ASSESSMENT OF PROJECTED C02
EMISSION REDUCTIONS FROM CHANGES
IN ELECTRICITY GENERATION AND USE
EPA 430-R-23-004
September 2023
oB>A
ELECTRICITY SECTOR
EMISSIONS IMPACTS
OF THE INFLATION
REDUCTION ACT
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This report - Electricity Sector Emissions
Impacts of the Inflation Reduction Act - is
responsive to the requirement in the Low
Emissions Electricity Program under the
Inflation Reduction Act section 60107(5),
that the Environmental Protection Agency
"assess ... the reductions in greenhouse
gas emissions that result from changes in
domestic electricity generation and use
that are anticipated to occur on an annual
basis through fiscal year 2031.
1 P.L. 117-169 (August 16, 2022), 136 STAT. 269, 42 U.S.C. 7435(a)
(5), Clean Air Act section 135(a)(5).
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ACKNOWLEDGEMENTS
This report was developed Py EPA's Office of Atmospheric Protection (OAP). It relies upon the
academic literature as well as data and modeling contriPutions from RTI International, the
Massachusetts Institute of Technology, the National RenewaPle Energy LaPoratory, the Pacific
Northwest National LaPoratory, Lawrence Berkeley National LaPoratory, and OnLocation, Inc.
Support for the report's production was provided Py RTI International, Inc.
PEER REVIEW
This report was peer reviewed Py six external and independent experts in a process
independently coordinated Py RTI International, Inc. EPA gratefully acknowledges the following
peer reviewers for their useful comments and suggestions: Aaron Bergman, John Bistline,
Eliot Crowe, Bri-Mathias Hodge, Jared Langevin, and Yuanrong Zhou. The information and views
expressed in this report do not necessarily represent those of the peer reviewers, who also Pear
no responsiPility for any remaining errors or omissions. Appendix H provides more information
aPout the peer review.
RECOMMENDED CITATION
EPA. 2023. Electricity Sector Emissions Impacts of the Inflation Reduction Act: Assessment
of projected C02 emission reductions from changes in electricity generation and use. U.S.
Environmental Protection Agency, EPA 430-R-23-004.
CONTACT US
emissions-impacts-inflation-reduction-act-report@epa.QQv
DATA AVAILABILITY
Data from the analyses in this report can Pe accessed on the following wePsite: https://www.
epa.Qov/inflation-red uction-act/electric-sector-emissions-im pacts-inflation-reduction-act
3
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Contents
List of Figures 5
List of Tables 6
List of Abbreviations 7
Executive Summary 8
Chapter 1 Introduction 16
1.1 Overview of the Inflation Reduction Act of 2022 18
1.2 Estimating Emission Reductions 22
1.2.1 Scope, Conventions, and Methodology 22
1.2.2 Multi-Sector or Electric-Sector Models Used or Cited 27
1.2.3 Scenarios and Sensitivities 31
1.2.4 Caveats and Limitations 33
1.3 Economy-Wide CO2 Emissions Reductions 38
1.3.1 Economy-Wide CO2 Emissions Snapshot 38
1.3.2 Economy-Wide Emissions Analysis and Results 39
Chapter 2 Electricity 50
2.1 Electric Sector Snapshot 50
2.2 Key Electric Sector IRA Provisions 53
2.3 Electric Sector Analysis and Results 55
Chapter 3 Transportation 68
3.1 Transportation Sector Snapshot 68
3.2 Key Transportation Sector IRA Provisions 72
3.3 Transportation Sector Analysis and Results 73
Chapter 4 Buildings 78
4.1 Buildings Sector Snapshot 78
4.2 Key Buildings Sector IRA Provisions 81
4.3 Buildings Sector Analysis and Results 82
Chapter 5 Industry 88
5.1 Industrial Sector Snapshot 88
5.2 Key IRA Provisions for Industry 90
5.3 Industry Sector Analysis and Results 93
Chapter 6 Conclusions 98
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List of Figures
Figure ES.l U.S. electricity sector C02 emissions 10
Figure ES.2 Summary of economy-wide and end-use sector C02 emissions reductions
from 2005 for the IRA and No IRA scenarios 12
Figure 1.1 Economy-wide C02 emissions from fossil fuel combustion and industrial
processes by end-use sector, 2005-2021 38
Figure 1.2 Economy-wide C02 emissions 40
Figure 1.3 Summary of economy-wide and end-use sector C02 emissions reductions
from 2005 for the IRA and No IRA scenarios 41
Figure 1.4 Fossil energy consumption by fuel 43
Figure 1.5 Economy-wide C02 emissions sensitivities 45
Figure 2.1 C02 emissions from the electric sector by fuel in the United States, compared to
economy-wide C02 emissions, 2005-2021 51
Figure 2.2 Electricity generation shares by fuel type, 2005-2021 52
Figure 2.3 Electric sector C02 emissions 59
Figure 2.4 Electric sector generation by technology (TWh), 2021, 2030, and 2035 60
Figure 2.5 Historical and projected capacity additions by technology, IRA scenario 62
Figure 2.6 Sensitivity of electric-sector emissions to IRA implementation
(multiple models) and natural gas prices, technology cost, and
constrained deployment (ReEDS-only) 64
Figure 2.7 Cross-model comparison of NOx and S02 emissions by model over time 67
Figure 3.1 C02 emissions from the transportation sector compared to economy- wide C02
emissions, 2005-2021 69
Figure 3.2 C02 emissions from the transportation sector, by end use, for
1990-2021 70
Figure 3.3 Transportation sector C02 emissions 74
Figure 3.4 Electric vehicle percent share of new sales 75
Figure 3.5 Electricity demand from light-duty vehicles in the transportation sector 77
Figure 4.1 C02 emissions from the buildings sector compared to economy-
wide C02 emissions, 2005-2021 79
Figure 4.2 Buildings sector C02 emissions 83
Figure 5.1 C02 emissions from the industrial sector compared to economy-wide
C02 emissions, 2005-2021 89
Figure 5.2 Industry energy-related C02 emissions (direct + indirect) by subsector, excluding
process emissions 91
Figure 5.3 Industrial sector combustion and indirect C02 emissions 95
Figure 5.4 Industry carbon capture and sequestration 96
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List of Tables
Table ES.l Summary of ranges of C02 emissions reductions from 2005 13
Table 1.1 Characteristics of multi-sector and electric-sector models in this analysis 26
Table 1.2 Summary of IRA provisions represented in energy models in this report 28
Table 1.3 Sensitivity scenarios by model. The dots indicate which models (columns) ran
each sensitivity scenario (rows) 31
Table 1.4 Summary of ranges of C02 emissions reductions from 2005 42
Table 1.5 Economy-wide C02 emissions changes (percentage points of 2005 emissions)
relative to the IRA Moderate scenario 45
Table 2.1 Electric sector C02 emissions changes (percentage points of 2005 emissions)
relative to the I RA Moderate scenario 65
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List of Abbreviations
ACEEE
American Council for an Energy-Efficient
kW
kilowatt
Economy
kWh
kilowatt hour
AEO
Annual Energy Outlook
LBNL
Lawrence Berkeley National LaPoratory
AIM
American Innovation and Manufacturing (Act)
LDV
light-duty vehicle
ATB
Annual Technology Baseline ("populated
LEEP
Low Emission Electricity Program
framework" managed Py NREL)
LP
linear program
BECCS
Pioenergy with carPon capture and storage
LPO
Loan Programs Office, United States
BEV
Pattery electric vehicle
Department of Energy
Btu
British thermal unit
MAC
marginal aPatement cost
CAGR
compound annual growth rate
MARKAL
Market Allocation
CARB
California Air Resources Board
MIT
Massachusetts Institute of Technology
CBO
Congressional Budget Office
Mt
million metric tons
CCS
carPon capture and sequestration
MWh
megawatt hour
CGS
Center for GloPal SustainaPility
N/A
not applicaPle
ecus
carPon capture, use, and storage
NAICS
North American Industry Classification System
ch4
methane
NEMS
National Energy Modeling System
co2
carPon dioxide
NETL
National Energy Technology LaPoratory
CPRG
Climate Pollution Reduction Grant
NGCC
natural gas comPined cycle (method for CO2
CSP
concentrating solar-thermal power
capture)
DAC
direct air capture
NHTSA
National Highway Traffic Safety Administration
dGen
DistriPuted Generation Market Demand Model
NOx
nitrous oxide
(NREL)
NRDC
Natural Resources Defense Council
DOE
United States Department of Energy
NREL
National RenewaPle Energy LaPoratory
El
Energy Innovation
O&M
operations and maintenance
EIA
United States Energy Information
OP
Office of Policy, United States Department of
Administration
Energy
EIR
Energy Infrastructure Reinvestment (Program)
PE
partial equiliPrium
EPA
Environmental Protection Agency
PHEV
plug-in hyprid electric vehicle
EPRI
Electric Power Research Institute
PM
particulate matter
EPS
Energy Policy Simulator
PNNL
Pacific Northwest National LaPoratory
EV
electric vehicle
PPSM
Power Plant Screening Methodology
FT
Fischer-Tropsch synthesis (for Piofuels)
PTC
production tax credit
g/mi
grams per mile
PV
photovoltaic
GCAM
GloPal Change Analysis Model
Re EDS
Regional Energy Deployment System
GHG
greenhouse gas
REGEN
Regional Economy, Greenhouse Gas, and Energy
GHGI
Greenhouse Gas Inventory (report)
(model)
GHGRP
Greenhouse Gas Reporting Program
REPEAT
Rapid Energy Policy Evaluation and Analysis
GIS
geographic information system
Toolkit (model)
GW
gigawatt
RFF
Resources for the Future
H2
hydrogen
RHG
Rhodium Group
HFC
hydrofluorocarPon
RIO
Regional Investment and Operations (model)
HGL
hydrocarPon gas liquids
sf6
sulfur hexafluoride
HUD
United States Department of Housing and
SMR
steam methane reformation (method for
UrPan Development
producing hydrogen)
HVAC
heating, ventilation, and air conditioning
sox
sulfur oxides
IIJA
2021 Infrastructure Investment and JoPs Act
so2
sulfur dioxide
IPM
Integrated Planning Model (EPA's)
T&S
transport and storage
IRA
Inflation Reduction Act of 2022
TWh
terawatt-hour
ITC
investment tax credit
UMD
University of Maryland
JEDI
JoPs and Economic Development Impact
USDA
United States Department of Agriculture
(model, NREL)
USPS
United States Postal Service
kg
kilogram
USREP
U.S. Regional Energy Policy (model)
km
kilometer
VMT
vehicle miles traveled
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The Inflation Reduction Act of 2022 (IRA) represents a significant legislative commitment
to transform energy production and consumption, reduce the risks of climate change,
improve environmental quality, and simultaneously spur investments that create economic
opportunities. With a comprehensive system of economic incentives, the Act provides
substantial support for the development and use of clean energy across the economy. The
IRA promotes domestic manufacturing, well-paying jobs, and economic growth. The IRA is
expected to reduce greenhouse gas (GHG) emissions by encouraging the generation of low-
cost, low-emission electricity and the efficient use of clean energy in buildings, transportation,
and industry.
Executive Summary
This report presents results from state-of-the-art multi-sector and electric sector models to
assess how the IRA's provisions reduce C02 emissions. The report is responsive to §60107(5)
of the Low Emissions Electricity Program within the IRA, which requires EPA to assess "... the
reductions in greenhouse gas emissions that result from changes in domestic electricity
generation and use that are anticipated to occur on an annual basis through fiscal year 2031."
The report primarily focuses on carbon dioxide (C02) emissions because the vast majority of
direct electric sector GHG emissions are from fossil fuel combustion and the increased use of
clean electricity primarily offsets fossil fuel use in end-use sectors.
The report includes the projected reductions in C02 emissions due to the IRA provisions
represented in the models. Emissions projections are modeled in an "IRA scenario" that
incorporates the effects of the IRA incentives, and these are compared to projections in a "No
IRA scenario." (Both scenarios incorporate other state and federal policies finalized prior to the
IRA enactment—see Section 1.2). It is important to note that this report does not reflect rules
and regulations that are currently being developed or finalized.
8
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The report presents results from recent peer-reviewed research [1], reports from the
Department of Energy [2] and the National Renewable Energy Laboratory [3], and EPA-funded
modeling. The combined data include results from ten multi-sector models and four electric
sector models. Multi-sector models are the appropriate analytic tool to examine emission
reductions from changes in both generation and use. However, the power sector accounts for
most of the emission reductions, and the single-sector electricity models provide additional
complementary perspectives. The report primarily examines the central estimates from the
models to provide ranges and identify areas of agreement and disagreement; sensitivity
estimates from a subset of models are also explored.
Throughout this report, C02 emissions are reported in million metric tons of C02 (Mt C02). C02
emission reductions are also presented as percentage changes from 2005 for comparability
to other studies and to U.S. emission reduction goals. Reductions are primarily shown for
the years 2030 and 2035 with annual results in Appendix A. Although many provisions of
the IRA end in 2031, extending the analysis to 2035 is valuable for multiple reasons. First,
certain economic incentives (e.g., clean electricity tax credits, 45Y and 48E) extend beyond
2031. Second, certain advanced technologies supported by the IRA are expected to have
more significant impact after 2030. These include advances in electricity storage, carbon
capture and storage (CCS), nuclear, distributed generation, clean hydrogen production, and
geothermal energy. Third, the available literature extends to 2035.
The IRA spurs substantial emission reductions from the electric sector of 49 to 83% from
2005 levels in 2030. C02 emissions decline most steeply in the electricity sector (Figure ES.l).
Each blue line in the figure represents the output from a model that includes the effects of the
IRA provisions. The orange lines show model results without the IRA provisions. Importantly,
results for 2030 and 2035 are emphasized to the right of the chart—each colored shape
represents the C02 emissions in that year of either the IRA or No IRA cases from the models.
The median C02 emissions projections for each scenario is represented by the colored
horizontal bar.2 With one exception, the modeling shows that these emission reductions are
primarily accomplished through increasing use of solar and wind capacity and generation
enabled by a combination of incentives under the IRA; infrastructure buildout (in part enabled
by provisions of the Infrastructure Investment and Jobs Act (IIJA; also known as Bipartisan
Infrastructure Bill or BIL), and increased use of storage technologies. One of the models relies
heavily on fossil CCS to reduce emissions. A robust finding across all models is that generation
from low- and zero-emitting technologies (e.g. renewables or fossil CCS) increases while high-
emitting generation from coal and gas without CCS declines.
2 The median value of the results is presented in this report to provide a measure of central tendency, in addition to the
range. A median is used instead of a mean because a mean is more heavily influenced by outlier results.
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Figure ES.l
U.S. electricity sector CO2 emissions
(a)
Data Points and
Median Results
O
O
(0
(0
E
LU
o
+¦»
o
o
w
><
o
o
LU
2,500
2,000
1,500
1,000
500
2021
2025
2030
2035
(b)
A 8
e
—e-
2030
2005
In the IRA scenario, U.S. electricity sector
CO2 emissions fall to 49 to 83% (39% median)
below 2005 levels in 2030. In 2030, individual
models find that electricity CO2 emissions are
11 to 67% (34% median) below what they are
modeled to be in the No IRA scenario, with
the median difference falling to just over 50%
by 2035. Figure ES.l(a) shows absolute model
results for the emissions trajectories (No IRA
scenario in orange dashed lines, IRA Scenario
in blue) with the historical trend (in black [4]).
Data points to the right of Figure ES.l(a) show
individual model results from 2030 and 2035
(blue circles for IRA scenario results, orange
triangles for No IRA). Horizontal bars represent
the median of the model results. Figure ES.l(b)
shows the percent difference between the IRA and No IRA for each model (blue lines) and the median across the
models (black line).3 Accessible table available in the Data Annex.
2021
2025
2030
2035
T
£
A
2035
3 A handful of models show higher emissions under the IRA in 2025. In the No IRA scenario, these forward-looking
models have slightly higher levels of near-term investments in renewables in 2025 because the models foresee
the expiration of tax credits. Under the IRA scenario, tax credits are extended and investment does not exhibit a
near-term spike.
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The IRA impacts electric sector-related C02 emissions in three important ways: 1) provisions
that support clean electricity generation, 2) provisions that encourage the electrification of
end uses—increasing the amount of electricity demand and reducing emissions from fossil
combustion in the transportation, buildings, and industrial end-use sectors, and 3) provisions
that encourage energy efficiency—offsetting overall energy demand, reducing energy costs,
and decreasing the amount of spending required to decrease electricity sector emissions while
meeting the increased demand from electrification of end uses. To capture the full impact of
these provisions, multi-sector models represent both the electricity sector and how electricity
competes with fossil energy in the industrial, transportation, and buildings sector end uses.
Across the end-use sectors, emissions reductions are greater underthe IRA scenario than
the No IRA scenario. Buildings exhibits the greatest reductions from 2005 levels of direct
plus indirect C02 emissions from electricity followed by industry and transportation (Figure
ES.2). By 2030, the IRA drives C02 emission reductions in the transportation sector of 11-
25% from the 2005 level. In the buildings sector, emissions in 2030 fall 49-63%, and for the
industrial sector, the reduction is 17-43%. By 2035, results show that the IRA achieves even
further reductions from the 2005 level in these sectors (15-35% for transportation, 52-70% for
buildings, and 23-57% for industry). The C02 emissions from each of the end-use sectors under
the IRA and No IRA scenarios represent both the "direct" emissions from fossil fuel use in each
respective sector and "indirect" emissions from fossil fuels used in generating the electricity
consumed by each sector. Electrification leads to C02 emission reductions directly because it
displaces fossil fuel combustion in end-use sectors; in addition, as the share of zero emissions
generation increases, the indirect C02 emissions from electricity generation will continue to
decline. Electrifying efficiently will reduce new electricity demand, enable retirement of high-
emitting fossil fuel-based generation, and more rapidly increase the share of zero-emitting
generation. Transportation sector reductions are relatively small in 2030 in part because
it takes time for the expected, and significant, increase in new electric vehicle sales to be
reflected in the light-duty vehicle stock, and because the transportation sector includes C02
emissions from multiple transportation modes—personal transportation, trucking, public
transit, rail, air, and ships—that may not all see changes due to the IRA. The sectoral chapters
provide further information on the degree of electrification.
The IRA lowers economy-wide CO? emissions, which includes electricity generation and use,
by 35-43% by 2030 from 2005 levels. For comparison, economy-wide C02 emissions in the
No IRA scenario are 26-33% below 2005. By 2035, economy-wide emissions are projected to
continue to fall 36-55% relative to 2005 in the IRA scenarios (Figure ES.2 , Table ES.l).
11
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Figure ES.2
Summary of economy-wide and end-use sector C02 emissions
reductions from 2005 for the IRA and No IRA scenarios
Electricity
0
25
2030
2035
2030
2035
50 75
A i* AiliAA A A A.
0 • • .. .|. « .
A aaU|aaa aa A
Transportation (Direct + Indirect from Electricity)
• H- «
^ ~ aa|*
• 4 .
Buildings (Direct + Indirect from Electricity)
2030 A"a+a AJ
A " *|A f. . ..
Industry (Direct + Indirect from Electricity)
„„„„ A . I - A
2030
' I "
2035
I
2030
2035
~ ~ ~ A I A J
Economy-Wide
0
25 50 75
Percent Reduction from 2005 Levels (%)
100
^ No IRA
+ IRA
100
In the IRA scenario, economy-wide C02 emissions fall 35 to 43% (39% median, bottom panel) below 2005 levels by
2030. By the same year, electric power sector CO2 emissions fall 49 to 83% (69% median, top panel) below 2005 levels;
transportation sector CO2 emissions fall 11 to 25% (17% median); buildings sector CO2 emissions fall 49 to 63% (55%
median); and industry sector CO2 emissions fall 17 to 43% (36% median) below 2005 levels. Note that transportation,
buildings, and industry emissions include reductions from changes in direct combustion as well as indirect emissions
from electricity generation.4'55'7 Ranges are summarized in Table ES.l. Accessible table available in the Data Annex.
transportation, buildings, and industry CO2 emissions include reductions from changes in direct combustion as well
as indirect CO2 emissions from electricity generation. Except where reported separately, electric sector CO2 emissions
were allocated to the end-use sectors based on electricity consumption. Emissions are broken out into direct and
indirect in Appendix F.2.
5 The Bistline et al. study [1] presents a range for economy-wide emissions reduction from 2005 as 33-40% in 2030 and
43-48% in 2035 in the IRA scenario. This range is the reduction in net-GHG emissions from a model-reported 2005 value
for all models but two: MARKAL-NETL includes energy and non-energy CO2 only, and REGEN-EPRI includes only energy
CO2. Using data from the Bistline study and the model-reported 2005 values, the range of emissions reductions from
2005 for only energy and non-energy CO2 (comparable to the range presented in this report) is 33-42% in 2030 and
42-53% in 2035. The Bistline 2030 range is lower than the range in this report due to the Bistline calculations referencing
model-reported 2005 values, whereas this report references 2005 GHGI data.
5The Bistline et al. study presents a range for electricity emissions reduction from 2005 as 47-83% in 2030 and 66-87%
in 2035. This range is the reduction in electricity emissions from a model-reported 2005 value, whereas this report
references 2005 GHGI data.
7 Industrial process emissions are included in economy-wide CO2 emissions for models that report them, but excluded
from industry-specific emissions.
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Table ES.l
Summary of ranges of C02 emissions reductions from 2005
IRA
No IRA
Sector
Year
Min
Median
Max
Min
Median
Max
2030
49%
69%
83%
43%
50%
59%
Electricity
2035
67%
77%
87%
40%
53%
68%
2030
11%
17%
25%
9%
15%
22%
Transportation
2035
15%
27%
35%
13%
23%
28%
2030
49%
55%
63%
34%
42%
47%
Buildings
2035
52%
66%
70%
36%
45%
51%
2030
17%
36%
43%
6%
25%
33%
Industry
2035
23%
36%
57%
3%
27%
36%
2030
35%
39%
43%
26%
31%
33%
Economy-Wide
2035
36%
46%
55%
29%
33%
39%
For each sector and for the economy as a whole, model results show greater CO2 emissions reductions for the IRA
scenario compared to the No IRA scenario. This is true for the full range of minimum, median, and maximum reductions
reported by any of the models in both 2030 and 2035.
The range of reductions across models is wide, which reflects differences across IRA
representation, model structure, and assumptions. The above results reflect a multi-
model comparison of IRA Moderate scenarios, that is, results reflecting the central set
of unharmonized IRA assumptions reported by the models (see Section 1.2.3). Emissions
reductions differ because of several IRA-related factors, including the number of provisions
modeled (i.e., representing more provisions is a contributing factor to lower emissions) and
the representation of those provisions (e.g., the eligibility to receive bonus production and
investment tax credits). Additionally, the models differ structurally (e.g., spatial and temporal
resolution as well as technological and sectoral detail). Finally, the range of future C02
emissions trajectories for both the IRA and No IRA scenarios reflects assumptions about a
variety of important factors including the rate of improvement in technology costs, the ability
to deploy low-emission power generation technologies, energy prices, and economic growth.
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Emissions Assessment Framework
This report emphasizes the range of C02 emissions results across the ten multi-sector energy system
models and four electricity sector models listed below. The analytic strengths of these models include their
representation of highly complex techno-economic systems, consistent emissions and energy accounting
systems, and in the case of multi-sectoral models, interactions across sectors [5].8 Models, as tractable
representations of these complex systems, also have limitations (Section 1.2.4) including assumptions of
perfect information, competitive markets, and optimizing decision-makers. The multi-model results are
conditional on input and scenario assumptions. They should not be interpreted as statistical distributions
nor do they reflect the full range of uncertainty, which would be wider.
MULTI-SECTOR (10)
Energy Policy Simulatorfrom Energy Innovation (EPS-El)
Global Change Analysis Model, two versions
— one from the Center for Global Sustainability (GCAM-CGS),
— and another from the Pacific Northwest National Laboratory
(GCAM-PNNL)
Market Allocation model from the National Energy Technology
Laboratory (MARKAL-NETL)
National Energy Modeling System, three versions, one each from
— the Energy Information Administration (NEMS-EIA),
— the Office of Policy at the Department of Energy (NEMS-OP),
— and the Rhodium Group (NEMS-RHG)
Regional Economy, Greenhouse Gas, and Energy from Electric
Power Research Institute (REGEN-EPRI)
Regional Investment and Operations Model from the Princeton
REPEAT Project (RIO-REPEAT)
U.S. Regional Energy Policy model (Massachusetts Institute of
Technology) linked to the ReEDS power sector model (USREP-
ReEDS)
ELECTRICITY SECTOR (4)
Haiku from Resources for the Future (Haiku)
Integrated Planning Model, two versions, one each from
- the U.S. EPA (IPM-EPA)
— and from the Natural Resources Defense Council (IPM-NRDC)
Regional Energy Deployment System from the National Renewable
Energy Laboratory (ReEDS-NREL)
8 NEMS-EIA and USREP-ReEDS also incorporate macro-economic feedbacks. All models, except for one simulation
model, use optimization to resolve energy markets and technology choices.
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Emission reductions are sensitive to IRA implementation, technology costs, and deployment
constraints—with electric sector emissions reductions of up to 91% below 2005 levels in 2030
under advanced technology assumptions. Beyond the central set of IRA Moderate scenarios
discussed above, a subset of models explore Optimistic and Pessimistic IRA implementation
scenarios and find power sector C02 emissions in 2030 fall an additional 2.5 percentage points
on average in the Optimistic scenario and fall 3.3 fewer percentage points below 2005 levels in
the Pessimistic scenario relative to the IRA moderate implementation scenario. The sensitivity
scenarios analyzing technology find larger impacts—relative to the moderate technology
assumptions, power sector C02 emissions in 2030 fall an additional 7.2 percentage points below
2005 levels in the advanced technology scenario with low technology costs and fall 8.8 fewer
percentage points below 2005 levels in the scenario with technology deployment constraints.
Other sensitivities explored by fewer models include energy prices and economic growth.
High and low energy price scenarios can respectively decrease or increase power sector C02
emissions by amounts similar in magnitude to the IRA implementation sensitivity scenarios.
The effects of sensitivity scenarios for economic growth on power sector C02 emissions are an
order of magnitude smaller than the effects of the energy price sensitivities. With the caveat that
fewer models are represented in sensitivity scenarios, these sensitivities show that minimizing
deployment constraints and achieving low technology costs are key to greater power sector C02
emissions reductions.
The IRA is an extensive and complex piece of legislation to model for several reasons including
the number of provisions and the interpretation and detailed assumptions needed to represent
the provisions in a model. As with any energy and economic modeling, there are limitations and
caveats to the analysis (Section 1.2.4). There are clearlimitations in modeling related to the IRA
worth emphasizing here.
1. As noted above, the IRA provides significant incentives for investments in advanced clean
energy technologies (e.g., storage, CCS, nuclear, distributed generation, energy efficiency,
vehicle and building electrification, hydrogen, and geothermal). The costs and market
deployment of these technologies are challenging to model—on one hand, energy models
have tended to underestimate cost declines in clean energy technologies such as wind and
solar, while on the other hand many models may not explicitly capture recent cost increases in
materials and labor or rising interest rates that may offset some of the cost reductions driven
by IRA incentives. Representing barriers and bottlenecks to more rapid technology adoption,
such as scaling supply chains, infrastructure buildout, and siting and permitting also present
uncertainties and challenges.
2. Modeling certain IRA provisions, such as the structure of investment tax credits and residential
rebate programs, is uncertain because the impact of the provisions will depend on decisions
that were not yet final as of the time of the modeling cited in this report (e.g., U.S. Treasury
Department guidance for the clean hydrogen production tax credit [45V] and advanced
manufacturing production credit [48X] and consumer home energy rebate programs).
3. Although it is clear from the modeling that the IRA results in reduced costs for clean
technology and that this is expected to make additional federal, state, and private climate
action more likely, this report does not model the effects of these prospective additional
policy impacts of the IRA.
Future analyses of the impact of the IRA will benefit from additional information and modeling to
address these limitations.
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Across the United States and around the world, the harmful impacts of climate change are
increasingly apparent. Damage from unusual heat waves, prolonged drought, increasingly
strong storms, accelerating sea level rise, and the expanding range of disease-carrying
organisms are collectively affecting our economy and the health and welfare of human beings.
Drought threatens agricultural production; sea level rise and flooding destroy infrastructure;
and tropical illnesses can leave people less able to adjust to changing circumstances. While
these risks can affect individuals from all walks of life, those in low-income communities are
particularly vulnerable. In 2022 alone, extreme weather resulted in $172 billion in economic
damages and 474 deaths in the United States—and the trend in weather disasters continues
to get worse [6]. The world experienced its hottest days on record in July 2023 as average
worldwide temperatures reached 63° Fahrenheit (17.2° Celsius) [7], In recognition of these risks,
and the economic opportunities available in clean energy investment, Congress passed the
Inflation Reduction Act of 2022 (IRA).
CHAPTER 1
Introduction
The science of climate change is clear—reducing the risk of harm requires reducing emissions
of greenhouse gases (GHGs). There are many sources of these emissions from almost
every sector of the economy, but the use of fossil fuels in the energy sector is a significant
contributor to climate risks. Reducing emissions that contribute to climate change is an
urgent task, as every ton of GHGs emitted contributes further to climate damages. Importantly,
Congress noted in enacting the IRA that accelerating the transition to low-emission energy
gives the country an economic advantage and provides an opportunity to lead the world in
clean energy technologies while meeting science-based emissions reductions goals.
Congress also recognized, in both the IRA and the Infrastructure Investment and Jobs Act (I IJ A;
also known as the Bipartisan Infrastructure Bill or BIL) that reducing GHG emissions across all
sectors of the economy—as an imperative and as an opportunity—requires the transition of our
energy sources to cleaner technologies and the transition of our energy use to cleaner energy
16
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and improved efficiency. These transitions involve simultaneously increasing deployment of
low-emitting electricity generation including renewables, fossil fuels with carbon capture
and sequestration (CCS), nuclear, energy storage and hydrogen, increased efficient use of
electricity to replace fossil fuels in other sectors, and increased energy efficiency and demand
flexibility to moderate the increases in electricity demand and enabling infrastructure. The
legislation was consistent with recent research that suggests pathways for reducing GHG
emissions with reliable, affordable electricity, safe and reliable transportation, comfortable and
affordable buildings, and productive and competitive industry [8, 9], Reducing carbon dioxide
emissions from the economy is also an opportunity to improve public health, because the
extraction and combustion of fossil fuels are associated with air pollution that kills thousands
and harms even more [10].
Congress further recognized in the IRA and BIL that realizing these pathways requires historic
investment in the energy infrastructure of our country, particularly in long-lived power grid,
transportation, building, and industrial infrastructure, which often have useful lives of 20
to 50 years or longer [11,12], The needed technologies are readily available but need to be
deployed at a faster rate and at a much greater scale than has been previously achieved. As a
result, meeting U.S. and U.N. 2050 emissions goals [13-15] requires immediate action to begin
replacing existing equipment reaching the end of its life with lower-emitting, efficient, and
electrified technologies. The IRA recognized that this effort has been limited thus far by both
economic and non-economic barriers (see Text Box: Overcoming Deployment Challenges
for more on some of these barriers), and therefore established incentives to overcome
economic barriers to investment and initiatives to reduce technical and knowledge barriers to
implementation.
The IRA represents the largest commitment ever made by the federal government to invest in
the decarbonization of the U.S. economy. The Congressional Budget Office (CBO) estimates the
total support for the broad range of climate and clean energy programs, tax credits, and other
incentives authorized through the IRA at $391 billion from 2022 through 2031 [16]. Subsequent
analyses of the IRA provisions indicate that the market response may result in even more
investment in clean energy than anticipate by CBO with public sector incentives ranging from
$800 billion to $1.2 trillion over the ten-year period [17,18]. The IRA provisions are designed
to leverage private-sector investment with estimates of combined public and private-sector
investments over the same period spanning $1.8 to $2.9 trillion [17,18]. Higher investment in
clean energy would lead to higher economic activity, additional job creation, and additional
emission reductions.
The IRA aims to reduce emissions by incentivizing both the generation of low-cost, low-
emission electricity and its efficient use in buildings, transportation, and industry. Through
tax incentives, grants, and loan programs the IRA seeks to promote the production of clean
energy, domestic manufacturing, and job creation as well as help low-income and underserved
communities transition to a low-carbon economy [18]. The BIL provides complementary
funding, programs, and incentives for clean energy technologies. It includes $7.5 billion for
17
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electric vehicle (EV) charging stations, $65 billion for electric grid upgrades, $8 billion for clean
hydrogen hubs, and $6 billion for a civil nuclear credits program.
This report examines results from state-of-the-art modeling tools to show how the IRA reduces
emissions during the transition to a cleaner economy. The report presents estimates of IRA
investments in clean energy technologies. Consistent with Congress' direction for this report,
it also shows where further efforts for climate action offer opportunities to ensure that the
United States is on a path to meet our climate goals.
Early analyses of the IRA by models used to inform the legislative deliberations prior to passage
show significant reductions in GHG emissions [19-22], This report builds upon these early
analyses to meet EPA's statutory requirement under the Low Emission Electricity Program
(LEEP) of the IRA, which calls for an assessment of "the reductions in greenhouse emissions
that result from changes in domestic electricity generation and use that are anticipated to
occur on an annual basis through fiscal year 2031"9.
This introductory chapter describes the methodologies used to assess the emissions
reductions and present economy-wide emissions reductions and the underlying energy system
transformations. Chapters 2 through 5 will explore these reductions and transformations at
a sectoral level for the electric sector, transportation, buildings, and industry, respectively.
Chapter 6 concludes with a summary of the findings and suggestions for future analysis.
1.1 OVERVIEW OF THE INFLATION REDUCTION ACT OF 2022
The IRA accelerates the clean energy transition by promoting clean energy, vehicles, buildings,
and manufacturing through the inclusion of more than two dozen tax provisions and grant
programs. It also provides enhanced/bonus credits to projects that are in low-income
communities or energy communities, pay prevailing wages and use registered apprentices,
or meet certain domestic content requirements—all with the aim of promoting environmental
justice, strengthening America's energy security, creating good-paying, high-quality jobs,
and spurring shared economic growth. Initiatives established or expanded by the IRA also
seekto address deployment challenges, including practical limitations to implementing new
technology and barriers caused by imperfect information. Specific barriers are discussed in the
Text Box: Overcoming Deployment Challenges.
The IRA offers funding, programs, and incentives to accelerate the transition to a clean energy
economy and will likely drive significant deployment of new clean electricity generation and
use. Most provisions of the IRA became effective January 1, 2023. In addition to the abbreviated
summary below, more detail on specific IRA provisions is provided in subsequent sector-
specific chapters.
9 Inflation Reduction Act of 2022. section 60107(5), P.L. 117-169 (August 16, 2022), 136 STAT. 269, 42 U.S.C. 7435(a)(5),
Clean Air Act section 135(a)(5).
18
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Electric Generation
The IRA provides significant incentives, including several tax provisions and substantial grant
and loan programs to support this deployment of commercially available and innovative clean
energy technologies. These include:
¦ Renewable Production and Clean Energy Production Tax Credit (45 and 45Y):10 Facilities
generating net zero GHG electricity from wind, biomass, geothermal, solar, landfill and
trash, and hydropower and marine renewable energy that begin construction prior to
January 1, 2025, will receive a credit amount of 0.3 cents/kilowatt-hour (kWh) up to 1.5
cents/kWh.11
¦ Renewable Investment and Clean Energy Investment Tax Credit (48 and 48E): Fuel cell, solar,
geothermal, small wind, energy storage, biogas, microgrid controllers, and combined heat
and power properties receive credit up to 30% of the qualified investment.
¦ Nuclear Production Tax Credit (45U): Up to 1.5 cents/kWh for nuclear facilities producing
electricity from 2024 through 2032 and not eligible for the advanced nuclear power credit
(45J).
¦ $40 billion in loan authority to guarantee loans for innovative clean energy projects,
including carbon capture, new renewable systems, and nuclear.
¦ $250 billion in loan authority under the Energy Infrastructure Reinvestment (EIR) Program to
leverage existing fossil fuel infrastructure.
Multi-Sector
The IRA also includes several provisions that cover and affect more than one sector of the
economy. The ten multi-sector models in this study are well suited to estimate the effects of
these provisions, which include:
¦ Carbon Capture and Sequestration (45Q): Up to $85/metric ton of carbon dioxide (C02)
captured and sequestered from industrial and power generation, and up to $180/metric ton
for direct air capture. Credit can be claimed for 12 years after carbon capture equipment is
placed in service (construction must begin prior to 2033).
¦ Hydrogen Production Tax Credit (45V): Up to $3.00/kilogram (kg) for producers of clean
hydrogen at a qualified facility.
¦ Provides funding to the U.S. Department of Agriculture (USDA) for electric loans for
renewable energy under the Rural Electrification Act, including for projects that store
electricity.
10 The abbreviated references capture the section of the U.S. Tax Code where the provision appears (e.g., 26 U.S. Code
§45Y)
11 The 0.3 cents/kWh to 1.5 cents/kWh range reflects the IRA's two-tier credit regime in which the higher tier credit is
available for eligible projects that satisfy certain prevailing wage and apprenticeship requirements. The PTC as written
is expressed in 1992 dollars which reflects a full value of 2.75 cents/kWh in 2022.
19
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¦ Funding for the Department of Energy (DOE) Loan Programs Office increases to $40 billion,
supporting eligible projects that avoid, reduce, utilize, or sequester air pollutants or GHG
emissions, and employ new or significantly improved technologies.
¦ Energy Infrastructure Reinvestment financing of $250 billion for DOE for projects that (1)
retool, repower, repurpose, or replace energy infrastructure, where fossil fuel electricity
projects must have controls to avoid, reduce, utilize, or sequester air pollutants of GHG
emissions, or (2) enable operating infrastructure to avoid, reduce, utilize, or sequester air
pollutants or GHG emissions.
¦ Tribal energy loan guarantee program increased to $20 billion.
The IRA is expected to promote significant efficiency improvements and electrification in
energy end-use sectors—most prominently transportation, buildings, and industry. Energy-
efficient measures, like more efficient equipment and building envelope improvements, directly
decrease fossil emissions and reduce the amount of grid generation and capacity needed to
fuel end uses. Electrification reduces GHG emissions through lower-emitting electric fuel and,
inherently, more efficient electric technology like variable speed vehicle engines and building
heat pumps. Electrification provides emissions reductions now in most parts of the country,
and as lower-emitting or non-emitting generation is deployed, those reductions will increase.
Optimizing the efficiency of end uses will also further reduce emissions and ameliorate any
near-term emissions increases.
Transportation
EV uptake will increase in the near-term as a result of measures that reduce the cost to
purchase and manufacture them, incentivize the growth of manufacturing capacity and
onshore sourcing of critical minerals needed for their manufacture, incentivize buildout of
public charging infrastructure for plug-in electric vehicles, and promote modernization of the
electrical grid that will power them. It includes significant purchase incentives:
¦ Clean Vehicle Credit (30D): Up to $7,500 for new clean vehicles.
¦ Used Clean Vehicle Credits (25E): Up to $4,000 for used vehicles, which will have a strong
impact on affordability of these vehicles for a wide range of customers.
¦ Commercial Clean Vehicle Credit (45W): Up to $40,000 for commercial purchase of
medium-duty vehicles.
In addition, the IRA includes significant tax credits for certain charging infrastructure
equipment, and sizable incentives for investment in and production of clean electricity, such as
30% of the cost for a charging station. It will significantly reduce the manufacturing cost of EV
components through the provisions detailed in the "industry" section below.
20
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Buildings
New tax incentives and customer rebates in the IRA reduce the cost of energy-efficient and
efficiently electrified home and building upgrades, offset the cost of adding distributed
clean energy sources, and make constructing new energy-efficient and efficiently electrified
single and multi-family homes cheaper and easier. These incentives are also reinforced by IRA
funding to state, local, and tribal governments accompanied by federal partnerships, technical
assistance, and training. Relevant IRA programs include:
¦ Energy Efficient Home Improvement Credit (25C): Up to $3,200 annually in tax credits
to lower the cost of residential efficiency upgrades, including efficient electric end-use
appliances like heat pumps for heating, air conditioning, and water heating, as well as
building envelope measures, including insulation, doors, and windows.
¦ Residential Clean Energy Credit (25D): Up to 30% tax credit to lower cost of residential
energy including rooftop solar, wind, geothermal, and battery storage.
¦ New Energy Efficient Homes Tax Credit (45L): Up to $5,000 in tax credits for qualifying new
homes and up to $1,000 for each unit in a multi-family building. Each must, at minimum,
meet ENERGY STAR requirements.
¦ Energy Efficient Commercial Buildings Deduction (179D): A revamped deduction for energy
efficiency in commercial buildings with a $0.50-$l base credit per square foot, deductions
increase to up to $5 per square foot if wage and apprenticeship requirements are met.
¦ Nearly $9 billion for states and tribal governments for consumer home energy rebate
programs, prioritizing low-income homeowners.
¦ $1 billion for the Green and Resilient Retrofit Program for building benchmarking and
efficiency improvements at U.S. Department of Housing and Urban Development (HUD)-
assisted multi-family properties.
¦ $1.2 billion in grants for states and local government to update building codes and provide
training for building sector contractors.
Industry
The IRA includes incentives for the manufacture of fuels and technologies that promote
decarbonization:
¦ Carbon Capture and Sequestration (45Q): Up to $85/metric ton of C02 captured and
sequestered from industrial and power generation.
¦ Advanced Manufacturing Production Tax Credit (45X): Credit of varying rates for U.S.
manufacturers of clean energy components. Manufacturer production tax incentives of $351
kilowatt-hour (kWh) for U.S. production of battery cells.
21
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¦ $5 billion in advanced industrial facilities deployment funding and $5 billion in vehicle
manufacturing loans and grants.
¦ More than $4 billion in funding for encouraging use of low-carbon materials in federal
infrastructure.
Cross-cutting Funds and Grants
IRA creates two major new programs that will fund projects in multiple sectors. The
representation of these programs in the analyses varies across models and may not be
reflected in some results.
¦ $27 billion for a GHG Reduction Fund for competitive grants to strengthen institutions
that accelerate the transition to an equitable net-zero economy—investing in buildings,
distributed solar, and beyond. The fund consists of
— $14 billion for National Clean Investment Fund competition.
— $7 billion for Solar for All program.
— $6 billion for Clean Communities Investment Accelerator.
¦ $5 billion for Climate Pollution Reduction Grants for states, local governments, tribes, and
territories to develop and implement plans across sectors for reducing GHG emissions and
other harmful air pollution.
— $250 million for noncompetitive planning grants.
— $4.6 billion for competitive implementation grants
1.2 ESTIMATING EMISSION REDUCTIONS
This assessment of emission reductions relies upon available data and analysis from the peer-
reviewed literature, government reports, and modeling by EPA. The report incorporates results
from ten multi-sector energy system models and four electric sector models. This section
describes the report's methodology and models (1.2.1), scenarios (1.2.2), scope and conventions
(1.2.3), and caveats and limitations (1.2.4).
1.2.1 Methodology and Models
The economy is interrelated and complex, and the investments made in the IRA are far-
reaching—analyzing the impacts of these changes requires the use of sophisticated energy-
economy models that can capture the breadth of the IRA's incentives. To estimate the emission
reductions, this report relies upon modeling results from recent peer-reviewed literature,
government reports, and EPA-funded modeling and analysis. By leveraging the results from
multiple energy-economy models, this analysis characterizes the general trends in emission
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reductions, provides an estimate of the range of reductions, gives insights into what drivers of
emission reductions are robust across models, and identifies areas of uncertainty that could
lead to differences from central estimates.
The report incorporates results from the following four studies:
¦ A 2023 multi-model, peer-reviewed study, Emissions and Energy Impacts of the Inflation
Reduction Act, by Bistline et al. and published in Science. This study includes six multi-
sector models and three electric-sector models [1], Much like the present analysis, the study
contrasts two scenarios with and without IRA represented. A strength of this study is the
large number of models represented including the models that produced early analyses of
the IRA last summer.
¦ The Energy Information Administration's (ElA's) Annual Energy Outlook (AEO) 2023 using
the National Energy Modeling System (NEMS) model. This report includes the with and
without IRA scenarios.
¦ An economy-wide study, Investing in American Energy: Impacts of the Inflation Reduction
Act and Bipartisan Infrastructure Law on the U.S. Energy Economy and Emissions
Reductions, using a version of the NEMS supported by the Office of Policy at the DOE [2,
23]. The study contrasts with and without IRA scenarios and explores sensitivities to IRA
implementation and technology cost.
¦ An electric-sector study, Evaluating Impacts of the Inflation Reduction Act and Bipartisan
Infrastructure Law on the U.S. Power System, using NREL's ReEDS model [3]. This study
presents a rich sensitivity analysis of IRA implementation, technology cost, deployment
constraints, and fuel prices.
EPA-funded analysis of emissions reductions includes the use of two multi-sector models, the
Global Change Assessment Model (GCAM-PNNL) and the U.S. Regional Energy Policy Model
(MIT's USREP model) linked to the Regional Energy Deployment System (National Renewable
Energy Laboratory's [NREL] ReEDS), and an electric-sector model, IPM-EPA.
The following list describes the ten multi-sector models and four electric-sector models cited
and shown herein.
Multi-Sector Models in This Report
¦ Energy Policy Simulator from Energy Innovation LLC (EPS-El): EPS simulates major sectors
of the U.S. economy on an annual basis. The model tracks changes from business-as-usual
projections to examine how user-selected policies impact energy demand, costs, and
emissions.
¦ Global Change Analysis Model (GCAM-CGS) from Center for Global Sustainability (CGS):
GCAM-CGS is based on GCAM 5.3 and models the United States at the state level. It
includes detailed sector-specific, state-level climate policies across multiple sectors of the
U.S. economy. GCAM solves for prices of energy resources and the associated demand from
other sectors, recursively converging to an equilibrium.
23
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¦ Global Change Analysis Model (GCAM-PNNL) from Joint Global Change Research
Institute (JGRCI): GCAM-PNNL is based on GCAM 6.0 and models the United States as a
single region. It adds detailed sector-specific, climate policies across multiple sectors of
the U.S. economy. GCAM solves for prices of energy resources and the associated demand
from other sectors, recursively converging to an equilibrium.
¦ Market Allocation (MARKAL) from National Energy Technology Laboratory (NETL):
MARKAL solves a linear program defined by the nine U.S. census regions, accounting for
trade flows of energy in the form of electricity, gas, coal, and other fuels.
¦ National Energy Modeling System (NEMS-EIA) from the Energy Information
Administration: NEMS models the entire energy sector using submodules for 13 subsectors
and broader economic feedbacks. Supply-side models use least-cost optimization
approaches to track the evolution this system overtime. This study incorporates the with
IRA and without IRA scenarios from ElA's Annual Energy Outlookfor 2023.
¦ National Energy Modeling System (NEMS-OP) from the Office of Policy at the
Department of Energy: This version of NEMS incorporates more provisions of the IRA
than are represented in ElA's Annual Energy Outlookfor 2023 and includes more extensive
representation of industrial CCS, hydrogen production, and direct air capture technologies.
¦ National Energy Modeling System (NEMS-RHG) from Rhodium Group: This version of
NEMS incorporates more provisions of the IRA than are represented in ElA's Annual Energy
Outlookfor 2023 and includes more extensive representation of industrial CCS, hydrogen
production, and direct air capture technologies.
¦ Regional Economy, Greenhouse Gas, and Energy (REGEN) from Electric Power Research
Institute (EPRI): The U.S. REGEN model links a detailed power sector planning and dispatch
linear program model with a logit-choice energy end-use model.
¦ Regional Investment and Operations Model (RIO) from REPEAT: The combination of the
RIO supply-side model and EnergyPATHWAYS demand-side model developed by Evolved
Energy Research and used by the REPEAT project models detailed energy accounting across
sectors of the economy with special detail on infrastructure investment and efficiency.
¦ USREP-ReEDS: This modeling framework consists of the MIT U.S. Regional Energy Policy
(USREP) model, a computable general equilibrium model of the United States with 12
regions, linked to NREL's Regional Energy Deployment System (ReEDS) model, a capacity
planning model of the U.S. electricity system. The linked modeling system combines
ReEDS's spatial and technological detail with USREP's representation of other sectors and
the macroeconomy. Note that the version of ReEDS linked with USREP is the same as the
standalone version (see below) with one important exception. The linked version does
not have plant-level CCS retrofit decisions, which leads to less CCS adoption and higher
electric sector emissions in the linked model.12
12 Seethe USREP-ReEDS documentation for this work, entitled Economic and Environmental Impacts of the
Inflation Reduction Act: USREP-ReEDS Modeling Framework, https://cfpub.epa.aov/si/si public record Report.
cfim?dirEntrvld=558898&Lab=OAP
24
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Electricity Sector Models in This Report
¦ Haiku from Resources for the Future (RFF): Haiku is a perfect foresight model of the U.S.
electricity sector with state-level coverage and detailed sub-national policy representation.
¦ Integrated Planning Model (IPM-EPA)fromtheEPA: IPM is a detailed power-sector model
and provides projections of least-cost capacity expansion, electricity dispatch, and
emission control strategies for meeting electric demand and environmental, transmission,
dispatch, and reliability constraints [24, 25].
¦ Integrated Planning Model (IPM-NRDC) from the Natural Resources Defense Council
(NRDC): IPM is a linear programming model of power-sector capacity planning and
utilization.
¦ Regional Energy Deployment System (ReEDS) from the National Renewable Energy
Laboratory (NREL): ReEDS is a linear program of electricity supply and demand as well as
the provision of operating reserves for grid reliability at 134 different balancing areas.
Table 1.1 contains a list and characteristics of the multi-sector and electric-sector models used
in this analysis. The geographic coverage of the models is primarily the United States; however,
GCAM models all world regions. Spatial resolution ranges from a single region to state and
sub-state levels. Most of the models rely on optimization to find least-cost solutions or balance
supply and demand in energy markets; one model (EPS-El) uses a simulation framework.
Temporal resolution ranges from annual time steps to hourly representations of end-use. Most
of the models represent fuel price and energy demand endogenously, that is these variables
are solved for within the model instead of taken as inputs. The electric sector is represented
with perfect foresight in most models. Appendix B contains two summary tables of model
representation of emerging technologies (B.l) and policy representation in the No IRA scenario
(B.2). The models attempt to represent current policies, though there are variations about
which policies are covered. A notable difference with a few models (OP-NEMS, ReEDS, USREP-
ReEDS) is that BIL/IIJA has been excluded from the No IRA scenario but included in the IRA
scenario to highlight the full effect of both policy measures.
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Table 1.1
Characteristics of multi-sector and electric-sector models in this
analysis
Geographic
Coverage Model Type and Endogenous
and Spatial Equilibrium Temporal Endogenous Energy Electric
Model Resolution Approach Resolution Fuel Prices Demand Sector
Multi-Sector Models (10)
EPS-El
Energy Policy Simulator
(EPS)
Energy Innovation
50 U.S. states
and D.C.
Single national
region
Energy systems
Economy: System
dynamics
Annual for end use
Seasonal for electric
Yes, in IRA
Scenario
Yes
Recursive
dynamic
GCAM-CGS, GCAM-PNNL
(2)
Global Change Analysis
Model UMD-CGS, PNNL
50 U.S. states
and D.C.
States
Energy systems
Economy: Logit
choice
Annual for end use
UMD-CGS: 4
segments for
electric
PNNL: Annual for
electric
Yes
Yes
Recursive
dynamic
MARKAL-NETL
MARKet ALlocation
NETL DOE
Contiguous U.S.
9 Census regions
Energy systems
Economy: Least-
cost LP
Hourly for end use
12 segments for
electric
Yes
Yes
Perfect
foresight
NEMS-EIA, NEMS-OP,
50 U.S. states
Energy systems
Annual for end use
Yes
Yes
Perfect
IMEMS-RHG (3)
DOE Office of Policy -
National Energy Modeling
System
EIA, DOE Office of Policy,
and D.C.
Regions vary by
sector
Economy: 13
modules with least-
cost LP supply and
consumer adoption
demand
9 segments for
electric
foresight
Rhodium Group
REGEN-EPRI
Regional Economy,
Greenhouse Gas, and Energy
EPRI
Contiguous U.S.
16 regions
Energy systems
Energy end use:
Lagged logit
choice; Electricity:
Least-cost LP
Hourly for end use
120 segments for
electric
No
Yes
Perfect
foresight
RIO-REPEAT
RIO (supply-side),
EnergyPATHWAYS (demand-
side)
Evolved Energy Research
and ZERO Lab
Contiguous U.S.
27 regions
Energy systems
Economy-wide LP
Hourly for end use
1,080 segments for
energy supply
No
Yes
Perfect
foresight
USREP-ReEDS
U.S. Regional Energy Policy
Model and Regional Energy
Deployment System
MITandNREL
50 U.S. states
and D.C.
USREP 12 regions
ReEDS 134
regions
Energy-Economy
Economy: Constant
elasticity of
substitution
Power sector:
Least-cost LP
17 segments for
electric
Annual for other
Yes
Yes
Recursive
dynamic
Electric Sector Models (4)
Haiku-RFF Contiguous U.S. Electric sector 24 segments for No No Perfect
Haiku Power Sector Model ^ ^ electric foresight
„ , ., r . States Power sector PE: y
Resources for the Future , j. j., „
Least-cost LP
IPM-EPA, IPM-IMRDC (2)
Contiguous U.S.
Electric sector
24 segments for
Coal, natural
No
Perfect
Integrated Planning Model
EPA, NRDC
67 regions
Power sector PE:
Least-cost LP
electric
gas, biomass
foresight
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1.2.2 Scenarios and Sensitivities
The study is structured around two scenarios to evaluate the potential impacts of the IRA on
emissions:
¦ IRA: A scenario that reflects all federal and state policies enacted including the IRA.
¦ No IRA: A counterfactual scenario that reflects federal and state policies enacted except
for the IRA.
As noted in Section 1.1, the IRA is an extensive and complex piece of legislation to model
for several reasons including the numPer of provisions and the interpretation and detailed
assumptions needed to represent the provisions in a model. TaPle 1.2 summarizes which of
the 44 IRA provisions are represented in each model. It is Pased upon Bistline et al. [1] for
consistency across studies. It is not intended to Pe exhaustive Put attempts to capture most of
the high economic value and high leverage provisions. Note that the category "not applicaPle"
applies to provisions that cannot Pe modeled within the current model structure and scope.
This contrasts with provisions identified as "not modeled," meaning that the structure and
scope exist, Put the provision was not modeled for other reasons. Appendix C contains the IRA
implementation assumptions for the EPA-funded models GCAM-PNNL, USREP-ReEDS, and
IPM-EPA (C.l), IRA implementation sensitivity assumptions for USREP-ReEDS (C.2) and GCAM-
PNNL (C.3), and IRA implementation sensitivity guidance for models in the Bistline et al. paper
(C.4).
As shown in TaPle 1.2, no model represents all 44 provisions. The multi-sector models cover
Petween 16 and 32 provisions. The electric-sector models cover 6 to 8 provisions. All else
equal, greater coverage of provisions would Pe expected to lead to greater reductions.
However, some provisions provide greater emission reductions that others (see the sensitivity
discussions in Sections 1.3.2 and 2.3). Certain provisions are covered Py very few models (see,
for example, loan programs, the advanced manufacturing production credit (45X), and cross-
cutting funds and grants). Of the end-use sectors, industry has the lowest coverage of IRA
provisions.
To understand how changes to various assumptions affect emissions reductions, the report
explores several sensitivities as summarized Pelow.
¦ IRA implementation (Moderate, Pessimistic, Optimistic). Results in the report reflect the
Moderate IRA implementation scenarios, which is defined as the modeling team's central
case as presented in the literature. The Optimistic implementation means that scenario
is favoraPle toward emission reductions. The Pessimistic implementation means the
scenario is less favoraPle for emission reductions. Results are included for four multi-sector
models (EPS-El, GCAM-CGS, REGEN-EPRI, RIO-REPEAT) and two electric-sector models
(ReEDS-NREL, Haiku-RFF) that ran Poth Optimistic and Pessimistic scenarios from the
Bistline et al. study [1]. These sensitivities change the IRA implementation assumptions,
and the Puild rates and availability of clean generating technologies as summarized from
Appendix C.3. USREP-ReEDS ran similar scenarios, Put without restrictions on Puild rates
(see Appendix C.2).
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Table 1.2
Summary of IRA provisions represented in energy models in this report
Multi-
sector
Power sector
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
NEMS-EIA
NEMS-OP
NEMS-RHG
REGEN-EPRI
RIO-REPEAT
USREP-ReEDS
Haiku-RFF
IPM-EPA
IPM-NRDC
ReEDS-NREL
Total # of provisions covered for each model out of 44.
27
22
23
19
16
32
22
19
29
26
6
8
Section
Tax code
Program
Electricity
13101
45
Production tax credit (PTC) for electricity from renewables
13102
48
Investment tax credit (ITC) for energy property
13103
45(e), 45E(h)
Solar and wind facilities placed in low-Income communities
13105
45U
Zero-emission nuclear power PTC
13701
45Y
New clean electricity PTC
13702
48 E
New clean electricity ITC
13703
168(e)(3)(B)
Cost recovery for qualified property (13703)
22004
-
USDA assistance for rural electric cooperatives
50151
-
Transmission facility financing
~
~
13104
13204
22001
50141
50144
50145
13201
13202
13203
13401
13402
13403
13404
13704
60101
70002
Multi-Sector
45Q
45V
Credit for carbon oxide sequestration (CCS & DAC)
Clean hydrogen PTC
Electric loans for renewable energy
Funding for DOE Loan Programs Office
Energy infrastructure reinvestment financing
Tribal energy loan guarantee program
Transportation
40A, others
40
40B
30D
25E
45W
30C
45Z
Biodiesel and renewable fuels PTC
Second-generation biofuels PTC
Sustainable aviation fuel PTC
Clean vehicle credit
Credit for previously-owned clean vehicles
Qualified commercial clean vehicle credit
Alternative fuel vehicle refueling property credit
New clean fuel PTC
Clean heavy-duty vehicles
U.S. Postal Service clean fleets
Included
Not Included
~ Not Applicable
28
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Table 1.2
Summary of IRA provisions represented in energy models in this report
(continued)
13301
13302
13303
13304
30002
50121
50122
60502
13501
13502
50161
60113
Multiple
Multiple
Multiple
Multiple
25C
25D
179D
45L
48C
45X
Total # of provisions covered for each model out of 44.
Buildings
Energy efficient home improvement PTC
Residential clean energy PTC
Energy efficient commercial buildings deduction
New energy efficient homes credit
Green and resilient (HUD) retrofit program
Home energy performance-based, whole-house rebates
High-efficiency electric home rebate program
Assistance for federal buildings
Industry
Advanced energy project credit
Advanced manufacturing production credit
Advanced industrial facilities deployment program
Methane emissions reduction program
Vehicle manufacturing loans/grants
Low-carbon materials
Agriculture and forestry provisions
Oil and gas lease sales
Cross-Cutting Funds and Grants
27
Multi-sector
22
23
19
16
32
22
19
29
60103
-
Greenhouse gas reduction fund
_
60114
-
Climate pollution reduction grants
60201
-
Environmental and climate justice block grants
26
Power sector
cc
CO
o
LU
CD
ad
Included
Not Included
~
Not Applicable
29
-------
-- Transferability penalty for tax credits (PTC/ITC/45Q/45V): double and halve penalty.
— Energy community and domestic content bonus eligibility: ±20% from central case
within maximum bound of credit,
— Energy Infrastructure Reinvestment Program coverage multiplier: ±25% from central
case.
— Build rates for renewables: 7% lower build rate from central case in pessimistic;
unconstrained for optimistic.
Build rates for transmission: 1% annual build rate for pessimistic; unconstrained for
optimistic.
CCS availability: unavailable until 2030 in pessimistic; unconstrained for optimistic.
— EVs eligible for qualifying bonus credits: ±25% from central case.
-- Demand-side incentive programs adjustments: 20% loss in credit value for pessimistic;
10% gain in optimistic.
¦ Technology. These scenarios vary cost and non-cost technology assumptions.
— Cost and Performance. (All Advanced, Advanced Renewables). RcEDS, USREP-RcEDS,
and GCAM-PNNL ran the All Advanced scenario. Technology cost and performance
in ReEDS are changed from the moderate NREL annual technology baseline (ATB)
electricity costs to advanced (Adv) or conservative (Cons) ATB cost assumptions [26].
ReEDS also ran a scenario with only advanced renewables. USREP-ReEDS uses the
same electricity assumptions as RcEDS and lowers the costs of transportation, energy
efficiency, and CCS. GCAM lowered the costs of solar, wind, and electric vehicles (see
C.4).
— Constraints (Constrained, Constrained Renewables). These sensitivities attempt to
capture deployment challenges associated with permitting challenges, infrastructure
development, and inter-regional coordination between utilities and transmission
operators. It reduces the land or resource availability for wind, solar, geothermal, and
biomass. It constrains transmission builds to historical national averages and limits
builds to within transmission planning regions. It also doubles the cost of C02 pipeline,
injection, and storage infrastructure [3], ReEDS ran both sensitivities; USREP-ReEDS ran
only the all constrained case.
¦ Combined IRA Implementation and Advanced Technology Costs (Optimistic-Advanced,
Pessimistic-Advanced). This scenario combines assumptions on advanced technology
costs with IRA implementation assumptions (Optimistic for both USREP-ReEDS and OP-
NEMS; Pessimistic for only USREP-ReEDS).
¦ Fossil Energy Prices and Economic Growth (Moderate, Low, High). These sensitivities were
explored using the GCAM-PNNL model by taking the high and low oil and natural gas price
scenarios and the economic growth assumptions from the ElA's 2023 AEO. Standalone
ReEDS also explores the effects of natural gas prices using high and low prices from AEO
2022.
30
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Table 1.3
Sensitivity scenarios by model. The dots indicate which models
(columns) ran each sensitivity scenario (rows)
Multi-sector
Power sector
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
NEMS-EIA
NEMS-OP
NEMS-RHG
REGEN-EPRI
RIO-REPEAT
USREP-ReEDS
Haiku-RFF
IPM-EPA
IPM-NRDC
ReEDS-NREL
Sensitivity Scenario Categories
IRA Implementation
•
•
•
•
•
•
•
•
Technology Costs
•
•
•
Combined IRA Implementation and Advanced Technology Costs
•
•
•
Technology Constraints
•
•
Energy Prices
•
•
Economic Growth
•
1.2.3 Scope and Conventions
Scope
Gases. In assessing the reductions in greenhouse gases, the analysis focuses on C02
emissions. Over 99% of power-sector GHG emissions are from direct combustion of fossil
fuels in generation13. Emissions of two non-C02 GHGs, methane (CH4) and sulfur hexafluoride
(SF6), are closely linked to electricity generation and use. As a significant consumer of natural
gas, the electric sector may be linked to upstream emissions from natural gas extraction,
processing, transmission, and distribution. However, changes in natural gas emissions are not
examined herein for several reasons. First, the focus of the statutory language for the report is
on electricity generation and use but natural gas emissions come from upstream processes.
Second, the available literature on the effect of the IRA contains very limited reporting of
upstream natural gas emissions and cannot be assessed with the same robustness as C02
emissions. Note that the Methane Emissions Reduction Program within the IRA aims to reduce
methane emissions levels from oil and natural gas operations through financial and technical
assistance as well as a waste emissions charge (see the text box on the Methane Emissions
Reduction Program of the IRA). The emissions of SF6, a gas used as an electrical insulator and
arc quencher in electrical transmission and distribution equipment, are discussed in the text
box on Managing SF6 in an Electrifying Economy in Chapter 2.
13 See Table 2.11 of the U.S. Greenhouse Gas Inventory [4]
31
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Policy. Following the long-held convention used in the ElA's Annual Energy Outlook, the results
in this report reflect on-the-books policies and regulations. The results do not reflect proposed
regulations outside the scope of the IRA. In particular, the report does not model or attempt to
account for proposed regulations in transportation and the electric sector. In April 2023, the
EPA announced new proposed standards to further reduce harmful air pollutant emissions from
light-duty and medium-duty vehicles [27], as well as heavy-duty vehicles [28], starting with
model year 2027.14 In addition, in May 2023, the EPA proposed a rule that establishes emission
limits and guidelines for C02 from fossil fuel-fired power plants based on cost-effective and
available control technologies [29]. The proposed rule covers new gas-fired combustion
turbines, existing coal, oil and gas-fired steam generating units, and certain existing gas-fired
combustion turbines. These and other proposed regulations are not reflected in the modeling
results and projections shown in this report.
Conventions
Sector Definitions. The report presents emission reductions and energy consumption at the
national level across four sectors of the economy: electric power, transportation, buildings, and
industry. Within the power sector, capacity and generation include both large, central station
units and distributed generation. Transportation includes personal transportation, trucking,
public transit, rail, air, and ships. Buildings include commercial space and residential dwellings.
Industry includes production activities such as manufacturing, mining and extraction, and
refining—but does not include the electric sector. Note that non-combustion-related industrial
process emissions (e.g., from cement) are included.
Direct and Indirect Emissions. Two categories of emissions are reported for the
transportation, buildings, and industry sectors: direct and indirect. Direct emissions refer to
emissions from the combustion of fossil fuels within the sector (e.g., natural gas combustion
for home heating). Indirect emissions, as used within this report, refer only to emissions
associated with the electricity consumed in the sector. Emissions associated with other energy
conversion processes such as refining are included in the industry sector.
Historical Data Sources. Unless otherwise noted, all historical emissions are based on the
Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2021 [4] and historical energy
data are from the Energy Information Agency's Monthly Energy Review [30].
Results Reporting. As described below, the report leverages information from multiple models.
Results are typically reported as the multi-model minimum and maximum as well as the median
value. Medians do not weight the highest and lowest results as heavily as the mean. Consistent
with the majority of the results in the literature, the first reported model year is 2025. Results
are presented through 2035, beyond the year specified in the IRA language requesting this
assessment, for two reasons—some important IRA provisions extend past 2031 and most of
the models we use report projections in five-year increments. Extending to 2035 also captures
energy system impacts of the IRA that might not be immediately evident in data through
2031 (e.g., due to slow turnover in end-use capital stock). To estimate reductions in 2031, the
14 The proposed rules are not included in this analysis and are mentioned here for broader context of the transportation
sector.
32
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emissions need to be interpolated between 2030 and 2035. For tractability, the results do not
identify individual models, except for the electricity sector. For numbers reported in the text,
modeled results are rounded to two significant figures. The results from individual models are
presented in Appendix E. Supplemental results on electrification, direct emissions, sensitivity
analysis are presented in Appendix F.
Emissions reductions are presented in two ways: 1) relative to historical 2005 emission levels
and 2) as the percentage change between the No IRA and IRA scenarios for each model. The
former allows for straightforward comparisons to the literature. The latter better isolates the
change in emissions due to the IRA from changes in emissions in the No IRA scenario.
1.2.4 Caveats and Limitations
The models used in this report are simplified representations of the decision-making by all the
actors in the economy, and it is important to note that even the most sophisticated modeling
and analysis is subject to limitations [31].
The range of results shown across models, which use the Moderate IRA implementation
scenario, reflect differences in 1) model structure including technology availability, 2)
parametric uncertainty (e.g., technology costs), 3) assumptions and the calibration of the
No IRA scenario, and 4) interpretation and representation of the IRA provisions. There was
no systematic effort to harmonize assumptions across models, which may affect the relative
change in emissions between the scenarios [32], The value of a multi-model approach is that
the analysis reveals results that are robust despite these differences. It should be noted that the
multi-model results do not reflect the full range of uncertainty and should not be interpreted
as statistical distributions. The full range of uncertainty would cover many more variables and
require systematic testing of distributions of input parameters within the models [31, 33, 34],
Energy-economy models represent many highly complex economic activities including
energy supply and demand, technology choice, and level of investment. For tractability,
most models, including the optimizing frameworks cited within the report, make simplifying
assumptions that decision-making occurs under perfect information and markets are perfectly
competitive. Recent research suggests that explicitly modeling imperfect information and
firm-level decision-making in electricity markets do not significantly alter total new capacity
additions but may affect technologies shares [35]. Models represent these activities at various
levels of spatial (e.g., nation, state, balancing area) and temporal resolution (e.g., years to
hours). Representing technologies at finer-scale temporal and spatial resolution is becoming
increasing important to capture technology performance and system interactions [31, 36].
Some of the provisions of the IRA will affect parts of the economy that are difficult to analyze
in models, due to, for example, the level of technology aggregation in the model or the
details of the IRA provision. Other examples include specific technical characteristics of the
transportation, building, and industrial sectors, as well as characteristics of decision-making
by individual consumers and companies to invest in efficient and electrified vehicles or
appliances, complete energy efficiency measures for buildings or industrial plants, or purchase
33
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renewable electricity [37], There are also dynamics that are challenging to represent in models
(see the text box on Overcoming Deployment Challenges) and are therefore explored through
sensitivity analysis. Furthermore, economic events, such as recent increases in interest rates
and material and labor costs, may offset some of the IRA cost reductions that are not captured
explicitly by the models.
The implementation of the IRA depends upon government decisions--some of which have
or had not been made as of the time of the analyses. Some specific incentives provided by
the Act—such as rules about tax credits to be developed by the Department of the Treasury,
depend on guidance that either has yet to be issued (e.g., 45V) or was issued after the
provisions were modeled. These details will affect investment decisions and, consequently,
they will affect future emissions. To model the impacts of the IRA, modelers have made
assumptions about how these details will be resolved, and the scenarios reflect these
uncertainties.
Further, multiple technologies are rapidly advancing in a wide range of areas that could
significantly change what the future power sector looks like (see the text box on Promising
Innovations as well as the sensitivity analyses shown in Figures 1.5 and 2.6) and change how
those technologies are to be reflected in future modeling (e.g., distributed generation and
long-term storage [38]). Although these technology innovations are less likely to have a
significant impact on the 2030 modeling results, they could start to have a more substantive
impact in 2035 and beyond. Technology developers, energy companies, and private investors
are all investing heavily in a wide range of technologies, many of which will see their first large-
scale commercial application between now and 2030. How these technologies evolve could
greatly impact future technology choices and potential emission reductions after 2035. This
report attempts to capture some of these effects through technology sensitivities for a limited
number of models.
Additionally, the IRA represents an unprecedented level of support for clean energy
technologies and supply chains. The rate of deployment of these technologies is highly
uncertain. For example, solar and wind projects face significant interconnection queues
[39], Furthermore, the rate of expansion may lead to short-term increases in the costs of
technologies as supply chains respond to greater demands. These investment dynamics
may have small macro-economic effects on materials costs and interest rates that are not
represented in the current analysis [40].
Finally, the results of the models are presented at the national level. Some of the models (the
electricity sector models in particular), represent generation activities at a relatively fine scale
to account for differences in regional markets. The models reflect, for example, how some
areas are more conducive to solar or wind power development. These sub-national details are
beyond the scope of the report and are not yet widely reported in the literature.
34
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Overcoming Deployment Challenges
INCENTIVES & FUNDING
Many studies have
pointed to an economic
efficiency gap between
actual clean energy
deployment levels and
higher levels supported
by economic benefit-
cost analysis alone [41],
Economists often explain
the gap as the result of
market failures, defined as a violation of one or more
of the assumptions associated with the competitive
model, for example, the lack of complete
information about clean energy opportunities [42],
Economists also recognize a multitude of potential
market barriers, which are defined as transitional
issues such as supply chain issues or skilled labor
shortages. For example, high technology costs for
renewable energy can be described as a market
barrier but may not be a market failure, unless there
are systemic issues that prevent and not just delay
market corrections. Lastly, there are non-market
or institutional barriers defined as regulatory or
administrative measures, technical issues, and
environmental and social concerns [41],
The IRA provides significant incentives and funding
to overcome market failures facing technologies
that lower emissions, which are, in part, reflected
in the modeling contained herein. The IRA also
aims to overcome market barriers that may not
be reflected in the modeling, through programs
that increase the knowledge and access to key
technologies and facilitate access to capital and
labor to promote deployment. Although the IRA
and other policies address some of these market
obstacles, they will still influence the pace and
magnitude of the IRA's impact. Because of the rapid
pace at which climate change must be addressed,
overcoming these market failures and addressing
market barriers is a high priority. Key barriers across
electricity generation and end-use sectors are
described below.15 Specific policies responding to
these barriers are discussed in the sector-specific
chapters of the report:
Market Failures
¦ RDD&D as a positive externality. Firms
engaged in research, development,
demonstration, and deployment (RDD&D)
accrue private Penefits from that
investment, Put there are also spillover
Penefits, or positive externalities, as others
can learn from their experience, Pringing
down the costs of the technologies and
allowing even wider deployment. The
existence of these positive externalities
means that the market under-invests
in RDD&D. Historical analysis suggests
that puPlic investment helps correct this
type of market failure [43]. Many of the
IRA clean technology incentives, like the
Advanced Industrial Facilities Deployment
Program, can help correct under-
investment in RDD&D failure and move us
closer to the optimal level of technology
learning.
¦ Imperfect information/foresight.
Economic models often assume that
consumers and Pusinesses have perfect
information and foresight aPout future
conditions. In practice, cost-effective
solutions may not be selected if
stakeholders lack such information. For
example, lack of definitions of energy
performance levels for efficient electrified
technologies and lack of associated
labeling make it less clear what equipment
This list is meant to be illustrative, not exhaustive. Also, while many of the emission reductions projected in this report
are not directly linked to programs where EPA has enforcement authority, there are complementary enforceable
mechanisms embedded in LEEP. Specifically, Congress included funding and direction for EPA "to ensure that
reductions in greenhouse gas emissions are achieved through use of existing authorities...incorporating the
assessment" when directing EPA's implementation of the program".
35
-------
Overcoming Deployment Challenges
(continued)
is eligible for incentives at the state and
federal level, and about the relative energy
and emission performance of different
products generally [44],
¦ Risk aversion. The competitive economic
model assumes that consumers or
businesses are indifferent between two
options with equal net present value,
defined as the present value of a future
stream of benefits and costs of an
alternative, calculated at the appropriate
discount rate. In practice, consumers or
businesses may apply a higher discount
rate to new, unfamiliar technology
reflecting perceptions of risk. For example,
this affects renewable, efficient, and
electrified building technology across the
building supply chain—manufacturers,
retailers, contractors/trades, and
consumers [45, 46]. Risk aversion may
also be factored into financing available
for projects, resulting in less favorable
terms. Risk aversion can have a more
significant effect when equipment choices
are made with limited time and resources
when equipment has failed and must
be immediately replaced. Risk aversion
can be overcome by having trusted
sources communicating the benefits
of technologies to these stakeholders,
encouraging equipment replacement
before failure, and ensuring that
equipment suppliers and contractors have
appropriate equipment in inventory and
knowledge to inform consumer choices
when a replacement is needed [47],
¦ Split incentives. The competitive
economic model assumes that decision-
makers consider all the potential benefits
and costs of an alternative. This, however,
is not always true in practice. For example,
in commercial properties, including many
multi-family housing buildings, a landlord
or building management company may
pay for infrastructure while residents
or tenants pay for energy costs, giving
the building owner limited incentive
to invest in measures that can reduce
energy costs, like energy management,
efficient equipment, or building envelope
improvements [48].
¦ Institutions. Some energy markets are
managed as regulated monopolies, and
businesses and consumers may have
limited access to purchasing clean power
supply and the market actors may have
limited incentive to do so. The ability of
consumers to access, build, or purchase
electricity from zero emissions sources
is uneven across the United States due
to market design and policy issues that
can limit a consumer's ability to choose
and use low emissions electricity. Also,
utility business models, including
retail rate structures, can affect utility
decision-making. For example, utilities'
use of building efficiency as a strategy for
demand management may be discouraged
if compensation is directly related to
volume of sales [49].
36
-------
Overcoming Deployment Challenges
(continued)
Market Barriers and Transitions
¦ Status quo bias. The current employment
and historical development of each region
may be heavily concentrated in existing
fossil fuel industries. These industries
create state/local revenue streams and
may drive a hesitancy in local officials to
support transitions to new resources.
The same may apply to existing building
technology and the construction industry
and building trades.
¦ High relative costs of information
and up-front implementation costs of
energy-efficient technology. For most
consumers, energy costs (and potential
savings) are a relatively small part of the
overall budget. The time and effort to
select and implement energy-efficient
measures may be perceived as more costly
than current costs, even if they provide
savings overall. Also, the consumers for
whom energy is the largest part of their
budget are least able to invest in energy
efficiency measures. Product certifications
like ENERGY STAR and programs that
connect consumers to financing and
trusted contractors can play an important
role in significantly reducing the costs
of product research and quantifying
savings. IRA incentive programs, like the
whole home rebates, attempt to address
the investment challenges for low- and
moderate-income customers.
¦ Generating, transmission, and
pipeline constraints. In many areas,
electricity infrastructure may need to
be modernized to support significant
new electrified end uses and renewable
energy adoption, including providing
sufficient power supply and distribution
on the utility side and sufficient electrical
infrastructure in homes, commercial
buildings, and industrial plants [47], Also,
siting and interconnecting new utility-
scale renewables, transmission lines,
distribution infrastructure, and pipelines
for transporting hydrogen or C02 may face
interconnection scheduling hurdles and
siting opposition.
¦ Transitional expediency to meet
climate goals. Some market transitions
may eventually occur without policy
intervention within a competitive market,
but not on a time frame consistent with
climate goals. For example, a range of
supply chain issues, workforce capacity,
and equipment availability currently limit
the deployment of technology that can
significantly reduce GHG emissions [50].
¦ Timing of costs/availability of financing.
Implementing building measures often
incurs high up-front costs [47], This
interacts with the barrier of lack of
financing opportunities for renewable and
efficient building strategies, particularly
for small business, and low-income and
disadvantaged communities [51].
37
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1.3 ECONOMY-WIDE C02 EMISSIONS REDUCTIONS
1.3.1 Economy-Wide C02 Emissions Snapshot
Economy-wide C02 emissions in 2005 were 6,132 Mt C02/yr, which decreased to 5,032 Mt CO2/
yr in 2021 (Figure l.l)16. Across the three end-use sectors in 2021, transportation accounted for
35% of direct plus indirect emissions from electricity consumption17. Buildings accounts for
33% and industry (including industrial process emissions) accounts for 32%. Indirect emissions
from electricity comprise higher shares of emissions in Puildings and industry, thus power-
sector emission reductions have a greater effect on these sectors.
Figure 1.1
Economy-wide C02 emissions from fossil fuel combustion and
industrial processes by end-use sector, 2005-2021
6,000
O 4,000
o
c/>
c
o
'55
0)
£
LU
D 2,000
¦
¦o
c
UJ
2005
2021
Industry: Process
Industry: Indirect
Industry: Direct
Buildings: Indirect
Buildings: Direct
Transportation: Indirect
Transportation: Direct
Total emissions, both direct and indirect (from the use of fossil fuel to generate electricity consumption) in 2005 were
6,132 Mt CO^/yr, which decreased to 5,032 Mt COJyrin 2021. In 2021, transportation represented 35% of emissions (with
negligible indirect emissions from electricity consumed in the transportation sector, not visible in the figure), buildings
represented 33%, and industry (including process emissions) represented 32%. Accessible table available in the Data
Annex.
This does not include CO2 emissions or sequestration from land use activities.
Indirect emissions are apportioned electricity emissions based on the end-use sector share of total electricity
consumption.
38
-------
1.3.2 Economy-Wide Emissions Analysis and Results
Economy-wide C02 reductions from the IRA Moderate scenarios are 35-43% below 2005 levels
in 2030 across the multi-sector models with a 39% median reduction (Figure 1.2(a)). Looking
out to 2035, emissions reductions from IRA continue over time and lead to 36-55% declines
by 2035 from 2005 levels, with a median reduction of 46%. This reduction exceeds the 29-39%
decline by 2035 from 2005 in the No IRA scenario (33% median reduction). Comparing each
model's IRA scenario to the same model's No IRA scenario in 2030 shows that economy-wide
C02 emissions are reduced 5-22% under the IRA scenario relative to the No IRA scenario
(Figure 1.2(b)). The range of reductions reflects differences in four areas: model structure and
parameterization, input assumptions (e.g., technology costs, economic growth, and energy
prices), the number of provisions modeled, and the interpretation and implementation of those
provisions.
As shown in Figure 1.1, C02 emissions from electricity generation (all indirect emissions) are
31% of U.S. C02 emissions in 2021. When electricity-related emissions are distributed to their
end-use sectors, transportation CC>2 emissions account for the largest portion, 35%, of U.S.
COa emissions, buildings account for 33%, and industry accounts for 32%. Across the end-use
sectors, emissions reductions are greater under the IRA scenario than the No IRA scenario.
Buildings exhibits the greatest reductions from 2005 levels of direct plus indirect C02
emissions from electricity followed by industry and transportation (Figure 1.3).
-------
Figure 1.2
Economy-wide C02 emissions
(a)
6,000
o
o
(0
c
o
'<
E
o
c
o
o
LU
4,000
2,000
Historical " y
No IRA — A
IRA —
3
Data Points and
Median Results
o
o
2005
2021
2025
2030
2035
2030
2035
In the IRA scenario, economy-wide CO2 (b)
emissions fall 35 to 43% (39% median) below
2005 levels in 2030. In 2030, individual models
find that economy-wide CO2 emissions are
5 to 22% (11% median) below what they are
modeled to be in the No IRA scenario, with the
median difference falling to nearly 20% by 2035.
Figure 1.2(a) shows absolute model results for
the emissions trajectories (No IRA scenario in
orange dashed lines, IRA Scenario in blue) with
the historical trend (in black [4]). Data points to
the right of Figure 1.2(a) show individual model
results from 2030 and 2035 (blue circles for I RA
scenario results, orangetriangles for No IRA).
Horizontal bars represent the median of the 2021
model results. Figure 1.2(b) shows the percent
difference between the IRA and No IRA for each
model (blue lines) and the median across the
models (black line). Economy-wide emissions
are broken out into electricity and non-electricity
in Appendix F.2. Accessible table available in the
Data Annex.
Median Result —
1
a>
(D
¦10 o
fl>
3
3
-20 =
2025
2030
2035
40
-------
Figure 1.3
Summary of economy-wide and end-use sector C02 emissions
reductions from 2005 for the IRA and No IRA scenarios
Electricity
0 25 50 75 100
2030
2035
0 • • .. .I. M. •
A "VI" * * ~
..I-,
Transportation (Direct + Indirect from Electricity)
„„„„ A-*- I A
2030 ~ I , _
¦ -j"
2035 ^ 1 _l
Buildings (Direct + Indirect from Electricity)
2030 A -K A
-------
By 2030, the IRA drives C02 emission reductions in the transportation sector of 11-25% from
the 2005 level. In the buildings sector, emissions in 2030 fall 49-63%, and for the industrial
sector, the reduction is 17-43%. By 2035, results show that the IRA achieves even further
reductions from the 2005 level in these sectors (15-35% for transportation, 52-70% for
buildings, and 23-57% for industry). The C02 emissions from each of the end-use sectors under
the IRA and No IRA scenarios represent both the "direct" emissions from fossil fuel use in each
respective sector and "indirect" emissions from fossil fuels used in generating the electricity
consumed by each sector. Appendix F.l shows how the share of electricity changes across the
economy and in each of the end-use sectors. Appendix F.5 illustrates the percent reduction
from the No IRA to the IRA scenario across the models for economy-wide and sectoral
emissions.
Changes in economy-wide emissions may also be viewed through the lens of fossil fuel
consumption. The median consumption of coal and gas falls by roughly four quadrillion Btu
(Quads) in 2030 and 2035 with the IRA (Figure 1.4), due predominantly to decreasing demand
for those fuels in the electric power sector. Petroleum demand falls comparatively less, as its
primary use is in the transportation sector where electric vehicle adoption is not as rapid as
the shift towards renewable generation in most models. One model, MARKAL-NETL, exhibits
increased coal consumption under the IRA due to greater use of coal CCS technologies in
the electric sector. The consumption of petroleum products falls by roughly two quads. These
figures present all fossil energy use in the economy (i.e., not only fossil energy use in the
electric sector).
I Summary of ranges of C02 emissions reductions from 2005
IRA
No IRA
Sector
Year
Min
Median
Max
Min
Median
Max
Electricity
2030
2035
49%
67%
69%
77%
83%
87%
43%
40%
50%
53%
59%
68%
Transportation
2030
2035
11%
15%
17%
27%
25%
35%
9%
13%
15%
23%
22%
28%
Buildings
2030
2035
49%
52%
55%
66%
63%
70%
34%
36%
42%
45%
47%
51%
Industry
2030
17%
36%
43%
6%
25%
33%
2035
23%
36%
57%
3%
27%
36%
Economy-Wide
2030
2035
35%
36%
39%
46%
43%
55%
26%
29%
31%
33%
33%
39%
Model results show greater CO2 emissions reductions for the IRA scenario compared to the No IRA scenario for each
sector and for the economy as a whole. This is true for the minimum, median, and maximum reductions reported by all
models for both 2030 and 2035 results.
42
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Figure 1.4
Fossil energy consumption by fuel
Coal
Historical
Data Points and
Median Results Natural Gas
No IRA -A
-I I I L
£
¦i
*8
2005
Historical
2021 2025 2030 2035 2030 2035 2005
No IRA — A
IRA
Data Points and
Median Results Petroleum
Data Points and
Median Results
A
A
H
No IRA - A
Historical
2021 2025 2030 2035 2030 2035 2005
2021 2025 2030 2035 2030 2035
The IRA scenario results show a substantial decrease in the use of coal, gas, and petroleum for energy consumption when
compared to the No IRA results in 2030 and 2035. The orange dashed lines represent model results in the No IRA scenario, and the
blue lines represent model results from the IRA scenario. The right side of each panel shows data points from different models (blue
circles for IRA scenario results, and orange triangles for No IRA), and the horizontal bars represent medians in the years 2030 and
2035. The declining trend in coal consumption shown in the historic data continues in the No IRA scenarios and accelerates in the
IRA scenarios (note that one model, MARKAL-NETL, exhibits increased coal consumption under the IRA due to greater use of coal
CCS technologies.) The increase in natural gas consumption shown in the historic data generally levels off in the No IRA scenario and
generally declines in the IRA scenario, while petroleum consumption begins to fall in the No IRA scenario with a slightly accelerated
decline in the IRA scenario. Accessible tables available in the Data Annex.
43
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In addition to the IRA Moderate scenario, the following sensitivities are examined by a
limited set of models (Figure 1.5): IRA implementation, technology advances and deployment
constraints, combined IRA implementation and advanced technology, economic growth, and
fossil energy prices (see section 1.2.2 for scenario descriptions). Four models in the Bistline
et al. study [1] and USREP-ReEDS explored the sensitivity of economy-wide C02 emissions
reductions to IRA implementation and the value of IRA incentives (e.g., PTC, ITC, 45Q, 45V,
energy community bonuses, domestic content bonus) and technology build rates and
availability (e.g., renewables and transmission build rates, CCS availability, EVs' eligible for
bonus credits) (see Appendix C.2 and C.3 for details). GCAM-PNNL, USREP-ReEDS, and NEMS-
OP explored the additional sensitivities (see Appendix C.4 and [2] for details).
Results in Table 1.5 are presented as changes from the Moderate IRA scenario for data in Figure
1.5. For reference, a 1% change from 2005 economy-wide C02 emissions is 61 Mt C02. In 2030,
IRA implementation sensitivities increase reductions by up to 4 percentage points (pp) relative
to the Moderate IRA scenario when optimistic and increase emissions by up to 2.5 pp when
pessimistic, and over 7 pp by 2035. Advanced technology assumptions increase reductions
by up to 2 7 pp while constraining it increases emissions up to 3.6 pp. The largest median
impact is in the sensitivity case combining Optimistic IRA implementation with advanced
technology assumptions resulting in 2030 emissions falling an additional 4.8 pp below 2005
levels. This combined effect is greater than the median changes in either the Optimistic IRA
implementation sensitivity or the advanced technology sensitivity alone—indicating positive
interaction effects related to the IRA accelerating advanced technology adoption. High fossil
energy prices reduce C02 emissions relative to the Moderate IRA scenario by a greater amount
than low fossil energy prices increase emissions. Sensitivities in economic growth showed the
least significant impact on emissions changes, no greater than ±2 pp relative to the Moderate
IRA scenario. See Appendix F.4 for a summary of emissions reductions ranges including all
sensitivity scenarios.
44
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Figure 1.5
Economy-wide C02 emissions sensitivities
Combined Technology
IRA Implementation Technology + Implementation Fossil Energy Prices Economic Growth
+
X
*
+
*
X
*
+
+
+X
*
+
*
X
*
+ Pessimistic
* Optimistic
X Moderate
2030
2035
~
Constr
O Mod
O ClMod
All Adv
~
All Adv
2030
~
Constr
Mod
O QMod
o
All Adv
~
All Adv
<>Mod
~ Mod
^ ~ Pess-Adv
I
Opt-Adv
<>Mod
~ Mod
Pess-Adv
\i
Opt-Adv/ Opt-Adv
O Low
O Mod
O High
O Low
O Mod
OHigh
O High
O Mod
O Low
O High
OMod
O Low
~ USREP-ReEDS
O GCAM-PNNL
O NEMS-OP A ReEDS-NREL
2035
2030
2035
2030
2035
2030
2035
The largest median impact in economy-wide C02 emissions is in the sensitivity case combining Optimistic IRA
implementation with advanced technology assumptions, resulting in 2030 emissions falling an additional 4.8 pp below
2005 levels. This combined effect is greater than the median changes in either the Optimistic IRA implementation sensitivity
or the advanced technology sensitivity alone, indicating positive interaction effects related to the IRA accelerating advanced
technology adoption. Note that the figure has a y-axis break between 2000 and 0 to better show the results. Economy-wide
CO2 emissions are presented forthree sensitivity cases in the first panel, Moderate (black "x"), Optimistic (blue asterisk), and
Pessimistic (green plus sign), and the horizontal bars represent the medians of each sensitivity. These scenarios were run by
EPS-El, GCAM-CGS, REGEN-EPRI, RIO-REPEAT, and USREP-ReEDS. The shapes in panels 2-5 represent individual models
(circle for GCAM-PNNL, square for USREP-ReEDS, diamond for NEMS-OP). These sensitivities cover a range of results that
explore the effectiveness of the IRA to reduce emissions under different assumptions (see Section 1.2.2). Table 1.5 presents
changes relative to the Moderate IRA Implementation scenario measured in incremental percentage point (pp) changes from
2005 economy-wide CO2 emissions (e.g., a value of -1.0 would mean that underthat sensitivity, emissions are reduced an
additional 1 pp below 2005 levels—equivalent to an additional 61 Mt CO2 of mitigation). Accessible tables available in the Data
Annex
45
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Table 1.5
Economy-wide C02 emissions changes (percentage points of
2005 emissions) relative to the IRA Moderate scenario
IRA
Implementation
« £
% .2
E 1
'£ s
a Q>
Year Metric O a
Advanced ^
31
3
O
O
Constrained i§
Implementation
+ Tech
1 1
7j <0
0 ">
£ d)
O a
Fossil Energy
Prices
I I
Economic
Growth
f i
2030 Min -4.0 0.7
-2.7
-6.4
-
-
Median -1.7 1.6
UD
ro
CO
1
-4.8 -1.7
-3.5 0.8
1.5 -1.6
Max 1.0 2.5
-0.9
-3.2
-
-
2035 Min
-3.1
Median -1.3
0.3
1.8
-4.6
-6.1
-3.0 4.3 -5.1 -3.3
-4.5
1.5
1.9 -2.0
Max
-0.4 7.4
-1.4
-4.0
The largest median impact in economy-wide C02 emissions is in the sensitivity case combining Optimistic IRA
implementation with advanced technology assumptions resulting in 2030 emissions falling an additional 4.8 pp below
2005 levels. This combined effect is greater than the median changes in either the Optimistic IRA implementation sensitivity
or the advanced technology sensitivity alone, indicating positive interaction effects related to the IRA accelerating advanced
technology adoption. Table 1.5 presents changes relative to the Moderate IRA Implementation scenario for all sensitivity
scenarios, measured in incremental percentage point (pp) changes from 2005 economy-wide CO2 emissions (e.g., a value
of -1.0 would mean that underthat sensitivity, emissions are reduced an additional 1 pp below 2005 levels—equivalent to an
additional 61 Mt CO2 of mitigation). For a summary of the percent reduction of all IRA sensitivities from the No IRA scenario,
see Appendix F.4.2.
46
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Promising Technological Innovation
Although this report
focuses primarily
on modeling of IRA,
additional information
aPout what might
happen under IRA
can Pe gleaned from
considering projects
under development
today. Because a
comPination of IRA, BIL, and CHIPs, provide
significant funding for development of new
technologies, not all the new technologies
that may Pe availaPle in the 2030 time frame
and Peyond are fully reflected in all the models
used in the present. The following highlights a
suPset of the technologies in advanced stages
of development that modelers may want to
consider in future efforts to model IRA and
other energy/climate policies.
At least four areas related to IRA modeling
are experiencing rapid technology advances.
These include energy storage, advanced
nuclear, CCS, and distriPuted generation. With
regards to energy storage, there are multiple
projects under development. While these
projects include demonstration projects at
utilities, they also include construction of
full-scale factories to supply an increasing
demand. These storage projects include a
wide range of technologies moving Peyond
traditional lithium-ion technologies. DOE's
pathways report for Long Duration Energy
Storage (LDES) focuses on interday LDES (10
to 36 hours) and multiday/week LDES (36 to
160+ hours). The report notes a wide range of
technologies that can meet these needs. For
interday, this includes traditional and novel
pumped storage, gravity Pased, compressed
air energy storage (CAES system), liquid air,
and liquid C02. Both in the United States and
internationally, developers are pursuing all
these technologies. For instance, in California,
Hydrostor is developing a 500 megawatt
(MW) CAES system.22
With regards to nuclear, there are at least
two areas of significant development: small
modular reactors and micro reactors. In
Poth cases, these technologies hope to take
advantage of Poth technology improvements
in the nuclear plants themselves and
just as importantly, improvements in
manufacturaPility Py developing smaller,
largely factory-Puilt modules that can take
advantage of mass production. While the
first of these projects will rely significantly
on federal funding, utilities are also showing
interest in these technologies without
additional federal funding. For instance,
PacifiCorp is partnering with Terrapower
and the federal government on a first of kind
345 MW sodium-cooled fast reactor with
integrated molten salt storage that will allow
for storage that could Poost generation to
500 MW. While this first plant is a federal-
private partnership, PacificCorp's most recent
integrated resource plan suggests that they
are looking at Puilding two more plants Py
2033 [52], All three of these projects take
advantage of the fact that Puilding these
A
TECHNOLOGICAL
INNOVATION
22 Energy storage is rapidly expanding at levels that may outperform cost and performance assumptions assumed in
some current modeling applications.
47
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Promising Technological Innovation
(continued)
projects at repurposed coal plants offers
an opportunity for significant savings. DOE
suggests that Puilding on existing sites
can save 15 to 35% of capital costs and that
recently retired coal plant sites could host
nearly 65 gigawatts (GW) of new nuclear
generation [53]. In addition to the Terrapower
projects, DOE's Advanced Nuclear Liftoff
Report cites additional puPlic/private projects
under development including projects with
X-Energy and Terrapower [54],
For carPon capture, there are at least two
promising trends. First are improvements
to existing post-comPustion CCS projects.
DOE is cosponsoring a numPer of projects
where technology developers are looking
at advanced sorPents. For instance, DOE
is working with the City of Springfield,
and technology developers BASF and
Linde on a 10-MW pilot-scale project. DOE
indicates that, "Based on results from small
pilot studies, techno-economic analysis
indicates the Linde-BASF technology can
provide a significant reduction in capital
costs compared to the NETL Pase case for a
supercritical pulverized coal power plant with
C02 capture." Another promising approach
is precomPustion processes. NETPower has
announced plans to Puild a commercial-size
version of their precomPustion natural gas
generation process with operation scheduled
to Pegin in 2026 [55].
DistriPuted generation, demand management,
and energy storage also have significant
aPility to provide low-cost, low-GHG power.
This is Poth through the development of
improved solar and storage technologies
Put also through Petter integration with the
grid. For instance, there is a growing trend in
integrating individual distriPuted generation
projects into virtual power plants that can
provide reliaPle, low-cost, low-GHG power.
The Rocky Mountain Institute suggests that 60
GW of virtual power plants could Pe availaPle
Py 2030 [56],
48
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The Methane Emissions Reduction
Program of the IRA
Although this report
focuses on C02 emissions
from combustion, methane
emissions from the
petroleum and natural gas
sector are significant. EPA's
proposed New Source
Performance Standards
METHANE EMISSIONS ancj Emissions Guidelines
for Oil and Natural Gas
Operations, and the Methane Emissions Reduction
Program aim to reduce these emissions. Methane
(or CH4) is the primary component of natural
gas, which can be used either as a chemical
feedstock or as fuel in power plants or residential
and commercial buildings. As natural gas travels
through the interconnected systems—exploration,
production, processing, storage (sometimes),
and transmission—from the wellhead to the
consumer, methane emissions are released into
the atmosphere in a variety of ways. With a global
warming potential (GWP) 28 times greater than
that of an equivalent mass of C02, methane is a
potent GHG. Methane emissions from natural gas
and petroleum systems were about 3.7% of total
GHG emissions in 2021.
The IRA provides new authorities under Section
136 of the Clean Air Act to reduce methane
emissions from the petroleum and natural gas
sector through the creation of the Methane
Emissions Reduction Program. The Methane
Emissions Reduction Program includes the
following components:
¦ Financial and Technical Assistance—$1.55
billion to reduce methane emissions from the
petroleum and natural gas sector by providing
financial and technical assistance for preparing
and submitting GHG reports, monitoring
methane emissions, and reducing methane
and other GHG emissions from petroleum
and natural gas systems, including mitigating
legacy air pollution, improving and deploying
equipment to reduce emissions, supporting
innovation, permanently shutting in and
plugging wells, mitigating health effects in
low-income and disadvantaged communities,
improving climate resiliency, and supporting
environmental restoration. The program
specifies that at least $700 million must be used
for activities at marginal conventional wells.
¦ Waste Emissions Charge—establishes a
waste emissions charge for methane from
applicable facilities that report more than
25,000 metric tons of C02 equivalent per year
to the Greenhouse Gas Reporting Program
(GHGRP) petroleum and natural gas systems
source category and that exceed statutorily
specified waste emissions thresholds. Waste
emissions charges start at $900 per metric
ton for emissions reported in 2024, increasing
to $1,200 for 2025 emissions, and $1,500
for emissions years from 2026 and on. This
program includes flexibilities and exemptions,
and requires revisions to GHGRP regulations
for petroleum and natural gas systems (Subpart
W) within 2 years.
New resources and programs in the IRA are
complementary to proposed Clean Air Act
standards for oil and gas operations, which will
incentivize early implementation of innovative
methane reduction technologies and support
methane mitigation and monitoring activities.
These complementary efforts will allow the United
States to achieve greater methane emissions
reductions more quickly. As background, on
November 15, 2021, the EPA proposed new source
performance standards and emission guidelines
for new and existing crude oil and natural gas
facilities (86 FR 63110). On November 11, 2022, the
EPA issued a supplemental proposal that updated,
strengthened, and expanded upon its November
2021 proposal. The EPA expects to issue a final rule
in 2023.
-------
CHAPTER 2
Electricity
2.1 ELECTRIC SECTOR SNAPSHOT
In 2021 the U.S. electric power sector
produced 1,540 Mt C02, making it the
second largest contributor to U.S.
greenhouse gas emissions (after the
transportation sector) [4], However,
emissions from the power sector are
projected to decline considerably in the
coming decades. The investments from the
IRA are expected to further accelerate this
trend.
Electric sector-related C02 emissions
come from the combustion of fossil fuels to
generate electricity. Although natural gas
is the largest U.S. fuel source for electricity
generation (38% of total generation in
2021), followed by coal-fired generation
(22%), coal is the primary contributor
of GHGs, contributing 910 Mt C02 in
2021, followed by natural gas with 613 Mt
C02. Most of the remaining electricity
delivered to the United States came from
low- or zero-emitting sources, including
renewable and nuclear sources, at 19% each.
The remaining 2% of generation comes
from other generating sources including
petroleum (see Figure 2.1).
Electric Demand
Expectations and projections of future
electric demand are a central element of
any electric sector analysis and are subject
to a myriad of influences that are uncertain.
Two countervailing elements, both of which feature
prominently in IRA, are central. The IRA includes many
provisions that incentivize efficient electrification across
industries to facilitate decarbonization efforts across the
economy. These provisions will lead to greater electric demand
even with efficient performance. The IRA also includes many
provisions that focus on further energy efficiency to reduce
energy use across those same industries. Together, along with
changes to electric demand across the economy due to other
important economic influences, these trends will influence
electric demand expectations, and thus any analysis and
projection for the electric sector. These influences can affect
demand on an annual basis, but also on an hourly basis, affecting
the peak demand periods. The net effect of these influences
is the most critical aspect, from an analytical and modeling
perspective. This important component is explored in sensitivity
and alternative scenario exploration, which can be found in this
report. As additional work is done to explore how electrification
and energy efficiency provisions of IRA influence future
expectations of C02 emissions, the analytical and modeling
community will be able to provide more detailed analysis to
inform these important dimensions.
50
-------
Figure 2.1
C02 emissions from the electric sector by fuel in the United States,
compared to economy-wide C02 emissions, 2005-2021
6,000
>
CNJ
O
o
— 4,000
t/>
t/>
E
LD
o
•*->
O
0J
(/)
> 2,000
o
o
LD
iiiiiuiiuy
2005
2021
Coal
Natural Gas
Petroleum
~
Other Sectors
Electric sector C02 emissions have declined overtime: 2021 emissions are 36% lower than 2005 levels. This overall trend
reflects a larger decline in coal C02 emissions (54% below 2005 in 2021) and an increase in C02 emissions from natural
gas combustion (92% above 2005 in 2021). Power sector emissions are shown by fuel: coal (brown), natural gas (gray), and
petroleum (magenta). The remaining economy-wide C02 emissions in other sectors are represented by the outlined bar.
Accessible table available in the Data Annex.
Supply of, and therefore emissions from, coal-fired electricity declined Py 54% Petween
2000 and 2021, from 1,943 terawatt-hours (TWh) in 2000, peaking in 2007 at 1,998 TWh, and
declining to 893 TWh Py 2021. Coal-fired electricity-generating units delivered 53% of total
generation in 2000 and 23% in 2021. Natural gas-fired generation Pegan exceeding coal-fired
generation in 2016. In 2022, renewaPle generation—including wind, solar, and hydroelectric
power—also surpassed coal generation [57], Generation from natural gas increased from 518
TWh in 2000 to 1,474 TWh in 2021. Generation from renewaPles increased from 315 TWh in
2000 to 790 TWh in 2021, with nearly all of the increase in renewaPle generation over that
period coming from wind and solar, with hydroelectric remaining relatively staple.
51
-------
Looking forward, generation from cleaner forms of electric generating technologies is
expected to continue to grow, amplified by the IRA (see Figure 2.2). As lower-emitting
generation increases its share of the generation mix, C02 emissions from the power sector
are expected to fall. Subsequent sections in this chapter discuss these trends.
In May 2023, the EPA proposed a rule that establishes emission limits and guidelines for
C02from fossil fuel-fired power plants based on cost-effective and available control
technologies [29]. The proposed rule covers new gas-fired combustion turbines, existing
coal, oil and gas-fired steam generating units, and certain existing gas-fired combustion
turbines. The proposal is not reflected in the modeling results and projections shown in this
report.
Figure 2.2
Electricity generation shares by fuel type, 2005-2021
4,000 -
3,000 -
o
as
o
o
LU
2,000
1,000 -
2005
2010
2015
2020
Coal
Natural Gas
Petroleum
Nuclear
Renewables
Other
Since 2000, natural gas and renewable generation have steadily risen while generation from coal has decreased
substantially. Source: EIA [30]. Accessible table available in the Data Annex.
52
-------
2.2 KEY ELECTRIC SECTOR IRA PROVISIONS
The IRA includes the following major provisions and incentives for the electric sector:
¦ Tax incentives and rebates
— Extension of Renewable Electricity Production Tax Credit (Section 45)
— Extension of Renewable Energy Investment Tax Credit (Section 48)
— Clean Electricity Production Tax Credits (45Y)
— Clean Electricity Investment Tax Credits (48E)
— Nuclear Power Production Tax Credit (45U)
— Carbon Capture and Sequestration Tax Credit (45Q)
¦ Funding and Financing
— Greenhouse Gas Reduction Fund (GGRF)
— Climate Pollution Reduction Grants (CPRG) program
— USDA Assistance for Rural Electric Cooperatives
— Grants to Facilitate the Siting of Interstate Electricity Transmission Lines
The IRA modified and extended the availability of the existing Renewable Energy Investment
and Renewable Electricity Production Tax Credits. The investment tax credit (ITC) is a 30%
tax credit on the up-front capital costs of a project in the year the facility is placed in service.
The production tax credit (PTC) provides 1.5 cents per kWh of electricity for the first 10 years
a zero- or negative-GHG emissions project operates. The IRA extends the availability of the
ITC and PTC until at least 2032. After 2032, the tax credits remain available until the power
sector achieves a 75% reduction in C02 emission from 2022 levels, after which they begin to
phase out. Qualifying facilities for the Clean Electricity ITC and PTC are expanded to include
zero- or negative-emitting generating technologies. The provisions also include wage and
apprenticeship requirements and allow for bonus credits to be earned if certain domestic
content or energy community requirements are met.
The Nuclear Power Production Tax Credit (45U) is a new tax credit that provides financial
assistance to existing nuclear facilities. The tax credit provides up to $15/megawatt hour (MWh)
for existing nuclear that meets prevailing wage and apprenticeship requirements and earns
an average of $25/MWh or less in electricity revenues. Credit is phased out for facilities with
average revenues earned above $25/MWh. The credit is available through 2032. Separately,
the IRA provides tax credits for both production and investment for new advanced reactors
generating electricity (section 45Y and 48D, respectively).23 The advanced reactor production
credit is 0.3 cents multiplied by the kWh base rate for 10 years and starts in 2025.24
23 Advanced reactor facilities that qualify for production or investment tax credits may only benefit from one, the
production credit or the investment credit, but not both.
24 Energy communities, including coal communities that are new, will receive 10% on top of this credit. The advanced
reactors that qualify for this production credit are those that generate electricity, come into service after December 31,
2024, and have a zero greenhouse gas emissions rate. The production credits will only be provided to qualified facilities
for 10 years starting when the facility is placed into service.
53
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The IRA also modified and extended the availability of the existing Carbon Capture &
Sequestration Tax Credit (45Q). The tax credit provides up to $85/metric ton for CCS
facilities.25 The date by which construction must begin was extended through 2032 and
includes wage and apprenticeship requirements to be eligible for higher credit amounts, which
can be claimed for 12 years.
Under the IRA, various provisions can alter the incentive levels of tax credits and incentives
depending upon how much of a particular project uses domestic content, and whether project
developers are paying prevailing wages in the locality where the project is built. While most
models do not represent these elements in a detailed manner, they can be explored through
sensitivity analysis and side cases (see Section 2.3).
Other relevant tax credit provisions impacting the power sector include New Advanced
Manufacturing Production Tax Credit (45X), which creates a tax credit for the production of
clean energy technology components that are produced in the United States and the new
Clean Hydrogen Production Tax Credit (45V), which creates a new 10-year incentive for clean
hydrogen production.
The IRA also provides investments into the power sector in the forms of funding and financing
provisions. For example, $9.7 billion is available for financial assistance to rural electric
cooperatives to purchase renewable energy, renewable energy systems, zero-emissions
systems, and carbon capture and storage systems; $5 billion is available for the DOE Loan
Programs Office for the cost of providing financial support in the form of loans and guarantees
to 1) retool, repower, repurpose, or replace energy infrastructure, or 2) enable operating energy
infrastructure to avoid, reduce, utilize, or sequester GHG emissions; and $2 billion to DOE for
direct loans for construction or modification of electric transmission facilities.
Additional IRA provisions target distributed generation, energy storage, energy efficiency, and
end-use electrification, with most of these funds supporting distribution grid and end-use
projects.26 Other IRA provisions that would likely impact distributed clean energy technologies
include:
¦ Energy Credit for Solar and Wind in Low-Income Communities
¦ Rural Energy for America Program (REAP)
¦ Greenhouse Gas Reduction Fund
¦ Climate Pollution Reduction Grants
There are numerous developing low-GHG distributed energy technologies, including nuclear
(small modular reactors and microreactors) and innovative energy storage technologies, that
the IRA will help encourage. These technologies can be applied as a foundation for microgrids,
which provide local resiliency and as parts of virtual power plants and can substitute for higher
emitting fossil fuel-fired peaking units.
25 The iRA also includes an incentive of $180/metric ton for direct air capture (DAC) facilities.
F" Building-based i RA provisions are specified in Section 4.2.
54
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Environmental Justice
The IRA includes historic investments in
environmental justice programs to improve public
health, reduce pollution, and revitalize communities
that are marginalized, underserved, and
overburdened by pollution. Across IRA programs
there are also specific requirements to engage
these communities and ensure benefits from these
programs accrue to them.
In addition to funding to reduce air pollution
from trucks and heavy-duty vehicles (Sec. 60101)
and ports (Sec. 60102), the IRA provides funds
for grants and technical assistance to schools
serving low-income communities to address air
pollution hazards (Sec. 60105). The Environmental
and Climate Justice Block Grants program (Sec.
60201) will advance environmental justice and
support projects like community-led air pollution
monitoring, prevention, and remediation;
mitigating climate and health risks from extreme
heat and wildfires; climate resiliency and
adaptation; and reducing indoor air pollution. The
Greenhouse Gas Reduction Fund (Sec. 60103) will
provide competitive grants to mobilize financing
and leverage private capital for clean energy and
climate projects that reduce pollution—with an
emphasis on projects that benefit low-income and
disadvantaged communities.
The Low Emissions Electricity Program (Sec.
60107) provides funding for education, technical
assistance, and partnerships within low-income
and disadvantaged communities with respect to
reductions in greenhouse gas emissions that result
from domestic electricity generation and use.
While the primary focus of this report is to discuss
the impacts of the IRA on C02 emissions, changes
in other air pollutants will also occur. Resulting
climate and air quality benefits will be both broadly
distributed and important to communities with
environmental justice concerns.
Environmental hazards can be inequitably
distributed in the United States, with people of
color and low-income populations consistently
bearing a disproportionate burden of
environmental pollution in some areas. The EPA
defines environmental justice as "the fair treatment
and meaningful involvement of all people regardless
of race, color, national origin, or income, with
respect to the development, implementation, and
enforcement of environmental laws, regulations,
and policies." This goal will be achieved when
everyone enjoys the same degree of protection
from environmental and health hazards, and equal
access to the decision-making process to have a
healthy environment in which to live, learn, and work
[60],
EPA has examined the results of its power sector
programs through an environmental justice lens to
better understand the impacts of those programs
on plants located nearby areas of potential concern.
In one evaluation using proximity analysis, a
frequently used approach to examine potential
impacts on people who reside nearby a pollution
source, EPA found that 10% of people in the
contiguous United States live within three miles
of a power plant reporting emissions to EPA under
various provisions of the Clean Air Act. These are
Map of Power Plants Covered by EPA's ARP and CSAPR Programs
-------
Environmental Justice (continued)
mostly gas-fired power plants, with approximately
2% of the population living near coal- or oil-fired
plants. Compared to the national average, the
population living near power plants is characterized
by a higher percentage of people of color and low-
income population.
In tracking changes in the power sector as the
IRA is implemented, EPA will assess the impacts
on disadvantaged communities at various scales.
Recent advancements in environmental justice-
screening methodologies for the power sector that
recognize that air pollution can travel significant
distances can enhance our ability to consider the
disadvantaged communities that are most exposed
to air pollution from each power plant. For example,
EPA recently developed the Power Plant Screening
Methodology (PPSM). The PPSM incorporates
several peer-reviewed approaches that combine
air quality modeling with demographic and
socioeconomic data to identify geographic areas
potentially exposed to air pollution by power plants
and quantify the relative potential for environmental
justice concern in those areas. This information
enables EPA to provide a screening-level look at
the relative potential for power plants to affect
disadvantaged communities.
This methodology utilizes two approaches to
identify areas that are potentially affected by
different types of pollutants from each plant:
proximity analysis and long-range downwind
transport. Each of these approaches uses air quality
modeling combined with GIS analysis to identify
census block groups that are potentially affected
by air pollution from each of the power plants. The
proximity analysis approach focuses on the air
quality impacts within 50 km of the source. The
long-range downwind transport approach focuses
on potential air quality impacts within 24 hours of
potential emissions release, reaching distances
that are considerably greater than 50 km from the
sources.
These recent advancements will enable EPA to
provide a more robust analysis of how air pollution
exposures are changing over time in disadvantaged
communities. In future analyses, we plan to
characterize the extent to which emissions are
decreasing and the generation mix is changing
at power plants located nearby and upwind of
disadvantaged communities.
To learn more about criteria air pollutant emissions
in the context of IRA implementation, see Text Box:
Criteria Air Pollutants.
Managing SF6 in an Electrifying Economy
Although the focus of this report is on C02, emissions of sulfur hexafluoride
(SF6), the most potent GHG known (100-year GWP = 23,500), need to be
managed to meet long-term emission reduction targets. SF6 is used as an
electrical insulator and arc quencher in electrical transmission and distribution
equipment (e.g., circuit breakers), and it can be emitted when equipment leaks
or is installed, serviced, or disposed. As electrical transmission and distribution
(T&D) networks expand, more electrical equipment will be required to support
these networks, potentially increasing emissions of SF6. Fortunately, options
to reduce SF6 emissions are available, including recovery and recycling of SF6
during servicing and disposal, leak detection and repair, replacement of leaky
SF6 equipment with more leak-tight SF6 equipment before the end of the old SF6
56
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Managing SF6 in an Electrifying Economy
(continued)
equipment's normal service life, and replacement
of SF6 equipment with equipment using other
insulating gases or not-in-kind technologies either
before or at the end of the SF6 equipment's normal
service life. To ensure that increasing emissions
of SF6 do not undermine the climate benefits of
decarbonization, it will be important to deploy
these mitigation options as T&D networks grow,
for example through programs such as EPA's SF6
Emission Reduction Partnership for Electric Power
Systems [61].
¦ The 2021 U.S. SF6 emissions from the
manufacture and use of electrical equipment
are estimated to have totaled 6.0 Mt of carbon
dioxide equivalents (C02e) (Inventory of
U.S. Greenhouse Gas Emissions and Sinks:
1990-2021, referred to below as "U.S. GHGI"
[4]). Of this total, 5.6 Mt C02e are estimated
to have been emitted from electrical T&D
networks while 0.4 Mt C02e are estimated to
have been emitted from electrical equipment
manufacturing. In the same year C02 emissions
from the power sector were 1,541 Mt C02e.
¦ Without electrification related to the IRA, U.S.
SF6 emissions in 2030 and 2035 would be 7.8
and 9.1 Mt C02e, respectively. These emissions
projections assume emissions would grow at
a rate of 3% per year from 2021 to 2035, which
is the growth rate assumed by the California
Air Resources Board (CARB) in their analysis of
the impacts of their SF6 regulations [62], This
growth rate is broadly supported by the trends
in SF6 emissions and SF6 equipment banks
(nameplate capacities) estimated in the U.S.
GHGI and/or reported under EPA's Greenhouse
Gas Reporting Program (GHGRP). The SF6
emissions estimated for this source in the U.S.
GHGI grew by 2% per year between 2017 and
2021, while equipment banks reported under
the GHGRP grew by 4% per year over the same
period.27
¦ With electrification, U.S. SF6 emissions in
2030 and 2035 would be 9.1 and 11.6 Mt C02e,
respectively. These emissions projections
assume that T&D networks would grow 1.8% per
year faster than in the "without electrification"
scenario and that the average level of SF6
mitigation would be the same as in the "without
electrification" scenario.
¦ With electrification and full deployment of
SF6 mitigation options, U.S. SF6 emissions in
2030 and 2035 would be 4.9 and 5.7 Mt C02e,
respectively. These emissions projections
assume that 43% of 2030 U.S. emissions and
40% of 2035 U.S. emissions can be reduced
through implementation of options other
than uptake of SF6-free equipment, including
improved servicing practices and SF6 recycling,
and that 4% of 2030 U.S. emissions and 11% of
2035 U.S. emissions can be reduced through
uptake of SF6-free equipment.28 Of note, about
half of the emissions avoided through use
of SF6-free equipment are dependent on the
availability of equipment that uses alternative
insulating gases. The current U.S. supplier
of these gases is planning to phase out their
production by the end of 2025, which may
affect their availability for this application.
2.3 ELECTRIC SECTOR ANALYSIS AND
RESULTS
Even before the enactment of the IRA, most
long-term modeling showed significant
The fact that emissions are growing more slowly than banks implies that utilities are reducing their emission rates,
probably by deploying recovery and recycling, LDAR, and replacement of leaky SF5 equipment with other SF5
equipment. (SF5 substitutes have not yet achieved significant market penetration.) Eventually, electrical T&D systems
will exhaust the reduction potential of these conservation measures and the growth rate for emissions will approach
that of the banks.
Reduction options are assumed to be applicable only to emissions from electrical T&D, not to emissions from electrical
57
-------
declines in power sector-related emissions in the coming decades. The investments from the
IRA further accelerate this expected trend. The No IRA power sector emission projections show
a large range in expected C02 emission outcomes by 2035, from 40-68% below 2005 levels,
with the median of 53% below 2005 levels (780 to 1,430 Mt C02, with the median of 1,130 Mt
C02). Comparing across the same set of models, by 2035 the range in projected C02 emissions
due to IRA both greatly declines and tightens, to 67-87% below 2005 levels, with the median
of 77% below 2005 levels (320 to 780 Mt C02, with the median of 540 Mt C02), suggesting an
agreement across models in the direction and magnitude of change in emissions expected for
the sector. The reduced emissions intensity of electricity generation also further amplifies the
emissions benefits of electrification (see Chapters 3 Transportation, 4 Buildings, and 5 Industry
for more information).
The projected decline in C02 emissions in the power sector is achieved through a combination
of projected declines in electric generation from fossil fuel-based generation technologies
(coal, natural gas, and petroleum) without CCS (referred to in later figures as "high-emitting"
generation technologies). There are significant increases in electric generation from low- and
zero-emitting generation technologies (most models favor new solar and wind, and to a lesser
extent fossil fuel-fired generation with CCS, while new nuclear is generally not favored by the
models).
Across all the models included in this report, generation from low- and zero-emitting
technologies is projected to increase, both relative to current (2021) levels and also across
scenarios with and without representation of the IRA. In 2021, generation from low- and
zero-emitting sources totaled 1,550 TWh. Generation in the No IRA scenarios is projected to
increase for low- and zero-emitting sources, ranging from 1,860 to 3,570 TWh by 2035, with a
median of 2,330 TWh. This range increases further in the scenarios with the IRA provisions from
2,440 to 5,260 TWh by 2035, with a median of 3,350 TWh.
Tax credits like the Clean Electricity Investment and Production Tax Credits (48E, 45Y) and
the Nuclear Power Production Tax Credit (45U) are the primary drivers for the increases seen
in model projections of renewable and nuclear generation technologies relative to future
projections under the No IRA scenario. Of renewable technologies, solar and wind see the
largest increases in generation; generation in the IRA scenario from wind and solar together
ranges from 1,100 to 4,490 TWh, with the median of 2,460 TWh in 2035. Given the expiration
of the Nuclear Power Production Tax Credit after 2032, the long-term outcome for nuclear
generation remains relatively uncertain, with some model projections showing generation
falling precipitously, while others show levels remaining relatively flat. Across all models,
nuclear generation in the IRA scenario is between 40 and 800 TWh in 2035 (with a median of
710 TWh), compared to 780 TWh in 2021.
equipment manufacturing. Options otherthan SF6-free equipment are assumed to be partially implemented in the
baseline, decreasing their remaining reduction potential in later years. On the other hand, the reduction potential of
adopting SF6-free equipment is assumed to grow slowly overtime as new SF6-free equipment replaces retiring SF6
equipment in each successive year. The figures here assume that replacement of SF6-containing equipment with SF6-
free equipment begins in 2025.
58
-------
Figure 2.3
Electric sector C02 emissions
(a)
O
O
(0
c
o
'<0
(0
'E
LU
o
o
2,500
2,000
1,500
O 1,000
o
<
'o
o
o
LU
500
Data Points and
Median Results
A
A
8 O
l
1
a 8
A
0
1~
A §
8
0
8
0
8
2005
2021
2025
2030
2035
2030
2035
In the IRA scenario, U.S. electricity sector CO2 ^
emissions fall to 49 to 83% (39% median) below
2005 levels in 2030. In 2030, individual models
find that electricity CO2 emissions are 11 to 67%
(34% median) below what they are modeled
to be in the No IRA scenario, with the median
difference falling to just over 50% by 2035.
Figure 2.3(a) shows absolute model results for
the emissions trajectories (No IRA scenario in
orange dashed lines, IRA Scenario in blue) with
the historical trend (in black [4]). Data points to
the right of Figure 2.3(a) show individual model
results from 2030 and 2035 (blue circles for IRA
scenario results, orange triangles for No IRA).
Horizontal bars represent the median of the 2021 2025 2030 2035
model results. Figure 2.3(b) shows the percent
difference between the IRA and No IRA for each model (blue lines) and the median across the models (black line).2
Accessible table available in the Data Annex.
Median Result — \
1 1
1
-20
-40
-60
a>
-*
(D
o
(D
o
3
A handful of models show higher emissions underthe IRA in 2025. In the No IRA scenario, these forward-looking
models have slightly higher levels of near-term investments in renewables in 2025 because the models foresee the
expiration of tax credits. Underthe IRA scenario, tax credits are extended and investment does not exhibit a near-term
spike.
59
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Figure 2.4
Electric sector generation by technology (TWh), 2021, 2030, and 2035
t- OLD OLD OLD OLD O ID OLD OLD OLD OLD OLD OLD OLD OLD OLD
CN CO CO COCO COCO CO CO CO CO CO CO COCO CO CO COCO COCO COCO COCO COCO COCO
O OO OO OO OO OO OO OO OO OO OO OO OO OO OO
CN CN CN CN CN CN CN CN CN CN CN CN CN CN CM CN CN CN CM CN CN CN CN CN CN CN CN CN CN
6,000 -
4,000
2,000 -
c
o
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c
2,000 -
1,000 -
L
-1,000 -
-2,000 -
li
4
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LU
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Biomass w/ CCS
Biomass w/o CCS
Coal w/ CCS
Coal w/o CCS
Gas w/ CCS
Gas w/o CCS
Geothermal
Hydro
Hydrogen
Nuclear
Petroleum
Solar
Wind
Across all the models, generation
from low- and zero-emitting
generation technologies is projected
to increase relative to current
(2021) with a greater increase in
the IRA scenarios than in the Mo
IRA scenarios. Generation from
uncontrolled fossil declines relative to
2021 with a greater decrease in the i RA
scenarios. Total generation generally
increases in most models relative
to 2021. This reflects the increase
in demand from the electrification
of end uses but demand would be
higher without energy efficiency
measures. The upper bars represent
the generation mix for 2030 and 2035
across all models, by technology, in
the IRA scenario. The bars in the lower
panel show the absolute change in
generation for each technology in the
IRA scenario, relative to the No IRA
scenario. Biomass, petroleum, and
geothermal generation is reported
by a number of models, but not large
enough to be visible in the figure. Note
that hydrogen generation is negligible,
reported only by REGEN-EPRi, and not
visible in the figure.
60
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The Carbon Capture and Sequestration Tax Credit provision has a significant impact on future
outcomes of CCS technologies in the power sector. Four of 14 models project relatively low
levels of CCS expansion in the No IRA scenarios, ranging from 3 to 28 TWh of CCS generation
by 2035 across all models, with a median of 10 TWh. In IRA scenarios with the updated Carbon
Capture and Sequestration Tax Credit, 11 of 14 models show CCS expansion, ranging from 2
to 1,380 TWh by 2035 across all models, with a median of 150 TWh. There is a range of new
capacity deployment across models. In general, the models deploy more wind and solar
capacity relative to other technologies, along with energy storage technologies. The tax credits
and incentives under IRA help spur greater investments in solar and wind. However, magnitudes
vary greatly by model, with an average of 54 GW/yr with IRA versus 27 GW/yr without.
Electric-sector emission reductions are achieved through a shift toward investments in zero-
and low-emission technologies such as solar, wind, storage, and fossil generation with CCS and
away from high-emitting coal and gas generation (Figure 2.5). Both panels of the figure use the
same color scheme with the left-hand panel representing historical additions and retirements
in generating capacity and the right side showing average annual capacity additions for 2021-
2035 as projected by the modeling. Note, for example, that natural gas generation spikes in
the early 2000s as depicted by the gray part of the columns. Wind capacity has grown since
the mid-2000s and solar capacity since about 2010. Retirements of generating capacity are
depicted below the zero line, including the shutdown of some nuclear capacity and the recent
retirements of some coal capacity. The right-side panel shows large average annual increases
in solar and wind capacity, followed by gas, electricity storage, some coal, and some coal with
CCS. The median total average annual capacity addition from 2021-2031 is approximately 60
GW, which is comparable to the largest historical annual capacity addition in 2002 as well as
the projected capacity additions for 2023 [58]. The range of total capacity additions across the
models vary widely from as low as 26 GW/yr to 125 GW/yr, which suggests very different power
systems development paths.
All the model projections indicate new wind and solar with storage as the preferred mix
of new capacity deployed. Other technology types are shown to have outcomes that are
more mixed. For example, most models show that existing nuclear is retired, largely due to
an assumption about the expected lifetime of existing nuclear units. Natural gas capacity
outcomes are diverse across models, with some showing notable levels of new capacity
while others show notable levels of retirement.30 Most models show a notable amount of coal
capacity being retired. Several models show that fossil fuel-fired plants with CCS (natural gas
and coal) are economic with IRA. Each model's technology-specific capital cost assumptions
(outlined in Appendix D) and natural gas price assumptions influence the relative economic
competitiveness of each technology and its level of projected future deployment.
Most models show the capacity factor for natural gas declining in the No !RA scenario and declining by a greater
amount in the IRA scenario. In 2021 the capacity factor for natural gas generation was 54%, underthe No IRA scenario
the median capacity factor falls to 33% (22% min, 83% max) in 2030 and 33% (19% min, 83% max) in 2035, and under
the IRA scenario the natural gas capacity factor falls to a median of 26% (14% min, 82% max) in 2030 and 21% (11% min,
82% max) in 2035. The models that project construction of gas CCS generally show higher projected capacity factors
for natural gas. See Appendix E, Table E.3. For 2021 capacity factors, see https://www.energy.gov/ne/articles/what-
generation-capacity.
61
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Figure 2.5
Historical and projected capacity additions by technology, IRA
scenario
Historical Annual Capacity Changes
Avg. Annual Capacity Changes (2021-35)
1960
1980
2000
2020
H ~ o CL OT
I P <3
CL CL i
HI HI OT
X 2
w
o
fh a Si 41
W ' ' OT
" i i
o
a.
LU
LLI
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In addition to the two scenarios discussed thus far in this chapter, the following sensitivities
are examined by a limited set of models (Figure 2.6): IRA implementation, technology advances
and deployment constraints, combined IRA implementation and advanced technology,
economic growth, and fossil energy prices (see Section 1.2.2 for scenario descriptions). Six
models in the Bistline et al. study [1] and USREP-ReEDS explored the IRA implementation
sensitivity of power sector C02 emissions reductions to the value of IRA incentives (e.g., PTC,
ITC, 45Q, 45V, energy community bonuses, domestic content bonus) and technology build
rates and availability (e.g., renewables and transmission build rates, CCS availability, EVs
eligible for bonus credits) (see Appendices C.2 and C.3 for details). GCAM-PNNL, NEMS-OP,
USREP-ReEDS, and ReEDS-NREL ran the remaining sensitivity scenarios (see Appendix C.4, [2],
and [3] for details).
Results in Table 2.1 are presented as changes from the Moderate IRA scenario for data in Figure
2.6. For reference, a 1% change from 2005 electricity C02 emissions is 24 Mt C02. In 2030, IRA
implementation sensitivities increase reductions by up to 11 percentage points (pp) relative
to the Moderate IRA scenario when optimistic and increase emissions by up to 6.5 pp when
pessimistic, and nearly 19 pp by 2035. Advanced technology assumptions increase reductions
by up to 7,7 pp while constraining it increases emissions up to 12 pp. The advanced technology
assumptions also lead to the greatest projection of electricity emissions reductions from 2005
by 2030, 91% [3]. The largest median impact is in the sensitivity case combining Optimistic IRA
implementation with advanced technology assumptions, resulting in 2030 emissions falling an
additional 9.4 pp below 2005 levels. This combined effect is greater than the median changes
in either the Optimistic IRA implementation sensitivity or the advanced technology sensitivity
alone—indicating positive interaction effects related to the IRA accelerating advanced
technology adoption. Low fossil energy prices lessen C02 emissions reductions relative to the
Moderate IRA scenario by a greater amount than high fossil energy prices reduce emissions.
Sensitivities in economic growth showed the least significant impact on emissions changes,
no greater than ±1 pp relative to the Moderate IRA scenario. Sec Appendix F.4 for a summary of
emissions reductions ranges including all sensitivity scenarios.
63
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Figure 2.6
Sensitivity of electric-sector emissions to IRA implementation
(multiple models) and natural gas prices, technology cost, and
constrained deployment (ReEDS-only)
Combined Technology
IRA Implementation Technology + Implementation Fossil Energy Prices Economic Growth
-
+
*
X
+
+
X
+
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All-Ad*
a
Mot
All-Adv
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~ Mot
Low
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igh
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A
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Mod
~
All-Adv
-------
Table 2.1
Electric sector C02 emissions changes (percentage points of 2005
emissions) relative to the IRA Moderate scenario
IRA
Implementation
Technology
Implementation
+ Tech
Fossil
Energy
Prices
Economic
Growth
Year
Metric
Optimistic
Pessimistic
All Advanced
Constrained
Advanced
Renewables
Conservative
Renewables
Optimistic +
Advanced
Pessimistic +
Advanced
f I
1 I
2030
Min
Median
Max
-11.0 -2.0
-2.7 3.2
2.6 6.5
-7.7 6.1
-6.8 8.8 -6.7 9.6
0.2 11.5
-9.6
-9.4 -5.5
-9.2
-2.5 0.2
-2.3 2.7
-2.1 5.2
0.2 -0.2
2035
Min
Median
Max
-8.0 -1.4
-1.5 2.0
0.0 18.8
-8.1
-3.9 7.4
0.3
-8.8
-7.4 -5.7
-6.0
-2.7 0.3
0.3 -0.4
The largest median impact in electric sector CO2 emissions is in the sensitivity case combining Optimistic IRA implementation
with advanced technology assumptions resulting in 2030 emissions falling an additional 9.4 pp below 2005 levels. This combined
effect is greaterthan the median changes in eitherthe Optimistic IRA implementation sensitivity orthe advanced technology
sensitivity alone, indicating positive interaction effects related to the IRA accelerating advanced technology adoption. Table 2.1
presents changes relative to the Moderate IRA Implementation scenario for all sensitivity scenarios, measured in incremental
percentage point (pp) changes from 2005 economy-wide CO2 emissions (e.g., a value of -1.0 would mean that underthat sensitivity,
emissions are reduced an additional 1 percentage point below 2005 levels—equivalent to an additional 24 Mt CO2 of mitigation). For a
summary of the percent reduction of all IRA sensitivities from the No IRA scenario, see Appendix F.4.2.
65
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Criteria Air Pollutants
SULFUR DIOXIDE (S02)
The primary focus of this report is to discuss the
impacts of the IRA on reducing C02 pollution from
the atmosphere. However, changes in sources
with C02 emissions likely affect the emissions of
other air pollutants from those sources, including
oxides of nitrogen (that also contribute to ground-
level ozone pollution as well as particulate matter
pollution) and sulfur dioxide (that also contributes
to particulate matter pollution). These pollutants
are found all over the United States and can harm
human health and damage the environment.
Sulfur dioxide, or S02, is
a highly reactive gas that
is generated primarily
when sulfur-containing
coal is burned within
power plants and large
industrial sources. Short-
term exposures to S02
can harm the respiratory
system and make
breathing difficult. People with asthma, particularly
children, are especially sensitive to these effects of
S02. High concentrations of S02 in the air generally
also lead to the formation of other sulfur oxides
(SOx), which can react with other compounds in the
atmosphere to form small particles or even sulfuric
acid. Sulfate particles are a primary constituent
of particulate matter (PM) pollution, which may
penetrate deeply into the lungs, and in sufficient
quantity can contribute to both lung and heart
problems.
In addition to the direct contribution of S02 to
the formation of acid rain, deposition of sulfate
particles can also stain and damage stone and other
materials, including culturally important objects
such as statues and monuments. Furthermore,
sulfate PM can reduce visibility by causing haze
in parts of the country, including many of our
treasured national parks and wilderness areas.
NITROGEN OXIDES (NO*)
Sulfur is not the only
constituent of PM.
Nitrogen oxide (NOx)
emissions from cars,
trucks and buses, power
plants, and off-road
equipment contribute
to the formation of fine
particle pollution as
well as ground-level
ozone. Ozone can aggravate lung diseases such as
asthma, emphysema, and chronic bronchitis and
increase the frequency of asthma attacks, leading to
increased school absences, medication use, visits
to doctors and emergency rooms, and hospital
admissions. Ecologically, ozone can affect sensitive
vegetation and ecosystems, destroying tissues
and killing organisms. This pollution can result in
substantial damage to crops, forests, parks, wildlife
refuges, and wilderness areas.
Emissions of the pollutants described above have
been decreasing. Between 2005 and 2019, the
United States saw a reduction in S02 emissions
from 14.5 to 2.0 million short tons economy-wide,
and NOx emissions fell from 20.4 to 8.7 million
short tons. Modeling suggests the IRA will result
in substantial reductions in emissions of both S02
and NOx. Up to an additional 0.5 million short tons
for each pollutant are expected to be reduced by
2035 compared to a No IRA scenario, resulting in
significant health and environmental benefits.
To learn more about how EPA will assess
impacts of these pollutants on disadvantaged
communities as the power sector evolves under
IRA implementation, see Text Box: Environmental
Justice.
66
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Figure 2.7
Cross-model comparison of NOx and S02 emissions by model over time
NOx
(0
(0
E
LU
4-
><
2 3-
1 -
Historical
No IRA — A
2010
IRA — o
i i i
2021 2025 2030
l
Data Points and
Median Results
A
**¦
so2
O 3
w 13
(0
in
E
LU
IRA — o
i i i i
Data Points and
Median Results
£
2035 2030 2035
2010
2021 2025 2030 2035 2030 2035
The median modeled projections for both NOx and S02 emissions fall in the IRA scenario relative to the No IRA scenario in
2030 and 2035. Model results for the emissions trajectories are shown as orange dashed lines for the No IRA scenario and in
blue for the IRA scenario. Data points to the right of the figure show individual model results from 2030 and 2035 (blue circles for
IRA scenario results, orange triangles for No IRA). Horizontal bars represent the median of the model results. Accessible tables
available in the Data Annex.
67
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CHAPTER 3
Transportation
3.1 TRANSPORTATION SECTOR SNAPSHOT
The transportation sector is the largest U.S. source of CQ2 emissions, representing 35% of
C02 emissions [4] (see Figure 3.1), followed by the buildings and industry sectors. Within the
transportation sector, light-duty trucks (including sport utility vehicles, pickup trucks, and
minivans) are the largest contributor at 37% of transportation emissions, followed by medium-
and heavy-duty trucks (23%), passenger cars (21%), aviation (9%), marine (3%), and rail (2%).
Passenger cars and light-duty trucks combined represent 58% of transportation C02 emissions,
and thus comprise 20% of total U.S. C02 emissions [4] (see Figure 3.2). Americans drove an
average of 13,476 miles per year [63] and spent $1.6 trillion (9.8% of total national household
spending) on transportation in 2021, the fourth largest household expenditure category after
health care, housing, and food [64],
Transportation C02 emissions rose from about 1,484 Mt C02/yr in 1990 to 1,863 Mt C02/yr in
2005. Transportation emissions then declined to 1,757 Mt C02/yr in 2021, despite a rise of
vehicle miles traveled from 2.99 million to 3.13 million [65] and an increase in population. This
decline is due to a range of effects from fuel economy and tailpipe emissions standards, as
well as the adoption of various technologies, including hybrids, plug-in hybrids (PHEVs), and
full battery electric vehicles (BEVs). While nearly all of the energy used for transportation was
supplied by petroleum-based products, electricity use in BEVs and PHEVs has recently begun
to increase [4],
Over the past decade, automakers have developed a range of electrification technologies,
including hybrid electric vehicles and, in recent years, plug-in electric vehicles (PEVs), which
include PHEVs and BEVs. Before the IRA became law, analysts were already projecting that
significantly increased penetration of PEVs would occur in the U.S. and global markets.
68
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Figure 3.1
C02 emissions from the transportation sector compared to economy-
wide C02 emissions, 2005-2021
6,000
o
o
4,000 -
t/>
c
o
'<75
E
LU
0)
t/>
Z)
T3
c
LU
2,000 -
2005
2021
Transportation: Direct
Transportation: Indirect
~
Other Sectors
Transportation CO2 emissions have remained relatively flat since 2005 and thus represent an increasing share of total
CO2 emissions. The transportation sector represents the largest share of CO2 emissions in the United States. Direct
transportation CO2 emissions comprise the majority of total emissions and indirect emissions from electricity use have
been negligible and are not visible in the figure (transportation emissions shown in blue). Remaining economy-wide CO2
emissions are represented by the outlined bar. Accessible table available in the Data Annex.
69
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Figure 3.2
C02 emissions from the transportation sector, by end use, for
1990-2021
2,000
1,500
¦lil| li _||ll ¦
ill
o
o
(0
(0
'E
LU
1,000
500 -
0-
Other
Pipelines
Water
Aircraft
MHD Trucks
LD Trucks
Passenger Cars
1990
2000
2010
2020
Emissions in the light-duty sector peaked in 2007 and began to decrease despite an increase in vehicle miles
traveled. This decrease is due to increased efficiency driven by regulations and advanced technology. "Other"
includes rail, buses, motorcycles, and lubricants. Truck emissions are split into light-duty (LD) and medium- and
heavy-duty (MHD). These are domestic-only CO2 emissions.32 Accessible table available in the Data Annex.
32 Emissions resulting from the combustion of fuels used for international transport activities, termed
international bunkerfuels underthe UNFCCC, are not included in national emission totals, but are reported
separately based upon location of fuel sales. See Chapter 3 of the Inventory of U.S. Greenhouse Gas Emissions
and Sinks: 1990-2021 [4],
70
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In 2021, IHS Markit predicted a nearly 40% U.S. PEV share by 2030 [66],33 and Bloomberg New
Energy Finance projections suggest that under current policy and market conditions, and prior
to the IRA, the United States was on pace to reach a 40-50% PEV share by 2050 [67], A 2022
survey by Consumer Reports shows that more than a third of Americans would either seriously
consider or definitely buy or lease a BEV today, if they were in the market for a vehicle [68]. A
report by the Environmental Defense Fund and Environmental Resources Management (ERM)
shows how virtually every major manufacturer of light-duty vehicles is already planning to
introduce widespread electrification across their global fleets in the coming years [69].
Advancements in technology have enabled these increases in fuel economy and decreased
usage of fossil fuels in the transportation sector. For example, battery costs have decreased
significantly in the last 20+ years, declining 89% between 2008 and 2022 [70]; micromobility34
has seen a growth in ridership in the United States, increasing from 2.4 million rides in 2011
to 112 million rides in 2021 [72], and an NREL study estimates that high adoption of shared
micromobility can save 2.3 billion gasoline-equivalent gallons per year nationwide [73]. Sales
of electric vehicles (BEVs and PHEVs) were about 120,000 worldwide in 2012, but has seen a
dramatic increase to 6.6 million in 2021. About half were sold in China in 2021 (3.3 million, 2.7
million were BEVs), representing 16% of domestic car sales. In Europe, electric vehicle sales
increased in 2021 more than 65% year-on-year to 2.3 million and accounted for 17% of Europe's
auto sales in 2021 [74], In the United States, about 608,000 electric vehicles were sold in 2021,
with BEVs making up 73%. Combined U.S. EV/PHEV production reached 4% of all new vehicles
in model year 2021, and is projected to reach a new high of 8% of all production in model year
2022 [75],
Recent regulations from the EPA and National Highway Traffic Safety Administration (NHTSA)
have set more stringent emissions and fuel economy standards. In December 2021, the EPA
issued new GHG emission standards for new passenger cars and light-duty trucks, requiring
automakers to reach an industry-wide target of 161 C02 grams per mile (g/mi) in model year
2026, steadily decreasing from 202 C02 g/mi in model year 2023 [76]. In March 2022, NHTSA
finalized fuel economy standards that increase fuel economy to a fleetwide average of 49 mpg
by 2026. In December 2022, EPA issued a final rule that set new standards to reduce nitrogen
oxide (NOx) pollution from heavy-duty vehicles and engines starting in model year 2027. In
addition, several other countries and localities have issued their own rules that will transition
new light-duty and heavy-duty sales to zero-emission vehicles in the coming years [77],
43 The table indicates 32.3% BEVs and combined 39.7% BEV, PHEV, and range-extended electric vehicle (REX) in 2030.
44 The Federal Highway Administration broadly defines micromobility as any small, low-speed, human- or electric-
powered transportation device, including bicycles, scooters, electric-assist bicycles, electric scooters, and other small,
lightweight, wheeled conveyances [71].
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In addition, on April 12, 2023, EPA announced new proposed standards to further reduce
harmful air pollutant emissions from light-duty and medium-duty vehicles [27], as well as
heavy-duty vehicles [28], starting with model year 2027.35 These proposed standards are not
reflected in the modeling results and projections shown in this report. Beyond the scope of
the IRA and the new vehicle standards, there remain other actions that can further decarPonize
the U.S. transportation sector. In January 2023, the departments of Energy, Transportation,
and Housing and UrPan Development, and the Environmental Protection Agency released The
U.S. National Blueprint for Transportation Decarbonization [78], a framework of strategies and
actions to remove all emissions from the transportation sector Py 2050.
3.2 KEY TRANSPORTATION SECTOR IRA PROVISIONS
The IRA includes the following major provisions and incentives for the transportation sector:
¦ Tax incentives and rebates
— Biodiesel and Renewable Fuels Production Tax Credit (40A, others)
— Second-generation Biofuels Production Tax Credit (40)
— Sustainable Aviation Fuel Production Tax Credit (40B)
— Clean Vehicle Credit (30D)
— Credit for Previously Owned Clean Vehicles (25E)
— Qualified Commercial Clean Vehicle Credit (45W)
— Alternative Fuel Vehicle Refueling Property Credit (30C)
— New Clean Fuel Production Tax Credit (45Z)
— Clean Hydrogen Production Tax Credit (45Q)
¦ Programs
— Clean Vehicles
— U.S. Postal Service Clean Fleets
The IRA extends numerous existing tax provisions that encourage production of fuels. These
include tax credits for renewable diesel and biodiesel used as fuel, the alternative fuels tax
credit, and the second-generation biofuel producer tax credit. The alternative fuel charging or
refueling property tax credit is extended, but modified so that it applies to property placed in
service in a low-income area.
New fuel-related provisions are created by the IRA. A new tax credit supports the sale or
mixture of sustainable aviation fuel. Another provision supports the production of clean
hydrogen for use in the transportation sector. A new clean fuel credit (which begins in 2025) is
established that depends on the emissions factor associated with the fuel.
35 The proposed rules are not included in this analysis and are mentioned here for broader context of the transportation
sector.
72
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In addition to support for fuels, the IRA includes provisions to support production and sales
of clean vehicles. The refundaPle tax credit for partial electric vehicles is modified to include
requirements for the use of critical minerals in production and for Pattery components. A new
tax credit is created supporting clean commercial vehicles (with more suPstantial potential
credits for larger commercial vehicles and smaller credits for smaller vehicles). Another new
credit is targeted at the purchase of used plug-in and fuel cell clean vehicles—this credit is
intended to support clean vehicle purchases Py lower income consumers.
New funding and loan programs are also created Py the IRA that will affect emissions from the
transportation sector. The U.S. Postal Service Clean Fleets program supports the purchase
of zero-emitting vehicles. The Advanced Technology Vehicle Manufacturing Loan Program
supports for production of advanced vehicles and their components. Through the Domestic
Manufacturing Conversion Program, cost-share grants are availaPle for domestic production of
clean vehicles.
While the Preadth of scope of the models included in this analysis offer a great deal of insight
to the effects of the IRA, many models reflect the IRA incentives in different ways and may
not Pe aPle to reflect some of the incentives in the transportation sector. This is due in part
to the mix of multi-sector and electricity sector models, which have varying degrees of
representation of the transportation sector.
3.3 TRANSPORTATION SECTOR ANALYSIS AND RESULTS
Transportation C02 emissions have Peen declining in recent history due to emissions and
fuel economy regulations and technological advances. In 2021, transportation emissions had
declined Py aPout 6% relative to 2005. Across the various models, the No IRA scenario had
emissions declining from Petween 13 and 28% in 2035 relative to 2005, with a median decline
of 23%. Investments and policies set forth in the IRA results in further reduced emissions, with a
range of 15 to 35% in 2035 relative to 2005, and a median decline of 27% (Figure 3.3).
This decline in emissions is largely due to the increased use of advanced technologies in the
transportation sector, such as electric cars and trucks. In the No IRA scenario, the models
project electric vehicles' share of the market (new sales) increases from 4% in 2021 to a range
of 12-43% (median 24%) in 2030, and 15-59% (median 38%) in 2035. With the IRA provisions
that range increases to 15-54% (median 36%) in 2030, and 18-81% (median 43%) in 2035 (Figure
3.4). This is similar to an analysis by the International Council on Clean Transportation (ICCT)
that finds the IRA results in a range of 48-61% EV sales share in the light-duty sector and a
range of 39-48% zero-emissions vehicle (ZEV) sales share in 2030 [79]. The decline in EV sales
share in the IRA scenario from 2030 to 2035 exhibited by some models represents the expiring
tax credits for EV purchases. In the IRA sensitivity scenarios, electric vehicle sales shares
reach as high as 70% in 2030 for NEMS-OP in the combined Optimistic IRA implementation
plus Advanced technology scenario and 93% in 2035 for RIO-REPEAT in the Optimistic IRA
Implementation scenario.
73
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Figure 3.3
Transportation sector C02 emissions
(a)
O
O
(0
c
o
'<0
in
Data Points and
Median Results
2,500
2,000
| 1,500
LU
o
o
o
o
w
(5
•c
o
Q.
in
1,000
500
No IRA — A
i
8 o
-i-A.
2021
2025
2030
2035
2030
2035
(b)
¦5 ?
2005
In the IRA scenario, transportation sector
CO2 emissions fall to 11 to 25% (17% median)
below 2005 levels in 2030. In 2030, individual
models find that transportation CO2 emissions
are 1 to 7% (2% median) below what they are
modeled to be in the No IRA scenario, with the
median difference falling to just over 5% by 2035.
Figure 3.3(a) shows absolute model results for
the emissions trajectories (No IRA scenario in
orange dashed lines, IRA Scenario in blue) with
the historical trend (in black [4]). Data points to
the right of Figure 3.3(a) show individual model
results from 2030 and 2035 (blue circles for IRA
scenario results, orange triangles for No IRA).
Horizontal bars represent the median of the
model results. Figure 3.3(b) shows the percent
difference between the IRA and No IRA for each
model (blue lines) and the median across the models (black line). Transportation emissions are broken out into direct and
indirect in Appendix F.2.35'37 Accessible table available in the Data Annex.
Median Result —
1 1
1
¦10
¦15
o
a>
o
3
2021
2025
2030
2035
35 USREP-ReEDS shows a slight uptickin transportation sector emissions in 2025 (Figure 3.3(a)). Falling electricity prices
increase the demand for transportation services and therefore emissions. By 2030, the IRA provisions supporting EV
adoption offset the electricity effects and emissions fall.
37 A handful of models show higher total transportation emissions under the IRA in 2025 (Figure 3.3(b)). In the No
IRA scenario, the forward-looking models have slightly higher levels of near-term investment in renewables in
2025 because the models forsee the expiration of tax credits. Under the IRA scenario, tax credits are extended and
investment does not exhibit a near-term spike.
74
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Figure 3.4
Electric vehicle percent share of new sales
Data Points and
Median Results
Modeled projections for the IRA scenario (blue lines) shows an increase over the No IRA scenario (orange dashed lines)
in 2030 and 2035, but both scenarios show increasing EV sales compared to historical data (blacklines). The right side
of the figure shows data points from different models as blue circles for the IRA scenario, orange triangles for the No IRA
scenario, and the horizontal lines represent the median, for both scenarios in the years 2030 and 2035. Historical data
come from the EPA Automotive Trends Report, 2022 [75]. Note: fewer models have this information, this chart contains
data from EPS-El, GCAM-CGS, GCAM-PNNL, NEMS-EIA, NEMS-OP, NEMS-RHG, REGEN-EPRI, RIO-REPEAT, and USREP-
ReEDS. Accessible table available in the Data Annex.
75
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This increase in electric vehicles' market share leads to an increase in electricity demand.
Figure 3.5 shows the increase in demand for electricity in the transportation sector from light-
duty passenger cars and trucks in the United States.
As the sector responsible for the largest source of U.S. C02 emissions, existing and future
technological improvements mean that this sector has the potential to show the largest
decrease in emissions. On-road transportation alone, including light-duty cars and trucks,
as well as medium- and heavy-duty trucks, represent 60% of emissions in the transportation
sector, and 21% of total U.S. GHG emissions. The shift from fossil fuel combustion to an
electrified fleet of vehicles is a rapidly developing, global phenomenon, as several countries
and regions around the world have set zero-emission targets for new cars sold within the next
10-20 years [80, 81],
This analysis demonstrates through a variety of energy sector and economy-wide models
how the provisions in the IRA reduce C02 emissions within the transportation sector. While
the modeling results show that the effects are not as large as they are in the buildings and
industrial sectors, there is still a substantial reduction in estimated emissions. These sectoral
differences reflect the inclusion of electric-sector emissions in values, and the transport share
of electricity is relatively low, so it does not benefit as much as when power sector emissions
decline by 2035. Modeling shows that the transportation-related provisions in the IRA can
result in a potential decline in transportation C02 emissions of 27% relative to 2005 levels by
2035, compared to a decline of 23% in the No IRA scenario compared to 2005 levels. However,
the modeling suite, consisting of multi-sector models and electricity sector models, may not
capture the full range of effects of the IRA in the transportation sector, let alone in subsectors
such as aviation or recent transportation trends such as the rise of micromobility {see Table
1.3 for a list of IRA provision coverage by model). Further regulations, such as EPA's proposed
greenhouse gas emission standards, will help drive further reductions in the transportation
sector. As discussed in Section 3.1, the U.S. National Blueprint for Transportation
Decarbonization outlines opportunities to reduce GHG emissions from the transportation
sector, covering options for every mode of travel. ElA's analysis of the Inflation Reduction Act
in Annual Energy Outlook (AEO) 2023 [82] has similar results, with the transportation sector
showing a 20% decrease in C02 emissions in the IRA scenario (which is incorporated into their
"Reference" scenario) compared to a 19% decrease in C02 emissions in the No IRA scenario
emissions in 2035 from 2005 levels.38 While the transportation sector may be the largest
contributor to U.S. C02 emissions, the IRA's provisions in the transportation sector mostly
affect the passenger cars and medium- and heavy-duty trucks subsectors, while a significant
portion of the transportation sector does not experience as much of a decline in emissions due
to the IRA (e.g., aircraft, water).
Future iterations of this analysis will need to evaluate any subsequent changes to the IRA
provisions. In addition, EPA's proposed greenhouse gas emission standards rules will need to
be implemented in the modeling assumptions upon their finalization. In addition, this analysis
is limited in its ability to provide projections for fuel use or transportation demand by mode.
This provides an opportunity to expand the scope of the models' capabilities in future research.
33 Historical values forthis calculation are from the U.S. Energy Information Administration (July 2023). Monthly Energy
Review [30].
76
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Figure 3.5
Electricity demand from light-duty vehicles in the transportation sector
No IRA -- A
IRA — O
2025
2030
2035
Model results show increased demand from light-duty vehicles, but the IRA scenario shows a more substantial
increase in 2030 and 2035. Individual model results are shown as orange triangles for No IRA and blue circles for IRA
scenarios in each year. Horizontal bars represent the median of the result for each scenario in each year. Accessible
table available in the Data Annex.
-------
4.1 BUILDINGS SECTOR SNAPSHOT
CHAPTER 4
Buildings
The buildings sector contributes over a third of U.S. C02 emissions. On-site fossil fuel
combustion at buildings is responsible for 11% of total U.S. C02 emissions and 22% of emissions
are from electricity used in buildings (see Figure 4.1) [4], When electricity use is accounted for,
the buildings sector is the second largest contributor of C02 emissions in the United States,
following the transportation sector. The largest sources of building emissions are heating,
cooling, and water heating. To substantially reduce building emissions, the most effective
strategies are to install energy efficient equipment to reduce energy use overall, ensure
that electric equipment is installed—when possible—to avoid fossil combustion, and ensure
buildings are constructed and operated in a way that uses energy efficiently.
The needed technologies are already being deployed in buildings but many—like air-source
heat pumps for air heating, air cooling, and water heating—are not currently used at the
scale necessary for substantial reductions. Because of the long life of buildings and building
equipment, it is an immediate priority to install efficient and lower-emitting technology
whenever existing buildings are retrofitted or new buildings are built. Immediate action
also allows more time to develop effective deployment strategies, to learn more about how
to optimize installation and operation as deployment scales, and to understand how these
changes can best take advantage of and enable a clean electric grid. To inform future actions,
this assessment projects the extent to which IRA spending contributes to energy-related
emissions have fallen.
78
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Figure 4.1
C02 emissions from the buildings sector compared to economy-
wide C02 emissions, 2005-2021
LU
6,000
8 4,000
in
c
o
'in
in
£
LU
u
in
3
2,000
2005
2021
Buildings: Direct
J Buildings: Indirect
~
Other Sectors
Since 2005, direct buildings sector CO2 emissions have remained essentially unchanged, and indirect emissions
(from the generation of electricity used to power buildings) have slowly decreased. Buildings direct emissions are
shown in gold and indirect emissions in gold with white cross-hatching. Remaining economy-wide CO2 emissions are
represented by the outlined bar. Accessible table available in the Data Annex.
79
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Residential buildings encompass both single-family homes (about 70% of households,
90 million homes), and multi-family buildings of varied sizes from duplexes to high-rises
(about 30% of households, over 37 million units) [83]. These figures include a growing number
of manufactured homes—7.5% of existing U.S. homes and 9% of new homes are manufactured
[84], Commercial buildings include any building that is not residential, industrial, or
agricultural, and they encompass 5.9 million buildings and a little over half of U.S. floorspace
[85, 85]. Currently, warehouse, office, and service buildings together account for roughly 48%
of all commercial buildings and 42% of total commercial building floorspace [86].
U.S. residential fuel consumption is split evenly between natural gas and electricity, with a small
percentage using other fossil fuels such as oil or propane for heating [30],39 In the commercial
sector, about a third of the energy consumed on-site is in the form of natural gas and two-
thirds is electricity [85].
Fuel use varies significantly across regions. For example, 50-70% of households in the Midwest
and West use natural gas as their primary heating fuel, but only a little over 30% of homes in the
South do so [87], The amount of electricity used for air conditioning also varies significantly
across regions. Regionally customized policies that account for this variation can optimize
buildings sector reductions [88].
Space heating, air conditioning, and water heating make up around 60% of energy used in
buildings and present the greatest opportunity for building emissions reductions—through
electrifying new heating, ventilation, and air conditioning (HVAC) and water heating with
energy efficient equipment (hereafter referred to as "efficient electrification") and through
lowered demand from better insulated buildings [86, 89] The remaining energy used in
buildings goes to activities like cooking and laundry (which also present opportunities for
efficient electrification) and other miscellaneous appliances that are generally electric like
refrigerators.
Residential buildings have a median age of a little over 40 years and commercial buildings
generally have lifetimes of 50 years or more, so decisions made now will have longstanding
effects [90, 91]. New buildings are designed to be more efficient because they must comply
with updated building efficiency codes, but additional energy management, lower-emitting
building equipment, and building envelope measures above current requirements can further
reduce long-term emissions. The efficiency of existing buildings can frequently be improved
through equipment and building envelope retrofits and efficient management and operation
of building systems because many were built before energy codes or when energy codes were
less stringent [92],
Currently, most buildings are heated with less efficient technologies—mainly fossil fuel-
burning furnaces, boilers, and electric resistance heating. However, this trend is shifting as
sales of air-source heat pumps, which provide more efficient and electrified heating and
cooling combined, surpassed sales of fossil fuel furnaces in 2022 by over 10%. Heat pump water
heaters lag the growth in space conditioning heat pumps, currently representing only 2% of
39 Currently, a small percentage of buildings, mostly homes, receive truck delivery of propane or fuel oil for space heating.
This is primarily concentrated in colder regions and in rural areas. A small number of homes also heat with wood.
80
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residential water heaters, and around 0.4% of commercial water heaters.40 IRA has the potential
to significantly increase deployment of heat pumps through tax credits and rePates, along with
programs to reduce Parriers to adoption (see Text Box: Overcoming Deployment Challenges).
The federal government already has a longstanding role in ensuring the energy performance
of Puildings. The U.S. Department of Energy sets enforceaPle standards for appliance energy
performance and develops model energy codes for state and local communities. The EPA
provides voluntary ENERGY STAR efficiency certifications for products, new residential
construction, and existing commercial Puildings [95, 96]. Through IRA funding, EPA is
expanding ENERGY STAR to Pring efficiency and emissions reductions to more residential and
commercial Puildings. The federal government also leads Py example Py implementing GHG
reduction measures in its own Puildings, through efforts like the federal Puilding performance
standard, and is also expanding that work through the IRA [97],
States commonly require electric and gas utilities to offer incentives for improvement in new
and existing Puilding envelopes and energy efficient products in residential and commercial
Puildings. These incentives will Pe supplemented through IRA tax credits and rePates. To
improve the performance of existing commercial Puildings, states and localities are Peginning
to adopt requirements for energy Penchmarking and for Puilding performance standards,
which require Puildings to meet either a certain energy or GHG performance level, or Poth.
Other sources of Puildings sector emissions include fugitive refrigerant or methane emission
from Puildings, emPodied emissions of construction materials (i.e., emissions produced during
manufacture of construction materials) [88], the effect of land use planning related to Puildings
(on Poth Puildings and transportation sector emissions) [98], and emissions from disposal of
construction waste. These are not specifically analyzed in this analysis. Another key concern
in the Puildings sector that is not addressed in this report is resilience to climate impacts.
IRA policies and programs do, however, address some of these priorities, through actions like
funding of the American Innovation and Manufacturing (AIM) Act to address refrigerants and
the Federal Buy Clean Initiative affecting construction materials [99].
4.2 KEY BUILDINGS SECTOR IRA PROVISIONS
The IRA includes the following policies and incentives relevant to the Puildings sector. See
Section 1.2.2 for which of these initiatives are represented in the modeling. That representation
may be limited based on the capabilities of the models and the extent to which the specifics of
the program have been determined.
40 The Air-Conditioning, Heating, and Refrigeration Institute (AHRI) reported 42% for residential space heating heat
pump sales in 2022. ENERGY STAR reported 2% for residential heat pump water heat sales in 2021. Using a combination
of Commercial Buildings Energy Consumption Survey (CBECS) data and AHRI data, commercial space heating sales for
2022 are estimated to be 14%. Using a combination of CBECS and ENERGY STAR data, commercial heat pump water
heating sales for 2021 are estimated to be 0.4% [86, 93, 94],
81
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¦ Tax incentives and rebates:
— Energy Efficiency Home Improvement Credit (25C)
— Residential Clean Energy Credit (25D)
— New Energy Efficient Homes Credit (45L)
— Energy Efficient Commercial Buildings Deduction (179D)
— Consumer home energy rebates
• Home Electrification and Appliance Rebates Program
• Home Efficiency Rebates Program
¦ Funding and financing:
— EPA Greenhouse Gas Reduction Fund
— Climate Pollution Reduction Grants
— DOE Building Energy Codes Technical Assistance
— DOE State-Based Home Energy Efficiency Contractor Training Grants
¦ Grants:
— HUD Green and Resilient Retrofit Program
— General Services Administration Assistance for Federal Buildings
¦ Programs:
— Labeling for Substantially Lower Carbon Construction Materials
— Low Emissions Electricity Program
The text box "Building Sector Measures Incented by the IRA" explores in more detail key
measures encouraged by these programs—including energy efficiency, efficient electrification,
and distributed renewable energy.
4.3 BUILDINGS SECTOR ANALYSIS AND RESULTS
Most of the major IRA provisions for buildings were covered by the models (see Table 1.3). The
projected emissions reductions in the buildings sectorfrom scenarios with and without IRAare
shown in Figure 4.2. Accounting for all modeled results, IRA provides a reduction in emissions
of 52-70% (median 66%) below 2005 levels in 2035. This is relative to the No IRA scenario,
which has a substantially smaller decrease of 36-51% (median 45%) below 2005 levels in 2035.
Median absolute reductions in 2030 and 2035 are 300 and 390 Mt C02, respectively (Figure
4.2(a)). Against each model's baseline (Figure 4.2(b)), emissions reductions are from 9 to 37%
in 2030 and 20 to 47% in 2035. In buildings, emissions predominantly fall due to changes in
indirect emissions (median indirect reductions in 2030 and 2035 are around 350 and 450 Mt
C02 respectively). Direct emissions fall by only around 37 Mt C02 per year in both 2030 and
2035 (see Figure F.2.3).
82
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Figure 4.2
Buildings sector C02 emissions
(a)
2,500
2,000
1,500
1,000
500
Historical
No IRA — A
fe::
i
i
Data Points and
Median Results
A
A
l
A
A
A
1
* O
O
O
O
§-
s
0
2005
2021 2025
2030
2035
2030 2035
In the IRA scenario, buildings sector CO2 ^
emissions fall to 49 to 63% (55% median) below
2005levels in 2030. In 2030, individual models
find that buildings CO2 emissions are 9 to 37%
(23% median) below what they are modeled
to be in the No IRA scenario, with the median
difference falling to 33% by 2035. Figure 4.2(a)
shows absolute model results forthe emissions
trajectories (No IRA scenario in orange dashed
lines, IRA Scenario in blue) with the historical
trend (in black [4]). Data points to the right of
Figure 4.2(a) show individual model results from
2030 and 2035 (blue circles for IRA scenario
results, orange triangles for No IRA). Horizontal
bars represent the median of the model results.
Figure 4.2(b) shows the percent difference
between the IRA and No IRA for each model (blue lines) and the median across the models (black line). Buildings
emissions are broken out into direct and indirect in Appendix F.2.41 Accessible table available in the Data Annex.
--30
I?
o
a>
o
3
73
>
2021
2025
2030
2035
NEMS-RHG shows higher total emissions under the IRA in 2025. Of the total emissions, the indirect emissions are
higher, though the direct emissions are lower. In the No IRA scenario, the forward-looking model has slightly higher
levels of near-term investment in renewables in 2025 because the model forseesthe expiration of tax credits. Under
the IRA scenario, tax credits are extended and investment does not exhibit a near-term spike. This leads to a projection
of greater indirect emissions from electricity generation underthe IRA in 2025.
83
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Under the IRA, the median electricity share of final energy increases by 1.9 percentage points
(pp) in 2030 and 2.1 pp in 2035. The maximum increase is 4.1 pp in 2030 and 4.7 in 2035.
However, electrification does not increase in all models. There is also a discernible increase
in electricity use in buildings over time—across the models there is a median 8% increase in
electricity's share of final energy from 2021 in the IRA scenario in 2030 and 13% in 2035, likely
reflecting a certain amount of building-based electrification in the near term.
The variation in results across models is at least in part because the multi-sector analyses vary
in the methods and the level of detail used in analysis of the buildings sector, as well as varying
assumptions about the uptake of IRA incentives. For details on how building policies are
represented in the different models, see Table 1.3 and Table C2.
Building on the modeling caveats and limitations provided in Section 1.2.4, there are two
types of uncertainties that are particularly applicable to the buildings sector that should
be acknowledged. The first type is uncertainty regarding IRA implementation. There is
uncertainty regarding the scale and nature of adoption of IRA buildings sector incentives,
which in part depends on initiatives that enable overcoming non-market barriers to adoption
(see the Overcoming Deployment Challenges Text Box in Section 1.3.2). The IRA provides
programmatic funding to target these barriers and to further support uptake of IRA incentives
and deployment of emission-reducing technologies. Program design at the federal and state
level will have a significant effect on the impact of IRA initiatives. There is also uncertainty
regarding technical assumptions, such as whether newly installed equipment will be operated
and maintained efficiently over the long term. Also, the buildings sector analysis is dependent
on analysis of the rate of power grid decarbonization, another key uncertainty.
The second type is uncertainty due to the limitations of representation of building sector
IRA policies and buildings sector modeling in the context of multi-sector models. Not all IRA
buildings-related programs are represented (see Table 1.3). The representation of capacity
building and technical assistance programs is particularly limited. More generally, multi-
sector models are limited in terms of the granularity with which they can represent efficient
technology adoption in the buildings sector and the behavior of energy demand [100-102], This
type of analysis would benefit from development of analytical methods that better reflect the
buildings sector in economy-wide modeling—something that has not been the focus of these
models in the past. The reflection of existing state- and local-level actions will also affect how
the impact of IRA is assessed.
84
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Buildings Sector Measures Incented by IRA
The IRA provides incentives for multiple strategies
to reduce C02 emissions from buildings, including
energy efficiency, efficient electrification, and
renewable energy from distributed renewables. This
section specifies the nature and potential impacts
of these measures. Potential impacts of energy
efficiency and efficient electrification measures are
explored in buildings sector-specific technology
scenario analyses separate from the multi-sector
modeling. The discussion of distributed renewable
energy specifies how it is represented in the multi-
sector modeling. Finally, the text box specifies how
IRA prioritizes these measures in low-income and
disadvantaged communities.
Energy efficient strategies
include efficient end-
use appliances and
equipment, energy and
building management
(including commissioning
and optimizing buildings
systems and controls),
and building envelope
measures such as higher performance insulation
and windows. Additionally, benchmarking energy
use for commercial buildings is an increasingly
prevalent way to make building operations more
efficient with minimal time and investment—studies
have estimated that benchmarking buildings can
drive energy efficiency improvements of 1-4%
annually. States and local communities are starting
to adopt policies that encourage benchmarking
[103-107],
In addition to directly acting to reduce emissions,
energy efficiency can reduce ratepayer and power
sector system costs. Ratepayers realize lower
energy bills through efficiency improvements.
Energy efficiency also enables the installation of
equipment with lower energy requirements by
reducing a building's heating, cooling, and water
heating load. This reduces equipment costs and,
in the case of efficient electrification, can avoid or
reduce electricity service upgrades in buildings.
ENERGY EFFICIENCY
As more end uses are electrified, a focus on energy
efficiency will moderate increases in electricity
system costs. A significant decrease in demand
from aggressive energy efficiency, along with a
focus on load flexibility and management, can
reduce total power demanded from the grid and
smooth out peak demand, thus reducing the
infrastructure investment needed for a low-
emitting electric grid.
A newly published study by LBNL and the Brattle
Group included an assessment of the potential
power system cost impacts of increased efficiency
and demand flexibility deployment in deep
decarbonization scenarios with high demand-side
electrification. While generally relevant to potential
IRA impacts, this study predates IRA and does
not reflect IRA policies. LBNL and Brattle found
that aggressive deployment of building efficiency
and demand flexibility measures alongside rapid
building electrification could offset more than a
third of the incremental grid investments required
to fully decarbonize the power system.42 In their
scenarios, LBNL and Brattle projected building
emission reductions of up to 46-67% below 2005 by
2030 (including reductions from direct combustion
and electricity). Demand reductions play a
foundational role in the scenarios, providing 44-
50% of reductions below 2005 levels in 2030. They
also compared costs of decarbonization scenarios
with efficient building energy use and demand
balancing against scenarios with less efficient
electric resistance heating and water heating. LBNL
and Brattle found that scenarios with demand-
side efficiency and flexibility measures resulted in
$6-25 billion less in annual power system costs in
2030 than the less efficient scenario, and $57-107
billion savings per year by 2050, around 35% of
incremental power system costs to decarbonize.
Efficient electrification will particularly reduce
emissions from inefficient electric and fossil-fueled
HVAC and water heating uses, as well as other uses
including commercial process heat and cooking.
42 Langevin et al., Demand-side solutions in the U.S. building sector could achieve deep emissions reductions and avoid
over $100 billion in power sector costs [108].
85
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Buildings Sector Measures Incented by IRA
(continued)
Heat pumps use half or less
of the electricity needed
for electric resistance
heating, and ENERGY STAR-
certified residential heat
pump models avoid more
than 4,500 pounds of GHG
emissions, on average, over
EFFICIENT their lifespan compared to
ELECTRIFICATION standard HVAC systems
[109,110]. Efficient
electrification results in emissions reductions in
almost all parts of the United States right now,
based on current grid emissions as compared to
conventional fuel emissions, and emissions per
kilowatt-hour will continue to decline in the future
as the grid decarbonizes. Cold weather climates in
the United States are the regions where specialized
cold weather heat pumps or backup heating may be
required to ensure sufficient heating during isolated
cold weather events [111].
To examine the potential impact of heat pumps
with efficient building measures in isolation, EPA
conducted a separate building-specific analysis for
this report using the DOE Scout tool [112]. For EPA,
LBNL and NREL analyzed three technical scenarios
for increased heat pump deployment for heating, air
conditioning, and water heating in the residential
and commercial buildings sectors and increased
building efficiency measures. These were compared
to a reference scenario with Annual Energy Outlook
(AEO) 2022 reference case assumptions.
The three growth scenarios do not explicitly model
IRA policy, but they assumed different levels
of deployment that are feasible under IRA. For
example, in 2030 heat pumps sales were projected
to reach 45% of residential HVAC sales in the Low
Scenario, 50% in the Central Scenario, and 63% in
the High Scenario.43 For reference, in 2022 heat
pumps were 42% of residential HVAC sales. The
High Scenario also assumes some accelerated
replacements of certain building components
that occur before the end of those components'
useful life. The scenarios assume building energy
efficiency improvements at EIA AEO 2022 reference
case (Low Scenario) or moderately above AEO 2022
(Central and High Scenario). See Appendix G.l for
details on all assumptions.
This analysis shows the significant reductions
that heat pumps and efficiency measures alone
can provide in the near term. The selected
improvements in heat pump use and building
efficiency resulted in emissions reductions ranging
from 46-51% of 2005 levels in 2030 and 52-60% of
2005 levels in 2035. The Central Scenario reached
50% reductions from 2005 levels in 2030 building
sector emissions and 57% in 2035. For more details
on the results, see Appendix G.l.
The emissions impact
of building-based
distributed renewablesis
not specifically quantified
in this analysis. However,
distributed renewables
and relevant IRA incentives
are reflected in modeling
assumptions. For example,
IRA modifications to the
25D residential clean energy
tax credit are accounted
for in all the models. The
credit was modified to add battery storage and
extended to 2032 with a phasedown to 2034. Also,
the Greenhouse Gas Reduction Fund is included
in model assumptions. It establishes the $7 billion
Solar for All competition will provide up to 60
grants to states, tribal governments, municipalities,
and nonprofits to build capacity for residential and
community solarinvestment in low-income and
disadvantaged communities. Table 1.3 shows three
RENEWABLE ENERGY
FROM DISTRIBUTED
RENEWABLES
43 Technical experts across NREL, LBNL, and EPA developed the scenarios to reflect feasible technology deployment
levels under IRA, informed by market research. The Scout analysis did not explicitly model IRA policy.
86
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Buildings Sector Measures Incented by IRA
(continued)
of the nine economy-wide models included the
Greenhouse Gas Reduction Fund in some way [113].
In practice, distributed renewables located at
buildings, sometimes paired with energy storage,
can generate low- or zero-carbon electricity
where the poweris consumed and may reduce
building owners' energy costs over time. Building-
based renewables can play a substantial role
in generation—currently, distributed solar
photovoltaics provide a third of all solar generation
[114]. Distributed renewable energy will reduce
demand for electricity from the grid as well as
losses in transmission of electricity. Distributed
renewables' role on the grid must be managed
to ensure grid stability, but well-managed
renewables can provide potential benefits to
the distribution grid, such as the grid stability
provided by renewables integrated into microgrids
[115]. Building owners can site renewables on
their buildings and properties (e.g., parking areas,
available land), or invest in certified green power
to provide additional incentives for renewable
generation [116].44 In the longer-term, renewable
energy generation could be stored in hydrogen
and used as a potential buildings fuel, but there are
multiple technical, policy, and other challenges that
must be addressed [117].
IRA also prioritizes the significant opportunity
for emissions reductions and ratepayer cost
savings from buildings located in low-income and
disadvantaged communities. In 2020, households
with an annual income below $60,000 accounted
for 50% of all household energy consumption [89].
Addressing this part of the buildings sector can
significantly reduce C02 emissions from buildings
and address key needs in energy affordability.
Twenty-seven percent of households experience
energy insecurity and over 25% experience a high
financial energy burden [118,119]. The IRA provides
significant home energy rebates and reduces
cost-share requirements based on income through
programs including the Home Electrification
and Appliance Rebates program, which requires
ENERGY STAR certification, and the Home Efficiency
Rebates program.
Additional IRA programs such as the Greenhouse
Gas Reduction Fund (GGRF) offer additional
substantial opportunities to provide financing that
could accelerate these transitions in low-income
and disadvantaged communities and address
specific barriers for these populations such as
poorer construction quality. As part of the GGRF,
the $6 billion Clean Communities Investment
Accelerator will provide grants to support nonprofit
organizations, enabling them to provide funding
and technical assistance to public, quasi-public,
not-for-profit, and nonprofit community lenders
working in low-income and disadvantaged
communities. Another part of the GGRF, the
$14 billion National Clean Investment Fund, will
provide grants to support national clean financing
institutions so that they can partner with the private
sector to provide accessible, affordable financing
for tens of thousands of clean technology projects
nationwide. Consistent with the Administration's
Justice40 Initiative, at least 40% of the funds
from the National Clean Investment Fund will be
dedicated to low-income and disadvantaged
communities.
Another policy concern is that if higher-income
communities electrify first, low-income and
disadvantaged communities would be left to pay
higher gas bills due to a declining revenue base
for the gas utilities. To address this concern, these
communities should be prioritized for investments
in both energy efficiency and efficient electric
upgrades in buildings in the nearterm. While no
specific results are provided on the impact of IRA
on these communities, many of the programs may
be reflected in assumptions of some of the models.
44 The impacts of green power investments are not analyzed as part of this analysis.
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CHAPTER 5
Industry
5.1 INDUSTRIAL SECTOR SNAPSHOT
More than 280,000 manufacturing facilities are currently operating in the United States [120].
These vary widely in the products they produce, their energy consumption, energy intensity,
size, number of employees, and emissions of greenhouse gases [121].46 In 2021 the U.S.
industrial sector (which accounts for manufacturing, mining, and construction, and including
non-combustion process emissions) emitted over 1,600 Mt of C02, or nearly 32% of U.S.
C02 emissions [4], Addressing industrial emissions requires reducing emissions from direct
combustion and Industrial processes—the predominant emissions of heavy industry—and
addressing the emissions associated with electricity use, which play a much greater role in light
industry emissions.
In the industrial sector, C02 emissions originate primarily from three different sources, and they
vary in potential for significant emission reductions:
¦ Direct emissions from the combustion of fuels on site account for 50% of industrial
CQ2 emissions (15% of total U.S. C02 emissions) [4], Manufacturers burn fuels at their
plants to produce heat, steam, and electricity to run industrial processes and contribute
electric power to the plant. Emission reduction potential comes from energy efficiency
improvements and upgrades to process heaters, boilers and steam systems, process
improvements, and plant operation and management.
45 The federal government defines manufacturing by the NAICS according to codes 31-33. Portions of the industries of
mining and agriculture are separately catalogued in the NAICS system and are not part of manufacturing; however, they
are related to and provide inputs to many of the manufacturing sectors.
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Figure 5.1
C02 emissions from the industrial sector compared to economy-wide
C02 emissions, 2005-2021
6,000
4,000
2,000
2005
2021
Industry: Direct
Industry: Indirect
Industry: Process
~
Other Sectors
Direct industry CO2 emissions have been consistent since 2005, but indirect emissions (from the generation of
electricity used by industry) have fallen. Industry direct combustion and indirect emissions are shown in green
(with black cross-hatching for indirect emissions) and non-energy industrial process emissions are shown in gray.
The remaining economy-wide emissions in other sectors are represented by the outlined bar. The industry sector
represents a marginally smaller portion of U.S. CO2 emissions than buildings or transportation. Accessible table
available in the Data Annex.
89
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¦ Direct process emissions, by-products from industrial processes, account for about 24% of
industrial C02 emissions (7% of total U.S. C02 emissions). These emissions primarily result
from the transformation of raw materials, particularly in the manufacture of cement.
¦ Indirect emissions from electricity purchased from the grid and used at the manufacturing
plant account for around 28% of industrial C02 emissions in the sector (9% of total U.S.
emissions) [4], This electricity supplies core manufacturing process or support equipment
(e.g., motors) and other facility needs (e.g., heating, lighting). As the grid decarbonizes, the
use of grid power could be a significant source of emissions reductions.
The IRA provides incentives for new and retrofitted industrial infrastructure to reduce
emissions across these categories. The long lifetimes of industrial equipment means that
immediate action is needed to take advantage of opportunities when new equipment is being
installed or old equipment is being replaced. Industrial equipment is estimated to have an
average lifetime of 10-30 years or more. For example, the average lifetime of an industrial boiler
is 25 years [122],
The nature of industrial infrastructure also differs across heavy and light industry (see Figure 5.2
for emissions by subsector). In heavy industry, equipment is frequently specialized to the
industrial process and heavy industry frequently requires more intensive heat. This applies
to chemicals, petroleum refining, steel, and cement—sectors with higher emissions and
energy intensities than others—where there are a limited number of plants, but equipment
replacements are infrequent and cost-intensive. By contrast, light industry emissions tend to
resemble buildings sector emissions, with heating, cooling, and electric-powered equipment
being primary energy uses [123]. A list of light industry NAICS codes and corresponding energy
use can be found in Appendix G, Table G.2.1.
C02 is the major greenhouse gas emitted from the industrial sector, though there are
significant emissions of other GHGs, primarily: methane from fossil fuel production; nitrous
oxides from agriculture as well as fertilizer and chemical production; and fluorinated gases
from metals production and other industrial processes. Non-C02 emissions are about 15% of
total industrial GHG emissions.
5.2 KEY IRA PROVISIONS FOR INDUSTRY
The IRA sets forth provisions that affect industry and manufacturing across all its sectors. There
is potential not only to transform the industrial sector's use of fuel and resulting emissions, but
products used by other sectors.
The IRA includes the following policies and incentives relevant to the industrial sector. See
Section 1.2.2 for which of these initiatives are represented in the modeling. That representation
may be limited based on the capabilities of the models and the extent to which the specifics of
the program have been determined.
¦ Expansion of Advanced Energy Project Credit (48C) to include industrial projects that
reduce greenhouse gas emissions by at least 20% at a facility
90
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Figure 5.2
Industry energy-related C02 emissions (direct + indirect) by subsector,
excluding process emissions
100
75
50
25
_
Food Processing: 8%
I
— Light Industry
Other Light Industry: 15%
Other Heavy Industry: 8%
Iron and Steel: 9%
Paper: 13%
— Heavy Industry
Refining: 22%
Chemicals: 23%
2021
Heavy industry sectors comprise the majority of 2021 industry energy-
related emissions (77%, dark blue), with light industry comprising the
remaining portion (23%, light blue). This figure shows the composition of
industrial sector CO2 emissions (including direct CO2 emissions and indirect
CO2 emissions from electricity use) by subsector [124],
91
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¦ Advanced Manufacturing Production Credit (45X)
¦ Clean Hydrogen Production Tax Credit (45V)
¦ Carbon Capture and Sequestration Tax Credit (45Q)
¦ Advanced Industrial Facilities Deployment Program
¦ Vehicle Manufacturing Loans and Grants
¦ Development of Environmental Product Declarations including lifecycle greenhouse gas
emissions
¦ Low-Carbon Materials Labeling for Construction Materials and Funding for Federal
Procurement
¦ Biodiesel, Advanced Biofuels, and Sustainable Aviation Fuel Incentives
¦ Greenhouse Gas Reduction Fund
¦ Climate Pollution Reduction Grants
¦ Methane Emissions Reduction Program
¦ Agriculture and Forestry Provisions
¦ Oil and Gas Leases
Potential industrial sector mitigation measures that could be incentivized by IRA include
energy efficiency, efficient electrification, hyd rogen, carbon capture, and other advanced
manufacturing processes that reduce emissions [125]. Each industrial subsector will take
advantage of a unique combination of these incentives.
Across light and heavy industry, energy efficiency can play a significant role in reducing
direct and indirect emissions. Retrofitting existing plants and building new plants with
efficient equipment can be complemented with benchmarking energy use of facilities for
ongoing improvement. Ongoing benchmarking of energy use can lower energy intensity of
manufacturing plants by 14% across heavy and light industry [125]. Electrification can play a
greater role in light industry as many processes require lower-temperature heat,
IRA programs also encourage lower-emitting fuel use economy-wide in ways that affect the
industry as the producer of fuel and energy technology and as a consumer of many fuels. These
include incentives for renewables, hyd rogen, biofuels, and sustainable aviation fuels. Hydrogen
and biofuels are potential ways to fuel high-temperature industrial processes. Methane
incentives will also help reduce emissions from the industrial sector, particularly in oil and gas
refining. Funding for implementation of the AIM Act will also impact both industrial production
and use of hydrofluorocarbons (HFCs).
92
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Incentives for carbon capture, use, and storage (CCUS) will ultimately accelerate emissions
reductions across a variety of industries and lead to CCUS deployment in the industrial sector,
fuel production, and the power sector. In the industrial sector, CCUS is a potential solution to
mitigate fossil combustion as well as the bulk of process emissions from cement production.
Finally, advanced technology tax credits and funding encourage industry-specific advances,
and development of federal Environmental Product Declarations will provide demand from
government and private entities for less carbon-intensive products.
5.3 INDUSTRY SECTOR ANALYSIS AND RESULTS
Multi-sector modeling produces a wide range of potential emissions from industry. The results
presented are for direct emissions for combustion and indirect emissions from electricity
generation only and exclude non-combustion process emissions. Process emissions are
excluded from the presentation of results because not all models report industrial process
emissions, and those that do cover differing emissions categories. Reductions in direct plus
indirect emissions from 2005 levels in 2030 from the IRA range from 17-43% (median 36%) and
23-57% (median 36%) in 2035, versus 6-33% (median 25%) in 2030 and 3-36% (median 27%) in
2035 without it. Median absolute reductions in 2030 and 2035 are approximately 130 and 190
Mt C02 (Figure 5.3(a)). Individual models find that industry sector C02 emissions are 2 to 21%
(12% median) below what they are modeled to be in the No IRA scenario, and in 2035 emissions
are 9 to 33% below (17% median) (Figure 5.3(b)). The additional emissions reductions with the
IRA are mainly due to changes in indirect emissions. In 2030, reductions in indirect emissions
account for around two-thirds of additional industry emissions reductions over the No IRA
scenario, increasing to around three-quarters by 2035 (see Figure F.2.4). Increases in electricity
use in the industrial sector under the IRA as compared to the No IRA scenario are minimal.
Under the IRA, the median electricity share of final energy increases by .06 percentage points
(pp) in both 2030 and 2035. The maximum increase is 3.3 pp in 2030 and just 1.3 pp in 2035.
Electrification does not increase in all models.
Much of this variation is likely due to differences in how the industrial sector is reflected in
modeling assumptions and how the specific model's dynamics affect the sector.46 Below and
in Appendix G, we provide some sector-specific information that helps to supplement the
economy-wide analysis.
Building on the modeling caveats and limitations provided in Section 1.2.4, specifically
applicable uncertainties for the building sector also apply to the industrial sector. The first
type of uncertainty is due to limitations in the level of detail with which the industrial sector
is modeled in the multi-sector models. This applies to characteristics of the sector as well as
to the policies represented—the IRA industrial sector policies are represented the least of all
45 For example, only three of the ten multi-sector models in this study include the advanced manufacturing production
credit (45X). Models that do not include this credit may see less U.S.-based manufacturing than if it was included, also
potentially resulting in less electricity demand in this sector. See Table 1.3 for a summary of IRA provisions represented
across the multi-sector and power-sector models.
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sectors across the multi-sector modeling (see Table 1.2). The second type of uncertainty is due
to lack of knowledge of how IRA programs will be implemented. For example, it is uncertain
the extent to which relevant IRA tax credits will be used by industry and what technologies IRA
programs will promote.
Seven models project moderate use of CCS in Industry, ranging from approximately 0-240
additional Mt C02/yr captured in 2035 in the IRA scenario compared to the No IRA scenario.
Figure 5.4 shows captured and sequestered C02 emissions in industry in 2030 and 2035, with
and without the IRA. Avoided emissions from industry CCS use are reflected in emissions
projections in Figure 5.3. The model with the largest amount of industrial CCS is USREP-ReEDS,
projecting 240 Mt C02/yr in 2030 in the IRA scenario and 0 in the No IRA scenario. NEMS-RHG
shows 190 Mt C02/yr in 2035 in the IRA scenario and 59 Mt C02/yr in the No IRA scenario, for
a net addition of over 130 Mt C02/yr sequestered, followed by RIO-REPEAT with a net addition
of 120 Mt C02/yr, and EPS-El with a net addition of nearly 100 ML C02/yr in 2035. GCAM-CGS,
NEMS-OP, and MARKAL-NETL deploy less CCS with no greater than 40 Mt C02/yr in 2035..47
® Most models show higher CCS amounts in 2035 than in 2030. The model results for CCS depend on CCS cost relative
to the IRA subsidy (fixed at 085/t). Costs vary by CCS technology assumptions and model structure. USREP-ReEDS, a
CGE model, has increasing capital costs that, in effect, reduce the penetration of CCS between 2030 and 2035.
94
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Figure 5.3
Industrial sector combustion and indirect C02 emissions.
(a)
Data Points and
Median Results
O
O
2,500
2,000
1,500
(0
(0
'E
LU
o 1 ,ooo
o
><
k.
-+-»
W)
D
¦D
_C
500
No IRA — A
A
A
&
A
A
O
.O.
¦A-g-
o
0
2005
2021
2025
2030
2035
2030
2035
In the IRA scenario, industrial sector CO2 (b)
emissions fall to 17 to 43% (36% median) below
2005 levels in 2030. In 2030, individual models
find that industrial CO2 emissions are 2 to 21%
(12% median) below what they are modeled
to be in the No IRA scenario, with the median
difference falling to 17% by 2035. Figure 5.3(a)
shows absolute model results for the emissions
trajectories (No IRA scenario in orange dashed
lines, IRA Scenario in blue) with the historical
trend (in black [4]). Data points to the right of
Figure 5.3(a) show individual model results from
2030 and 2035 (blue circles for IRA scenario
results, orange triangles for No IRA). Horizontal
bars representthe median of the model results. 2021 2025 2030 2035
Figure 5.3(b) shows the percent difference
between the IRA and No IRA for each model (blue lines) and the median across the models (black line)
emissions are broken out into direct and indirect in Appendix F.2.48/19 Accessible table available in the
Industry
Data Annex.
48 Note that industrial process emissions are not included in Figure 5.3. Not all models report industrial process
emissions, and those that do cover differing emissions.
49 NEMS-RHG shows higher total emissions under the IRA in 2025. In the No IRA scenario, the forward-looking model has
slightly higher levels of near-term investment in renewables in 2025 because the model forsees the expiration of tax
credits. Underthe IRA scenario, tax credits are extended and investment does not exhibit a near-term spike. This leads
to a projection of greater indirect emissions from electricity generation underthe IRA in 2025.
95
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Figure 5.4
Industry carbon capture and sequestration
No IRA IRA
Carbon sequestered from the industrial sector grows significantly by 2035 with the IRA. Results are
shown by model with the IRA (right panel) and without (left panel) in 2030 (left bar, light blue) and 2035
(right bar, dark blue). Accessible table available in the Data Annex.
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Hydrogen
Hydrogen offers
unique solutions for
decarbonization in
various sectors because
of its potential to provide
dispatchable, clean
energy with long-term
storage and seasonal
HYDROGEN capabilities. However,
while there are zero direct
C02 emissions from hydrogen combustion, it is
important to acknowledge that different processes
used to produce hydrogen result in different levels
of GHG emissions. In both the 2021 Infrastructure
Investment and Jobs Act (IIJA) and the 2022 IRA,
Congress recognized that different methods of
hydrogen production generate different amounts
of GHG emissions and included extensive policy
support and financial incentives for increased
development of hydrogen produced through
low-GHG-emitting methods. The magnitude of
these incentives is anticipated to accelerate the
production of low-GHG hydrogen for use in a
broad range of applications across many sectors,
including the utility power sector. For example,
Bistline et al. [1] notes that the 45V tax credits for
hydrogen combined with credits for captured C02,
increase the amount of low-emission hydrogen
production. Most of the models in the Bistline et
al. analysis show little change in total hydrogen
production, with only one model showing an
increase of 0.6 quads from 1.4 quads to 2.0 quads,
as a result of the IRA provisions. However, there
is a substantial shift in the processing inputs and
technologies used to produce hydrogen, with the
models showing a move from predominantly relying
on steam methane reforming (SMR), a high-emitting
production technology, to SMR with CCS, other
processes with CCS, and electrolysis, all relatively
low-emissions production technologies (see
supplemental materials and Figure S23 from the
Bistline et al. publication).50
50 Separate reports may provide more granular exploration of the emissions impact of varying hydrogen production
methods and PTC structures (e.g., Ricks et al. [126]).
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Climate change is already harming communities and imposing economic costs around
the world. In 2022, the United States passed the Inflation Reduction Act (IRA), which
provides a broad range of strategies to reduce greenhouse gas (GHG) emissions across
multiple sectors of the economy while simultaneously promoting domestic manufacturing and
well-paying jobs. These measures include incentives for clean energy and carbon management,
support for accelerating efficient electrification and energy efficiency, policies for reducing
methane emissions, and many other provisions affecting electricity generation, transportation,
buildings, and industry.
Conclusions
CHAPTER 6
To assess the effects of these measures on GHG emissions (with a focus on combustion
emissions) multiple modeling tools are used in this report. The models vary, ranging in scope
(e.g., full energy system vs. power sector only) and resolution (e.g., level of technological and
sectoral detail). As the report details, these models have different strengths and weaknesses,
but they contribute important analytical perspectives to the results.
This analysis presents modeling results through 20-35 from two scenarios, a No IRA scenario
that reflects current, finalized federal and state policies enacted except for the IRA, and an IRA
scenario, that reflects the current federal and state policies enacted in addition to modeled
provisions of the IRA. Modeled results show that the IRA results in C02 emissions reductions
not only economy-wide, but also specifically from the electricity generation, transportation,
buildings, and industrial sectors (see Figure 1.2).
For comparison, in the No IRA scenario economy-wide C02 emissions decline from 6,130 Mt
CC>2/yr in 2005 to a median projection of 4,100 Mt C02/yr in 2035, representing a reduction of
33%. The IRA scenario shows a substantially larger reduction, to a median of 3,300 Mt C02/yr in
2035, which represents a 46% decline from 2005 levels. (The projected range for 2035 in the
IRA scenario is 2,800-3,900 Mt C02/yr). In 2030, the IRA scenario shows median C02 emissions
11% lower than the No IRA scenario, and 19% lower by 2035.
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These economy-wide results are presented with several caveats. First, current modeling does
not include the impact of proposed (and potential) federal, state, and private policies that
are not yet final. Second, the final details of certain IRA provisions such as the structure of
tax credits, are still under consideration by the Treasury Department. Third, several important
provisions of the IRA are not amenable to modeling using currently available tools, and so
are not included in this analysis. Additionally, there is currently limited data on advanced
technologies that are encouraged by the IRA, such as carbon capture and storage (CCS)
technologies. All of these caveats increase the uncertainty of model results. Despite the
caveats, the modeling shows that the IRA reduces costs and increases acceptance of clean
technology, and the legislation is expected to make future climate measures more likely.
Summarizing the results for individual sectors:
Electricity:
¦ C02 emissions from electricity generation are projected to decline in the IRA scenario from
2005 levels by 67% to 87% in 2035 with a median reduction of 77%. This is significantly more
than the decline in the No IRA scenario between 2005 and 2035 of 40% to 68%. In terms
of emissions quantities, electric sector emissions in 2005 were 2,400 Mt C02/yr—the IRA
scenario shows a decline by 2035 to a range of 320 to 780 Mt C02/yr, compared to the No
IRA scenario decline by 2035 to a range of 780 to 1,400 Mt C02/yr.
Transportation:
¦ Total direct (from combustion of fossil fuels) and indirect (from combustion of fuels to
generate electricity consumed in the transportation sector) C02 emissions are projected
to decline in the IRA scenario from 2005 levels by 15% to 35% in 2035 with a median
reduction of 27%. This is more than the decline in the No IRA scenario between 2005 and
2035 of 13% to 28%. In terms of emissions quantities, transportation sector emissions in
2005 were 1,863 Mt C02/yr--the IRA scenario shows a decline by 2035 to a range of 1,200
to 1,600 Mt C02/yr compared to the No IRA scenario decline by 2035 to a range of 1,300 to
1,600 Mt C02/yr.
Buildings:
¦ Total direct and indirect buildings C02 emissions decline in the IRA scenario from 2005
levels by 52% to 70% in 2035 with a median reduction of 66%. This is more than the decline
in the No IRA scenario between 2005 and 2035 of 36% to 51%. In terms of emissions
quantities, buildings sector emissions in 2005 were 2,245 Mt C02/yr—the IRA scenario
shows a decline in 2035 to a range of 670 to 1,100 Mt C02/yr compared to the No IRA
scenario decline by 2035 to a range of 1,100 to 1,400 Mt C02/yr.
99
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Industry;
¦ Total direct and indirect industry C02 emissions decline in the IRA scenario from 2005
levels by 23% to 56% in 2035 with a median reduction of 36%. This is significantly more
than the decline in the No IRA scenario between 2005 and 2035 of 3% to 36%. In terms
of emissions quantities, industry sector emissions in 2005 were 1,587 Mt C02/yr—the IRA
scenario shows a decline in 2035 to a range of 690 to 1,200 Mt C02/yr compared to the No
IRA scenario decline by 2035 to a range of 1,000 to 1,500 Mt COz/yr.
In the electric sector, C02 emissions reductions from 2021 to 2035 (in both the No IRA and IRA
scenarios) are primarily driven by the shift away from high-emitting generation sources-coal
and natural-fired gas generation without CCS—to low- or zero-emitting generation sources
(wind, solar, etc.). This shift is consistent across all models (see Figure 2.4). The additional
reductions in the IRA scenario result from tax credits like the Clean Electricity Investment and
Production Tax Credits (48E, 45Y) and the Nuclear Power Production Tax Credit (45U). Across
all but one model, solar and wind sec the largest increases in generation.
In the transportation sector, the reduction in C02 emissions from the IRA is driven primarily
by the increase in electric vehicles' (EV) share of new sales (see Figure 3.4), which is projected
to rise from 4% in 2021 to between 15% and 54% in 2030, with a median market share of 36%
in the IRA scenario. This level of EV sales represents an increase from the No IRA scenario,
which shows a median market share of 38% in 2035. This increase in EV sales share leads to an
increase in demand for electricity and results in a decrease in fossil fuel consumption in the
transportation sector (see Figure 3.5). As with EV sales share, demand for electricity increases
in the No IRA scenario, but increases even more in the IRA scenario. These changes are due
to transportation-specific provisions in the IRA, such as the clean vehicle credit (13401), the
commercial clean vehicle credit (13403), and the credit for previously owned clean vehicles
(13402), among others, which incentivizes demand for EVs.
For the buildings sector, most emissions are associated with the generation of electricity
consumed in buildings, but approximately a third of emissions are from direct fossil fuel
combustion, primarily for space and water heating (see Figure 4.2). The modeling shows that
the IRA results in buildings sector reductions through the decarbonization of the electricity
consumed by the buildings sector, and also through incentives for energy efficiency and
electrification—reducing total energy required for buildings and fossil fuel use. IRA provisions
for improvements for existing homes (25C, 25D, and home rebate programs), new homes (45L),
and commercial buildings (179D) along with the Greenhouse Gas Reduction Fund and Climate
Pollution Reduction Grants all contribute to reduced sector emissions.
The industrial sector has three sources of C02 emissions: direct emissions from on-site
combustion, indirect emissions from electricity used in the sector, and direct process
emissions (not included in the industry results). The IRA scenario results show a substantial
decrease in direct and indirect industry CO- emissions, and, consistent with results from
the transportation and buildings sector results, reductions are primarily associated with
the indirect emissions from the generation of electricity consumed by the industrial sector
100
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(see Figure 5.3). The results reflect IRA provisions that provide incentives for industrial
energy efficiency, low- or zero-emitting electricity generation, and advanced manufacturing
processes. Some of these include the extension of the advanced energy project credit (48C),
the advanced manufacturing production credit (45X), and low-carbon materials funding.
In addition to the IRA and No IRA scenarios discussed above, this report includes cases
that explore alternative assumptions for the following factors for a subset of models: IRA
implementation, technology costs, fossil energy prices, and economic growth. The above
results are for the IRA Moderate implementation scenarios; that is, results reflecting the
central set of unharmonized assumptions reported by each model. Several models explored
Optimistic and Pessimistic IRA implementation scenarios that vary the representation of IRA
provisions (e.g., tax credit transferability penalties, domestic content bonus eligibility, and the
uptake of demand side programs) as well as limitations on the build rates of renewables and
CCS availability in the Pessimistic implementation scenario. Electricity sector C02 emissions
in 2030 fall an additional 2.5 percentage points below 2005 levels in the Optimistic scenario
and fall 3.3 fewer percentage points in the Pessimistic scenario relative to the IRA Moderate
implementation scenario. The sensitivity scenarios analyzing only technology costs and
deployment find larger impacts—relative to the moderate technology assumptions, power
sector C02 emissions in 2030 fall an additional 7.2 percentage points below 2005 levels in the
advanced technology scenario with low technology costs and fall 8.8 fewer percentage points
below 2005 levels in the scenario with technology deployment constraints. Other sensitivities
explored in this analysis include high and low energy prices and economic growth. High and
low energy price scenarios can respectively decrease or increase power sector C02 emissions
by amounts similar in magnitude to the IRA implementation sensitivity scenarios. The effects
of sensitivity scenarios for economic growth on power sector C02 emissions are an order
of magnitude smaller than the effects of the energy price sensitivities. With the caveat that
fewer models are represented in sensitivity scenarios, these sensitivities show that minimizing
deployment constraints and achieving low technology costs are key to greater power sector
C02 emissions reductions.
Potential future analyses of the IRA can better address factors that are uncertain at this time-
such as guidance on tax credit provisions that have yet to be finalized (e.g., clean hydrogen and
advanced manufacturing production tax credits), the evolution of complementary federal, state
and local policies, and the rate of technological improvement. New information, and improved
economic tools such as more detailed sectoral models, can contribute to better understanding
of the IRA impacts on the energy sector. Better harmonization across model inputs and
assumptions along with more sensitivity testing can also reduce uncertainties and enhance our
understanding of key drivers of emissions reductions. The provisions of the IRA are expected
to make additional federal, state, and other climate policies and measures more cost effective.
The exploration of results at regional and state levels can help inform such actions. Better
understanding of the more detailed impacts of the IRA is likely going forward.
101
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