
823
782
758
753
766
747
736
712
659
630
596
561
539
514
499
494
482
475
475
496
the view that) the following
methodology may provide the most
accurate method for forecasting the
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Shipments
(millions)
Fmt 4702
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Cumulative Shipments
(millions)
4.01
5.46
4.94
7.09
5.09
4.60
4.69
4.65
5.27
4.78
4.45
4.23
4.91
4.61
4.63
4.94
5.31
5.75
6.15
6.21
5.96
6.42
6.58
6.59
7.02
7.66
8.14
7.38
6.00
6.27
7.20
7.43
7.31
6.80
6.73
6.29
7.47
7.99
8.24
8.68
9.08
9.34
8.88
8.97
8.99
9.52
9.84
10.39
10.56
10.93
10.90
11.98
13.02
13.18
13.37
14.84
15.90
16.69
16.73
15.39
15.09
14.37
14.27
26.23
31.68
36.62
43.71
48.79
53.39
58.08
62.73
68.00
72.78
77.23
81.45
86.36
90.98
95.61
100.56
105.87
111.61
117.76
123.97
129.93
136.35
142.94
149.53
156.54
164.21
172.35
179.73
185.72
192.00
199.19
206.62
213.93
220.73
227.46
233.75
241.22
249.20
257.44
266.12
275.20
284.53
293.41
302.37
311.37
320.88
330.72
341.11
351.68
362.60
373.51
385.49
398.51
411.69
425.05
439.89
455.79
472.48
489.21
504.60
519.69
534.06
548.34
incremental cost of efficiency given the
potential impact of long-term product
price trends or technological learning:
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Federal Register / Vol. 76, No. 35 / Tuesday, February 22, 2011 / Proposed Rules
• When sufficiently long-term data
are available on the cost trends for
equipment or technologies for particular
efficiency design options, an empirical
experience curve fit to the available data
may be used to forecast future costs of
such design option technologies. If a
statistical evaluation indicates a low
level of confidence in estimates of the
design option cost trend, this method
should not be used to forecast costs.
• When sufficiently long term data
are not available for forecasting the cost
of products or equipment using specific
efficiency-improving components, the
experience curve cost trend for the
product or equipment as a whole should
be applied to both the product or
equipment price and the incremental
product or equipment price.
• When sufficiently long term data
are not available for a specific product
or equipment, it may be appropriate to
apply the experience curve cost trend
for a similar product or equipment, or
a product or equipment grouping that
includes the product or equipment at
issue, to both the product or equipment
price and the incremental product or
equipment price. Alternatively, DOE
may use experience curve parameters
from review studies that may indicate
that certain parameter ranges apply to
certain classes or groups of products or
equipment that include the product or
equipment under analysis. If data are
not available for estimating a price
trend, DOE may use a constant real
price trend as in past rulemakings.
In other words, when data are
available to help guide DOE in
projecting potential cost reductions over
time for a particular appliance or
equipment, DOE plans to use these data
as part of its analyses. In those instances
where such data are unavailable, DOE
will continue to employ the methods it
currently uses, which is to hold costs at
a fixed level for purposes of long-term
impact projections.
For the energy conservation standards
analysis for refrigerators, refrigeratorfreezers and freezers, long-term data are
available on overall product costs. DOE
is therefore considering use of the long
term trend in product price to forecast
the long term trend in the incremental
cost of efficiency. DOE posts updated
national impact analysis spreadsheets
that incorporate price trend forecasting
at https://www.eere.energy.gov/
buildings/appliance_standards for
public review.
To improve the accuracy and
reliability of price forecasts, DOE may
periodically review the performance of
equipment and incremental efficiency
cost forecasts and may make further
methodological improvements that
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improve forecast accuracy and
reliability.
In the next section, DOE seeks
information on all of the issues covered
in this section, as well as additional
topics.
General Discussion of Potential
Consumer Welfare Impacts
DOE also notes that the economics
literature provides a wide-ranging
discussion of how consumers trade-off
upfront costs and energy savings in the
absence of government intervention.
Much of this literature attempts to
explain why consumers appear to
undervalue energy efficiency
improvements. This undervaluation
suggests that regulation that promotes
energy efficiency can produce
significant net private gains (as well as
producing social gains by, for example,
reducing pollution). There is evidence
that consumers undervalue future
energy savings as a result of (1) a lack
of information, (2) a lack of sufficient
savings to warrant delaying or altering
purchases (e.g. an inefficient ventilation
fan in a new building or the delayed
replacement of a water pump), (3)
inconsistent (e.g. excessive short-term)
weighting of future energy cost savings
relative to available returns on other
investments, (4) computational or other
difficulties associated with the
evaluation of relevant tradeoffs, and (5)
a divergence in incentives (e.g. renter
versus owner; builder v. purchaser). In
the abstract, it may be difficult to say
how a welfare gain from correcting
under-investment compares in
magnitude to the potential welfare
losses associated with no longer
purchasing a machine or switching to an
imperfect substitute, both of which still
exist in this framework.
Other literature indicates that with
less than perfect foresight and
uncertainty about the future, consumers
may trade off these types of investments
at a higher than expected rate between
current consumption and uncertain
future energy cost savings. Some studies
suggest that this seeming
undervaluation may be explained in
certain circumstances by differences
between tested and actual energy
savings, or by uncertainty and
irreversibility of energy investments.
The mix of evidence in the empirical
literature suggests that if feasible,
analysis of regulations mandating
energy efficiency improvements should
explore the potential for both welfare
gains and losses and move toward fuller
economic framework where all relevant
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changes can be quantified.2 While DOE
is not prepared at present to provide a
fuller quantifiable framework for this
discussion, DOE seeks comments on
how to assess these issues.3
Issues on Which DOE Seeks Comment
and Information
Data Sources
1. DOE seeks data related to observed
trends in historical costs, retail prices,
and shipment efficiencies of products
and equipment covered by the Energy
Conservation Standards program.
2. DOE seeks data related to observed
trends in historical costs, retail prices,
and shipment efficiencies of products
and equipment that, while not covered
by the Energy Conservation Standards
program, may be of use to DOE with
respect to its treatment of technology
learning curves and consumer welfare
impacts.
3. DOE seeks data related to historical
costs and prices of covered products
and equipment delineated by efficiency
level.
4. DOE seeks information on the
appropriate range of values for learning
parameters found in the relevant
literature, either in the aggregate or
associated with specific appliances,
equipment, technologies, or production
processes.
Potential Methodologies
1. DOE specifically seeks comment on
the methodology described in the
‘‘Background’’ section above.
2. DOE seeks information on
alternative methodologies for
forecasting equipment price trends in its
analyses.
3. DOE seeks comment on how
changes in other product attributes,
including efficiency, could be
‘‘normalized’’ or ‘‘corrected’’ based on
historical data.
4. DOE seeks comment on methods
for calculating changes in historical
costs or prices, including the use of the
PPI and CPI.
5. DOE seeks comment on methods of
deriving historical production volumes.
6. DOE seeks comment on the details
of the method, data and references
2 A good review of the literature related to this
issue can be found in Gillingham, K., R. Newell, K.
Palmer. (2009). ‘‘Energy Efficiency Economics and
Policy,’’ Annual Review of Resource Economics, 1:
597–619; and Tietenberg, T. (2009). ‘‘Energy
Efficiency Policy: Pipe Dream or Pipeline to the
Future?’’ Review of Environmental Economics and
Policy. Vol. 3, No. 2: 304–320.
3 A draft paper, ‘‘Notes on the Economics of
Household Energy Consumption and Technology
Choice,’’ proposes a broad theoretical framework on
which an empirical model might be based and is
posted on the DOE Web site along with this notice
at https://www.eere.energy.gov/buildings/
appliance_standards.
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Federal Register / Vol. 76, No. 35 / Tuesday, February 22, 2011 / Proposed Rules
described in the draft paper ‘‘Using the
Experience Curve Approach for
Appliance Price Forecasting’’ posted on
the DOE Web site at https://
www.eere.energy.gov/buildings/
appliance_standards.
7. DOE seeks comment on data
sources and analytical methods for
estimating potential consumer welfare
impacts from energy conservation
standards, including information on
specific consumer subgroups of
products regulated under the energy
conservation program.
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Procedural Issues
1. DOE seeks comment on the details
of how equipment price forecasts and
consumer welfare impacts may be
incorporated into specific downstream
analyses that rely on the engineering
analysis outputs and what other
methodological changes to those
analyses might be merited.
2. DOE seeks comment on products or
equipment, or groups of products or
equipment, that are likely to have the
greatest and least improvement in price
forecast accuracy from the application
of experience curve methodology.
3. DOE seeks information on
alternative methods for modeling
persistent price trends for regulated
products or equipment.
General Analysis Methodology
1. DOE seeks comments and
information regarding additional ways
of improving the accounting of costs
and benefits in its energy conservation
standards analysis, including comment
on benefits and costs that may not have
been included in energy conservation
standards analyses to date.
2. DOE seeks information on how
standards can affect the dynamics of
innovation and investment in U.S.
appliance and equipment industries.
3. DOE seeks comment on ways in
which standards-induced innovation
and investment might impact the
competitiveness of U.S. products and
companies in the global marketplace.
4. DOE seeks comment on the
additional global benefits that may arise
from standards that may encourage U.S.
appliances and equipment to have
efficiency performance levels exceeding
the efficiency performance levels of
appliances and equipment in other
countries.
The purpose of this NODA is to solicit
feedback from industry, manufacturers,
academia, consumer groups, efficiency
advocates, government agencies, and
other stakeholders on issues related
price forecasts in DOE’s engineering
analyses for Energy Conservation
Standards rulemakings. DOE is
VerDate Mar<15>2010
16:42 Feb 18, 2011
Jkt 223001
specifically interested in information
and sources of data related to covered
products and equipment that could be
used in formulating a methodology
regarding long term equipment price
forecasts, and a methodology regarding
consumer welfare impacts. Respondents
are advised that DOE is under no
obligation to acknowledge receipt of the
information received or provide
feedback to respondents with respect to
any information submitted under this
NODA. Responses to this NODA do not
bind DOE to any further actions related
to this topic.
Issued in Washington, DC, on February 15,
2011.
Cathy Zoi,
Assistant Secretary, Energy Efficiency and
Renewable Energy.
[FR Doc. 2011–3873 Filed 2–18–11; 8:45 am]
BILLING CODE 6450–01–P
DEPARTMENT OF THE INTERIOR
Office of Surface Mining Reclamation
and Enforcement
30 CFR Part 901
[SATS No. AL–076–FOR; Docket ID: OSM–
2010–0020]
Alabama Regulatory Program
Office of Surface Mining
Reclamation and Enforcement, Interior.
ACTION: Proposed rule; public comment
period and opportunity for public
hearing on proposed amendment.
AGENCY:
We, the Office of Surface
Mining Reclamation and Enforcement
(OSM), are announcing receipt of a
proposed amendment to the Alabama
regulatory program (Alabama program)
under the Surface Mining Control and
Reclamation Act of 1977 (SMCRA or the
Act). Alabama proposes revisions to its
Program regarding their license fees,
annual license updates, and blaster
certification fees. Alabama intends to
revise its program to improve
operational efficiency. The fees will be
used to recover Alabama’s anticipated
costs of reviewing, administering, and
enforcing Alabama’s licensing and
blaster certification requirements.
This document gives the times and
locations that the Alabama program and
proposed amendment to that program
are available for your inspection, the
comment period during which you may
submit written comments on the
amendment, and the procedures that we
will follow for the public hearing, if one
is requested.
DATES: We will accept written
comments on this amendment until
SUMMARY:
PO 00000
Frm 00007
Fmt 4702
Sfmt 4702
4 p.m., c.s.t., March 24, 2011. If
requested, we will hold a public hearing
on the amendment on March 21, 2011.
We will accept requests to speak at a
hearing until 4 p.m., c.s.t. on March 9,
2011.
ADDRESSES: You may submit comments,
identified by SATS No. AL–076–FOR by
any of the following methods:
• E-mail: swilson@osmre.gov. Include
‘‘SATS No. AL–076–FOR’’ in the subject
line of the message.
• Mail/Hand Delivery: Sherry Wilson,
Director, Birmingham Field Office,
Office of Surface Mining Reclamation
and Enforcement, 135 Gemini Circle,
Suite 215, Homewood, Alabama 35209.
• Fax: (205) 290–7280.
• Federal eRulemaking Portal: https://
www.regulations.gov. Follow the
instructions for submitting comments.
Instructions: All submissions received
must include the agency name and
docket number for this rulemaking. For
detailed instructions on submitting
comments and additional information
on the rulemaking process, see the
‘‘Public Comment Procedures’’ heading
of the SUPPLEMENTARY INFORMATION
section of this document.
Docket: For access to the docket to
review copies of the Alabama program,
this amendment, a listing of any
scheduled public hearings, and all
written comments received in response
to this document, you must go to the
address listed below during normal
business hours, Monday through Friday,
excluding holidays. You may receive
one free copy of the amendment by
contacting OSM’s Birmingham Field
Office or going to https://
www.regulations.gov.
Sherry Wilson, Director, Birmingham
Field Office, Office of Surface Mining
Reclamation and Enforcement, 135
Gemini Circle, Suite 215, Homewood,
Alabama 35209, Telephone: (205) 290–
7282, E-mail: swilson@osmre.gov.
In addition, you may review a copy of
the amendment during regular business
hours at the following location:
Alabama Surface Mining Commission,
1811 Second Ave., P.O. Box 2390,
Jasper, Alabama 35502–2390,
Telephone: (205) 221–4130.
FOR FURTHER INFORMATION CONTACT:
Sherry Wilson, Director, Birmingham
Field Office. Telephone: (205) 290–
7282. E-mail: swilson@osmre.gov.
SUPPLEMENTARY INFORMATION:
I. Background on the Alabama Program
II. Description of the Proposed Amendment
III. Public Comment Procedures
IV. Procedural Determinations
I. Background on the Alabama Program
Section 503(a) of the Act permits a
State to assume primacy for the
E:\FR\FM\22FEP1.SGM
22FEP1
]]>Agencies
[Federal Register Volume 76, Number 35 (Tuesday, February 22, 2011)]
[Proposed Rules]
[Pages 9696-9700]
From the Federal Register Online via the Government Printing Office [www.gpo.gov]
[FR Doc No: 2011-3873]
=======================================================================
-----------------------------------------------------------------------
DEPARTMENT OF ENERGY
10 CFR Parts 430 and 431
[Docket No. EE-2008-BT-STD-0012]
Equipment Price Forecasting in Energy Conservation Standards
Analysis
AGENCY: Office of Energy Efficiency and Renewable Energy, Department of
Energy.
ACTION: Notice of data availability; request for comment.
-----------------------------------------------------------------------
SUMMARY: The U.S. Department of Energy (DOE) seeks information related
to potential technical improvements its energy conservation standards
rulemaking analysis, and requests comment on corresponding revisions to
the analysis for energy conservation standards for refrigerators,
refrigerator-freezers and freezers.
DATES: Written comments and information are requested on or before
March 24, 2011.
ADDRESSES: Interested persons are encouraged to submit comments using
the Federal eRulemaking Portal at https://www.regulations.gov. Follow
the instructions for submitting comments. Alternatively, interested
persons may submit comments, identified by docket number EE-2008-BT-
STD-0012, by any of the following methods:
E-mail: to ResRefFreez-2008-STD-0012@hq.doe.gov. Include
EE-2008-BT-STD-0012 in the subject line of the message.
Mail: Ms. Brenda Edwards, U.S. Department of Energy,
Building Technologies Program, Mailstop EE-2J, Equipment Price
Forecasting in Energy Conservation Standards Analysis, EE-2008-BT-STD-
0012, 1000 Independence Avenue, SW., Washington, DC 20585-0121. Phone:
(202) 586-2945. Please submit one signed paper original.
Hand Delivery/Courier: Ms. Brenda Edwards, U.S. Department
of Energy, Building Technologies Program, 6th Floor, 950 L'Enfant
Plaza, SW., Washington, DC 20024. Phone: (202) 586-2945. Please submit
one signed paper original.
Instructions: All submissions received must include the agency name
and docket number for this rulemaking.
Docket: For access to the docket to read background documents, or
comments received, go to the Federal eRulemaking Portal at https://www.regulations.gov.
FOR FURTHER INFORMATION CONTACT: Requests for additional information
may be sent to Mr. John Cymbalsky, U.S. Department of Energy, Office of
Energy Efficiency and Renewable Energy, Building Technologies Program,
EE-2J, 1000 Independence Avenue, SW., Washington, DC 20585-0121.
Telephone: 202-586-4617. E-mail: Lucas.Adin@ee.doe.gov.
In the office of the General Counsel, contact Ms. Elizabeth Kohl,
U.S. Department of Energy, Office of the General Counsel, GC-71,1000
Independence Ave., SW., Room 6A-179, Washington, DC 20585. Telephone:
202-586-7796; E-mail: Elizabeth.Kohl@hq.doe.gov.
SUPPLEMENTARY INFORMATION: On January 18, 2011, the President issued
Executive Order (the Order) 13563, meant to ensure that regulations
seek more affordable, less intrusive means to achieve policy goals, and
that agencies give careful consideration to the benefits and costs of
those regulations. Among other things, the Order requires agencies
propose or adopt a regulation only upon a reasoned determination that
its benefits justify its costs, the regulation imposes the least burden
on society consistent with obtaining the regulatory objectives, and
that in choosing among alternative regulatory approaches, agencies
choose those approaches that maximize net benefits.
The Order also contains provisions that bear on the analysis of
benefits and costs. It provides that agencies must ``use the best
available techniques to quantify anticipated present and future
benefits and costs as accurately as possible.'' In subsequent guidance
on February 2, 2011, the Office of Information and Regulatory Affairs
explained that such techniques include ``identifying changing future
compliance costs that might result from technological innovation or
anticipated behavioral changes.''
In light of the Order, DOE has examined its processes for
establishing energy efficiency standards for consumer products and
commercial equipment. In examining its analytical approaches for
developing these regulations, DOE has developed a supplemental approach
to help quantify the impacts flowing from the setting of efficiency
levels for a given product or equipment. This approach is intended to
improve accuracy in the assessment of future compliance costs. As part
of this notice, DOE is soliciting comment on the potential inclusion of
this approach for its future rulemaking activities. Additionally, DOE
is seeking comment on the merits of adopting this approach within the
context of its ongoing rulemaking to set standards for refrigerators,
refrigerator-freezers, and
[[Page 9697]]
freezers (collectively, ``refrigeration products'').
Price Forecast Methodology
One of the key estimates that DOE currently makes during the
analysis of energy conservation standards is the impact of efficiency
regulations on equipment price. DOE uses its engineering analysis--
which determines a given appliance's cost as a function of its
efficiency (through the development of cost-efficiency curves)--as the
basis for estimating these equipment price impacts. The technology
costs derived in the engineering analyses form the basis for product
prices used in the national impact analysis that estimates regulatory
impacts for products sold over the 30-year analysis period.
Consequently, the price projections affect the economic impacts
calculated for any potential energy conservation standard levels.
Currently, DOE's analyses assume that the manufacturer costs and
retail prices of products meeting various efficiency levels remain
fixed, in real terms, after the compliance date and throughout the
period of the analysis. This assumption is conservative. Examination of
historical price data for certain appliances and equipment that have
been subject to energy conservation standards indicates that the
assumption of constant real prices and costs may, in many cases, over-
estimate long-term appliance and equipment price trends. Economic
literature and historical data suggest that the real costs of covered
products and equipment may in fact trend downward over time according
to ``learning'' or ``experience'' curves. A draft paper, ``Using the
Experience Curve Approach for Appliance Price Forecasting,'' posted on
the DOE Web site along with this notice at https://www.eere.energy.gov/buildings/appliance_standards, provides a summary of the data and
literature currently available to DOE that is relevant to price
forecasts for selected appliances and equipment.
In light of these data and DOE's aim to improve the accuracy and
robustness of its analyses, DOE is considering assessing future costs
by incorporating learning over time, consistent with the analysis in
the currently available literature, in its analysis of regulatory
options in the energy conservation standards for refrigeration
products, in an attempt to create a more accurate and robust forecast
of the pricing effects that accompany amended energy efficiency
standards for these products. The consequences of this approach are
outlined below. DOE is also considering applying this approach
generally to its energy conservation standards-related analyses for
appliance and commercial equipment.
DOE seeks comment on the merits of this approach, particularly with
respect to its application to an analysis of potential energy
efficiency standards for refrigeration products and the data presented
in this notice.
In addition, DOE requests information regarding the potential for
improving the methodology for projecting the cost of efficiency
improvements over the analysis period in general. DOE provides
additional background in the following paragraphs and seeks input on
three broad categories: (1) Data sources; (2) potential methodologies;
and (3) procedural issues.
Background
Forecast Method. An extensive economic literature discusses the
``learning'' or ``experience'' curve phenomenon, typically based on
observations in the manufacturing sector.\1\ In the experience curve
method, the real cost of production is related to the cumulative
production or ``experience'' with a product. To explain the empirical
relationship, the theory of technology learning is used to substantiate
a decline in the cost of producing a given product as firms accumulate
experience with the technology. A common functional relationship used
to model the evolution of production costs in this case is:
---------------------------------------------------------------------------
\1\ See, for example, the review paper: Weiss, M., Junginger,
H.M., Patel, M.K., Blok, K., (2010a). A Review of Experience Curve
Analyses for Energy Demand Technologies. Technological Forecasting &
Social Change. 77:411-428, which provides an extensive list of
studies that have performed experience curve analyses.
---------------------------------------------------------------------------
Y = aX-b,
where a is an initial price (or cost), b is a positive constant
known as the learning rate parameter, X is cumulative production,
and Y is the price as a function of cumulative production. Thus, as
experience (production) accumulates, the cost of producing the next
unit decreases. The percentage reduction in cost that occurs with
each doubling of cumulative production is known as the learning rate
(LR), given by:
LR = 1-2-b
DOE's current price forecast methodology is a special case of the
forecast equations specified above, but to date, DOE has assumed that
the learning rate parameter is 0 in its energy conservation standards
analysis. This notice describes an approach for improving this
assumption and estimating non-zero learning rate parameters consistent
with historical cost data.
Data. In typical learning curve formulations, the learning rate
parameter is derived using two historical data series: Cumulative
production and price (or cost). On the basis of previous rulemakings,
DOE is aware of several relevant data sets. Annual shipments (for
calculating cumulative production) of several appliances can be found
in industry publications (e.g., Appliance Magazine) and industry
association (e.g., the Air-Conditioning, Heating, and Refrigeration
Institute (AHRI), the Association of Home Appliance Manufacturers
(AHAM) Fact Book, etc.) data sets. Historical shipment-weighted
efficiency data could be gathered from these sources, as well as from
the Energy Information Administration (EIA). Historical price or cost
data for several products could be derived from the Bureau of Labor
Statistics' (BLS) Producer Price Index (PPI) and/or Consumer Price
Index (CPI).
Table 1 provides these data for refrigerators, refrigerator-
freezers, and freezers (including compacts). The inflation-adjusted
price index is derived from CPI data for 1947 to 1997 and PPI data from
1998 to 2009. The inflation-adjusted price is derived from a current
price estimate for refrigerator-freezers that is then scaled over time
by the inflation-adjusted price index. DOE estimates that cumulative
refrigerator, refrigerator-freezer, and freezer shipments are 22.22
million in 1946 and then they increase each year with the current year
shipments.
[[Page 9698]]
Table 1--Historical Data Regarding Refrigerator, Refrigerator-Freezer, and Freezer Prices and Shipments
--------------------------------------------------------------------------------------------------------------------------------------------------------
Inflation-adjusted Inflation-adjusted Cumulative Shipments
Year price index price (2009$) Shipments (millions) (millions)
--------------------------------------------------------------------------------------------------------------------------------------------------------
1947................................................ 3.95 $4,132 4.01 26.23
1948................................................ 4.03 4,218 5.46 31.68
1949................................................ 3.96 4,144 4.94 36.62
1950................................................ 3.83 4,001 7.09 43.71
1951................................................ 3.73 3,906 5.09 48.79
1952................................................ 3.52 3,686 4.60 53.39
1953................................................ 3.37 3,522 4.69 58.08
1954................................................ 3.12 3,258 4.65 62.73
1955................................................ 2.94 3,071 5.27 68.00
1956................................................ 2.50 2,611 4.78 72.78
1957................................................ 2.22 2,326 4.45 77.23
1958................................................ 2.09 2,186 4.23 81.45
1959................................................ 2.07 2,164 4.91 86.36
1960................................................ 1.99 2,081 4.61 90.98
1961................................................ 1.94 2,032 4.63 95.61
1962................................................ 1.88 1,967 4.94 100.56
1963................................................ 1.81 1,890 5.31 105.87
1964................................................ 1.75 1,829 5.75 111.61
1965................................................ 1.67 1,747 6.15 117.76
1966................................................ 1.56 1,633 6.21 123.97
1967................................................ 1.51 1,581 5.96 129.93
1968................................................ 1.47 1,536 6.42 136.35
1969................................................ 1.42 1,482 6.58 142.94
1970................................................ 1.38 1,439 6.59 149.53
1971................................................ 1.35 1,410 7.02 156.54
1972................................................ 1.31 1,366 7.66 164.21
1973................................................ 1.23 1,289 8.14 172.35
1974................................................ 1.17 1,226 7.38 179.73
1975................................................ 1.21 1,262 6.00 185.72
1976................................................ 1.20 1,250 6.27 192.00
1977................................................ 1.16 1,217 7.20 199.19
1978................................................ 1.15 1,200 7.43 206.62
1979................................................ 1.09 1,137 7.31 213.93
1980................................................ 1.02 1,062 6.80 220.73
1981................................................ 0.99 1,031 6.73 227.46
1982................................................ 1.01 1,055 6.29 233.75
1983................................................ 1.01 1,055 7.47 241.22
1984................................................ 0.98 1,028 7.99 249.20
1985................................................ 0.94 984 8.24 257.44
1986................................................ 0.92 957 8.68 266.12
1987................................................ 0.88 923 9.08 275.20
1988................................................ 0.86 895 9.34 284.53
1989................................................ 0.83 872 8.88 293.41
1990................................................ 0.79 823 8.97 302.37
1991................................................ 0.75 782 8.99 311.37
1992................................................ 0.72 758 9.52 320.88
1993................................................ 0.72 753 9.84 330.72
1994................................................ 0.73 766 10.39 341.11
1995................................................ 0.71 747 10.56 351.68
1996................................................ 0.70 736 10.93 362.60
1997................................................ 0.68 712 10.90 373.51
1998................................................ 0.63 659 11.98 385.49
1999................................................ 0.60 630 13.02 398.51
2000................................................ 0.57 596 13.18 411.69
2001................................................ 0.54 561 13.37 425.05
2002................................................ 0.52 539 14.84 439.89
2003................................................ 0.49 514 15.90 455.79
2004................................................ 0.48 499 16.69 472.48
2005................................................ 0.47 494 16.73 489.21
2006................................................ 0.46 482 15.39 504.60
2007................................................ 0.45 475 15.09 519.69
2008................................................ 0.45 475 14.37 534.06
2009................................................ 0.47 496 14.27 548.34
--------------------------------------------------------------------------------------------------------------------------------------------------------
Application to Standards. Given the information currently available
to DOE, DOE believes (and invites comments on the view that) the
following methodology may provide the most accurate method for
forecasting the incremental cost of efficiency given the potential
impact of long-term product price trends or technological learning:
[[Page 9699]]
When sufficiently long-term data are available on the cost
trends for equipment or technologies for particular efficiency design
options, an empirical experience curve fit to the available data may be
used to forecast future costs of such design option technologies. If a
statistical evaluation indicates a low level of confidence in estimates
of the design option cost trend, this method should not be used to
forecast costs.
When sufficiently long term data are not available for
forecasting the cost of products or equipment using specific
efficiency-improving components, the experience curve cost trend for
the product or equipment as a whole should be applied to both the
product or equipment price and the incremental product or equipment
price.
When sufficiently long term data are not available for a
specific product or equipment, it may be appropriate to apply the
experience curve cost trend for a similar product or equipment, or a
product or equipment grouping that includes the product or equipment at
issue, to both the product or equipment price and the incremental
product or equipment price. Alternatively, DOE may use experience curve
parameters from review studies that may indicate that certain parameter
ranges apply to certain classes or groups of products or equipment that
include the product or equipment under analysis. If data are not
available for estimating a price trend, DOE may use a constant real
price trend as in past rulemakings.
In other words, when data are available to help guide DOE in
projecting potential cost reductions over time for a particular
appliance or equipment, DOE plans to use these data as part of its
analyses. In those instances where such data are unavailable, DOE will
continue to employ the methods it currently uses, which is to hold
costs at a fixed level for purposes of long-term impact projections.
For the energy conservation standards analysis for refrigerators,
refrigerator-freezers and freezers, long-term data are available on
overall product costs. DOE is therefore considering use of the long
term trend in product price to forecast the long term trend in the
incremental cost of efficiency. DOE posts updated national impact
analysis spreadsheets that incorporate price trend forecasting at
https://www.eere.energy.gov/buildings/appliance_standards for public
review.
To improve the accuracy and reliability of price forecasts, DOE may
periodically review the performance of equipment and incremental
efficiency cost forecasts and may make further methodological
improvements that improve forecast accuracy and reliability.
In the next section, DOE seeks information on all of the issues
covered in this section, as well as additional topics.
General Discussion of Potential Consumer Welfare Impacts
DOE also notes that the economics literature provides a wide-
ranging discussion of how consumers trade-off upfront costs and energy
savings in the absence of government intervention. Much of this
literature attempts to explain why consumers appear to undervalue
energy efficiency improvements. This undervaluation suggests that
regulation that promotes energy efficiency can produce significant net
private gains (as well as producing social gains by, for example,
reducing pollution). There is evidence that consumers undervalue future
energy savings as a result of (1) a lack of information, (2) a lack of
sufficient savings to warrant delaying or altering purchases (e.g. an
inefficient ventilation fan in a new building or the delayed
replacement of a water pump), (3) inconsistent (e.g. excessive short-
term) weighting of future energy cost savings relative to available
returns on other investments, (4) computational or other difficulties
associated with the evaluation of relevant tradeoffs, and (5) a
divergence in incentives (e.g. renter versus owner; builder v.
purchaser). In the abstract, it may be difficult to say how a welfare
gain from correcting under-investment compares in magnitude to the
potential welfare losses associated with no longer purchasing a machine
or switching to an imperfect substitute, both of which still exist in
this framework.
Other literature indicates that with less than perfect foresight
and uncertainty about the future, consumers may trade off these types
of investments at a higher than expected rate between current
consumption and uncertain future energy cost savings. Some studies
suggest that this seeming undervaluation may be explained in certain
circumstances by differences between tested and actual energy savings,
or by uncertainty and irreversibility of energy investments.
The mix of evidence in the empirical literature suggests that if
feasible, analysis of regulations mandating energy efficiency
improvements should explore the potential for both welfare gains and
losses and move toward fuller economic framework where all relevant
changes can be quantified.\2\ While DOE is not prepared at present to
provide a fuller quantifiable framework for this discussion, DOE seeks
comments on how to assess these issues.\3\
---------------------------------------------------------------------------
\2\ A good review of the literature related to this issue can be
found in Gillingham, K., R. Newell, K. Palmer. (2009). ``Energy
Efficiency Economics and Policy,'' Annual Review of Resource
Economics, 1: 597-619; and Tietenberg, T. (2009). ``Energy
Efficiency Policy: Pipe Dream or Pipeline to the Future?'' Review of
Environmental Economics and Policy. Vol. 3, No. 2: 304-320.
\3\ A draft paper, ``Notes on the Economics of Household Energy
Consumption and Technology Choice,'' proposes a broad theoretical
framework on which an empirical model might be based and is posted
on the DOE Web site along with this notice at https://www.eere.energy.gov/buildings/appliance_standards.
---------------------------------------------------------------------------
Issues on Which DOE Seeks Comment and Information
Data Sources
1. DOE seeks data related to observed trends in historical costs,
retail prices, and shipment efficiencies of products and equipment
covered by the Energy Conservation Standards program.
2. DOE seeks data related to observed trends in historical costs,
retail prices, and shipment efficiencies of products and equipment
that, while not covered by the Energy Conservation Standards program,
may be of use to DOE with respect to its treatment of technology
learning curves and consumer welfare impacts.
3. DOE seeks data related to historical costs and prices of covered
products and equipment delineated by efficiency level.
4. DOE seeks information on the appropriate range of values for
learning parameters found in the relevant literature, either in the
aggregate or associated with specific appliances, equipment,
technologies, or production processes.
Potential Methodologies
1. DOE specifically seeks comment on the methodology described in
the ``Background'' section above.
2. DOE seeks information on alternative methodologies for
forecasting equipment price trends in its analyses.
3. DOE seeks comment on how changes in other product attributes,
including efficiency, could be ``normalized'' or ``corrected'' based on
historical data.
4. DOE seeks comment on methods for calculating changes in
historical costs or prices, including the use of the PPI and CPI.
5. DOE seeks comment on methods of deriving historical production
volumes.
6. DOE seeks comment on the details of the method, data and
references
[[Page 9700]]
described in the draft paper ``Using the Experience Curve Approach for
Appliance Price Forecasting'' posted on the DOE Web site at https://www.eere.energy.gov/buildings/appliance_standards.
7. DOE seeks comment on data sources and analytical methods for
estimating potential consumer welfare impacts from energy conservation
standards, including information on specific consumer subgroups of
products regulated under the energy conservation program.
Procedural Issues
1. DOE seeks comment on the details of how equipment price
forecasts and consumer welfare impacts may be incorporated into
specific downstream analyses that rely on the engineering analysis
outputs and what other methodological changes to those analyses might
be merited.
2. DOE seeks comment on products or equipment, or groups of
products or equipment, that are likely to have the greatest and least
improvement in price forecast accuracy from the application of
experience curve methodology.
3. DOE seeks information on alternative methods for modeling
persistent price trends for regulated products or equipment.
General Analysis Methodology
1. DOE seeks comments and information regarding additional ways of
improving the accounting of costs and benefits in its energy
conservation standards analysis, including comment on benefits and
costs that may not have been included in energy conservation standards
analyses to date.
2. DOE seeks information on how standards can affect the dynamics
of innovation and investment in U.S. appliance and equipment
industries.
3. DOE seeks comment on ways in which standards-induced innovation
and investment might impact the competitiveness of U.S. products and
companies in the global marketplace.
4. DOE seeks comment on the additional global benefits that may
arise from standards that may encourage U.S. appliances and equipment
to have efficiency performance levels exceeding the efficiency
performance levels of appliances and equipment in other countries.
The purpose of this NODA is to solicit feedback from industry,
manufacturers, academia, consumer groups, efficiency advocates,
government agencies, and other stakeholders on issues related price
forecasts in DOE's engineering analyses for Energy Conservation
Standards rulemakings. DOE is specifically interested in information
and sources of data related to covered products and equipment that
could be used in formulating a methodology regarding long term
equipment price forecasts, and a methodology regarding consumer welfare
impacts. Respondents are advised that DOE is under no obligation to
acknowledge receipt of the information received or provide feedback to
respondents with respect to any information submitted under this NODA.
Responses to this NODA do not bind DOE to any further actions related
to this topic.
Issued in Washington, DC, on February 15, 2011.
Cathy Zoi,
Assistant Secretary, Energy Efficiency and Renewable Energy.
[FR Doc. 2011-3873 Filed 2-18-11; 8:45 am]
BILLING CODE 6450-01-P
