[Federal Register Volume 87, Number 76 (Wednesday, April 20, 2022)]
[Rules and Regulations]
[Pages 23421-23431]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-08427]
========================================================================
Rules and Regulations
Federal Register
________________________________________________________________________
This section of the FEDERAL REGISTER contains regulatory documents
having general applicability and legal effect, most of which are keyed
to and codified in the Code of Federal Regulations, which is published
under 50 titles pursuant to 44 U.S.C. 1510.
The Code of Federal Regulations is sold by the Superintendent of Documents.
========================================================================
Federal Register / Vol. 87, No. 76 / Wednesday, April 20, 2022 /
Rules and Regulations
[[Page 23421]]
DEPARTMENT OF ENERGY
10 CFR Part 431
[EERE-2013-BT-STD-0030]
RIN 1904-AD01
Energy Conservation Program: Energy Conservation Standards for
Commercial Packaged Boilers; Response to United States Court of Appeals
for the District of Columbia Circuit Remand in American Public Gas
Association v. United States Department of Energy
AGENCY: Office of Energy Efficiency and Renewable Energy, Department of
Energy.
ACTION: Final rule; supplemental response to comments.
-----------------------------------------------------------------------
SUMMARY: On January 10, 2020, a final rule amending energy conservation
standards for commercial packaged boilers was published in the Federal
Register. The American Public Gas Association, Air-conditioning,
Heating, and Refrigeration Institute, and Spire Inc. filed petitions
for review of the final rule in the United States Courts of Appeals for
the District of Columbia Circuit (``D.C. Circuit''), Fourth Circuit,
and Eight Circuit, respectively. These petitions were consolidated in
the D.C. Circuit. In its January 18, 2022, opinion, the D.C. Circuit
remanded the final rule to the Department of Energy (``DOE'') to
supplement its responses to the following three issues raised during
the public comment period: The random assignment of boilers to
buildings, forecasted fuel prices, and estimated burner operating
hours. This document provides additional explanation regarding these
three issues.
DATES: This supplemental response to comments document is effective
April 20, 2022. The effective date of the final rule was March 10,
2020. Compliance with the amended standards established for commercial
packaged boilers in that final rule is required on and after January
10, 2023.
ADDRESSES: Docket: The docket for this activity, which includes Federal
Register notices, comments, and other supporting documents/materials,
is available for review at www.regulations.gov. All documents in the
docket are listed in the www.regulations.gov index. However, some
documents listed in the index, such as those containing information
that is exempt from public disclosure, may not be publicly available.
The docket web page can be found at www.regulations.gov/docket/EERE-2013-BT-STD-0030. The docket web page contains instructions on how
to access all documents, including public comments, in the docket.
FOR FURTHER INFORMATION CONTACT: Ms. Julia Hegarty, U.S. Department of
Energy, Office of Energy Efficiency and Renewable Energy, Building
Technologies Office, EE-5B, 1000 Independence Avenue SW, Washington, DC
20585-0121. Telephone: (240) 597-6737. Email: [email protected].
Mr. Pete Cochran, U.S. Department of Energy, Office of the General
Counsel, GC-33, 1000 Independence Avenue SW, Washington, DC 20585-0121.
Telephone: (202) 586-9496. Email: [email protected].
For further information on how to review the docket, contact the
Appliance and Equipment Standards Program staff at (202) 287-1445 or by
email: [email protected].
SUPPLEMENTARY INFORMATION:
Table of Contents
I. Overview
II. Background
III. Supplemental Response to Comments
A. Random Assignment of Boiler Efficiency to Buildings
B. Fuel Prices
C. Burner Operating Hours
IV. Procedural Issues and Regulatory Review
I. Overview
In its January 18, 2022, opinion, the United States Court of
Appeals for the District of Columbia Circuit remanded to the Department
of Energy (``DOE'') the final rule, Energy Conservation Program: Energy
Conservation Standards for Commercial Packaged Boilers, EERE-2013-BT-
STD-0030. See American Public Gas Association v. United States
Department of Energy, No. 20-1068 (Jan. 18, 2022), 2022 WL 151923. In
its opinion, the court determined that DOE failed to provide meaningful
responses to comments with respect to three distinct issues related to
the modeling used during the rulemaking proceeding: (1) The random
assignment of boilers to buildings; (2) forecasted fuel prices; and (3)
estimated burner operating hours. As a result, the court concluded that
DOE failed to adequately explain why the rule satisfies the applicable
clear and convincing evidence standard. To afford DOE the opportunity
to cure these ``failures to explain,'' the court remanded the final
rule to DOE for the agency to take appropriate remedial action within
90 days. In this document, DOE provides further explanation addressing
the three issues the court identified.
II. Background
The American Society of Heating, Refrigerating, and Air-
Conditioning Engineers (``ASHRAE'') Standard 90.1 (ASHRAE Standard
90.1), ``Energy Standard for Buildings Except Low-Rise Residential
Buildings,'' sets industry energy efficiency levels for, among other
things, commercial packaged boilers (``CPBs''). The Energy Policy and
Conservation Act (``EPCA'') directs that if ASHRAE amends Standard
90.1, DOE must adopt amended standards at the new ASHRAE efficiency
level, unless DOE determines, supported by clear and convincing
evidence, that adoption of a more stringent level would produce
significant additional conservation of energy and would be
technologically feasible and economically justified. (42 U.S.C.
6313(a)(6)(A)(ii)) Under EPCA, DOE must also review energy efficiency
standards for CPBs every six years and determine, based on clear and
convincing evidence, whether adoption of a more stringent standard
would result in significant additional conservation of energy and is
technologically feasible and economically justified. (42 U.S.C.
6313(a)(6)(C)) In determining whether a proposed standard is
economically justified, EPCA requires DOE to consider the following
seven factors: (1) Economic impacts on manufacturers and consumers; (2)
changes in total installation and operating costs for the covered
product, i.e., life-cycle costs; (3) total energy savings; (4) any
likely
[[Page 23422]]
decrease in a product's utility or performance; (5) impacts on
competition as determined by the Attorney General; (6) need for
national energy conversation; and (7) other factors DOE considers
relevant. (42 U.S.C. 6313(a)(6)(B)(ii))
As ASHRAE has not amended the standards for CPBs since 2007,\1\ DOE
initiated the required 6-year lookback review in 2013.\2\ DOE proposed
amended standards for CPBs in a notice of proposed rulemaking published
on March 24, 2016. 81 FR 15836. Subsequently, DOE issued a final rule
amending standards for CPBs that was published on January 10, 2020. 85
FR 1592 (``January 2020 Final Rule'').
---------------------------------------------------------------------------
\1\ DOE adopted the 2007 ASHRAE standards in a final rule
published on July 22, 2009. 74 FR 36312.
\2\ DOE initiated the rulemaking process with a preliminary
framework document that was published on September 3, 2013. 78 FR
54197.
---------------------------------------------------------------------------
III. Supplemental Response to Comments
In response to the remand in American Public Gas Association v.
United States Department of Energy, the following discussion
supplements the January 2020 Final Rule explanation of and response to
comments regarding the assignment of boiler efficiencies to buildings,
forecasted fuel prices, and estimated burner operating hours. The
following discussion provides additional detail of the analyses
presented in the final technical support document (``TSD'')
accompanying the January 2020 Final Rule.
A. Random Assignment of Boiler Efficiency to Buildings
DOE's initial response to stakeholders regarding the assignment of
boiler efficiencies to buildings in the Monte Carlo model used to
calculate life-cycle cost (``LCC'') changes is in section IV.F.11 of
the January 2020 Final Rule. 85 FR 1592, 1637-1638.
The LCC calculates, at the consumer level, the discounted savings
in operating costs (less maintenance and repair costs) throughout the
estimated life of the covered equipment, compared to any increase in
the installed cost for the equipment likely to result directly from the
imposition of the standard. In conducting the LCC analysis, DOE first
forecasts equipment shipments in the absence of new or amended
standards (``no-new-standards case''), including the distribution of
equipment efficiency across all consumers. To estimate the impact that
new or amended standards would have on LCC (and energy savings), DOE
then uses a ``roll-up'' scenario, which takes into consideration the
same market failures as in the no-new-standards scenario, as discussed
further below, to determine what changes will occur under the new
standards. A roll-up scenario assumes that equipment efficiencies in
the no-new-standards case, which do not meet the standard level under
consideration, would ``roll up'' to the lowest efficiency required to
meet the new efficiency standard level. For example, the January 2020
Final Rule established a minimum thermal efficiency of 84 percent for
small gas-fired hot water CPBs (the product class with the largest
number of shipments). But DOE estimates that in 2020 approximately 81.3
percent of the market for small gas-fired hot water CPBs already meets
this minimum thermal efficiency.\3\ As a result, DOE's analysis rolls
up only the remaining 18.7 percent of the market, comprised of the
least-efficient CPBs available, to the new minimum thermal efficiency
of 84%. This roll-up in efficiencies results in the projected LCC and
energy savings from the amended standard by forcing the less than 20%
segment of the market that purchases lower efficiency CPBs to purchase
a more-efficient, minimally compliant CPB. Consumers already purchasing
higher efficiency equipment, more than 80% of the market in this
example, are not impacted by a new or amended standard set at a lower
efficiency level and, as a result, do not account for any of the LCC or
energy savings projected to result from the amended rule.
---------------------------------------------------------------------------
\3\ See appendix 8H of the final rule TSD.
---------------------------------------------------------------------------
To conduct its LCC analysis, DOE has developed spreadsheet models
combined with a commercially available program (i.e., Crystal Ball).
This allows DOE to explicitly model both the uncertainty and the
variability in the inputs to the model using Monte Carlo simulation and
probability distributions. The LCC results are displayed as
distributions of impacts compared to the baseline conditions. Results
are based on 10,000 samples per Monte Carlo simulation run.
As discussed in the January 2020 Final Rule \4\ and the
accompanying TSD,\5\ to develop the no-new-standards case, DOE
assembled data on the share of models in each equipment class,
separated by draft type,\6\ based on the Air-Conditioning, Heating and
Refrigeration Institute (``AHRI'') certification directory and on
shipments data submitted by AHRI for small gas-fired hot water
(``SGHW'') and large gas-fired hot water (``LGHW'') equipment classes
broken down by efficiency. DOE utilized these data to develop the no-
new-standards case efficiency distribution for each CPB equipment
class. The efficiency distribution developed by DOE for each product
class resulted in a shipment-weighted average efficiency that was
consistent with the shipment-weighted values submitted by AHRI. This
efficiency distribution was then used in assigning the efficiencies of
installed CPBs under the no-new standards case.
---------------------------------------------------------------------------
\4\ 85 FR 1592, 1635-1636.
\5\ See section 8.2.2.9 of chapter 8 of the final rule TSD, and
appendix 8H of the final rule TSD.
\6\ The regulations for commercial packaged boilers prior to the
January 2020 Final Rule listed 10 equipment classes with
corresponding energy efficiency standards for each. 10 CFR 431.87;
January 2019 edition. These equipment classes were based on (1) size
(rated input), (2) heating media (hot water or steam), and (3) type
of fuel used (oil or gas). Commercial packaged boilers are further
classified according to draft type (i.e., the means by which
combustion gases are moved through the unit's stack.).
---------------------------------------------------------------------------
To conduct the Monte Carlo simulation for the LCC analysis of a
given product class in which the efficiencies of installed models are
forecast over the analysis period, DOE developed a building sample from
the Energy Information Administration's (``EIA'') 2012 Commercial
Building Energy Consumption Survey (``CBECS 2012'') \7\ and the 2009
Residential Energy Consumption Survey (``RECS 2009'').\8\ CBECS is a
national sample survey that collects information on the stock of U.S.
commercial buildings, including their energy-related building
characteristics and energy usage data (consumption and expenditures).
Commercial buildings include all buildings in which at least half of
the floorspace is used for a purpose that is not residential,
industrial, or agricultural. Similarly, RECS is a nationally
representative sample of housing units that collects energy
characteristics on the housing unit, usage patterns, and household
demographics. This information is combined with data from energy
suppliers to these homes to estimate energy costs and usage for
heating, cooling, appliances and other end uses.
---------------------------------------------------------------------------
\7\ EIA, 2012 Commercial Building Energy Consumption Survey,
www.eia.gov/consumption/commercial/ (Last accessed January 20,
2022).
\8\ EIA, 2009 Residential Energy Consumption Survey,
www.eia.gov/consumption/residential/ (Last accessed January 20,
2022).
---------------------------------------------------------------------------
Each building in the sample was then assigned a boiler efficiency
sampled from the no-new-standards case efficiency distribution for the
appropriate equipment class. DOE was not able to assign a CPB
efficiency to a building in the no-new-standards case based on building
characteristics, since CBECS 2012 and RECS 2009 did not provide enough
information to distinguish installed boilers by
[[Page 23423]]
application type, distribution system, or return water temperature, and
there were no shipments data disaggregating boiler efficiency by region
or other criteria. The efficiency of a boiler was assigned based on the
forecasted efficiency distribution (which is constrained by the
shipment and model data collected by DOE and submitted by AHRI) and
accounts for consumers that are already purchasing efficient CPBs.\9\
---------------------------------------------------------------------------
\9\ Appendix 8H of the final rule TSD shows the no-new-standards
case efficiency distributions for all product classes.
---------------------------------------------------------------------------
For example, as previously discussed, the January 2020 Final Rule
established a minimum thermal efficiency of 84 percent for small gas-
fired hot water CPBs (the product class with the largest number of
shipments), but DOE estimates that in 2020 approximately 81.3 percent
of the market for small gas-fired hot water CPBs already meets this
minimum thermal efficiency and thus will not be impacted by the final
rule. The assignment of CPB efficiency in the LCC accounts for this
distribution (e.g., as models with at least an 84 percent efficiency
represent approximately 81.3 percent of the market, there was an 81.3-
percent chance that a building would be assigned a boiler with an 84
percent efficiency or higher).
As noted in the January 2020 Final Rule, AHRI and Burnham Holdings
commented that the random assignment of no-new-standards case
efficiencies (sampled from the developed efficiency distribution) in
the LCC model is not correct, as this inherently assumes that the
purchasers do not pay attention to costs and benefits in a world
without standards. 85 FR 1592, 1637-1638. Instead, AHRI proposed an
alternate approach that assigned the highest boiler efficiencies to
scenarios involving the shortest payback periods. 85 FR 1592, 1637. In
other words, AHRI assumed there were no market failures affecting
consumer boiler purchases.
While DOE acknowledges that economic factors may play a role when
building owners or builders decide on what type of boiler to install,
assignment of boiler efficiency for a given installation, based solely
on economic measures such as life-cycle cost or simple payback period,
most likely would not fully and accurately reflect actual real-world
installations. There are a number of commercial sector market failures
discussed in the economics literature, including a number of case
studies, that illustrate how purchasing decisions with respect to
energy efficiency are likely to not be completely correlated with
energy use, as described below. DOE noted some of these market failures
affecting purchasing decisions in sections IV.F.11 and VI.A of the
January 2020 Final Rule, such as information asymmetry and the high
costs of gathering and analyzing relevant information, the misaligned
incentives between building owners (or landlords) and building
operators, and the external benefits of improved energy efficiency
(such as climate and health benefits) not captured by users of the
equipment. 85 FR 1592, 1638, 1676. DOE also noted these same market
failures in the March 2016 notice of proposed rulemaking. 81 FR 15836,
15913. The following discussion further expands on these market
failures impacting the commercial sector and supplements DOE's
discussion from the January 2020 Final Rule. Additionally, DOE has
since become aware of several case studies and sources of data specific
to the commercial packaged boiler market that support DOE's conclusion
regarding the existence of market failures and DOE's assignment of
boiler efficiency in the no-new-standards case. These case studies and
sources of data further supplement and expand upon DOE's conclusion in
the January 2020 Final Rule that an assignment of boiler efficiency
based solely on calculated payback, without consideration of these
market failures, ``reflects an overly optimistic and unrealistic
working market'' and ``may unreasonably bias the results.'' 85 FR 1592,
1637.
There are several market failures or barriers that affect energy
decisions generally. Some of those that affect the commercial sector
specifically are detailed below. However, more generally, there are
several behavioral factors that can influence the purchasing decisions
of complicated multi-attribute products, such as boilers. For example,
consumers (or decision makers in an organization) are highly influenced
by choice architecture, defined as the framing of the decision, the
surrounding circumstances of the purchase, the alternatives available,
and how they're presented for any given choice scenario.\10\ The same
consumer or decision maker may make different choices depending on the
characteristics of the decision context (e.g., the timing of the
purchase, competing demands for funds), which have nothing to do with
the characteristics of the alternatives themselves or their prices.
Consumers or decision makers also face a variety of other behavioral
phenomena including loss aversion, sensitivity to information salience,
and other forms of bounded rationality.\11\ Thaler, who won the Nobel
Prize in Economics in 2017 for his contributions to behavioral
economics, and Sunstein point out that these behavioral factors are
strongest when the decisions are complex and infrequent, when feedback
on the decision is muted and slow, and when there is a high degree of
information asymmetry.\12\ These characteristics describe almost all
purchasing situations of appliances and equipment, including CPBs. The
installation of a new or replacement CPB in a commercial building is a
complex, technical decision involving many actors and is done very
infrequently, as evidenced by the CPB mean lifetime of nearly 25 years.
85 FR 1592, 1634. Additionally, it would take at least one full heating
season for any impacts on operating costs to be fully apparent.
Further, if the purchaser of the CPB is not the entity paying the
energy costs (e.g., a building owner and tenant), there may be little
to no feedback on the purchase. These behavioral factors are in
addition to the more specific market failures described as follows.
---------------------------------------------------------------------------
\10\ Thaler, R.H., Sunstein, C.R., and Balz, J.P. (2014).
``Choice Architecture'' in The Behavioral Foundations of Public
Policy, Eldar Shafir (ed).
\11\ Thaler, R.H., and Bernartzi, S. (2004). ``Save More
Tomorrow: Using Behavioral Economics in Increase Employee Savings,''
Journal of Political Economy 112(1), S164-S187. See also Klemick,
H., et al. (2015) ``Heavy-Duty Trucking and the Energy Efficiency
Paradox: Evidence from Focus Groups and Interviews,'' Transportation
Research Part A: Policy & Practice, 77, 154-166. (providing evidence
that loss aversion and other market failures can affect otherwise
profit-maximizing firms).
\12\ Thaler, R.H., and Sunstein, C.R. (2008). Nudge: Improving
Decisions on Health, Wealth, and Happiness. New Haven, CT: Yale
University Press.
---------------------------------------------------------------------------
It is often assumed that because commercial and industrial
customers are businesses that have trained or experienced individuals
making decisions regarding investments in cost-saving measures, some of
the commonly observed market failures present in the general population
of residential customers should not be as prevalent in a commercial
setting. However, there are many characteristics of organizational
structure and historic circumstance in commercial settings that can
lead to underinvestment in energy efficiency.
First, a recognized problem in commercial settings is the
principal-agent problem, where the building owner (or building
developer) selects the equipment and the tenant (or subsequent building
owner) pays for energy costs.13 14 Indeed, more than a
[[Page 23424]]
quarter of commercial buildings with a boiler in the CBECS 2012 sample
are occupied at least in part by a tenant, not the building owner
(indicating that, in DOE's experience, the building owner likely is not
responsible for paying energy costs). Additionally, some commercial
buildings have multiple tenants. There are other similar misaligned
incentives embedded in the organizational structure within a given firm
or business that can impact the choice of a CPB. For example, if one
department or individual within an organization is responsible for
capital expenditures (and therefore equipment selection) while a
separate department or individual is responsible for paying the energy
bills, a market failure similar to the principal-agent problem can
result.\15\ Additionally, managers may have other responsibilities and
often have other incentives besides operating cost minimization, such
as satisfying shareholder expectations, which can sometimes be focused
on short-term returns.\16\ Decision-making related to commercial
buildings is highly complex and involves gathering information from and
for a variety of different market actors. It is common to see
conflicting goals across various actors within the same organization as
well as information asymmetries between market actors in the energy
efficiency context in commercial building construction.\17\
---------------------------------------------------------------------------
\13\ Vernon, D., and Meier, A. (2012). ``Identification and
quantification of principal-agent problems affecting energy
efficiency investments and use decisions in the trucking industry,''
Energy Policy, 49, 266-273.
\14\ Blum, H. and Sathaye, J. (2010). ``Quantitative Analysis of
the Principal-Agent Problem in Commercial Buildings in the U.S.:
Focus on Central Space Heating and Cooling,'' Lawrence Berkeley
National Laboratory, LBNL-3557E. (Available at: escholarship.org/uc/item/6p1525mg) (Last accessed January 20, 2022).
\15\ Prindle, B., Sathaye, J., Murtishaw, S., Crossley, D.,
Watt, G., Hughes, J., and de Visser, E. (2007). ``Quantifying the
effects of market failures in the end-use of energy,'' Final Draft
Report Prepared for International Energy Agency. (Available from
International Energy Agency, Head of Publications Service, 9 rue de
la Federation, 75739 Paris, Cedex 15 France).
\16\ Bushee, B.J. (1998). ``The influence of institutional
investors on myopic R&D investment behavior,'' Accounting Review,
305-333.
DeCanio, S.J. (1993). ``Barriers Within Firms to Energy
Efficient Investments,'' Energy Policy, 21(9), 906-914. (explaining
the connection between short-termism and underinvestment in energy
efficiency).
\17\ International Energy Agency (IEA). (2007). Mind the Gap:
Quantifying Principal-Agent Problems in Energy Efficiency. OECD Pub.
(Available at: www.iea.org/reports/mind-the-gap) (Last accessed
January 20, 2022)
---------------------------------------------------------------------------
Second, the nature of the organizational structure and design can
influence priorities for capital budgeting, resulting in choices that
do not necessarily maximize profitability.\18\ Even factors as simple
as unmotivated staff or lack of priority-setting and/or a lack of a
long-term energy strategy can have a sizable effect on the likelihood
that an energy efficient investment will be undertaken.\19\ U.S. tax
rules for commercial buildings may incentivize lower capital
expenditures, since capital costs must be depreciated over many years,
whereas operating costs can be fully deducted from taxable income or
passed through directly to building tenants.\20\
---------------------------------------------------------------------------
\18\ DeCanio, S.J. (1994). ``Agency and control problems in US
corporations: the case of energy-efficient investment projects,''
Journal of the Economics of Business, 1(1), 105-124.
Stole, L.A., and Zwiebel, J. (1996). ``Organizational design and
technology choice under intrafirm bargaining,'' The American
Economic Review, 195-222.
\19\ Rohdin, P., and Thollander, P. (2006). ``Barriers to and
driving forces for energy efficiency in the non-energy intensive
manufacturing industry in Sweden,'' Energy, 31(12), 1836-1844.
Takahashi, M and Asano, H (2007). ``Energy Use Affected by
Principal-Agent Problem in Japanese Commercial Office Space
Leasing,'' In Quantifying the Effects of Market Failures in the End-
Use of Energy. American Council for an Energy-Efficient Economy.
February 2007.
Visser, E and Harmelink, M (2007). ``The Case of Energy Use in
Commercial Offices in the Netherlands,'' In Quantifying the Effects
of Market Failures in the End-Use of Energy. American Council for an
Energy-Efficient Economy. February 2007.
Bjorndalen, J. and Bugge, J. (2007). ``Market Barriers Related
to Commercial Office Space Leasing in Norway,'' In Quantifying the
Effects of Market Failures in the End-Use of Energy. American
Council for an Energy-Efficient Economy. February 2007.
Schleich, J. (2009). ``Barriers to energy efficiency: A
comparison across the German commercial and services sector,''
Ecological Economics, 68(7), 2150-2159.
Muthulingam, S., et al. (2013). ``Energy Efficiency in Small and
Medium-Sized Manufacturing Firms,'' Manufacturing & Service
Operations Management, 15(4), 596-612. (Finding that manager
inattention contributed to the non-adoption of energy efficiency
initiatives).
Boyd, G.A., Curtis, E.M. (2014). ``Evidence of an `energy
management gap' in US manufacturing: Spillovers from firm management
practices to energy efficiency,'' Journal of Environmental Economics
and Management, 68(3), 463-479.
\20\ Lovins, A. (1992). Energy-Efficient Buildings:
Institutional Barriers and Opportunities. (Available at: rmi.org/insight/energy-efficient-buildings-institutional-barriers-and-opportunities/) (Last accessed January 20, 2022).
---------------------------------------------------------------------------
Third, there are asymmetric information and other potential market
failures in financial markets in general, which can affect decisions by
firms with regard to their choice among alternative investment options,
with energy efficiency being one such option.\21\ Asymmetric
information in financial markets is particularly pronounced with regard
to energy efficiency investments.\22\ There is a dearth of information
about risk and volatility related to energy efficiency investments, and
energy efficiency investment metrics may not be as visible to
investment managers,\23\ which can bias firms towards more certain or
familiar options. This market failure results not because the returns
from energy efficiency as an investment are inherently riskier, but
because information about the risk itself tends not to be available in
the same way it is for other types of investment, like stocks or bonds.
In some cases energy efficiency is not a formal investment category
used by financial managers, and if there is a formal category for
energy efficiency within the investment portfolio options assessed by
financial managers, they are seen as weakly strategic and not seen as
likely to increase competitive advantage.\24\ This information
asymmetry extends to commercial investors, lenders, and real-estate
financing, which is biased against new and perhaps unfamiliar
technology (even though it may be economically beneficial).\25\ Another
market failure known as the first-mover disadvantage can exacerbate
this bias against adopting new technologies, as the successful
integration of new technology in a particular context by one actor
generates information about cost-savings, and other actors in the
market can then benefit from that information by following suit; yet
because the first to adopt a new technology bears the risk but cannot
keep to themselves all the informational benefits, firms may
[[Page 23425]]
inefficiently underinvest in new technologies.\26\
---------------------------------------------------------------------------
\21\ Fazzari, S.M., Hubbard, R.G., Petersen, B.C., Blinder,
A.S., and Poterba, J.M. (1988). ``Financing constraints and
corporate investment,'' Brookings Papers on Economic Activity,
1988(1), 141-206.
Cummins, J.G., Hassett, K.A., Hubbard, R.G., Hall, R.E., and
Caballero, R.J. (1994). ``A reconsideration of investment behavior
using tax reforms as natural experiments,'' Brookings Papers on
Economic Activity, 1994(2), 1-74.
DeCanio, S.J., and Watkins, W.E. (1998). ``Investment in energy
efficiency: do the characteristics of firms matter?'' Review of
Economics and Statistics, 80(1), 95-107.
Hubbard R.G. and Kashyap A. (1992). ``Internal Net Worth and the
Investment Process: An Application to U.S. Agriculture,'' Journal of
Political Economy, 100, 506-534.
\22\ Mills, E., Kromer, S., Weiss, G., and Mathew, P.A. (2006).
``From volatility to value: analysing and managing financial and
performance risk in energy savings projects,'' Energy Policy, 34(2),
188-199.
Jollands, N., Waide, P., Ellis, M., Onoda, T., Laustsen, J.,
Tanaka, K., and Meier, A. (2010). ``The 25 IEA energy efficiency
policy recommendations to the G8 Gleneagles Plan of Action,'' Energy
Policy, 38(11), 6409-6418.
\23\ Reed, J.H., Johnson, K., Riggert, J., and Oh, A.D. (2004).
``Who plays and who decides: The structure and operation of the
commercial building market,'' U.S. Department of Energy Office of
Building Technology, State and Community Programs. (Available at:
www1.eere.energy.gov/buildings/publications/pdfs/commercial_initiative/who_plays_who_decides.pdf) (Last accessed
January 20, 2022).
\24\ Cooremans, C. (2012). ``Investment in energy efficiency: do
the characteristics of investments matter?'' Energy Efficiency,
5(4), 497-518.
\25\ Lovins 1992, op. cit.
The Atmospheric Fund. (2017). Money on the table: Why investors
miss out on the energy efficiency market. (Available at: taf.ca/
publications/money-table-investors-energy-efficiency-market/) (Last
accessed January 20, 2022).
\26\ Blumstein, C. and Taylor, M. (2013). Rethinking the Energy-
Efficiency Gap: Producers, Intermediaries, and Innovation. Energy
Institute at Haas Working Paper 243. (Available at:
haas.berkeley.edu/wp-content/uploads/WP243.pdf) (Last accessed April
6, 2022).
---------------------------------------------------------------------------
In sum, the commercial and industrial sectors face many market
failures that can result in an under-investment in energy efficiency.
This means that discount rates implied by hurdle rates \27\ and
required payback periods of many firms are higher than the appropriate
cost of capital for the investment.\28\ The preceding arguments for the
existence of market failures in the commercial and industrial sectors
are corroborated by empirical evidence. One study in particular showed
evidence of substantial gains in energy efficiency that could have been
achieved without negative repercussions on profitability, but the
investments had not been undertaken by firms.\29\ The study found that
multiple organizational and institutional factors caused firms to
require shorter payback periods and higher returns than the cost of
capital for alternative investments of similar risk. Another study
demonstrated similar results with firms requiring very short payback
periods of 1-2 years in order to adopt energy-saving projects, implying
hurdle rates of 50 to 100 percent, despite the potential economic
benefits.\30\ A number of other case studies similarly demonstrate the
existence of market failures preventing the adoption of energy-
efficient technologies in a variety of commercial sectors around the
world, including office buildings,\31\ supermarkets,\32\ and the
electric motor market.\33\
---------------------------------------------------------------------------
\27\ A hurdle rate is the minimum rate of return on a project or
investment required by an organization or investor. It is determined
by assessing capital costs, operating costs, and an estimate of
risks and opportunities.
\28\ DeCanio 1994, op. cit.
\29\ DeCanio, S.J. (1998). ``The Efficiency Paradox:
Bureaucratic and Organizational Barriers to Profitable Energy-Saving
Investments,'' Energy Policy, 26(5), 441-454.
\30\ Andersen, S.T., and Newell, R.G. (2004). ``Information
programs for technology adoption: the case of energy-efficiency
audits,'' Resource and Energy Economics, 26, 27-50.
\31\ Prindle 2007, op. cit.
Howarth, R.B., Haddad, B.M., and Paton, B. (2000). ``The
economics of energy efficiency: insights from voluntary
participation programs,'' Energy Policy, 28, 477-486.
\32\ Klemick, H., Kopits, E., Wolverton, A. (2017). ``Potential
Barriers to Improving Energy Efficiency in Commercial Buildings: The
Case of Supermarket Refrigeration,'' Journal of Benefit-Cost
Analysis, 8(1), 115-145.
\33\ de Almeida, E.L.F. (1998). ``Energy efficiency and the
limits of market forces: The example of the electric motor market in
France'', Energy Policy, 26(8), 643-653.
Xenergy, Inc. (1998). United States Industrial Electric Motor
Systems Market Opportunity Assessment. (Available at:
www.energy.gov/sites/default/files/2014/04/f15/mtrmkt.pdf) (Last
accessed January 20, 2022).
---------------------------------------------------------------------------
The existence of market failures in the commercial and industrial
sectors is well supported by the economics literature and by a number
of case studies. If DOE developed an efficiency distribution that
assigned boiler efficiency in the no-new-standards case solely
according to energy use or economic considerations such as life-cycle
cost or payback period, the resulting distribution of efficiencies
within the building sample would not reflect any of the market failures
or behavioral factors above. DOE thus concludes such a distribution
would not be representative of the CPB market. Further, even if a
specific building/organization is not subject to the market failures
above, the purchasing decision of CPB efficiency can be highly complex
and influenced by a number of factors not captured by the building
characteristics available in the CBECS or RECS samples. These factors
can lead to building owners choosing a CPB efficiency that deviates
from the efficiency predicted using only energy use or economic
considerations such as life-cycle cost or payback period (as calculated
using the information from CBECS 2012 or RECS 2009).
DOE notes that EIA's Annual Energy Outlook \34\ (``AEO'') is
another energy use model that implicitly includes market failures in
the commercial sector. In particular, the commercial demand module \35\
includes behavioral rules regarding capital purchases such that in
replacement and retrofit decisions, there is a strong bias in favor of
equipment of the same technology (e.g., boiler efficiency) despite the
potential economic benefit of choosing other technology options.
Additionally, the module assumes a distribution of time preferences
regarding current versus future expenditures. For space heating,
approximately half of the total commercial floorspace is assigned one
of the two highest time preference premiums. This translates into very
high discount rates (and hurdle rates) and represents floorspace for
which equipment with the lowest capital cost will almost always be
purchased without consideration of operating costs. DOE's assumptions
regarding market failures are therefore consistent with other prominent
energy consumption models.
---------------------------------------------------------------------------
\34\ EIA, Annual Energy Outlook, www.eia.gov/outlooks/aeo/ (Last
accessed January 25, 2022).
\35\ For further details, see: www.eia.gov/outlooks/aeo/assumptions/pdf/commercial.pdf (Last accessed January 25, 2022).
---------------------------------------------------------------------------
Although the January 2020 rulemaking record sufficiently supports
DOE's approach, DOE conducted an additional search after the January
2020 Final Rule was issued for documentation of actual recent gas-fired
commercial hot water boiler installations that included efficiency
details, to further supplement DOE's conclusions that market failures
cause consumers to base purchasing decisions on factors other than
minimizing payback periods.\36\ This additional documentation, as
discussed in more detail below, further reinforces the validity of
DOE's approach to assigning boiler efficiencies in the January 2020
Final Rule.
---------------------------------------------------------------------------
\36\ DOE issued the January 2020 Final Rule in December 2016. In
accordance with the error correction process in 10 CFR 430.5, DOE
did not immediately submit the rule to the Federal Register for
publication in order to allow the public and DOE the opportunity to
identify any errors in the regulatory text. Following litigation in
the Ninth Circuit, see Natural Res. Def. Council, Inc. v. Perry, 940
F.3d 1072 (9th Cir. 2019), the Department submitted the rule that
was issued in December 2016 to the Federal Register for publication
in December 2019. The rule was subsequently published on January 10,
2020.
---------------------------------------------------------------------------
First, DOE obtained data from the Federal Energy Management Program
(``FEMP'') \37\ on commercial gas-fired hot water boiler installations
in government buildings from 2000 to 2013. DOE divided the data into
the same North and Rest of Country regions \38\ as considered in the
2007 residential furnace final rule. 72 FR 65136, 65146-65147 (Nov. 19,
2007).
[[Page 23426]]
One might expect that highly efficient condensing boilers would be more
common in colder climates. However, these data show that in warm
climates in the Rest of Country states, including California, Texas,
Oklahoma, Hawaii, and others, condensing boilers, which are generally
more efficient, were typically installed (95 percent of buildings had a
condensing boiler installation out of 60 buildings, with one building
installing both condensing and non-condensing boilers). In contrast, in
colder climates in the North, including West Virginia, New Jersey,
Washington, and others, non-condensing boilers, which are generally
less efficient, are not uncommon (47 percent of buildings had a non-
condensing boiler installation out of 19 buildings).\39\ DOE
acknowledges that condensing fractions are likely higher for the
buildings in the FEMP data during this time period compared to other
commercial buildings due to Federal mandates and management goals
related to energy efficiency and conservation. DOE also acknowledges
the small sample size of buildings with CPB installations obtained from
FEMP. However, using economic criteria based on energy use or payback
period alone, one might not predict that non-condensing gas-fired
boilers would be more likely installed in colder climates. These real-
world installations are indicative of complex decision-making.
---------------------------------------------------------------------------
\37\ Prior to 2014, FEMP had separate minimum energy efficiency
designations for condensing and non-condensing gas-fired commercial
hot water boilers, meaning that under Federal requirements for
procuring energy efficient equipment the initial decision of whether
to install a condensing or non-condensing unit was left to the
Federal agency. (Available at web.archive.org/web/20130114025912/http://www1.eere.energy.gov:80/femp/technologies/eep_boilers.html)
(Last accessed January 20, 2022). Since 2014, FEMP mandates
condensing gas-fired commercial hot water boilers, except when an
agency demonstrates that selecting the FEMP designated efficiency
level may not be cost effective. (Available at: energy.gov/eere/femp/federal-energy-management-program) (Last accessed January 20,
2022).
\38\ The Northern region comprises states with population-
weighted heating degree days (HDD) equal to or greater than 5,000.
This includes Alaska, Colorado, Connecticut, Idaho, Illinois,
Indiana, Iowa, Kansas, Maine, Massachusetts, Michigan, Minnesota,
Missouri, Montana, Nebraska, New Hampshire, New Jersey, New York,
North Dakota, Ohio, Oregon, Pennsylvania, Rhode Island, South
Dakota, Utah, Vermont, Washington, West Virginia, Wisconsin, and
Wyoming. Rest of Country region comprises states with population-
weighted HDD less than 5,000. This includes Alabama, Arizona,
Arkansas, California, Delaware, Florida, Georgia, Hawaii, Kentucky,
Louisiana, Maryland, Mississippi, Nevada, New Mexico, North
Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and
the District of Columbia.
\39\ FEMP gas-fired hot water boiler building data (Available
at: www.regulations.gov/document/EERE-2013-BT-STD-0030-0101).
---------------------------------------------------------------------------
DOE also gathered recent installation data and case studies for
areas within the North region that demonstrate a significant fraction
of installations are for non-condensing commercial boilers. Data on
building permits from Milwaukee \40\ indicate that there are many
installations of gas-fired non-condensing hot water boilers in a very
cold climate (46 percent of buildings had a non-condensing boiler
installed out of 50 remodeled buildings).41 42 In a study in
Massachusetts, interviewed manufacturers stated that they expect the
market for non-condensing boilers to persist for some replacement
situations.\43\ In a study of 105 multifamily buildings in Minnesota
(ranging in size from 5 units to over 50 units), 85 percent of
buildings with a gas-fired boiler have a non-condensing gas boiler
despite the cold climate.\44\ These studies indicate that a cold
climate (and therefore a large heating load) does not necessarily mean
that high-efficiency boilers will predominate. Additionally, in the
case of an emergency replacement (e.g., a boiler failing in the middle
of winter), buildings are likely to adopt a familiar ``like-for-like''
replacement with the same technology. If the existing technology is
non-condensing, then these emergency replacements are likely to be non-
condensing as well, even in a cold climate.
---------------------------------------------------------------------------
\40\ DOE examined building permit data from several
jurisdictions in different states, however only the City of
Milwaukee data contained the necessary information to determine
boiler efficiency for individual permits.
\41\ City of Milwaukee Land Management System. Boiler New Permit
(10/24/2016-08/11/2017). (Available at: aca-prod.accela.com/MILWAUKEE/Default.aspx) (Last accessed January 20, 2022).
\42\ Boiler model data was used to determine efficiency and
type.
\43\ DNV-GL. (2017). Gas Boiler Market Characterization Study
Phase II--Final Report. (Available at: ma-eeac.org/wp-content/uploads/Gas-Boiler-Market-Characterization-Study-Phase-II-Final-Report.pdf) (Last accessed January 20, 2022).
\44\ Minnesota Department of Commerce. (2013). Minnesota
Multifamily Rental Characterization Study. (Available at:
slipstreaminc.org/sites/default/files/documents/research/minnesota-multifamily-rental-characterization-study.pdf) (Last accessed
January 20, 2022).
---------------------------------------------------------------------------
Finally, DOE also examined the data available in Northwest Energy
Efficiency Alliance's 2019 Commercial Building Stock Assessment
``CBSA''), published in May 2020.\45\ The CBSA is a regional study
characterizing the energy consumption and building characteristics of
commercial buildings throughout the Northwest region of the country.
The study consists of detailed site visits to 932 commercial buildings
across 12 building types and includes on-site assessments, building
staff interviews, and utility submission of energy consumption data.
The rated boiler efficiency is a key variable captured by CBSA, with
efficiencies of installed boilers ranging from below 80 percent to 97
percent. For gas-fired hot water boilers, an efficiency of 85 percent
and below is generally considered to be non-condensing.
---------------------------------------------------------------------------
\45\ The final report and all data files are available at:
neea.org/data/commercial-building-stock-assessments (Last accessed
January 25, 2022). The data file specific to boilers is
hydronic_systems-boilers.xlsx.
---------------------------------------------------------------------------
DOE specifically examined the subset of buildings with gas-fired,
mechanical draft, hot water boilers whose function includes space
heating. DOE limited the subset of buildings to those with a boiler
input capacity equal to or greater than 300,000 Btu/h to match the CPB
equipment class definitions. Building characteristics include the
conditioned floor area and the annual, weather-normalized gas
consumption in therms \46\ (i.e., normalized to the weather in a
typical year). Some buildings have multiple identical boilers staged
together into one system (with a boiler system input capacity equal to
the sum of each individual boiler's input capacity).\47\ Some buildings
are served by multiple boiler systems, likely servicing different
sections of the building. In these cases, the conditioned floor area
and facility gas consumption were split evenly among the number of
boiler systems for ease of comparison. In total this subset represents
53 boiler systems, although not every building includes a complete set
of data. Table III.1 shows the number of boiler systems above and below
a rated efficiency of 86 percent, across a number of different
characteristics. For each characteristic, the sample is approximately
divided into two similarly sized subsets, with an additional subset
showing the extreme end of the distribution.
---------------------------------------------------------------------------
\46\ One therm is equal to 100,000 BTUs.
\47\ Staging multiple boilers together may be desired in order
to provide redundancy, or to manage average and peak heating loads.
Table III.1--Number of Buildings * in CBSA by Boiler Efficiency Across
Selected Characteristics
------------------------------------------------------------------------
Rated efficiency Rated efficiency at
below 86 percent or above 86 percent
------------------------------------------------------------------------
conditioned floor area per boiler system
------------------------------------------------------------------------
<70,000 sq ft............... 9 14
>=70,000 sq ft.............. 13 14
>=100,000 sq ft............. 5 6
------------------------------------------------------------------------
[[Page 23427]]
boiler system input capacity
------------------------------------------------------------------------
<2,500,000 Btu/h............ 10 17
>=2,500,000 Btu/h........... 14 12
>=5,000,000 Btu/h........... 8 6
------------------------------------------------------------------------
annual, weather-normalized facility gas consumption per boiler system
------------------------------------------------------------------------
<35,000 therms.............. 12 14
>=35,000 therms............. 11 14
>=100,000 therms............ 6 6
------------------------------------------------------------------------
* Buildings with a gas-fired, hot water, mechanical draft boiler whose
function includes space heating and with an input capacity equal to or
greater than 300,000 Bth/h.
Across each characteristic, there is a lack of any strong
correlation with the efficiency of the existing boiler system.
Buildings with boilers servicing a larger conditioned floor area do not
preferentially have higher efficiency boilers. The same is true for
buildings with higher capacity boilers installed, and for buildings
with higher annual gas consumption. Additionally, neither the buildings
with the largest conditioned floor area, the buildings with the largest
capacity boilers, nor the buildings with the highest annual weather-
normalized gas consumption have a systematic preference for high
efficiency boilers. Without the consideration of potential market
failures, one would expect a correlation with boiler efficiency.\48\
---------------------------------------------------------------------------
\48\ The 2019 CBSA also includes 7 buildings with a gas-fired,
hot water, natural draft boiler system; 24 buildings with a gas-
fired steam boiler system; and 5 buildings with an oil-fired, hot
water boiler system. Of the 24 buildings with steam boilers, only 3
have boiler efficiencies greater than 85 percent. Only 1 building
has a higher efficiency oil-fired boiler.
---------------------------------------------------------------------------
These examples indicate that CPB purchasing decisions are most
likely subject to several market failures. These decisions can be
complex and are not always made based on total building energy use,
life-cycle cost, or payback period estimates. The data show that
condensing and non-condensing boilers are installed in a variety of
building types and that the building characteristics do not correlate
strongly with the existing boiler efficiency.
For these reasons, DOE selected a random assignment of CPB boiler
efficiency (sampled from the developed efficiency distribution, which
is consistent with the overall shipment-weighted efficiency data
submitted by AHRI) as a more appropriate representation of the market
than if that assignment was based on energy use or payback period only.
DOE acknowledges that a random sampling from a distribution of boiler
efficiency is an approximation of what takes place in the commercial
boiler market. However, given the factors discussed in the preceding
paragraphs, DOE explains that an approach that relied only on apparent
cost-effectiveness criteria using the information available in the
CBECS or RECS samples would lead to a more unrepresentative estimate of
the potential impact on the CPB market from an energy conservation
standard compared to DOE's current approach.
At the present time, there are insufficient data to analyze site-
specific economics that take into account a multitude of technical and
other non-economic decision-making criteria in the analyses, as well as
model the effects of various market failures, on a building-by-building
level. In the absence of such a model and the necessary supporting
data, DOE concludes that using a random assignment sampled from the
developed efficiency distributions (consistent with stakeholder-
submitted data) is a reasonable approach, one that simulates behavior
in the CPB market, where market failures result in purchasing decisions
not being perfectly aligned with economic interests, more realistically
than relying only on apparent cost-effectiveness criteria derived from
the limited information in CBECS or RECS. DOE further emphasizes that
its approach does not assume that all purchasers of CPBs make
economically irrational decisions (i.e., the lack of a correlation is
not the same as a negative correlation). As part of the random
assignment, some buildings with large heating loads will be assigned
higher efficiency CPBs, and some buildings with particularly low
heating loads will be assigned baseline CPBs, which aligns with the
available data. By using this approach, DOE acknowledges the
uncertainty inherent in the data and minimizes any bias in the analysis
by using random assignment, as opposed to assuming certain market
conditions that are unsupported given the available evidence.
Finally, even if DOE were to assume the random assignment approach
produced some overstatement of the economic benefits of the new
standards--because one were to conclude that even with all of those
market failures there may be more strictly rational purchasers in the
market than the random distribution accounts for--for all of the
reasons discussed above any such overstatement would be small and would
not alter DOE's conclusion that the revised standards are economically
justified. That is particularly clear given that DOE considers numerous
factors in addition to any savings to consumers. For instance, the
January 2020 Final Rule is expected to result in cumulative emission
reductions of 16 million metric tons of carbon dioxide and 41 thousand
tons of nitrogen oxides, among other pollutants. The present monetized
value of the nitrogen oxide emissions reduction, for example, is
estimated to be $35 million at a 7-percent discount rate and $99
million at a 3-percent discount rate. 85 FR 1592, 1597. There are also
many significant unquantified benefits from the Rule, including
additional environmental and public health benefits. When considering
these benefits together with the other statutory factors listed in 42
U.S.C. 6313(a)(6)(B)(ii), DOE has an abiding conviction that its
determination that the benefits of the standard exceed its burdens,
i.e., the standard is economically justified, is highly probable to be
true. As a result, DOE
[[Page 23428]]
found clear and convincing evidence that the standard was economically
justified.
B. Fuel Prices
DOE clarifies its response to stakeholders in section IV.F.4 of the
January 2020 Final Rule regarding the estimation of energy prices in
the LCC analysis. 85 FR 1592, 1631-32.
As described in the January 2020 Final Rule and final rule TSD, DOE
developed marginal energy prices (electricity, natural gas, and fuel
oil) for use in the LCC analysis.\49\ A marginal energy price reflects
the cost or benefit of adding or subtracting one additional unit of
energy consumption. The starting point for the estimation of marginal
energy prices is with publicly available average energy prices
published by the EIA in various publications (Form 826 data, natural
gas prices, and State Energy Data System).\50\ These data are
disaggregated by state and by month and can be aggregated into the same
reportable domains used in RECS and census divisions used in CBECS. The
price data by month allow DOE to separately estimate winter (heating
season) and non-winter (cooling season) energy prices. The detailed
breakdown of these average energy prices by fuel type, region, and
month is available in appendix 8C of the final rule TSD.
---------------------------------------------------------------------------
\49\ See section IV.F.4 of the January 2020 Final Rule, sections
8.2.2.2 and 8.2.2.3 of chapter 8 of the final rule TSD, and appendix
8C of the final rule TSD.
\50\ Form EIA-826 is now Form EIA-861M. Available at:
www.eia.gov/electricity/data/eia861m/ (Last accessed January 25,
2022).
Natural gas prices available at: www.eia.gov/naturalgas/ (Last
accessed January 25, 2022).
State Energy Data System available at: www.eia.gov/state/seds/
(Last accessed January 25, 2022).
---------------------------------------------------------------------------
EIA data additionally provides historical monthly energy
consumption and total energy expenditures by state. By analyzing how
total expenditures change with changes in energy consumption, DOE can
estimate seasonal marginal energy price factors. These changes in
expenditures are due to the marginal changes in energy consumption and
exclude, for example, fixed costs, connection fees, and other
surcharges. In a regression of total expenditures versus total energy
consumption, the slope represents the marginal price. DOE used a 10-
year average across the same regional divisions in either RECS or CBECS
to determine seasonal marginal price factors in order to transform the
average energy prices into marginal energy prices. The detailed
breakdown of these marginal energy price factors by fuel type and
region, for both winter and non-winter months, is available in appendix
8C of the final rule TSD.
These detailed estimates of marginal energy prices are then used in
the LCC and NIA analyses. To project energy prices in future years, DOE
relied on energy price projections from EIA's AEO to develop energy
price indices over time and scaled marginal prices accordingly.
In response to the notice of proposed rulemaking published prior to
the January 2020 Final Rule, DOE received comments on marginal energy
prices and, in particular, on the accuracy of the marginal rates paid
by larger load consumers. DOE noted that the Gas Associations (American
Gas Association, American Public Gas Association) commented that the
analysis should adjust the energy price calculation methodology using
marginal prices to use a tariff-based approach to make the analysis
more robust. Spire commented that DOE used erroneous utility marginal
energy pricing and forecasts in its analysis resulting in overstated
benefits, and that consumers with large loads do not pay the same
marginal rates as an average commercial consumer. PG&E agreed with
Spire that larger consumers pay less for utilities. And AHRI commented
that the marginal gas rates do not accurately reflect what larger
consumers pay. 85 FR 1592, 1632. DOE further acknowledged comments from
Spire asserting that EIA data is completely inaccurate for its largest
consumers and that transport rates are typically used, and from Phoenix
Energy Management stating that the largest consumers also hedge gas
prices by buying and selling futures and commenting that it is
extremely difficult to figure out what the true cost of the energy is.
Id.
Regarding the usage of EIA data and comparisons to tariff data, DOE
emphasizes that the EIA data provide complete coverage of all utilities
and all customers, including larger commercial and industrial utility
customers that may have discounted energy prices. The actual rates paid
by individual customers are captured and reflected in the EIA data and
are averaged over all customers in a state. DOE has previously compared
these two approaches for determining marginal energy price factors in
the residential sector. In a September 2016 supplemental notice of
proposed rulemaking for residential furnaces, DOE compared its marginal
natural gas price approach using EIA data with marginal natural gas
price factors determined from residential tariffs submitted by
stakeholders. 81 FR 65719, 65784 (Sept. 23, 2016). The submitted
tariffs represented only a small subset of utilities and states and
were not nationally representative, but DOE found that its marginal
price factors were generally comparable to those computed from the
tariff data (averaging across rate tiers).\51\ DOE noted that a full
tariff-based analysis would require information on each household's
total baseline gas consumption (to establish which rate tier is
applicable) and how many customers are served by a utility on a given
tariff. These data were not available in the public domain. By relying
on EIA data, DOE noted, its marginal price factors represented all
utilities and all states, averaging over all customers, and was
therefore ``more representative of a large group of consumers with
diverse baseline gas usage levels than an approach that uses only
tariffs.'' 81 FR 65719, 65784. While the above comparative analysis was
conducted for residential consumers, the general conclusions regarding
the accuracy of EIA data relative to tariff data remain the same for
commercial consumers. DOE uses EIA data for determining both
residential and commercial electricity prices and the nature of the
data is the same for both sectors. DOE further notes that not all
operators of CPBs are larger load utility customers. As reflected in
the building sample derived from CBECS 2012 and RECS 2009 data, there
are a range of buildings with varying characteristics, including multi-
family residential buildings, that operate CPBs. The buildings in the
LCC sample have varying heating load, square footage, and boiler
capacity. Operators of CPBs are varied, some large and some smaller,
and thus the determination of the applicable marginal energy price
should reflect the average operator of CPBs.
---------------------------------------------------------------------------
\51\ See appendix 8E of the TSD for the 2016 supplemental notice
of proposed rulemaking for residential furnaces for a direct
comparison, available at: www.regulations.gov/document/EERE-2014-BT-STD-0031-0217 (Last accessed January 25, 2022).
---------------------------------------------------------------------------
DOE's approach is based on the largest, most comprehensive, most
granular national data sets on commercial energy prices that are
publicly available from EIA. The data from EIA are the highest quality
energy price data available to DOE. The resulting estimated marginal
energy prices do represent an average across all commercial customers
in a given region (state or group of states for RECS, census division
for CBECS). Some customers may have a lower marginal energy price,
while others may have a higher marginal energy price. With respect to
large customers who may pay a lower
[[Page 23429]]
energy price, no tariffs were submitted to DOE during the rulemaking
for analysis. Tariffs for individual non-residential customers can be
very complex and generally depend on both total energy use and peak
demand (especially for electricity). These tariffs vary significantly
from one utility to another. While DOE was unable to identify data to
provide a basis for determining a potentially lower price for larger
commercial and industrial utility customers, either on a state-by-state
basis or in a nationally representative manner, the historic data on
which DOE did rely includes such discounts. The EIA data include both
large non-residential customers with a potentially lower rate as well
as more typical non-residential customers with a potentially higher
rate. Thus, to the extent larger consumers of energy pay lower marginal
rates, those lower rates are already incorporated into the EIA data,
which would drive down EIA's marginal rates for all consumers. If DOE
were to adjust downward the marginal energy price for a small subset of
individual customers in the LCC Monte Carlo sample as suggested by
commenters, it would also have to adjust upward the marginal energy
price for all other customers in the sample to maintain the same
marginal energy price averaged over all customers. Even assuming DOE
could accomplish those adjustments in a reliable or accurate way, this
upward adjustment in marginal energy price would affect the majority of
buildings in the LCC sample. Operational cost savings would therefore
both decrease and increase for different buildings in the LCC sample,
yielding substantially the same overall average LCC savings result as
DOE's current estimate.
In summary, DOE's current approach utilizes an estimate of marginal
energy prices and captures the impact of actual utility rates paid by
all customers, including those that enjoy lower marginal rates for
whatever reason, in an aggregated fashion. Adjustments to this
methodology are unlikely to change the average LCC results and
therefore the conclusions of the January 2020 Final Rule are
insensitive to this issue.
C. Burner Operating Hours
DOE clarifies its response to stakeholders in section IV.F.11 of
the January 2020 Final Rule regarding the estimation of burner
operating hours (``BOHs'') in the LCC analysis. 85 FR 1592, 1637.
BOHs are used to estimate energy consumption of elements other than
the heating element (e.g., electronic controls, fans). The BOHs are not
used to estimate the amount of fuel consumed to meet a heating load but
are the result of a separate heating load estimation and an assumed CPB
capacity. Instead, heating load and the efficiency of the CPB are used
to determine fuel consumption. As a result, CPBs with the same
efficiency level, but different capacities will have different BOHs in
meeting the same heating load. For example, in meeting a specific
heating load a CPB with a lower capacity will have higher BOHs than a
similarly efficient CPB with a higher capacity. The lower capacity CPB
will burn fuel at a lower rate so it will need to be on longer to meet
the heating load as compared to a larger capacity CPB, which will burn
fuel at a higher rate. While the hours of operation differ between the
CPBs of different capacities, the amount of fuel burned is the same
(i.e., the heating load and unit efficiency, not hours of operation,
dictate fuel consumption). BOHs are therefore not a crucial component
of determining operating costs in the LCC analysis. Operating costs are
dominated by fuel consumption to meet the heating load, which as
described in further detail below, is not dependent on any assumptions
regarding BOHs.
A full discussion of boiler energy use and the determination of
BOHs is available in chapter 7 and appendix 7B of the final rule
TSD.\52\ BOHs represent the amount of time the burner operates at full
load. BOHs are not a primary input parameter separately estimated by
DOE, but rather a derived quantity that is largely determined from the
space heating fuel consumption reported in CBECS 2012 or RECS 2009. As
described previously, CBECS and RECS are large, nationally
representative surveys and the energy consumption and expenditure
estimates are derived directly from utility billing data. CBECS and
RECS data are the most robust energy consumption data for space heating
available to DOE. CBECS and RECS form the basis of the LCC Monte Carlo
sample for CPBs and both CBECS and RECS report space heating fuel
consumption for each building in the surveys (determined from utility
bill data). DOE estimated each building's heating load from this
reported fuel consumption, coupled with estimates of the historical
boiler efficiency, building shell efficiency, and adjustments for
average climate conditions in each region.\53\ BOHs are then calculated
using the building heating load and the efficiency of the CPB of that
building. BOHs are utilized to estimate auxiliary electricity
consumption for the circulating pump, draft inducer (if applicable),
igniter, and standby power.\54\
---------------------------------------------------------------------------
\52\ Figure 7.3.1 in chapter 7 of the final rule TSD provides an
overview of the energy use methodology.
\53\ See equation 7.4 in the final rule TSD. Equation 7.5 shows
the adjustment to average climate conditions. See appendix 7B for
the derivation of existing boiler efficiency in 2012 and 2009 (the
sample years for CBECS and RECS).
\54\ See equation 7.9 and section 7.3.3 of the final rule TSD.
---------------------------------------------------------------------------
In the January 2020 Final Rule DOE included comments from AHRI in
which AHRI posited that either due to DOE's sizing assumption and/or
due to the use of the CBECS energy use data in the sample itself, the
energy use model produced excessively high operating hours in some
instances and that these distort the economic results; and that AHRI's
consultant suggested that a more logical approach for estimating may be
to use directly measured data or estimated load data. 85 FR 1592, 1637.
As discussed, DOE derived the BOHs from CBECS and RECS data. BOH
values are determined from building heating loads, which are themselves
derived from reported fuel consumption data taken form large,
nationally representative surveys. DOE therefore has a high degree of
confidence in the resulting building heating loads. The presence of
high BOHs in some instances is not an indication of an error, but due
to the representative boiler capacity assigned in that instance.\55\
However, the building heating load and resulting fuel consumption are
fixed and these are the primary determinant of operating costs.
Furthermore, adjusting the BOHs downward in some instances would
require adjusting upward the BOHs in other instances to maintain the
same average capacity, yielding the substantially the same overall
average LCC results.
---------------------------------------------------------------------------
\55\ The engineering analysis and all downstream analyses
utilize a representative capacity (or rated input) that aligns with
the highest number of shipments. Using a representative capacity
allows DOE to analyze certain equipment characteristics as a proxy
for that equipment class. See section 5.2.1 in chapter 5 of the
final rule TSD.
---------------------------------------------------------------------------
Once each building's heating load is determined, DOE can estimate
BOHs in both the no-new-standards case and all potential standards
cases using the assigned boiler efficiency, boiler capacity, and the
number of boilers assigned to each building, with adjustments made for
estimated return water temperatures and part load operation.\56\ BOHs
are constrained in
[[Page 23430]]
the model to be, at most, 5,840 hours per year (two thirds of a year),
although the vast majority of boilers have BOHs that are significantly
lower than this maximum value.\57\ For all but one product class, the
median BOHs are below 1,000 hours. For context, 1,000 hours of
operation represents approximately 8-9 hours per day for 4 months or 5-
6 hours per day for 6 months. These median values are not unreasonable
expectations for when the burner is on during the winter heating season
in a commercial building, depending on the local climate. Furthermore,
some commercial buildings may require heating for longer periods during
the day during winter, including possibly 24 hours a day (e.g.,
hospitals). BOHs of over 2000 hours represent one end of the
distribution and only apply to a subset of buildings where heating
loads are driven higher by climate, size, age, etc.; similarly, some
buildings have BOHs under 500 hours, representing the other end of the
distribution. Given that the median BOHs derived from the estimated
building heating loads represent reasonable operating conditions, DOE
therefore has no reason to suspect the building heating loads derived
from CBECS and RECS are erroneous.
---------------------------------------------------------------------------
\56\ See equation 7.3 in the final rule TSD. See appendix 7B for
a detailed discussion of adjustments made for return water
temperature and part-load operation.
\57\ Table 7B.2.8 in appendix 7B of the final rule TSD displays
the distribution of BOHs for each CPB equipment class.
---------------------------------------------------------------------------
BOHs are inversely related to the number of boilers and overall
boiler capacity assigned to each building. This means that in a
building with multiple boilers, each individual boiler has fewer BOHs
to meet the building heating load compared to another building with a
similar building heating load with only a single boiler at the same
capacity. The same is also true when comparing two single boilers of
different capacity; the higher capacity boiler will have lower BOHs to
meet the same building heating load. Larger capacity CPBs are typically
installed in buildings with larger heating loads, but these loads are
not necessarily proportional to the increase in CPB capacity.
Therefore, it is not unusual for the larger capacity CPB equipment
classes to have lower median BOHs in some instances.
Because BOHs are a derived quantity and not a primary input
parameter, the estimated fuel consumption of each building in the LCC
sample would be the same regardless of the assigned boiler capacity and
number of boilers in a given building. BOHs do not affect the fuel
consumption of the sample building. The annual fuel consumption in the
no-new-standards and standards cases is largely set by the building
heating load determined from CBECS or RECS, coupled with the assigned
boiler efficiency. There may be individual buildings in the LCC sample
at the extreme ends of the distribution with high or low BOHs due to
the assigned boiler capacity. If, in the field, a larger capacity
boiler (or multiple boilers) with the same efficiency were installed
instead in that building, BOHs would go down but overall fuel
consumption would remain the same to match the building heating load.
Similarly, at the low end of the distribution, if a lower capacity
boiler were installed in the field instead, BOHs would increase but
fuel consumption would remain the same. The only impact of changes to
BOHs would be with electricity consumption. Electricity consumption
while the boiler is on would decrease with decreasing BOHs and increase
with increasing BOHs; however, electricity consumption is a minor
component of overall operating costs.\58\ Adjustments to these BOHs at
either end of the distribution would yield an overall average LCC
savings result substantially the same as DOE's current estimate. In
summary, higher and lower capacities may be present in the field (with
correspondingly lower and higher BOHs), however the net result of any
adjustments would be a minimal impact to average LCC savings and the
percentage of negatively impacted consumers.
---------------------------------------------------------------------------
\58\ The number of standby hours would increase with decreasing
BOHs. Total standby electricity consumption (for those CPBs with
standby power) would therefore increase, however this represents an
even smaller fraction of total operating costs and would have a
negligible impact on LCC results.
---------------------------------------------------------------------------
As an illustration of the small impact of electricity consumption
adjustments, a small gas-fired hot water CPB at a thermal efficiency of
84 percent with a typical heating load has an estimated average annual
fuel use of 863.7 million Btus per year (``MMBtu/yr'') and an estimated
average annual electricity consumption of 683.5 kilowatt-hours per year
(``kWh/yr'').\59\ Assuming this CPB is in New England, with a
commercial natural gas price of $10.56/MMBtu and a commercial
electricity price of $0.15/kWh,\60\ this results in an annual operating
cost of $9,121 for natural gas and $103 for electricity. The
electricity consumption of the auxiliary equipment and standby power
accounts for approximately 1 percent of total energy costs. The
difference in electricity consumption between efficiency levels is an
even smaller fraction, compared to the difference in natural gas
consumption between efficiency levels. Changes to BOHs both upward and
downward would have a negligible impact on overall LCC savings results
given that the fuel consumption is the dominant factor and it is
determined by the heating load and assigned boiler efficiency.
Therefore, the conclusions of the January 2020 Final Rule are
insensitive to adjustments to BOHs.
---------------------------------------------------------------------------
\59\ See table 7.4.1 in chapter 7 of the final rule TSD.
\60\ See section 8.2.2.2 in chapter 8 of the final rule TSD.
---------------------------------------------------------------------------
IV. Procedural Issues and Regulatory Review
DOE has concluded that the determinations made pursuant to the
various procedural requirements applicable to the January 2020 Final
Rule remain unchanged for this supplemental response to comments. These
determinations are set forth in the January 2020 Final Rule. 85 FR
1592, 1676-1681. Because the rule was remanded without vacatur for
further explanation, DOE was able to provide this explanation without
opening another notice and comment period. See Chamber of Commerce v.
SEC, 443 F.3d 890, 900 (D.C. Cir. 2006).
In the alternative, however, DOE finds that, pursuant to the
Administrative Procedure Act, 5 U.S.C. 553(b), there is good cause to
not issue a separate notice to solicit public comment on the
supplemental responses to comments contained in this document. This
document does not change the determinations made by DOE in the January
2020 Final Rule, but is a supplement to that final rule, which already
went through notice and comment. This document provides further
explanation to the response to comments already provided. In addition,
this supplement to the January 2020 Final Rule is issued pursuant to a
court order directing DOE to provide supplemental responses to certain
comments within 90 days. Issuing a separate notice to solicit public
comment during that time period would be impracticable, unnecessary,
and contrary to the public interest.
Signing Authority
This document of the Department of Energy was signed on April 14,
2022, by Kelly J. Speakes-Backman, Principal Deputy Assistant Secretary
for Energy Efficiency and Renewable Energy, pursuant to delegated
authority from the Secretary of Energy. That document with the original
signature and date is maintained by DOE. For administrative purposes
only, and in compliance with requirements of the Office of the Federal
Register, the undersigned DOE Federal Register Liaison Officer has been
[[Page 23431]]
authorized to sign and submit the document in electronic format for
publication, as an official document of the Department of Energy. This
administrative process in no way alters the legal effect of this
document upon publication in the Federal Register.
Signed in Washington, DC, on April 15, 2022.
Treena V. Garrett,
Federal Register Liaison Officer, U.S. Department of Energy.
[FR Doc. 2022-08427 Filed 4-19-22; 8:45 am]
BILLING CODE 6450-01-P