ASSESSMENT OF PROJECTED C02
EMISSION REDUCTIONS FROM CHANGES
IN ELECTRICITY GENERATION AND USE
APPENDIX
EPA 430-R-23-004
September 2023
oB>A
ELECTRICITY SECTOR
EMISSIONS IMPACTS
OF THE INFLATION
REDUCTION ACT
-------
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).
-------
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-
Appendix. U.S. Environmental Protection Agency, EPA 430-R-23-004.
CONTACT US
emissions-impacts-inflation-reduction-act-report@epa.gov
DATA AVAILABILITY
Data from the analyses in this report can Pe accessed on the following wePsite: https://www.
epa.gov/inflation-red uction-act/electric-sector-emissions-im pacts-inflation-reduction-act
3
-------
Appendices
Appendix A: Summary Table of Annual Emissions and Reductions
Appendix B: Supplementary Model Description
Appendix C: IRA Implementation and Sensitivity Assumptions
Appendix D: Input Assumptions
Appendix E: Results by Model
Appendix F: Supplemental Results
Appendix G: Supplemental EPA Analyses
Appendix H: Peer Review Process
-------
Appendix A: Summary Table of Annual Emissions and
Reductions
Table A.l.l Annual economy-wide C02 emissions (Mt C02/yr).
TableA.1.2 Difference between No IRA and IRA economy-wide CO2 emissions.
Absolute Difference (Mt C02/yr)
% Difference
Year
Min
Median
Max
Min
Median
Max
2024
-75.5
47.9
401.3
-1.6
1.0
8.4
2025
-100.7
67.2
535.0
-2.2
1.4
11.4
2026
26.7
152.7
624.1
0.6
3.4
13.4
2027
88.6
245.2
713.2
2.1
5.4
15.5
2028
123.2
327.3
802.3
2.9
7.3
17.5
2029
158.0
405.2
891.4
3.8
9.4
19.6
2030
192.6
478.6
980.4
4.6
11.4
21.7
2031
302.7
532.8
1,003.1
7.3
12.5
22.4
2032
394.3
550.6
1,025.6
9.1
13.1
23.0
2033
396.3
584.1
1,048.2
9.1
13.9
23.7
2034
411.6
664.4
1,070.7
9.5
16.0
24.4
2035
413.8
750.7
1,093.2
9.6
18.5
25.5
A-l
-------
Table A.1.3 Difference in economy-wide C02 emissions from 2005 and 2021 for
the No IRA scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
20.6
23.5
27.1
3.3
6.8
11.1
2025
21.5
25.2
30.1
4.4
8.9
14.9
2026
22.7
26.1
30.5
5.8
10.0
15.3
2027
23.8
27.2
31.1
7.1
11.2
16.0
2028
24.9
28.4
31.8
8.5
12.7
16.9
2029
25.8
29.8
32.5
9.6
14.4
17.8
2030
26.4
30.7
33.2
10.3
15.6
18.7
2031
26.9
31.4
34.2
10.9
16.4
19.9
2032
27.4
31.9
35.2
11.5
17.0
21.1
2033
27.9
32.2
36.6
12.1
17.4
22.7
2034
28.4
32.6
38.0
12.8
17.9
24.5
2035
28.9
33.2
39.5
13.4
18.6
26.3
Table A.1.4 Difference in economy-wide CO2 emissions from 2005 and 2021 for
the IRA scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
21.1
24.0
29.0
3.9
7.4
13.5
2025
22.2
26.0
32.7
5.2
9.8
18.0
2026
25.7
28.4
34.8
9.5
12.8
20.5
2027
29.2
31.0
36.8
13.8
15.9
23.0
2028
31.5
33.3
38.9
16.5
18.8
25.5
2029
33.2
36.3
41.0
18.6
22.3
28.1
2030
34.6
39.5
43.0
20.4
26.2
30.6
2031
35.2
40.7
43.9
21.1
27.6
31.6
2032
35.4
41.6
45.8
21.3
28.8
34.0
2033
35.7
43.1
48.9
21.6
30.8
37.7
2034
36.1
45.0
51.9
22.1
32.9
41.4
2035
36.3
46.4
54.9
22.4
34.7
45.1
A-2
-------
Table A.2.1 Annual electric sector C02 emissions (Mt C02/yr).
No IRA
IRA
Year
Min
Median
Max
Min
Median
Max
2024
1,203
1,388
1,463
1,136
1,284
1,481
2025
1,091
1,332
1,438
1,001
1,192
1,460
2026
1,068
1,305
1,409
937
1,101
1,374
2027
1,046
1,260
1,383
857
1,027
1,339
2028
1,024
1,238
1,379
709
938
1,304
2029
1,002
1,217
1,379
561
857
1,268
2030
980
1,189
1,380
414
755
1,233
2031
940
1,173
1,390
396
708
1,136
2032
899
1,161
1,400
378
676
1,039
2033
857
1,156
1,410
359
623
943
2034
816
1,146
1,420
341
584
846
2035
775
1,133
1,430
323
544
784
Table A.2.2 Difference between No IRA and IRA electric sector CO2 emissions.
Absolute Difference (Mt C02/yr)
% Difference
Year
Min
Median
Max
Min
Median
Max
2024
-78.2
55.3
243.0
-6.5
4.2
17.6
2025
-104.2
93.1
324.1
-9.6
7.3
24.5
2026
-4.2
143.8
378.3
-0.4
11.0
28.8
2027
39.4
215.8
455.1
2.9
16.5
34.7
2028
75.3
274.6
578.3
5.5
24.0
44.9
2029
102.8
360.6
701.5
8.1
30.2
55.5
2030
125.4
405.8
824.7
10.6
34.1
66.6
2031
219.8
406.6
828.1
18.3
34.2
67.7
2032
272.1
421.5
831.5
25.3
34.8
68.8
2033
283.8
441.5
835.0
26.3
40.0
69.9
2034
295.6
514.4
838.4
27.4
45.6
71.1
2035
307.4
556.8
862.0
28.5
51.8
72.2
A-3
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Table A.2.3 Difference in electric sector C02 emissions from 2005 and 2021 for the
No IRA scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
39.0
42.2
49.9
5.0
9.9
21.9
2025
40.1
44.5
54.6
6.7
13.6
29.2
2026
41.3
45.6
55.5
8.6
15.2
30.7
2027
42.4
47.5
56.4
10.3
18.2
32.1
2028
42.5
48.4
57.3
10.5
19.6
33.5
2029
42.5
49.3
58.3
10.5
21.0
35.0
2030
42.5
50.5
59.2
10.5
22.8
36.4
2031
42.1
51.1
60.8
9.8
23.9
39.0
2032
41.7
51.6
62.6
9.2
24.6
41.7
2033
41.3
51.9
64.3
8.5
25.0
44.4
2034
40.8
52.2
66.0
7.8
25.6
47.0
2035
40.4
52.8
67.7
7.2
26.5
49.7
Table A.2.4 Difference in electric sector CO2 emissions from 2005 and 2021 for the
IRA scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
38.3
46.5
52.7
3.9
16.6
26.3
2025
39.1
50.3
58.3
5.2
22.6
35.0
2026
42.7
54.1
61.0
10.8
28.5
39.2
2027
44.2
57.2
64.3
13.1
33.4
44.4
2028
45.7
60.9
70.4
15.4
39.1
54.0
2029
47.2
64.3
76.6
17.7
44.4
63.6
2030
48.6
68.6
82.8
20.0
51.0
73.2
2031
52.7
70.5
83.5
26.3
54.0
74.3
2032
56.7
71.8
84.3
32.5
56.2
75.5
2033
60.7
74.0
85.0
38.8
59.5
76.7
2034
64.7
75.7
85.8
45.1
62.2
77.8
2035
67.3
77.3
86.5
49.1
64.7
79.0
A-4
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Table A.3.1 Annual transportation C02 emissions (Mt C02/yr).
Table A.3.2 Difference between No IRA and IRA transportation CO2 emissions.
Absolute Difference (Mt C02/yr)
% Difference
Year
Min
Median
Max
Min
Median
Max
2024
-6.9
3.6
39.7
-0.4
0.2
2.4
2025
-9.2
3.2
53.0
-0.5
0.2
3.3
2026
-2.9
9.8
64.0
-0.2
0.6
4.0
2027
3.2
16.8
74.9
0.2
0.9
4.8
2028
7.2
21.5
85.8
0.5
1.2
5.5
2029
11.2
28.4
96.7
0.7
1.8
6.3
2030
15.0
35.5
107.8
0.9
2.4
7.2
2031
15.2
48.9
111.6
0.9
3.3
6.9
2032
15.0
62.5
144.0
0.9
4.2
9.1
2033
16.5
71.6
176.4
1.0
4.8
11.5
2034
19.4
73.1
208.8
1.2
5.0
14.0
2035
22.6
84.0
241.2
1.4
5.9
16.7
A-5
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Table A.3.3 Difference in transportation C02 emissions from 2005 and 2021 for the
No IRA scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
2.4
7.5
12.1
-3.5
2.0
6.8
2025
1.3
8.1
14.3
-4.6
2.6
9.1
2026
3.2
9.4
15.7
-2.6
4.0
10.7
2027
5.1
10.7
17.2
-0.6
5.3
12.2
2028
6.5
12.1
18.6
0.8
6.8
13.7
2029
7.6
13.7
20.1
2.1
8.5
15.3
2030
8.7
15.3
21.5
3.2
10.2
16.8
2031
9.7
17.0
22.9
4.3
11.9
18.2
2032
10.7
18.6
24.2
5.3
13.7
19.6
2033
11.7
19.8
25.5
6.3
14.9
21.0
2034
12.9
21.2
26.8
7.6
16.4
22.4
2035
13.3
22.8
28.1
8.1
18.1
23.8
Table A.3.4 Difference in transportation CO2 emissions from 2005 and 2021 for the
IRA scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
2.8
7.5
13.5
-3.1
2.0
8.3
2025
1.8
8.2
16.2
-4.1
2.6
11.1
2026
4.4
9.8
17.9
-1.3
4.4
13.0
2027
6.3
11.6
19.7
0.6
6.2
14.9
2028
7.9
13.5
21.5
2.4
8.3
16.8
2029
9.9
15.5
23.3
4.5
10.4
18.6
2030
11.1
17.4
25.0
5.7
12.4
20.5
2031
12.0
19.7
26.0
6.7
14.8
21.5
2032
12.8
22.2
27.9
7.5
17.5
23.6
2033
13.4
23.6
30.0
8.2
19.0
25.7
2034
14.0
25.0
32.0
8.8
20.5
27.9
2035
14.5
26.6
35.3
9.4
22.2
31.4
A-6
-------
Table A.4.1 Annual buildings C02 emissions (Mt C02/yr).
Table A.4.2 Difference between No IRA and IRA buildings CO2 emissions.
Absolute Difference (Mt C02/yr)
% Difference
Year
Min
Median
Max
Min
Median
Max
2024
-53.3
19.9
231.9
-3.7
1.4
14.7
2025
-71.1
42.2
309.3
-5.2
3.0
19.8
2026
16.1
90.8
358.3
1.2
6.2
23.2
2027
59.9
151.6
407.3
4.6
10.6
26.7
2028
76.2
217.6
456.4
6.1
15.4
30.2
2029
92.5
270.8
505.4
7.6
20.1
33.8
2030
108.8
303.6
554.4
9.2
23.0
37.5
2031
160.4
313.6
578.7
13.8
24.0
39.4
2032
212.0
316.6
603.1
18.4
24.7
41.3
2033
263.6
349.9
627.4
19.6
28.1
43.3
2034
267.8
370.7
651.7
20.4
30.7
45.3
2035
243.4
389.4
676.1
20.1
33.3
47.3
A-7
-------
Table A.4.3 Difference in buildings C02 emissions from 2005 and 2021 for the No
IRA scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
25.3
32.4
36.7
-2.5
7.2
13.2
2025
24.6
34.6
39.9
-3.3
10.4
17.6
2026
28.0
36.5
40.9
1.2
12.9
18.9
2027
31.3
38.0
42.5
5.8
14.9
21.1
2028
32.7
39.1
44.1
7.7
16.5
23.4
2029
33.4
40.2
45.8
8.7
18.0
25.6
2030
34.1
41.8
47.4
9.6
20.1
27.9
2031
34.6
42.2
48.1
10.2
20.8
28.8
2032
35.0
42.3
48.8
10.9
20.8
29.7
2033
35.5
42.9
49.4
11.5
21.7
30.7
2034
35.9
43.9
50.1
12.1
23.0
31.6
2035
36.3
44.8
50.8
12.7
24.4
32.5
Table A.4.4 Difference in buildings CO2 emissions from 2005 and 2021 for the IRA
scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
25.1
36.3
40.3
-2.7
12.6
18.2
2025
24.5
39.3
44.7
-3.6
16.8
24.2
2026
29.8
42.5
48.4
3.8
21.2
29.3
2027
35.2
45.1
52.1
11.2
24.6
34.3
2028
40.6
49.0
55.8
18.5
30.0
39.4
2029
46.0
52.2
59.5
25.9
34.5
44.4
2030
49.2
54.7
63.2
30.3
37.8
49.5
2031
50.5
56.1
64.6
32.1
39.8
51.5
2032
50.7
58.0
66.0
32.4
42.5
53.4
2033
51.0
60.5
67.5
32.8
45.8
55.4
2034
51.6
62.8
68.9
33.7
49.0
57.3
2035
52.0
65.7
70.3
34.2
53.0
59.3
A-8
-------
Table A.5.1 Annual industry C02 emissions (Mt C02/yr).
Table A.5.2 Difference between No IRA and IRA industry CO2 emissions.
Absolute Difference (Mt C02/yr)
% Difference
Year
Min
Median
Max
Min
Median
Max
2024
-21.3
18.3
127.3
-1.9
1.5
9.0
2025
-28.4
30.4
169.7
-2.6
2.4
11.5
2026
2.9
46.2
198.3
0.2
3.9
13.4
2027
7.6
62.3
227.0
0.6
5.6
15.3
2028
12.2
89.6
255.5
0.9
7.5
17.2
2029
16.8
113.8
284.1
1.3
9.6
19.0
2030
21.5
134.0
312.8
1.6
11.6
20.9
2031
46.3
146.2
321.9
3.4
12.6
21.4
2032
71.2
157.0
331.1
5.3
13.1
21.9
2033
96.0
176.6
340.3
7.1
13.9
25.3
2034
96.2
184.8
349.4
8.9
15.6
28.9
2035
95.8
191.4
358.6
9.2
16.9
32.6
A-9
-------
Table A.5.3 Difference in industry C02 emissions from 2005 and 2021 for the No
IRA scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
11.3
22.3
29.1
-15.3
-1.0
7.8
2025
7.4
22.0
31.1
-20.4
-1.4
10.4
2026
7.0
22.5
31.4
-20.9
-0.7
10.8
2027
6.7
23.1
31.7
-21.3
0.1
11.2
2028
6.3
23.7
32.0
-21.8
0.8
11.5
2029
6.0
24.2
32.2
-22.3
1.5
11.9
2030
5.6
24.8
32.6
-22.7
2.2
12.4
2031
5.2
25.2
33.2
-23.3
2.8
13.2
2032
4.7
25.6
33.9
-23.9
3.2
14.0
2033
4.3
26.0
34.5
-24.5
3.8
14.8
2034
3.8
26.4
35.1
-25.0
4.2
15.6
2035
3.4
26.8
35.7
-25.6
4.8
16.4
Table A.5.4 Difference in industry CO2 emissions from 2005 and 2021 for the IRA
scenario (% reduction).
2005
2021
Year
Min
Median
Max
Min
Median
Max
2024
15.1
26.5
30.8
-10.4
4.3
10.1
2025
12.4
27.5
33.4
-13.8
5.8
13.4
2026
13.8
29.6
34.4
-12.0
8.5
14.7
2027
15.2
31.6
35.4
-10.2
11.2
16.0
2028
16.6
33.5
36.7
-8.4
13.4
17.7
2029
17.1
34.9
39.7
-7.7
15.4
21.5
2030
17.2
36.1
42.6
-7.7
17.0
25.4
2031
18.3
36.2
45.4
-6.2
17.0
29.0
2032
19.5
36.0
48.2
-4.7
16.8
32.7
2033
20.7
35.9
51.0
-3.2
16.6
36.3
2034
21.8
35.9
53.8
-1.6
16.6
40.0
2035
23.0
35.8
56.6
-0.1
16.5
43.6
A-10
-------
Appendix B: Supplementary Model Description
Table B.l Model representations of emerging technologies.
The following abbreviations are used in the table below: CCS, carbon capture and storage; H2, hydrogen; T&S, transport and
storage; O&M, operations and maintenance. Electric sector models are designated with * (others are energy system models).
Analysis
Abbreviation
CCS
Technologies
CO2 Transport and
Storage
H2
Production
H2 Transport and
Storage
Carbon Dioxide
Removal
Energy Storage
Technologies
EPS-El
ฆ Power: Fossil fuel
(not included in
IRA analysis)
ฆ Industrial: Fossil
fuel use and
processes
ฆ Direct air capture
(not included in
IRA analysis)
Not explicitly
modeled, but costs
are included in CCS
costs.
H2can be produced
via five different
production
pathways, including
steam methane
reforming and
electrolysis
None
DAC: One
representative
technology
powered by
electricity
Battery storage,
existing pumped
hydro
GCAM-CGS
ฆ -Power: CCS for
new coal, NGCC,
and biomass with
different capture
assumptions
ฆ -Industrial
processes
ฆ -Liquid fuel
production
CO2T&S on a
regional basis with
costs for
investments in
pipeline and
injection capacity,
as well as ongoing
O&M costs.
H2can be produced
with electrolysis.
Exogenously
specified H2
transport costs.
BECCS: Power
generation or liquid
fuel production
Battery storage,
concentrated solar
power
B-l
-------
Analysis
Abbreviation
CCS
Technologies
CO2 Transport and
Storage
H2
Production
H2 Transport and
Storage
Carbon Dioxide
Removal
Energy Storage
Technologies
GCAM-PNNL
ฆ Power: CCS for
new coal, NGCC,
and biomass with
different capture
assumptions
ฆ Industrial
processes
ฆ Liquid fuel
production
CO2T&S on a
regional basis with
costs for
investments in
pipeline and
injection capacity,
as well as ongoing
O&M costs.
H2can be produced
with fossil
resources, biomass,
or electrolysis.
Fossil and biomass
H2technologies can
be used with CCS.
Endogenous
representation of
H2 transport and
storage with new
dedicated
infrastructure.
ฆ DAC: Three
representative
technologies
(high-
temperature with
heat provided by
natural gas or
electricity and
low-temperature
with electricity)
ฆ BECCS: Power
generation, liquid
fuel production,
or hydrogen
production
Battery storage,
concentrated solar
power
MARKAL-
NETL
ฆ -Power: CCS for
new coal, NGCC,
and biomass;
retrofits for coal
and NGCC
ฆ -Industrial
processes
ฆ -Hydrogen
production
ฆ -Direct air
capture
Fixed cost of CO2
transport, injection,
and long-term
monitoring. CO2
storage reservoir
capacity varies by
region
H2can be produced
with fossil
resources, biomass,
or electrolysis.
Fossil and biomass
Ha technologies can
be used with CCS.
Local, midsize, and
central production
options.
Transport costs
from central H2 vary
by settlement type.
Liquid H2 can be
imported by truck
or pipeline.
Distributed
production
technologies
combine
production and
refueling
capabilities.
DAC: High
temperature with
heat from natural
gas
Battery storage, H2
storage, existing
pumped hydro
NEMS-EIA
ฆ Power: CCS for
new and retrofit
coal, NGCC,
petroleum
ฆ Industry: CCS for
EOR
Transport and
storage costs
differentiated by
region
None
None
None
Diurnal (battery)
storage, pumped
hydro
B-2
-------
Analysis
Abbreviation
CCS
Technologies
CO2 Transport and
Storage
H2
Production
H2 Transport and
Storage
Carbon Dioxide
Removal
Energy Storage
Technologies
NEMS-OP
ฆ Power: CCS for
new and retrofit
coal, NGCC,
biomass
ฆ Industry: cement,
iron and steel,
refining (e.g.,
hydrogen,
ethanol)
ฆ Direct Air
Capture
Transport and
storage costs
differentiated by
region
Hydrogen
produced via fossil
resources, biomass,
or electrolysis
Available but not
used for this
analysis
Direct air capture
using electricity or
natural gas
Diurnal (battery)
storage, pumped
hydro
NEMS-RHG
ฆ Power: CCS for
new coal and
NGCC (Allam
cycle); retrofits
for coal and
NGCC
ฆ Industrial
processes
ฆ Hydrogen
production
ฆ Direct air capture
Regional CO2 T&S
costs
H2can be produced
with fossil
resources or
electrolysis. Fossil
can be retrofitted
with CCS.
Representation of
existing
infrastructure.
DAC: Median cost
estimate among
DAC technology
pathways
Battery storage,
concentrated solar
power, existing
pumped hydro
REGEN-EPRI
ฆ Power: CCS for
new coal, NGCC,
and biomass with
different capture
assumptions;
retrofits for
existing coal and
NGCC
ฆ Industrial
processes
ฆ Hydrogen
production
ฆ -Direct air
capture
Regional CO2 T&S
with costs for
investments in
pipeline and
injection capacity,
as well as O&M
costs. Investments
in inter-regional
CO2 pipeline
capacity can be
made to access
capacity in
neighboring
regions.
H2can be produced
with fossil
resources, biomass,
or electrolysis.
Fossil and biomass
^technologies can
be used with CCS.
Endogenous
representation of
H2 transport and
storage with new
dedicated
infrastructure or
blending gas
commodities
through existing
natural gas
infrastructure.
ฆ DAC: Four
representative
technologies
(high-
temperature with
heat provided by
natural gas or
electricity and
low-temperature
with gas and/or
electricity)
ฆ BECCS: Power
generation or
hydrogen
production
Battery storage
(endogenous
duration),
concentrated solar
power, compressed
air, H2 storage,
existing pumped
hydro
B-3
-------
Analysis
Abbreviation
CCS
Technologies
CO2 Transport and
Storage
H2
Production
H2 Transport and
Storage
Carbon Dioxide
Removal
Energy Storage
Technologies
RIO-REPEAT
ฆ Power: CCS for
new NGCC and
new biomass with
different capture
assumptions;
retrofits for
existing coal and
NGCC;
repowering
existing gas and
coal to NGCC
with CCS
ฆ -Industrial
processes
ฆ -Hydrogen
production
Inter-zonal CO2 T&S
through the
expansion of a CO2
transport network,
including pipeline
capital and O&M
costs, injection
costs, and spur line
costs to connect
into the trunkline
system.
H2can be produced
from natural gas
(steam methane
reformation with or
without CCS,
autothermal
reformation with
CCS), biomass with
CCS or electrolysis.
Endogenous
representation of
H2 transport with
dedicated
infrastructure or
limited blending in
existing natural gas
infrastructure.
Endogenous
hydrogen storage
technologies.
ฆ Direct air capture
ฆ BECCS: Power
generation, H2
production, or H2
production with
renewable fuel
production.
Battery storage
(endogenous
duration), thermal
energy storage, H2
storage, existing
pumped hydro
USREP-
ReEDS
Energy-intensive
industries, oil and
gas, electricity. For
ReEDS, the
Standard Scenario
2022 version was
used (plant-
specific upgrades
are not available in
this version).
Explicitly modeled
for electricity (see
ReEDS); Not
explicitly modeled
for other sectors.
None
None
DAC: One
representative
technology
powered by
electricity
ReEDS pump-
storage hydro,
batteries, and
compressed air
energy storage
Haiku-RFF*
ฆ Power: CCS for
new coal and
NGCC
EPA CO2 T&S costs
(step function for
each state). Total
CO2 storage and
utilization options
is scaled to 100
million short tons in
2030, doubling
every five years
thereafter.
None
None
None
Battery storage (4-
hr duration),
existing pumped
hydro
B-4
-------
Analysis
Abbreviation
CCS
Technologies
CO2 Transport and
Storage
H2
Production
H2 Transport and
Storage
Carbon Dioxide
Removal
Energy Storage
Technologies
IPM-EPA*
ฆ Power: CCS for
new and existing
coal, and new and
existing NGCC
with different
capture
assumption
ฆ Power: Detailed
modeling of
different carbon
sinks and the
costs of building
CO2 pipelines
from source to
sink.
Not captured.
Hydrogen is
modeled
exogenously and
assumed to be
available at $l/kg
delivered price to
the power sector.
Not captured.
Hydrogen is
modeled
exogenously and
assumed to be
available at $l/kg
delivered price to
the power sector.
None
Battery storage of
varying duration,
pumped hydro.
IPM-NRDC*
ฆ Power: CCS
retrofits (90% and
99% capture) for
coal and NGCC,
CCS for new
NGCC
Assumptions for
CO2 storage
capacity/cost from
based on GeoCAT
(2021) in 37 of 48
states. CO2
transport based on
$228k/in-mi for
pipelines.
None
None
None
Battery storage
(4/8/10-hr
duration), paired 4-
hr battery with
solar, existing
pumped hydro and
other storage
ReEDS-
NREL*
ฆ Power: CCS for
new and retrofits
for coal and
NGCC
ฆ Newbiomass
with CCS, DAC,
and H2
production
modeled but not
considered in this
analysis
Spatially explicit
cost, investment,
and operation for
CO2 T&S, including
capital and O&M of
pipeline, injection,
and storage.
Pipelines can be
built between any
ReEDS regions, as
well as between a
region and a
storage reservoir.
Available in ReEDS
but not considered
in this analysis.
Available in ReEDS
but not considered
in this analysis.
Available in ReEDS
but not considered
in this analysis.
Battery storage,
pumped hydro
storage (existing
and new/uprates),
compressed air,
concentrated solar
power
B-5
-------
Table B.2 Policy representation in No IRA scenario and calibration assumptions.
Analysis
Abbreviation
Federal
Policies
State/Local
Policies
Other Calibration
Assumptions
EPS-El
Policies and regulations through
August 2022, which includes key
components of IIJA that have
quantifiable emissions reductions
State-level renewable portfolio
standards are aggregated and
represented.
Calibrated to the 2022 AEO High Oil
and Gas Supply scenario. GDP
projections correspond closely to CBO.
Technology costs were calibrated
based on NREL ATB 2022.
GCAM-CGS
Corporate Average Fuel Economy
Standards
BIL EV charging infrastructure
See tables 2-6 of The Bevond 50
Scenario: Technical Aooendix
State-level renewable portfolio
standards modeled.
Local policies were aggregated at the
state level or assumed to be embedded
in federal or state policy.
GDP, population, primary energy prices,
and hydro generation from EIA AEO
2022.
GCAM-PNNL
Corporate Average Fuel Economy
Standards
BIL EV charging infrastructure
See tables 2-6 of The Bevond 50
Scenario: Technical Aooendix
State-level renewable portfolio
standards modeled.
Local policies were aggregated at the
state level or assumed to be embedded
in federal or state policy.
GDP, population, primary energy prices,
and hydro generation from EIA AEO
2023.
MARKAL-
NETL
All policies enacted as of September
2022, with exception to IRA and BIL
State-level renewable portfolio
standards modeled. Local policies were
aggregated from the state levels to the
Census regions levels.
EIA AEO 2021 for energy demand.
NEMS-EIA
All policies and regulations enacted as
of September, 2022.
State-level renewable portfolio
standards and clean energy standards
State and regional GHG programs
State LDV GHG standards and LDV,
MDV, and HDV ZEV mandates
State low-carbon fuel standards
Macro outlook from S&P Global IHS
Markit from September 2022.
NEMS-OP
All policies and regulations
represented in AEO 2022 and
including EPA LDV standards for
MY23-26, exclusive of BIL/IIJA.
State-level renewable portfolio
standards and clean energy standards
State and regional GHG programs
State LDV GHG standards and LDV,
MDV, and HDV ZEV mandates
State low-carbon fuel standards
Electric sector technology costs based
on NREL ATB 2022.
EV costs based on Argonne National
Laboratory estimates.
B-6
-------
Analysis
Abbreviation
Federal
Policies
State/Local
Policies
Other Calibration
Assumptions
NEMS-RHG
All policies enacted as of June 2022,
inclusive of 110A, EPA LDV standards
for MY23-26, and Good Neighbor
program.
State-level renewable portfolio
standards and clean energy standards
as ofJune 2022
State and regional GHG programs
State LDV GHG standards and LDV,
MDV, and HDV ZEV mandates
State low-carbon fuel standards
GDP aligned with EIA AEO 2022 Low
Economic Growth case.
Oil and gas resource availability aligned
with EIA AEO 2022 Reference case.
NREL ATB 2022 moderate costs for
most low- and zero-emitting
generation costs; RHG analysis for CCS
costs.
Conventional fossil generator costs
aligned with EIA AEO 2022 Reference
case.
REGEN-EPRI
Policies and regulations through
September 2022, including BIL/IIJA.
State and regional portfolio standards,
technology mandates, and carbon
pricing (electric sector and economy-
wide).
EIA AEO 2021 for service demand
growth and fuel prices. EPRI data for
technology cost and performance.
RIO-REPEAT
Policies and regulations as of January
2021 including:
ฆ EPA final rule on HFCs
ฆ Bipartisan Infrastructure Law
Policies and regulations as of January
2021
EIA AEO 2021
NREL ATB 2021
USREP-
ReEDS
ReEDS: All policies enacted as of
September 2022, with exception to
IRA and BIL.
USREP: CAFE and GHG Emissions
standards for light-duty vehicles as
reflected in AEO 2023
State-level renewable portfolio
standards modeled.
GDP and emissions projections
calibrated to EIA AEO 2023
Haiku-RFF*
Policies in AEO 2021 that affect
electricity demand are implicit in
parameters
State RPS's are aggregated to regional
levels
EIA AEO 2021 for NG and coal fuel
prices, electricity demand,
Initial calibration to AEO2021 for state
level generation, national generation for
NG and coal
NREL ATB 2022 for Solar, Wind, and
CCS capital costs
B-7
-------
Analysis
Abbreviation
Federal
Policies
State/Local
Policies
Other Calibration
Assumptions
IPM-EPA*
All policies enacted as of Summer
2022.
Includes proposed Good Neighbor
Program.
State-level renewable portfolio
standards and clean energy standards
modeled as of summer 2022.
State and regional GHG programs
including Colorado (HB21-1266),
Massachusetts (Massachusetts Senate
Bill 9), North Carolina (North Carolina
House Bill 951), Oregon (Oregon House
Bill 2021), and Washington (Washington
state SB5126)
Electricity demand data from EIA AEO
2021 augment with incremental
electricity demand from EV's as a result
of EPA's Final Rule to Revise Existing
National GHG Emissions Standards for
Passenger Cars and Light Trucks
Through Model Year 2026 (not reflected
in AEO 2021).
IPM-NRDC*
All policies enacted as of November
2021including BIL/IIJA.
State RPS/CES is explicitly modeled
reflecting state CES/RPS as of April
2022. ZEV mandates are included
through AEO 2022 electricity demand
projections.
EIA AEO for electricity demand and
conventional technology costs.
NREL ATB 2021 for renewables and
storage costs.
Firm builds and retirements based
multiple sources. Tends to have more
retirements than NEEDS database.
ReEDS-
NREL*
All policies enacted as of September
2022, except for IRA and BIL.
All policies enacted as of September
2022, with exception to IRA and BIL.
EIA AEO 2022
B-8
-------
Appendix C: IRA Implementation and Sensitivity Assumptions
TableC.l IRA provision implementation for GCAM-PNNL, USREP-ReEDS1, and IPM-EPA.2
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
Electricity
13101
Production tax credit
(PTC) for electricity from
renewables (45)
Modeled as a $26/MWh subsidy for
solar, wind and geothermal
technologies through 2024.
Assume that all projects pay
prevailing wages. A 7.5% reduction
in the credit value is assumed due to
the transferability provision.
Assume PWA requirements are
met. Apply $27.5/MWh to onshore
wind, utility-scale PV, and
biopower. Vary reduction in the
credit due transferability and
addition in credit for energy
community and domestic content
credit among scenarios.
Assume PWA requirements
are met. Apply $27.5/MWh to
onshore wind, utility-scale
PV, and biopower. 10% bonus
energy communities' credit
is provided to all storage
technologies and prorated
based on share of the total
IPM regional land area that
qualifies as an energy
community for solar and
wind units.
13102
Investment tax credit
(ITC) for energy property
(48)
Modeled as a 30% subsidy for
offshore wind and storage
technologies through 2024, with
the simplifying assumption that all
projects pay prevailing wages. A
7.5% reduction in the credit value is
assumed due to the transferability
provision.
Assume PWA requirements are
met. Apply 30% credit to offshore
wind, CSP, geothermal,
hydropower, nuclear, pumped
storage, battery storage, and
distributed PV. Vary reduction in
the credit due transferability and
addition in credit for energy
community and domestic content
credit among scenarios.
Assume PWA requirements
are met. Apply 30% credit to
offshore wind, CSP,
geothermal, hydropower,
nuclear, pumped storage,
battery storage, and
distributed PV. 10% bonus
energy communities' credit
is provided to all storage
technologies and prorated
based on share of the total
IPM regional land area that
qualifies as an energy
community for solar and
wind units.
1 For additional detail, see the USREP-ReEDS documentation for this work, entitled Economic and Environmental Impacts of the Inflation Reduction Act: USREP-ReEDS Modeling Framework.
https://cfpub.epa.gov/si/si public record Report.cfm?dirEntryld=358898&Lab=OAP
2 Details on the IRA scenario may be found here: https://www.epa.gov/power-sector-modeling/post-ira-2022-reference-case. For the No IRA scenario see: https://www.epa.gov/power~sector~
model ing/pre-ira-2022-reference-case.
C-l
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
13103
Solar and wind facilities
placed in low-Income
communities (45(e),
45E(h))
0.9 GW per year (50% of the
maximum total annual capacity
allowed to receive the low-
income community bonus) of
distributed PV added to the dGen
projections through 2032.
13105
Zero-emission nuclear
power PTC (45U)
Modeled as a $15/MWh subsidy for
nuclear technologies through 2030,
with the simplifying assumption
that all projects pay prevailing
wages.
Assume in combination with non-
federal incentives and zero-
emission credits, this provision
prevents the economic retirement
of nuclear plants. As such, model
Georgia Vogtle units 3&4 coming
online by 2025 and maintain nuclear
capacity at today's levels.
Assume PWA requirements are
met. Apply $27.5/MWh to nuclear
power production
No endogenous nuclear
retirements are allowed over
the forecast period.
13701
New clean electricity PTC
(45Y)
Same as 13101 through 2030, with
phasedown after 2030.
Same as 13101
Same as 13101
13702
New clean electricity ITC
(48 E)
Same as 13102 through 2030, with
phasedown after 2030.
Same as 13102
Same as 13102
13703
Cost recovery for
qualified property
(168(e)(3)(B))
Captured in ReEDS with the
financing calculations according
to Ho et al.5
22004
USDA assistance for rural
electric cooperatives
Not included.
50151
Transmission facility
financing
Not included.
3 Ho,J., Becker, J., Brown, M., Brown, P., Chernyakhovskiy, I., Cohen, S.,... Zhou, E. (2021). Regional Energy Deployment System (ReEDS) Model Documentation: Version 2020 (NREL/TP-6A20-78195).
NREL. https://www.nrel.gov/docs/fy21osti/78195.pdf
C-2
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
Multi-Sector
13104
Credit for carbon oxide
sequestration (45Q)
Electricity sector: Extension of
existing credits for captured CO2 at
$85/metric ton is implemented
through 2030. Assume this subsidy
will result in sequestration levels
consistent with analyses by
Rhodium Group and Edmonds et
al.4'5 Modeled this exogenously by
specifying sequestration across
various industrial sectors, resulting
in 130 MTCO2 and 140 MTCO2 annual
sequestration.
Industrial Sector: Same as power
sector with exogenously by
specifications for sequestration
across various industrial sectors,
resulting in 93 MtCC>2 and 89 MtCC>2
annual sequestration.
Assume PWA requirements are
met. Apply $85/metric ton credit
to industrial and power
applications. Assume -7.5% credit
for cost of monetization in the
power sector. Industrial CCS cost
assumptions based on National
Energy Laboratory's (NETL) report
on the cost of CCS by industry.
Apply $180/metric ton credit for
DAC.
Assume PWA requirements
are met. Apply $85/metric
ton credit to industrial and
power applications.
Power sector CCS cost
assumptions based on
Sargent and Lundy analysis
in support of EPA's IPM 2022
post-IRA Reference Case.
13204
Clean hydrogen PTC (45V)
Modeled as different subsidies to
hydrogen technologies depending
on their carbon intensities. Assume
that fossil hydrogen without CCS
doesn't qualify and fossil hydrogen
with CCS claims 45Q instead, and
that 50% of projects pay prevailing
wages.
Not applicable.
Hydrogen is available at a
delivered cost of $l/kg within
the power sector consistent
with DOE Hydrogen Shot
goal.
4 Larsen, J., King, B., Hiltbrand, G., & Herndon, W. (2021). Capturing the moment: Carbon capture in the American Jobs Plan. Rhodium Group, https://rhg.com/research/carbon-capture-american-iobs-
plan/
5 Edmonds, J., Nichols, C., Adamantiades, M., Bistline, J., Huster, J., Iyer, G., Johnson, N., Patel, P., Showalter, S., Victor, N., Waldhoff, S., Wise, M., & Wood, F. (2020). Could congressionally mandated
incentives lead to deployment of large-scale CO2 capture, facilities for enhanced oil recovery CO2 markets and geologic CO2 storage? Energy Policy, 146. https://doi.Org/10.1016/i.enpol.2020.111775
C-3
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
22001
Electric loans for
renewable energy
Loans are treated as an interest
rate reduction be calculating the
cumulative value of interest to be
paid on capital expenditures for
solar and wind capacity,
deducting the value of loans, and
calculating the implied (lower)
interest rate.
50141
Funding for DOE Loan
Programs Office
Loans are treated as an interest
rate reduction be calculating the
cumulative value of interest to be
paid on capital expenditures for
solar and wind capacity,
deducting the value of loans, and
calculating the implied (lower)
interest rate. About 80% of DOE
LPO funding to-date has been
spent on electricity-related
technologies.
50144
Energy infrastructure
reinvestment financing
Modeled as $250 billion in loans
and guarantees used to accelerate
the retirement of coal-fired power
generation and fund the
construction of renewable
electricity-generating capacity.
Estimate this to accelerate the
retirement of 38 GW of additional
coal-fired capacity beyond already-
scheduled retirements by 2030.
Loans are treated as an interest
rate reduction by calculating the
cumulative value of interest to be
paid on capital expenditures for
solar and wind capacity,
deducting the value of loans, and
calculating the implied (lower)
interest rate.
50145
Tribal energy loan
guarantee program
Loans are treated as an interest
rate reduction by calculating the
cumulative value of interest to be
paid on capital expenditures for
solar and wind capacity,
deducting the value of loans, and
calculating the implied (lower)
interest rate.
C-4
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
Transportation
13201
Biodiesel and renewable
fuels PTC (40A, others)
Implemented as subsidies for
biodiesel, cellulosic ethanol, FT
biofuels, cellulosic ethanol with
CCS, and FT biofuels with CCS.
Assume that jet fuel is the first
market for FT biofuel, and FT
biofuels therefore receive the
aviation fuel credit.
Not applicable.
13202
Second-generation
biofuels PTC (40)
See 13201
Not applicable.
13203
Sustainable aviation fuel
PTC (40 B)
See 13201
Not applicable.
13401
Clean vehicle credit (30D)
This tax credit has a maximum value
of $7,500 with an EV being eligible
for half of the credit if its battery
meets domestic assembly
requirements and other half of the
credit is contingent upon a specific
share of the minerals used in the
battery being sourced for North
American or other free trade
countries. Assume that the U.S. auto
manufacturing sector will reorient
itself so that all new EVs produced
by 2030 will meet domestic
assembly and mineral requirements,
and that by 2025, half of EVs sold
will meet these requirements.
Assume 89% of Americans meet the
income eligibility requirement.
Altogether, this yields an EV tax
credit with an effective value of
$6,673, implemented as a capital
cost reduction. Assume that for the
2031-2035 model period that the
tax credit takes on a value 40% of
the 2030 value because it is
scheduled to expire in 2032.
Assume $5625 to reflect
moderate assumption on vehicles
meeting critical battery and
mineral component requirements.
C-5
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
13402
Credit for previously
owned clean vehicles
(25 E)
Not applicable
13403
Qualified commercial
clean vehicle credit (45W)
This tax credit is modeled as a
$40,000 capital cost reduction for
electric heavy-duty freight trucks,
and a $7,500 capital cost reduction
for electric medium-duty and light-
duty freight trucks. Assume that for
the 2031-2035 model period that
the tax credit takes on a value 40%
of the 2030 value because it is
scheduled to expire in 2032.
Apply $40,000 as a reduction in
costs for all heavy-duty EVs. Apply
as a percent reduction in cost
based on a weighted average cost
of heavy-duty vehicles from NREL
ATB transportation data.
13404
Alternative fuel vehicle
refueling property credit
(30C)
This credit is assumed to be a
$1,000 property credit available for
light duty vehicle charging
infrastructure for individuals in rural
and low-income census tracts.
Based on census data, 17.4% of
Americans live in counties that are
either rural or low-income, so the
$1,000 property credit is modeled
as a weighted average national
subsidy of $174 for capital
infrastructure cost for EVs. Assume
that for the 2031-2035 model
period that the tax credit takes on a
value 40% of the 2030 value
because it is scheduled to expire in
2032.
Not included.
13704
New clean fuel PTC (45Z)
See 13201
Not applicable.
C-6
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
60101
Clean heavy-duty vehicles
$1B grant over 10 years (2022-
2031) assigned to the purchase of
new zero-emission buses.
Apportioned spending and
infrastructure costs based on
proportion used to support USPS
fleet (section 70002), so $433M
for vehicle purchases and $567M
for charging infrastructure.
70002
U.S. Postal Service clean
fleets
$1.3B grant allocated for the
purchase of new zero-emission
USPS vehicles and $1.7B for fleet
infrastructure and charging. Used
ATB vehicle cost data and USPS
VMT data to determine number of
vehicles purchased and gas saved
through replacement with EVs.
Then calculated the decline in oil
demand and increase in
electricity demand and introduce
as an exogenous shift to the
model.
Buildings
13301
Energy efficient home
improvement PTC (25C)
Modeled by improving shell
efficiency in residential buildings
based on the AEO 2022 "Alternative
Policies - Extended Credit" case.6
Apportion CBO estimated outlays
for this program ($12.5B) to
residential electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
80% applicability, 3.3 private:
public leverage.
6 U.S. Energy Information Administration (2022). Annual energy outlook 2022. https://www.eia.gov/outlooks/aeo/tables ref.php
C-7
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
13302
Residential clean energy
PTC (25D)
Modeled by updating the rooftop
ITC, which results in an additional
0.7GW/yr increase in electricity
generation from rooftop PV on the
lifetime of the credit through 2035.
Apportion CBO estimated outlays
for this program ($22.OB) to
residential electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
80% applicability, 3.3 private:
public leverage.
13303
Energy efficient
commercial buildings
deduction (179D)
This provision is estimated to
reduce commercial HVAC costs by
3%. Modeled this provision as a 3%
subsidy for commercial high-
efficiency heating and cooling
technologies in 2025 and 2030.
Apportion CBO estimated outlays
for this program ($362M) to
commercial energy efficiency
programs (with applicability and
leverage assumptions) and apply
exogenous shift in energy
demand to the model. Assume
80% applicability, 3.3 private:
public leverage.
13304
New energy efficient
homes credit (45L)
Same as 13301
Apportion CBO estimated outlays
for this program ($2B) to
residential electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
80% applicability, 1.0 private:
public leverage.
C-8
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
30002
Green and resilient (HUD)
retrofit program
Apportion CBO Budget Authority
for this program ($990M) to
residential electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
50% applicability, 4.0 private:
public leverage.
50121
Home energy
performance-based,
whole-house rebates
Same as 13301
Apportion CBO Budget Authority
for this program ($4.3B) to
residential electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
80% applicability, 1.6 private:
public leverage.
50122
High-efficiency electric
home rebate program
Modeled as a subsidy to high-
efficiency technologies in
residential buildings in 2025 and
2030. We assume that two-thirds of
consumers are eligible for this
credit, so we implemented this as a
weighted average across all
consumers with the effective value
of the credit modeled to be 66% of
each of the following: $1,750 to
electric heat pump water heaters,
$4,000 to electric heat pumps for
space heating, $420 to electric
ovens, $420 to electric heat pump
clothes dryers, $1,600 for high-
efficiency air conditioning.
Apportion CBO Budget Authority
for this program ($4.5B) to
residential electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
80% applicability, 1.4 private:
public leverage
C-9
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
60502
Assistance for federal
buildings
Apportion CBO Budget Authority
for this program ($3B) to
government energy efficiency
programs (with applicability and
leverage assumptions) and apply
exogenous shift in energy
demand to the model. Assume
50% applicability, 1.0 private:
public leverage
Industry
13501
Advanced energy project
credit (48C)
Used data from Berkley National
Laboratory to determine the cost
of saved electricity in industrial
sectors. Applied an applicability
haircut and private leverage ratio
to CBO Budget Authority ($10B)
and applied an exogenous shift in
electricity demand to the model.
13502
Advanced manufacturing
production credit (45X)
Calculated the implied cost
savings for wind and solar
capacity based on component-
level tax credits and new capacity
cost shares from NREL's JEDI
model. The discounts are applied
to the overnight capital cost of
these technologies in ReEDS. For
batteries, we calculated the value
of the $10/kW credit as a share of
total vehicle cost based on NREL
ATB transportation data.
50161
Advanced industrial
facilities deployment
program
Used data from Berkley National
Laboratory to determine the cost
of saved electricity in industrial
sectors, take an applicability
haircut and private leverage to
CBO Budget Authority ($5.8B),
and applied an exogenous shift in
electricity demand to the model.
C-10
-------
Section
Program
GCAM-PNNL
USREP-ReEDS
IPM-EPA
60113
Methane emissions
reduction program
This provision has a waste
emissions charge of $l,500/tCH4
($60/tCO2e) on fugitive methane,
which was modeled to reduce 2.92
MtChU (73 MTC02e) in the oil and
gas sector, using the EPA's MAC
curves for methane.7 Because this
waste emissions charge only
applies to sources covered under
the EPA's GHG Reporting Program
and that exceed statutorily-
specified waste emissions
thresholds, we assume that only
39% of the emissions reductions are
achieved,8 resulting in a reduction
of 1.14 MtChU (28 MtC02e) by 2030.
Not included.
Multiple
Vehicle manufacturing
loans/grants
Multiple
Low-carbon materials
Multiple
Agriculture and forestry
provisions
Allocate $8.5 billion to
Environmental Quality Incentives
Program, in which distribution of
funds is prioritized for reducing
enteric methane emissions from
ruminants. This was modeled as a
0.63 MtChU (16 MtC02e) reduction
in livestock methane emissions in
2030.
Multiple
Oil and gas lease sales
7 United States Environmental Protection Agency (2022). U.S. State-level Non-C02 Greenhouse Gas Mitigation Potential: 2025-2050. United States Environmental Protection Agency. Retrieved from
https://www.epa.gov/global-mitigation-non-co2-greenhouse-gases/us-state-level-non-co2-ghg-mitigation-report
8 Jenkins, Jesse D.; Farbes, Jamil; Jones, Ryan; and Mayfield, Erin N. (2022), REPEAT Project Section-by-Section Summary of Energy and Climate Policies in the 117th Congress, REPEAT Project,
http://bit.lv/REPEAT-Policies. doi: 10.5281/zenodo.6993118
C-ll
-------
Program
GCAM-PNNL
USREP-ReEDS
Other
60103
Greenhouse gas
reduction fund
Apportion CBO Budget Authority
for this program ($27B) to
residential electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
80% applicability, 3.0 private:
public leverage
60114
Climate pollution
reduction grants
Apportion CBO Budget Authority
for this program ($5B) to
residential electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
80% applicability, 1.0 private:
public leverage
60201
Environmental and
climate justice block
grants
Apportion CBO Budget Authority
for this program ($3250M) to
government electrification,
weatherization, and energy
efficiency programs (with
applicability and leverage
assumptions) and apply
exogenous shift in energy
demand to the model. Assume
50% applicability, 2.0 private:
public leverage.
IPM-EPA
C-12
-------
Table C.2 USREP-ReEDS IRA sensitivity assumptions.
Section
Description
Scenarios {Pessimistic, Moderate, Optimistic}
Electricity
13101
Production tax credit (PTC) for electricity from renewables (45)
{-12.5, -10.0, -7.5} % for monetization
{5,10,15} %for bonus credits
13102
Investment tax credit (ITC) for energy property (48)
{-12.5, -10.0, -7.5} % for monetization
{5,10,15} %for bonus credits
13103
Solar and wind facilities placed in low-Income communities
(45(e), 45E(h))
13105
Zero-emission nuclear power PTC (45U)
13701
New clean electricity PTC (45Y)
{-12.5, -10.0, -7.5} % for monetization
{5,10,15} %for bonus credits
13702
New clean electricity ITC (48E)
{-12.5, -10.0, -7.5} % for monetization
{5,10,15} %for bonus credits
13703
Cost recovery for qualified property (168(e)(3)(B))
22004
USDA Assistance for Rural Electric Cooperatives
Not Modeled
50151
Transmission facility financing
Not Modeled
Multi-Sector
13104
Credit for carbon oxide sequestration (45Q)
{-12.5, -10.0, -7.5} % for monetization in power sector
13204
Clean hydrogen PTC (45V)
Not Modeled
22001
Electric loans for renewable energy
50141
Funding for DOE Loan Programs Office
50144
Energy infrastructure reinvestment financing
50145
Tribal energy loan guarantee program
Transportation
13201
Biodiesel and renewable fuels PTC (40A, others)
Not Modeled
13202
Second-generation biofuels PTC (40)
Not Modeled
13203
Sustainable aviation fuel PTC (40B)
Not Modeled
13401
Clean vehicle credit (30D)
{$3750, $5625, $7500}
13402
Credit for previously owned clean vehicles (25E)
Not Modeled
13403
Qualified commercial clean vehicle credit (45W)
13404
Alternative fuel vehicle refueling property credit (30C)
Not Modeled
C-13
-------
Section
Description
Scenarios {Pessimistic, Moderate, Optimistic}
13704
New clean fuel PTC (45Z)
Not Modeled
60101
Clean heavy-duty vehicles
70002
U.S. Postal Service clean fleets
Buildings
13301
Energy efficient home improvement PTC (25C)
{-20, 0, 20} % federal spending
13302
Residential clean energy PTC (25D)
{-20, 0, 20} % federal spending
13303
Energy efficient commercial buildings deduction (179D)
{-20, 0, 20} % federal spending
13304
New energy efficient homes credit (45L)
{-20, 0, 20} % federal spending
30002
Green and resilient (HUD) retrofit program
{-20, 0, 20} % applicability
50121
Home energy performance-based, whole-house rebates
{-20, 0, 20} % leverage
50122
High-efficiency electric home rebate program
{-20, 0, 20} % leverage
60502
Assistance for federal buildings
{-20, 0, 20} % applicability
Industry
13501
Advanced energy project credit (48C)
{10, 25, 40} % applicability
13502
Advanced manufacturing production credit (45X)
{-1.32, -3.06, -4.80} % solar capital cost credit
{-3.56, -4.42, -5.27} % wind capital cost credit
Wind and solar vary by domestic production assumptions. No
variation for electric vehicles or offshore wind.
50161
Advanced industrial facilities deployment program
{25, 50, 75} % applicability
60113
Methane emissions reduction program
Not Modeled
Multiple
Vehicle manufacturing loans/grants
Not Modeled
Multiple
Low-carbon materials
Not Modeled
Multiple
Agriculture and forestry provisions
Not Modeled
Multiple
Oil and gas lease sales
Not Modeled
Other
60103
Greenhouse gas reduction fund
{-20, 0, 20} % leverage
{-20, 0, 20} % applicability
60114
Climate pollution reduction grants
{-20, 0, 20} % leverage
60201
Environmental and climate justice block grants
{-20, 0, 20} % leverage
{-20, 0, 20} % applicability
C-14
-------
Table C.3 IRA sensitivities for Bistline et al. (2023).
Pessimistic and Optimistic IRA sensitivities are intended to illustrate how alternate assumptions about IRA implementation and
related assumptions can alter the emissions and energy system impacts of IRA. Guidance for these harmonized scenarios is
flexible, given the variation across models in their scope, representation of IRA provisions, and specifications for central
estimates.9 In these scenarios, "Low" refers to the scenario with lower IRA impacts.
Assumption
Description
Pessimistic
(Low in Bistline)
Optimistic
(High in Bistline)
Transferability Penalty for Tax Credits
(PTC/ITC/45Q/45V)
% loss in credit value
2x central
0.5x central
Energy Communities Bonus Eligibility for
PTC/ITC
%
max(-20% from central, 0% of
credit)
min(+20%from central, 100%
of credit)
Domestic Content Bonus Eligibility for
PTC/ITC
%
max(-20% from central, 0% of
credit)
min(+20%from central, 100%
of credit)
1706 Coverage Multiplier
%
-25% from central
+25% from central
Build Rates for Renewables
Upper bound on CAGR
-7% from central
Unconstrained
Build Rates for Transmission
Upper bound on CAGR
1%
Unconstrained
CCS Availability10
None through 2030
Unconstrained
EVs Eligible for Qualifying Bonus Credits
% new sales
-25% from central
+25% from central
Demand-Side Incentive Haircuts for
Program Uptake11
% loss in credit value
+20% from central
-10% from central
Note:
All other external uncertainties (e.g., fuel prices, service demand, technological costs) are held fixed across these IRA sensitivities.
Low and high values are specified in relative terms from the central scenario since modeling teams may assume different parameter values in their central cases.
9 We aimed to make these specifications directionally consistent with scenarios from teams that have already conducted these sensitivities so that they do not need to re-run these scenarios.
10 Note that a central case likely has constraints on CCS deployment before 2030 due to project lead times and potentially growth rates associated with CO2 transport and storage, though there is
likely variation across models.
11 Combination of program overhead, participation given the pool of eligible participants, and other factors. Note that models with exogenous electricity demand can either choose not to adjust these
dimensions across low and high sensitivities or to use outputs from another model to inform electricity demand projections.
C-15
-------
Appendix D: Input Assumptions
Appendix D presents input assumptions for natural gas price and capital costs for electricity
generation by model.
Figure D.T Natural gas price assumptions.
Historical
r&>
1?
EPS-El
GCAM-CGS
GCAM-PNNL
Haiku-RFF
IPM-NRDC
MARKAL-NETL
- NEMS-EIA
NEMS-OP
NEMS-RHG
ReEDS-NREL
- REGEN-EPRI
ฆ ฆ ฆ RIO-REPEAT
USREP-ReEDS
D-l
-------
Figure D.2 Capital costs for battery storage, NGCC, solar PV, and onshore wind.
Battery (4-Hour)
NGCC
Solar PV
Onshore Wind
1,500-
1,200-
o
"oO
O
o
ฎ 900-
CL
CO
o
600-
nV> eiO oU o
^
^ o,ซ ป
^ ^
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v nP
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Advanced
Moderate
Conservative
EPS-El
GCAM-CGS
Haiku-RFF
IPM-EPA
IPM-NRDC
MARKAL-NETL
NEMS-RHG
NREL 2022 ATB - Adv.
NREL 2022 ATB - Con.
NREL2022ATB-Mod.
OP-NEMS-Adv
OP-NEMS-Mod
ReEDS-NREL
REGEN-EPRI
RIO-REPEAT
D-2
-------
Figure D.3 Capital costs for coal CCS retrofits and natural gas CCS.
Coal CCS Retrofits
New NGCC CCS
4,000-
o
g 3,000
CM
tfl
O
O
i5
'cL
co
O
2,000
1,000
qfo r{o c& np
jCS3
-------
Appendix E: Results by Model
Appendix E figures show results presented in this report by model.
Figure E.I Economy-wide CO2 emissions. Shown forthe No IRA and IRA
scenarios, along with the absolute and percent differences between
the two scenarios. Black dots represent median values.
No IRA
IRA
E 3,000
LU
Q
CD
ฆ+'
_ZJ
o
(/)
_Q
<
0>
>.
~C\|
o
o
CD
o
c
(D
(D
ij=
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= -800
I
T>
(ft
&
>
5,000
CM
O
O
4,000
Cfl
c
0
CO
CO
E
3,000
LU
Q)
O
C
CD
1
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tt=
C
CD
O
0
Q.
-I0r
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ ฆ ฆ RIO-REPEAT
USREP-ReEDS
E-l
-------
Figure E.2 Electricity sector C02 emissions. Shown for the No IRA and IRA
scenarios, along with the absolute and percent differences between
the two scenarios. Black dots represent median values.
a)
o
c
Q)
s
-------
Figure E.3 Transportation sector C02 emissions. Shown forthe No IRA and IRA
scenarios, along with the absolute and percent differences between
the two scenarios. Black dots represent median values.
No IRA
IRA
o
o
w
c
o
CO
w
E
HI
~~OJ
o
O
a)
o
c
CD
0
it
b
CD
o
w
-Q
<
1,800
1,500
1,200-
O
O
U)
c
o
w
w
E
LU
1,800
1,500
1,200
's
1
1 1
1 1
1 u
qO>
a>
o
c
0
u_
0)
!1=
C
cfp
jP
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
E-3
-------
Figure E.4 Buildings sector C02 emissions. Shown for the No IRA and IRA
scenarios, along with the absolute and percent differences between
the two scenarios. Black dots represent median values.
No IRA
IRA
o
o
w
c
o
CO
w
E
HI
~~OJ
o
O
a)
o
c
CD
L_
CD
tt
q -500
CD
ฆ41
o
o
c
0
i_
CD
!t=
C
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O
i
CD
Q.
-20
-30
V<\' ~ TVs
*
A
/
/
1
Vi"
u 1
1 1 1
csV
T3
r&
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
E-4
-------
Figure E.5 Industrial sector C02 emissions. Shown for the No IRA and IRA
scenarios, along with the absolute and percent differences between
the two scenarios. Black dots represent median values.
No IRA
IRA
oj 1,500
o
o
(/)
c
o
*(/)
a)
E
HI
~~OJ
O
o
a)
o
c
Q)
0
it
b
CD
o
w
-Q
<
1,000-
500-
csV
a>
o
c
0
0)
!t=
c
-------
Figure E.6 Coal Primary Energy. Shown for the No IRA and IRA scenarios, along
with the absolute and percent differences between the two scenarios.
Black dots represent median values.
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
- NEMS-EIA
NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ RIO-REPEAT
USREP-ReEDS
No IRA
E-6
-------
Figure E.7 Natural Gas Primary Energy. Shown forthe No IRA and IRA scenarios,
along with the absolute and percent differences between the two
scenarios. Black dots represent median values.
No IRA
IRA
~o
03
13
o
O)
<
0)
c
LU
03
.i 20-
&
CN
O
o
0)
o
c
CD
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it
b
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<
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-5.0
-7.5
CsV
<$>
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W
"D
0!
3
o
>
U)
i
-------
Figure E.8 Petroleum Primary Energy. Shown for the No IRA and IRA scenarios,
along with the absolute and percent differences between the two
scenarios. Black dots represent median values.
No IRA
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
NEMS-EIA
NEMS-OP
NEMS-RHG
REGEN-EPRI
RIO-REPEAT
USREP-ReEDS
E-8
-------
Figure E.9 Electricity generation from nuclear energy. Shown for the No IRA and
IRA scenarios, along with the absolute and percent differences
between the two scenarios. Black dots represent median values.
EPS-El
GCAM-CGS
GCAM-PNNL
Haiku-RFF
IPM-EPA
IPM-NRDC
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ReEDS-NREL
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
No IRA
CD n
O 0
c
CD
0) -200
It
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$
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d
o
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TO
s
0
ฃZ
CD
O
800
600
400
200
0
"L>
600
E-9
-------
Figure E.10 Electricity generation from solar power. Shown for the No IRA and IRA
scenarios, along with the absolute and percent differences between
the two scenarios. Black dots represent median values.
No IRA
IRA
CD
O
c
0)
CD
1
c
0
o
0
CL
.
-
r 1 1 1
L_
100
r&>
EPS-El
GCAM-CGS
GCAM-PNNL
Haiku-RFF
IPM-EPA
IPM-NRDC
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ReEDS-NREL
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
E-10
-------
Figure E.ll Electricity generation from wind energy. Shown for the No IRA and IRA
scenarios, along with the absolute and percent differences between
the two scenarios. Black dots represent median values.
EPS-El
GCAM-CGS
GCAM-PNNL
Haiku-RFF
IPM-EPA
IPM-NRDC
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ReEDS-NREL
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
No IRA
1,500
$
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TO
s
0
ฃZ
CD
O
2,500
2,000
1,500
1,000
500
_c
2,500
ฃ
H
2,000
c
o
1,500
TO
CD
1,000
c
(U
CD
500
E-ll
-------
Figure E.T2 Electricity generation from coal with CCS. Shown for the No IRA and
IRA scenarios, along with the absolute differences between the two
scenarios. Black dots represent median values.
No IRA
IRA
ฃZ
O
03
CD
ฃZ
CD
O
1,500-
1,000-
500
ots-
CD
O
C
0)
0
tt
cd
=3
-t'
O
-------
Figure E.13 Electricity generation from gas with CCS. Shown for the No IRA and
IRA scenarios, along with the absolute differences between the two
scenarios. Black dots represent median values.
No IRA
IRA
500-
ฃ1
.2 250 -
4-ป
TO
i
CD
c
Q)
0
<#'
500
c
0 250
4<1
ra
1
0
c
0
0
$
nfc>
GCAM-CGS
GCAM-PNNL
IPM-EPA
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
REGEN-EPRI
ฆ ฆ ฆ RIO-REPEAT
USREP-ReEDS
E-13
-------
Figure E.14 Electricity generation from coal without CCS. Shown for the No IRA
and I RA scenarios, along with the absolute and percent differences
between the two scenarios, Black dots represent median values.
No IRA
IRA
c
o
"ฆ+I
TO
J
CD
c
0
-200
~ -600
o
w
-Q
<
&
J3
nr
\
V
V.
1 I
U : 1
c
o
TO
CD
C
CD
CD
V- _
ฆ "vy
$ J? J?
EPS-El
GCAM-CGS
GCAM-PNNL
Haiku-RFF
IPM-EPA
iPM-NRDC
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ReEDS-NREL
ฆ REGEN-EPRI
ฆ ฆ ฆ RIO-REPEAT
USREP-ReEDS
E-14
-------
Figure E.15 Electricity generation from natural gas without CCS. Shown for the No
IRA and IRA scenarios, along with the absolute and percent differences
between the two scenarios, Black dots represent median values
EPS-El
GCAM-CGS
GCAM-PNNL
Haiku-RFF
IPM-EPA
IPM-NRDC
MARKAL-NETL
- NEMS-OP
NEMS-RHG
ReEDS-NREL
ฆ REGEN-EPRI
RIO-REPEAT
USREP-ReEDS
$
H
ci
o
'*-<'
CD
s
0
ฃZ
CD
O
2,500
2,000
1,500
1,000
500
0b
2,500-
2,000
c 1,500
O
CD 1,000
500
No IRA
E-15
-------
Figure E.16 Electricity generation from other energy sources. Shown for the No IRA
and IRA scenarios, along with the absolute and percent differences
between the two scenarios. Black dots represent median values. Other
energy sources include geothermal, hydro, biomass, and oil. No model
reported biomass or oil generation with CCS.
No IRA
IRA
500-
500-
EPS-El
GCAM-CGS
GCAM-PNNL
Haiku-RFF
IPM-EPA
IPM-NRDC
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ReEDS-NREL
ฆ REGEN-EPRI
ฆ ฆ ฆ RIO-REPEAT
USREP-ReEDS
E-16
-------
Figure E.17 Electric vehicle sales share. Shown for the No IRA and IRA scenarios
along with the absolute percent point and percentage differences
between the two scenarios, Black dots represent median values.
EPS-El
GCAM-CGS
GCAM-PNNL
- NEMS-EIA
- NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ ฆ ฆ RIO-REPEAT
USREP-ReEDS
No IRA
E-17
-------
Figure E.18 Electricity generation by energy source in 2030 and 2035. Shown for
the No IRA and IRA scenarios.
OLO OLO OLO OLO OLO OLO OLO OLO OLO OLO OLO OLO OLD OLD
COCO COCO COCO coco coco coco coco coco coco coco coco coco coco coco
oo oo oo oo oo oo oo oo oo oo oo oo oo oo
CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM
c
o
'*-ป
03
L
cd
c
a)
0
&
o
-i'
o
0
LU
6,000
4,000
2,000
6,000
4,000
2,000
Biomass w/ CCS
Biomassw/o CCS
Coal w/ CCS
Coal w/o CCS
Gas w/ CCS
Gas w/o CCS
Geothermal
Hydro
Hydrogen
Nuclear
Petroleum
Solar
Wind
E-18
-------
Figure E.19 Electricity capacity by energy source in 2030 and 2035. Shown for the
No IRA and IRA scenarios.
OlO OlO OlO OlO OlO OlO OlO OlO OlO OlO OlO OlO OlO OlO
coco coco coco coco coco coco coco coco coco coco coco coco coco coco
oo oo oo oo oo oo oo oo oo oo oo oo oo oo
CM CM CM CM CM CM CMCM CMCM CMCM CMCM CMCM CMCM CMCM CMCM CMCM CMCM CMCM
2,500
Biomass w/ CCS
Biomassw/o CCS
Coal w/ CCS
Coal w/o CCS
Gas w/ CCS
Gas w/o CCS
Geothermal
Hydro
Nuclear
Oil w/o CCS
Other
Solar
Storage
Wind
E-19
-------
Figure E.20 Economy-wide electricity percent share of final energy. Shown for the
No IRA and IRA scenarios along with the absolute percentage point
(pp) and percentage differences between the two scenarios. Black
dots represent median values.
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
NEMS-RHG
ฆ ฆ RIO-REPEAT
USREP-ReEDS
No IRA
E-20
-------
Figure E.21 Transportation sector electricity percent share of final energy. Shown
for the No IRA and IRA scenarios along with the absolute percentage
point (pp) and percentage differences between the two scenarios.
Black dots represent median values.
15
10
5
No IRA
J*
$
-------
Figure E.22 Buildings sector electricity percent share of final energy. Shown for the
No IRA and IRA scenarios along with the absolute percentage point
(pp) and percentage differences between the two scenarios. Black
dots represent median values.
E?
CD
ฃ=
LU 60
~ro
c
Li- 40
H
o
>
0 20
No IRA
EPS-El
GCAM-CGS
GCAM-PNNL
NEMS-RHG
ฆ RIO-REPEAT
USREP-ReEDS
40-
E-22
-------
Figure E.23 Industrial sector electricity percent share of final energy. Shown for the
No IRA and IRA scenarios along with the absolute percentage point
(pp) and percentage differences between the two scenarios. Black
dots represent median values.
O)
20
O 15
&
o
w 10
I
0*
No IRA
O)
L_
0
c
LU
ro
c
o
&
o
LU
0
o
c
0
4
c
0
o
L_
0
CL
20
15
o
^ 10
i
&
IRA
EPS-El
GCAM-CGS
GCAM-PNNL
NEMS-RHG
RIO-REPEAT
USREP-ReEDS
E-23
-------
Figure E.24 Economy-wide C02 combustion emissions. Shown for the No IRA and
IRA scenarios, along with the absolute and percent differences
between the two scenarios, Black dots represent median values.
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
>.
O
o
No IRA
5,500-
4.000
o
o
5,500
4,000
E-24
-------
Figure E.25 Transportation sector C02 combustion emissions. Shown for the No
IRA and IRA scenarios, along with the absolute and percent differences
between the two scenarios. Black dots represent median values.
No IRA
IRA
^ 2,000
o
o
C/)
c
o
"(/)
(/>
(D
o
w
-Q
<
1,500
1,000
-
ซ.
"
2,000
"^CNJ
o
o
1,500
C0
C
O
CO
CO
E 1,000
L_
l_
nS
LJ
1?
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
E-25
-------
Figure E.26 Buildings sector C02 combustion emissions. Shown for the No IRA and
IRA scenarios, along with the absolute and percent differences
between the two scenarios, Black dots represent median values.
O
o
w
c
o
w
w
O
O
CD
O
c
0)
I
CD
it
b
CD
-Iป
O
(/)
SI
<
No IRA
700
450
200
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
NEMS-EIA
NEMS-OP
NEMS-RHG
REGEN-EPRI
RIO-REPEAT
USREP-ReEDS
E-26
-------
Figure E.27 Buildings sector C02 combustion emissions. Shown for the No IRA and
IRA scenarios, along with the absolute and percent differences
between the two scenarios, Black dots represent median values.
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
1,200
1,000
No IRA
1,200
1,000 j-
E-27
-------
Figure E.28 Economy-wide C02 emissions from electricity production. Shown for
the No IRA and IRA scenarios, along with the absolute and percent
differences between the two scenarios. Black dots represent median
values.
No IRA
IRA
>, 4,000-
o
o
w
c
o
"w
w
E
HI
O
O
4,000
C\J
o
o
3,000
c/)
c
o
w
C/)
E
2,000
LU
0'
o
d)
o
c
0
-5'
^ J
i
CD
it
-10'
b
-i<
c
(D
U
i_
CD
-15'
~
U 1 I
i u
_l l
I u
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
REGEN-EPRI
RIO-REPEAT
E-28
-------
Figure E.29 Transportation sector C02 indirect emissions from electricity
consumed. Shown for the No IRA and IRA scenarios, along with the
absolute and percent differences between the two scenarios. Black
dots represent median values.
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
No IRA
E-29
-------
Figure E.30 Buildings sector C02 indirect emissions from electricity consumed.
Shown for the No IRA and IRA scenarios, along with the absolute and
percent differences between the two scenarios. Black dots represent
median values.
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
1,200
No IRA
1,200
E-30
-------
Figure E.31 Industrial sector C02 indirect emissions from electricity consumed.
Shown for the No IRA and IRA scenarios, along with the absolute and
percent differences between the two scenarios. Black dots represent
median values.
(D
O
C
CD
i_
(D
b
0
-fป
O
(/)
_Q
<
No IRA
500
0
-50
-100
-150
-200
EPS-El
GCAM-CGS
GCAM-PNNL
MARKAL-NETL
ฆ - NEMS-EIA
NEMS-OP
NEMS-RHG
ฆ REGEN-EPRI
ฆ ฆ RIO-REPEAT
USREP-ReEDS
500
E-31
-------
Table E.l Electricity generation from coal and gas technologies without carbon
capture. Shown forthe No IRA and IRA scenarios for years 2030 and
2035.
Coal
Gas
No IRA
IRA
No IRA
IRA
Model
2030
2035
2030
2035
2030
2035
2030
2035
EPS-El
434
408
189
18
1,779
1,828
1,008
712
GCAM-CGS
737
684
219
0
1,589
1,849
1,242
1,191
GCAM-PNNL
478
412
376
423
1,863
1,885
1,570
1,488
Haiku-RFF
873
794
562
500
1,010
964
713
667
IPM-EPA
554
466
243
44
1,791
1,883
1,754
1,341
IPM-NRDC
362
334
198
143
1,942
2,058
1,650
1,654
MARKAL-
NETL
670
684
515
203
1,892
2,018
2,021
1,427
NEMS-EIA
611
575
353
347
1,571
1,500
1,154
1,039
NEMS-OP
539
473
300
143
1,456
1,427
1,048
782
NEMS-RHG
485
446
138
205
1,260
1,185
821
947
REGEN-EPRI
668
594
465
151
1,192
1,366
1,323
872
RIO-REPEAT
654
493
475
236
972
838
812
445
ReEDS-NREL
696
334
1,303
695
USREP-
ReEDS
600
478
268
190
1,356
1,481
861
737
E-32
-------
Table E.2 Electricity generation capacity from coal and gas technologies without
carbon capture. Shown forthe No IRAand IRA scenarios for years 2030
and 2035.
Coal
Gas
No IRA
IRA
No IRA
IRA
Model
2030
2035
2030
2035
2030
2035
2030
2035
EPS-El
123
114
58
7
510
541
421
478
GCAM-CGS
223
206
66
0
594
641
478
457
GCAM-PNNL
64
55
52
59
257
260
218
207
Haiku-RFF
159
155
133
126
527
534
497
491
IPM-EPA
111
88
60
33
539
572
515
519
IPM-NRDC
85
68
63
54
550
578
512
520
MARKAL-
NETL
129
108
96
58
432
422
391
326
NEMS-EIA
118
108
105
93
515
563
525
558
NEMS-OP
112
100
90
70
595
666
581
638
NEMS-RHG
102
91
74
61
602
650
648
723
REGEN-EPRI
117
99
80
27
453
491
470
449
RIO-REPEAT
120
102
108
77
502
506
476
455
ReEDS-NREL
156
136
447
402
USREP-
ReEDS
152
144
149
141
473
510
406
399
E-33
-------
Table E.3 Electricity generation capacity factors for coal and gas technologies
without carbon capture. Shown for the No IRA and IRA scenarios for
years 2030 and 2035. Capacity factor is the ratio of electrical energy
produced by a generating technology over a year to the electrical
energy that could have been produced at continuous full power
operation during the year expressed as a percentage.
Coal
Gas
No IRA
IRA
No IRA
IRA
Model
2030
2035
2030
2035
2030
2035
2030
2035
EPS-El
40%
41%
37%
30%
40%
39%
27%
17%
GCAM-CGS
38%
38%
38%
0%7
31%
33%
30%
30%
GCAM-PNNL
85%
85%
83%
82%
83%
83%
82%
82%
Haiku-RFF
63%
58%
48%
45%
22%
21%
16%
16%
IPM-EPA
57%
61%
46%
15%
38%
38%
39%
30%
IPM-NRDC
49%
56%
36%
30%
40%
41%
37%
36%
MARKAL-
NETL
59%
72%
61%
40%
50%
55%
59%
50%
NEMS-EIA
59%
61%
38%
42%
35%
30%
25%
21%
NEMS-OP
55%
54%
38%
23%
28%
24%
21%
14%
NEMS-RHG
54%
56%
21%
38%
24%
21%
14%
15%
REGEN-EPRI
65%
68%
66%
64%
30%
32%
32%
22%
RIO-REPEAT
62%
55%
50%
35%
22%
19%
19%
11%
ReEDS-NREL
51%
28%
33%
20%
USREP-
ReEDS
45%
38%
20%
15%
33%
33%
24%
21%
1GCAM-CGS coal generation and capacity are beneath rounding tolerance, so CF has been set to 0%.
E-34
-------
Appendix F: Supplemental Results
F.1 Electrification
Appendix F.I summarizes electrification changes, both economy-wide and by sector.
Electrification is presented as the electricity share of final energy. Electrification and energy
efficiency are not broken out.
Figure F.l.l Electricity share of final energy. Economy-wide (total), transportation,
buildings, and industry share electricity of final energy over time for the
IRA (blue line and circles) and No IRA scenarios (orange dashed line
and triangles). Horizontal bars to the right of each panel represent the
median of model results.
Economy-Wide
Transportation
>*
O)
CD
C
LU
~03
c
60-
40 f-
O
>*
o
o
_0
LU
20-
2020
A 8
Alt?
A0 A
2025
2030
2035 2030 2035 2020
2025
2030
o
ฃซฆ
2035 2030 2035
F-l
-------
Table F.l.l Summary of electricity share of final energy.
Sector
Year
No IRA
IRA
Min
Median
Max
Min
Median
Max
Economy-Wide
2025
19%
21%
24%
19%
22%
26%
2030
19%
23%
26%
20%
24%
27%
2035
20%
25%
30%
22%
26%
30%
Transportation
2025
0%
1%
1%
0%
1%
2%
2030
1%
2%
4%
2%
4%
6%
2035
2%
4%
10%
4%
7%
15%
Buildings
2025
47%
48%
58%
47%
48%
59%
2030
47%
49%
59%
47%
51%
62%
2035
48%
50%
61%
48%
55%
66%
Industry
2025
13%
15%
18%
13%
16%
18%
2030
13%
15%
18%
13%
17%
18%
2035
12%
17%
19%
12%
17%
18%
F-2
-------
TableF.1.2 Differences in electricity share of final energy between IRAand NoIRA
scenarios.
Sector
Year
Percent Difference
Percentage Point Difference
Min
Median
Max
Min
Median
Max
Economy-Wide
2025
-2%
1%
16%
-0.47 pp
0.22 pp
3.54 pp
2030
-3%
4%
13%
-0.66 pp
0.875 pp
2.98 pp
2035
-3%
5%
12%
-0.71 pp
1.14 pp
3.01 pp
Transportation
2025
2%
30%
91%
0.01 pp
0.19 pp
0.78 pp
2030
12%
48%
233%
0.23 pp
1.62 pp
3.26 pp
2035
20%
57%
257%
0.81 pp
1.84 pp
5.46 pp
Buildings
2025
-0%
2%
3%
-0.06 pp
0.86 pp
1.89 pp
2030
-2%
4%
7%
-1.43 pp
1.92 pp
4.11 pp
2035
-3%
4%
9%
-1.82 pp
2.14 pp
4.7 pp
Industry
2025
-5%
1%
9%
-0.82 pp
0.11 pp
1.3 pp
2030
-3%
0%
22%
-0.47 pp
0.06 pp
3.3 pp
2035
-5%
0%
9%
-0.96 pp
0.06 pp
1.31 pp
F-3
-------
F.2 Direct vs Indirect CO2 Emissions
Appendix F.2 shows CO2 emissions presented in Figures 1.2, 2.3, 3.3, 4.2, and 5.3, broken out
into emissions from electricity and emissions from non-electricity. Blue lines show model
results for the IRA scenario and dashed orange lines show results for the No IRA scenario.
For each of the figures, individual model results are represented to the right of each panel-
blue circles are model results for 2030 and 2035 for the IRA scenario, orange triangles are
for the No IRA scenario, and the horizontal bars represent the median of model results.
Figure F.2.1 Economy-wide CO? emissions. Left panel for non-electricity includes
emissions from fuel combustion and industrial processes in every
sectorexcept for the electricity sector, on the right panel.
Non-Electricity
Electricity
4,000-
o
o
E
LU
2,000-
0-
2005
2021 2025 2030 2035
2030 2035
2005
2021 2025 2030 2035
2030 2035
Figure F.2.2 Transportation direct combustion and indirect CO2 emissions.
Horizontal bars to the right of each panel represent the median of
model results.
Direct Combustion
Indirect from Electricity
2,000
>, 1,500
o
o
E
LU
1,000-
Historical
No IRA
IRA
I
i I i
i | j
n
2005
2021 2025 2030 2035
2030 2035
2005
2021 2025 2030 2035
2030 2035
F-4
-------
Figure F.2.3 Buildings direct combustion and indirect C02 emissions.
Direct Combustion
2,000-
^ 1.500-
~~C\1
o
o
(/)
c
o
c/)
C/)
E
LU
1,000-
500-
Historical
No IRA
Indirect from Electricity
1
5
5-^S\
I
I 1 1 1
1 1 1
A
4-
&
A ฉ
2005
2021 2025 2030 2035 2030 2035 2005
2021 2025 2030 2035 2030 2035
Figure F.2.4 Industry direct combustion and indirect CO2 emissions.
Direct Combustion
1,500-
O 1,000-
o
CO
c
o
$ 500-
E
LU
No IRA
A
A o
Indirect from Electricity
2005
2021 2025 2030 2035 2030 2035 2005
-fฑf.
2021 2025 2030 2035 2030 2035
F-5
-------
F.3 Trends in Electricity Generation
Figure F.3.1 Electricity generation (in TWh) by technology and technology type. The
horizontal line in each panel represents generation in 2021, the orange
dots represent the range of modeled results in the No IRA scenario. The
orange and blue dashes represent the mean generation in the No IRA
and IRA scenarios, respectively. In the top panel, low or zero-emission
generation includes solar, wind, nuclear, biomass, hydro, geothermal,
and fossil with CCS, and high emitting generation includes unabated
natural gas, coal, and petroleum. In the middle panel, CCS includes all
fossil (coal, gas, petroleum) with CCS. In the third panel, other includes
hydro, geothermal, and biomass (with and without CCS), and coal and
natural gas are unabated.
Total Generation
Low or Zero Emitting Generation
High Emitting Generation
>,500
2 3,250
A
l
A
O
_ ง ง
"ง 1 =
O
A
A
2,600
S
K
| 1,300
2,600-
2 1,300-
Wind
ง
o
o
o
ง
&
I"
~ ง ^
ฉ 1-
" o
L.
4s
Natural Gas
Other
. e
-A G-
ง
o
8
A
&
o
A
^
ฉ
"1
^ ~~
_ ^
8
A _
ง
i-
1
A
1
"
- ง i ฐ
-i s
B
V
a No IRA O IRA
2021
IRA
No IRA
F-6
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Figure F.3.2 Difference in electricity generation by technology and technology type
between the IRA and No IRA scenarios. The blue dots represent the
difference between electricity generation in the IRA scenario and No
IRA scenario for each model. The blue dashes represent the median
difference for the results. In the top panel, low or zero-emission
generation includes solar, wind, nuclear, biomass, hydro, geothermal,
and fossil with CCS, and high emitting generation includes unabated
natural gas, coal, and petroleum. In the middle panel, CCS includes all
fossil (coal, gas, petroleum) with CCS. In the third panel, other includes
hydro, geothermal, and biomass (with and without CCS), and coal and
natural gas are unabated.
2,000-
1,000-
Total Generation
Low or Zero Emitting Generation
o
High Emitting Generation
-1,000-
-2,000-
2,000-
I 1,:
000-
2030
2035
Wind
2030
Solar
2035
Nuclear
2030
2035
CCS
ฃ -1,000-
t
-2,000-
2,000-
2030
2035
Coal
2030
2035 2030
Natural Gas
2035 2030
Other
2035
1.000-
-1,000-
-2,000-
2030
t
2035
2030
2035
I
2030
2035
IRA
F-7
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F.4 Sensitivity Analysis
Ranges for the "IRA Moderate Scenario" are those shown in Figure ES.2/1.3. Ranges for the
"IRA All Sensitivities" are those shown in Figure F.4.1 and include the Moderate IRA scenario
as well as all sensitivity scenarios presented in Figures 1.5 and 2.6. "Range change with
inclusion of all sensitivities" presents the changes to the C02 emissions reductions from
2005 range when all sensitivity scenarios are included.
Table F.4.1 Summary of C02 emissions reductions below 2005 levels for the
Moderate IRA scenario only and all IRA sensitivity scenarios.
Range Change with
IRA Moderate Scenario IRA All Sensitivities Inclusion of Sensitivities
Sector
Year
Min
Median
Max
Min
Median
Max
Min
Median
Max
Electricity
2030
49%
69%
83%
49%
72%
91%
0%
3%
8%
2035
67%
77%
87%
60%
79%
92%
-7%
2%
6%
Transportation
2030
11%
17%
25%
11%
17%
25%
0%
-1%
0%
2035
15%
27%
35%
15%
28%
41%
1%
5%
Buildings
2030
49%
55%
63%
49%
57%
71%
0%
3%
8%
2035
52%
66%
70%
52%
66%
77%
0%
7%
Industry
2030
17%
36%
43%
17%
38%
44%
0%
2%
1%
2035
23%
36%
57%
23%
41%
58%
5%
2%
Economy-Wide
2030
35%
39%
43%
35%
41%
46%
0%
1%
3%
2035
36%
46%
55%
36%
47%
58%
1%
3%
F-8
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Figure F.4.1 Range of C02 emissions reductions across all scenarios including
sensitivity cases.
Electricity C02 Emissions Reductions
fc iiju* AA ^
^ mm mm m Q
A * A iM AAA
2035
0 | ..
Transportation C02 Emissions Reductions (Direct + Indirect from Electricity)
2030
2035
Buildings C02 Emissions Reductions (Direct + Indirect from Electricity)
A " A
*"+ " A No IRA
A
IRA
iuau A i ^
^ ซ
Industry C02 Emissions Reductions (Direct + Indirect from Electricity)
^ M. A A I A A ^
2030 ^ 1 J ^
^ m
A A A A A | A A
2035 | _
^ MB *ป * *W
Economy-Wide C02 Emissions Reductions
A i' ~A
2030 m
2035 AH"' A
0 25 50 75 100
Percent Reduction from 2005 Levels (%)
In 2030 under the full range of the IRA scenario economy-wide emissions fall to 35 to 46% (41% median)
below 2005 levels; power-sector C02 emissions fall to 49 to 91% (72% median) below 2005 levels;
transportation sector C02 emissions fall to 11 to 25% (17% median) below 2005 levels in 2030; buildings
sector C02 emissions fall to 49 to 71% (57% median) below 2005 levels; and industry sector C02
emissions fall to 17 to 44% (38% 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.
F-9
-------
Figure F.4.2 Range of percent reduction of all IRA sensitivity scenarios from the No
IRA scenario.
Electricity
0 25 50 75 100
2030 ^ | mm wat j ซ
2035 0 ซ!ซ mป mm Q
Transportation (Direct + Indirect from Electricity)
2030
2035 0
Buildings (Direct + Indirect from Electricity)
2030 ฃ M M | H 0
2035 ^ mm Q
Industry (Direct + Indirect from Electricity)
2030 0 "h* ซ|ซw
2035 ^ ซ #
Economy-Wide
2030 # ฃ
2035 0 ป
-------
F.5 Summary of Emissions Reduction from No IRA
Appendix F.5 presents the range of percent reduction of the Moderate IRA scenario from the No IRA
scenario, summarizing figures 1.2(b), 2.3(b), 3.3(b), 4.2(b), and 5.3(b). See Appendix F.4 for this range
inclusive of all IRA sensitivity scenarios.
Figure F.5.1 Range of percent reduction of the Moderate IRA scenario from the No
IRA scenario.
Electricity
0 25 50 75 100
2030 0 #*l* * *4* 0
2035 0 . ฃ
Transportation (Direct + Indirect from Electricity)
2030
2035 # 0
Buildings (Direct + Indirect from Electricity)
2030 0 ^
2035 0 4 "
Industry (Direct + Indirect from Electricity)
2030 0 ป|ซ #
2035 | - 0
Economy-Wide
2030 0 4*
2035 # -
0 25 50 75 100
Percent Reduction from No IRA Scenario (%)
F-ll
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Appendix G: Supplemental EPA Analyses
G.1 Buildings Measures
This appendix details the assumptions for and results from the Scout technical scenarios
completed by NREL and LBNL for EPA, quantifying the emission impacts of heat pump and
building envelope measures. This is discussed in the Chapter 4 text box "Building Sector
Measures Incented by IRA."
Notes on Assumptions:
1. Technical experts across NREL, LBNL, and EPA developed the scenarios to reflect feasible
deployment of building envelope measures and heat pumps under IRA, informed by market
research. Deployment under the IRA is uncertain and could encompass a wide variety of
scenarios.
2. The scenarios analyzed for EPA draw significantly on assumptions developed for Langevin et al.
202212.
3. Building efficiency improvements follow the moderate scenario in Table 1 of Langevin et al.:
Building technologies with breakthrough performance/cost enter the market by 2035. The
Scout assumptions represent emerging heat pump, envelope, and control technologies
drawn from DOE roadmaps where available, see Langevin et al. Table 6.
Elevated building codes and standards to latest ENERGYSTAR/IECC/90.1 levels take effect in
2030
Additional near-term deployment of building envelope/control efficiency measures
4. Assumptions for technology performance improvement over time are shown in Table G.2.
5. Heat pump sales are exogenously specified based on a separate analysis conducted by
Guidehouse. Heat pump sales follow the definition provided for Table 7 in Langevin et al.: "Sales
shares are relative to total sales of unitary AC equipment plus heat pumps; rates for comparable
studies are typically relative to total heating equipment sales. Sales shares are exclusive to heat
pumps and do not include electric resistance technologies." The sales assumptions are shown
below in Table G.l.
6. For reference, 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%.
7. The high scenario is the only scenario that includes accelerated heat pump retrofits before the
end of useful life. Early retrofits assumptions are the same as those provided in for Table 8 in
Langevin et al. Specifically, it is assumed that the residential HVAC and water heating annual
early retrofit rates increase from 0.5% up to 2% by 2035. For commercial, the annual early
retrofit rates for HVAC and water heating increase from 0.9% to 3.6% in 2035.
8. The scenarios assume an 80% reduction in grid carbon dioxide emissions vs. 2005 levels by 2050.
12 https://escholarship.org/uc/item/6507pl61
G-l
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Table G.l.l Scout analysis efficiency and heat pump assumptions.
Scenario
Building Efficiency
Improvements
2030 Heat Pump Sales
Residential
Space Heating
Residential
Water Heating
Commercial
Space Heating
Commercial Water
Heating
Low
EIAAEO 2022
Reference Case
45%
10%
15%
3%
Central
Moderate Increases
from AEO 2022
50%
20%
20%
5%
High
Moderate Increases
from AEO 2022
63%
40%
25%
7%
Table G.1.2 Scout analysis technology performance assumptions.
Market-Available
Technology Performance Range
Scenario
Raise Floor (via
building codes and standards)
Raise Ceiling (via market entry
of emerging technology)
Low
BAU (AEO 2022 Reference Case)
BAU (AEO 2022 Reference Case)
Central
Moderate Improvement
(take effect in 2030)
Moderate Improvement
(market entry in 2035)
High
Moderate Improvement
(take effect in 2030)
Moderate Improvement
(market entry in 2035)
G-2
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Figure G.l.l Emissions reductions by end use type from Scout technical scenarios
for heat pump and building efficiency deployment. This graph shows
the initial trajectory of reductions in the EPA Scout scenarios. Energy
efficiency and electrification both contribute to an 50% decrease in
emissions from 2005 in the Low Scenario, 52% in the Central Scenario,
and 60% in the High scenario by 2035.
o
o
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G.2 Industrial Measures
In addition to the LEEP Assessment economy-wide modeling of the industrial sector as
whole, this appendix discusses supplemental analyses by EPA or researchers working with
EPA that specify emissions reduction measures and their impacts in industrial subsectors.
This work does not specifically analyze IRA policies but can provide insights to inform IRA
implementation.
A key strategy for reducing emissions in the industrial sector involves finding the unique
opportunities within the different industrial sectors. As part of an EPA analysis, Worrell and
Boyd analyzed industry by sector, and projected significant potential reductions, reducing
industrial emissions 86% by 2050. The emission reductions come from a wide range of
sources including energy efficiency, material efficiency, and efficient electrification
combined with grid decarbonization, and in some cases, technologies like hydrogen and
carbon capture applied to specific industries.13 While Worrell and Boyd did not estimate
potential reductions by 2035, they do offer near-term recommendations.
In contrast to the situation in heavy industry, there are many near-term lower-cost reduction
opportunities in light industry. Light industry emissions tend to resemble building sector
emissions. This resemblance is primarily due to similar levels of combustion of natural gas
and electricity consumption, and often heating, cooling, and water heating play significant
roles.14 Similar to buildings, energy efficiency and efficient electrification can deliver
significant reductions in the near term. A list of light industry NAICS codes and
corresponding energy use can be found below in Table G.2.1.
EPA analysis estimates energy efficiency could deliver 34% of the GHG emission reductions
from industry overall. However, many manufacturing companies have not taken important
first steps to efficiency savings, such as employing energy managers and conducting energy
assessments of their facilities.15 Projections suggest that these two actions could lower
energy intensity of manufacturing plants by 14%, across heavy and light industry.16 To
illustrate, the ENERGY STAR Program saw the cement industry improve energy intensity by
13% between 1997 and 2008, representing 60 trillion Btu in source energy saved, while U.S.-
based automobile producers reduced the fossil fuel consumption of their assembly plants
by 12% between 2000 and 2005.1718 EPA analysis shows that emissions from the energy-
intensive industrial sectors fell 26% between 2007-2017, while emissions
from manufacturing as a whole fell 5%.19 Further analysis from Duke University examines CO2
emissions per dollar of product shipped, using data from the Census of Manufactures by
NAICS code. It shows that there are many industries with widely distributed carbon
13 Ernst Worrell and Gale Boyd. Bottom-up estimates of deep decarbonization of U.S. manufacturing in 2050. Journal of Cleaner Productions,
2021. https://doi.Org/10.1016/i.iclepro.2021.129758.
14 Ernst Worrell and Gale Boyd. Bottom-up estimates of deep decarbonization of U.S. manufacturing in 2050. Journal of Cleaner Productions,
2021. https://doi.Org/10.1016/i.iclepro.2021.129758.
15 Gale Boyd, E. M. C., Su Zhang (2021). Impact of Strategic Energy Management Practices on Energy Efficiency: Evidence from Plant-Level Data.
Summer Study on Energy Efficiency in Industry 2021, Virtual, ACEEE.
16 Gale Boyd, E. M. C., Su Zhang (2021). Impact of Strategic Energy Management Practices on Energy Efficiency: Evidence from Plant-Level Data.
Summer Study on Energy Efficiency in Industry 2021, Virtual, ACEEE.
17 https://www.energystar.gov/industrial plants/measure-track-and-benchmark/energy-star-energy-1
18 https://www.energystar.gov/buildings/tools-and-resources/assessing-improvement-energv-efficiencv-us-auto-assembly-plants
19 Creason, Jared, Jameel Alsalam, Kong Chiu, and Allen A. Fawcett, 2021. Energy Intensive Manufacturing Industries and GHG Emissions.
Climate Change Economics, 12(3) DOI: 10.1142/S201000782150010X
G-4
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intensities across different plants, implying that some plants in these industries have been
able to significantly reduce emissions while others in the sector can do the same.20
The ENERGY STAR Program has also found specific near-term opportunities for
improvement in light industry energy efficiency through benchmarking and basic energy
management. Light manufacturing plants that participated in the ENERGY STAR Program's
Challenge for Industry, on average, achieved a 20% reduction in energy intensity within two
years.21 Light industry has significant efficient electrification opportunities, including
process and building heating requirements. Burners and fired heaters can be replaced with
electrically supplied equipment, including heat pumps.
EPA analysis also shows opportunity for electrification, particularly for light industry.
Decarbonization of electricity used within the sector has a meaningful effect on indirect
emissions, again with light industry playing a significant role. Light industry only uses 12% of
total manufacturing energy, but 44% of manufacturing electricity. Some companies,
particularly those that are consumer facing (which many light industrial companies are), may
have incentives to actively pursue renewable energy power purchases over and above
reductions in emissions from grid power.22
EPA analysis shows light industry and bulk chemical manufacturing are the sectors with the
greatest potential for significant reductions from energy efficiency and electrification.
Substantial efficiency reductions can also be found in refining, paper, and iron and steel.
Aluminum and glass, iron and steel, and paper have potential for substantial reductions from
electrification. While refining and cement have more limited electrification potential, they
have substantial potential for emissions reductions through material efficiency and carbon
capture, use, and storage (CCUS).23
Possible constraints to such electrification in the near term include necessary infrastructure
upgrades and capital turnover. The infrastructure issues reflect the fact that complete
electrification would double light industry electricity use; the ability to reliably deliver that
additional power to manufacturing plants would be a significant concern to industry. Capital
turnover would also impact the speed of any electrification since businesses are unlikely to
retire equipment early. In addition, policy needs to be nimble to offer incentives when that
turnover window opens for equipment to be replaced. If that window closes, then the
opportunity may not present itself again for many years.
The key barriers to energy efficiency and efficient electrification in industry include
technology costs, lack of equipment supply, incompatibility of new equipment with existing
plants, insufficient electrical supply to meet industry's needs, and lack of sufficient grid
infrastructure to deliver the needed power supply. In concert with IRA incentives, IRA
programs can help address these industry-specific barriers.
Overall, to achieve substantial goals in the long-term, efficiency and electrification will need
to be complemented with other technologies to reduce the carbon impact of fossil fuel use
and the emissions impact of other industrial processes. Combined heat and power has been
an important complementary technology in industries like chemicals, refining, food
20 Boyd etal (2011) Preliminary Analysis of the Distributions of Carbon and Energy Intensity for 27 Energy Intensive Trade Exposed Industrial
Sectors, Duke University Working Paper EE 11-03, https://sites.nicholasinstitute.duke.edu/environmentaleconomics/wp-
content/uploads/sites/3/2016/03/W P-EE-ll-03.pdf
21 https://www.energystar.gov/industrial_plants/results_energy_star_challenge_industry_2010_through_2020
22 Ernst Worrell and Gale Boyd. Bottom-up estimates of deep decarbonization of U.S. manufacturing in 2050. Journal of Cleaner Productions,
2021. https://doi.Org/10.1016/i.iclepro.2021.129758.. Detailed table is provided in Appendix D.
23 Ernst Worrell and Gale Boyd. Bottom-up estimates of deep decarbonization of U.S. manufacturing in 2050. Journal of Cleaner Productions,
2021. https://doi.org/10.1016/i.iclepro.2021.129758.. Detailed table is provided in Appendix D.
G-5
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processing, metals, and paperproviding significant efficiencies in on-site heat and power
production.24 On-site renewables, including biomass and geothermal, can also provide
electricity and replace fossil combustion needs in whole or in part. High-temperature
processes will still need some type of on-side fuel combustion. CCUS is a potential solution
to mitigate fossil combustion as well as the bulk of process emissions from cement
production. Hydrogen is another potential low-GHG fuel under study for high-temperature
industrial processes. Most of these technologies can benefit from near-term Research
Development Demonstration & Deployment (RDD&D) to scale up to a level of deployment
that delivers significant reductions.
24https://betterbuildingssolutioncenter.energy.gov/sites/default/files/attachments/CHP Technical Potential Studv.pdf
G-6
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Table G.2.1 Light Industry by NAICS with associated energy use (source: First Use
of Energy for All Purposes (Fuel and Nonfuel), EIA Manufacturing Energy
Consumption Survey 2018). Units are trillion Btu.
NAICS
Code
Subsector and Industry
Total
Net
Electricit
y
Natural
Gas
Other25
3115
Dairy Product
124
39
81
4
3116
Animal Slaughtering and Processing
278
108
151
19
312
Beverage and Tobacco Products
118
49
54
15
3121
Beverages
111
46
50
15
3122
Tobacco
7
3
4
0
313
Textile Mills
64
37
23
4
314
Textile Product Mills
22
10
11
1
315
Apparel
4
2
2
0
316
Leather and Allied Products
2
1
1
0
321
Wood Products
388
71
68
323
Printing and Related Support
60
34
25
1
3254
Pharmaceuticals and Medicines
115
36
63
16
326
Plastics and Rubber Products
257
168
84
5
332
Fabricated Metal Products
257
124
126
7
333
Machinery
148
80
61
7
334
Computer and Electronic Products
110
76
33
1
335
Electrical Equip., Appliances, and
Components
85
38
37
10
336
Transportation Equipment
348
172
159
17
337
Furniture and Related Products
37
16
15
6
339
Miscellaneous
61
33
26
2
Total of non-energy intensive industry
2,347
1,143
1,074
130
Share of total
12%
44%
15%
1%
25 Other includes HGL (excluding natural gasoline), Coke, Coal, Breeze, and waste-derived fuels.
G-7
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Appendix H: Peer Review Process
Peer Review of the Report
Consistent with guidelines described in EPA's Peer Review Handbook,26-27 this report was subject to an
independent, external expert peer review that concluded in August 2023. The peer review
documentation is available at EPA's Science Inventory.28
Expert Peer Review
The expert review was managed by a contractor (RTI International) under the direction of a designated
independent EPA peer review leader, who prepared a peer review plan, the scope of work for the
review contract, and the charge for the reviewers. Reviewers worked individually (i.e., without contact
with other reviewers, colleagues, or EPA) to prepare written comments in response to the charge
questions.
The contractor identified, screened, and selected six reviewers who had no conflict of interest in
performing the review, and who collectively met the technical selection criteria provided by EPA. The
peer review charge directed reviewers to provide responses to the following questions during the main
review:
1. Are the writing level and graphics appropriate for an educated but general audience including
stakeholders and decision-makers?
2. Do the text, figures, and tables in the sector specific chapters clearly communicate the modeling
results? If not, please provide recommendations for improvement. Note that Appendices [E and
F] contains additional figures and alternative figure styles.
3. Does the executive summary provide sufficient context to understand the synthesized results?
4. Does the introductory chapter clearly explain the purpose of the report and provide appropriate
context for the sector chapter results?
5. Does the introductory chapter adequately explain the overall analytic framework of the project?
6. Are the inputs and scenarios clearly explained and documented in the introduction? If not,
please provide recommendations for improvement.
7. Is the cited literature accurately represented?
8. Are there any additional relevant data sources that are not included but could be incorporated
into this analysis?
9. The analysis presented in this report is multi-faceted, using results from several sophisticated
multi-sector and single-sector energy-economy models. Is the use of a multi-model approach,
incorporating multi-sector and power sector models, appropriate to estimate the potential
26 EPA, 2015: Peer Review Handbook, 4th Edition, 2015. United States Environmental Protection Agency, Programs of the Office of the Science
Advisor. Available online at https://www.epa.gov/osa/peer-review-handbook-4th-edition-2015.
27 EPA has determined that this report falls under the classification of "influential scientific information," as defined by OMB and further
described in the EPA Peer Review Handbook. This product is for science dissemination and communication purposes only and does not reflect
analysis of nor recommendations regarding any particular policy.
28 https://cfpub.epa.gov/si/
H-l
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effects of the energy- and climate-related provisions of the IRA? If not, please suggest other
approaches.
10. Does the report provide an assessment of reductions in greenhouse gas emissions that result
from changes in domestic electricity generation and use due to the Inflation Reduction Act of
2022 that are anticipated to occur on an annual basis through fiscal year 2031?
11. Is the draft report missing important findings or messages based on your review?
12. Do you have any recommendations for any key research that could be discussed but is not
mentioned? Do you have recommendations for future updates to the report that EPA should
consider?
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