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Regulatory Impact Analysis for the Proposed
New Source Performance Standards for
Greenhouse Gas Emissions from New,
Modified, and Reconstructed Fossil Fuel-Fired
Electric Generating Units; Emission Guidelines
for Greenhouse Gas Emissions from Existing
Fossil Fuel-Fired Electric Generating Units; and
Repeal of the Affordable Clean Energy Rule
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EPA-452/R-23-006
May 2023
Regulatory Impact Analysis for the Proposed New Source Performance Standards for
Greenhouse Gas Emissions from New, Modified, and Reconstructed Fossil Fuel-Fired Electric
Generating Units; Emission Guidelines for Greenhouse Gas Emissions from Existing Fossil
Fuel-Fired Electric Generating Units; and Repeal of the Affordable Clean Energy Rule
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, NC
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CONTACT INFORMATION
This document has been prepared by staff from the Office of Air and Radiation, U.S.
Environmental Protection Agency. Questions related to this document should be addressed to the
Air Economics Group in the Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, Office of Air and Radiation, Research Triangle Park, North Carolina 27711
(email: OAQPSeconomics@epa.gov).
ACKNOWLEDGEMENTS
In addition to U.S. EPA staff from the Office of Air and Radiation, personnel from the Office of
Policy of the U.S. Environmental Protection Agency contributed data and analysis to this
document.
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TABLE OF CONTENTS
Table of Contents i
Table of Tables v
Table of Figures x
Executive Summary ES-1
ES. 1 Introduction ES-1
ES.2 Regulatory Requirements ES-3
ES.3 Baseline and Analysis Years ES-8
ES.4 Emissions Impacts ES-9
ES.5 Compliance Costs ES-11
ES.6 Benefits ES-12
ES.6.1 Climate Benefits ES-13
ES.6.2 Health Benefits ES-13
ES.6.3 Additional Unqualified Benefits ES-14
ES.6.4 Total Climate and Health Benefits ES-14
ES.7 Economic Impacts ES-15
ES.8 Environmental Justice Impacts ES-17
ES.9 Comparison of Benefits and Costs ES-20
ES. 10 Proposed 11 1(d) Standards for Existing Natural Gas-Fired EGUs and Third Phase of the
Proposed 11 1(b) Standards for New Natural Gas-Fired EGUs ES-22
ES.10.1 Introduction ES-22
ES.10.2 Emissions Impacts ES-23
ES. 10.3 Cost Impacts ES-24
ES.10.4 Climate Benefits ES-25
ES.ll References ES-26
1 Introduction and Background 1-1
1.1 Introduction 1-1
1.2 Legal and Economic Basis for Rulemaking 1-3
1.2.1 Statutory Requirement 1-3
1.2.2 The Need for Air Emissions Regulation 1-5
1.3 Overview of Regulatory Impact Analysis 1-5
1.3.1 Repeal of Affordable Clean Energy (ACE) Rule 1-5
1.3.2 Baseline and Analysis Years 1-7
1.3.3 Best System of Emission Reduction (BSER) 1-8
1.3.4 Illustrative Scenarios 1-13
1.4 Organization of the Regulatory Impact Analysis 1-14
1.5 References 1-15
2 Industry Profile 2-1
2.1 Background 2-1
2.2 Power Sector Overview 2-1
2.2.1 Generation 2-1
2.2.2 Transmission 2-13
2.2.3 Distribution 2-14
2.3 Sales, Expenses, and Prices 2-15
2.3.1 Electricity Prices 2-16
2.3.2 Prices of Fossil Fuel Used for Generating Electricity 2-17
2.3.3 Changes in Electricity Intensity of the U.S. Economy from 2010 to 2021 2-18
3 Compliance Costs, Emissions, and Energy Impacts 3-1
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3.1 Overview 3-1
3.2 Illustrative Scenarios 3-1
3.3 Monitoring, Reporting, and Recordkeeping Costs 3-5
3.4 Power Sector Modeling Framework 3-7
3.5 EPA's Power Sector Modeling of the Baseline Run and Three Illustrative Scenarios 3-10
3.5.1 EPA's IPM Baseline Run v6.21 3-10
3.5.2 Methodology for Evaluating the Illustrative Scenarios 3-11
3.5.3 Methodology for Estimating Compliance Costs 3-14
3.6 Estimated Impacts of the Illustrative Scenarios 3-14
3.6.1 Emissions Reduction Assessment 3-14
3.6.2 Compliance Cost Assessment 3-17
3.6.3 Impacts on Fuel Use, Prices and Generation Mix 3-19
3.7 Limitations 3-32
3.8 References 3-35
4 Benefits Analysis 4-1
4.1 Introduction 4-1
4.2 Climate Benefits 4-2
4.3 Human Health Benefits 4-18
4.3.1 Air Quality Modeling Methodology 4-19
4.3.2 Selecting Air Pollution Health Endpoints to Quantify 4-20
4.3.3 Calculating Counts of Air Pollution Effects Using the Health Impact Function 4-25
4.3.4 Calculating the Economic Valuation of Health Impacts 4-27
4.3.5 Benefits Analysis Data Inputs 4-27
4.3.6 Quantifying Cases of Ozone-Attributable Premature Death 4-31
4.3.7 Quantifying Cases of PM2s-Attributable Premature Death 4-33
4.3.8 Characterizing Uncertainty in the Estimated Benefits 4-36
4.3.9 Estimated Number and Economic Value of Health Benefits 4-40
4.4 Additional Unquantified Benefits 4-55
4.4.1 Hazardous Air Pollutant Impacts 4-58
4.4.2 NO2 Health Benefits 4-60
4.4.3 SO2 Health Benefits 4-60
4.4.4 Ozone Welfare Benefits 4-61
4.4.5 NO2 and SO2 Welfare Benefits 4-61
4.4.6 Visibility Impairment Benefits 4-62
4.4.7 Water Quality and Availability Benefits 4-63
4.5 Total Monetized Benefits 4-68
4.6 References 4-76
5 Economic Impact Analysis 5-1
5.1 Energy Market Impacts 5-1
5.2 Social Costs 5-3
5.3 Small Entity Analysis 5-5
5.3.1 Overview 5-5
5.3.2 EGU Small Entity Analysis and Results 5-5
5.4 Labor Impacts 5-12
5.4.1 Overview of Methodology 5-13
5.4.2 Overview of Power Sector Employment 5-14
5.4.3 Projected Sectoral Employment Changes due to the Proposed Rule 5-15
5.4.4 Conclusions 5-17
5.5 References 5-18
6 Environmental Justice Impacts 6-1
6.1 Introduction 6-1
6.2 Analyzing EJ Impacts in This Proposal 6-3
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6.3 Qualitative Assessment of Climate Impacts 6-4
6.4 Demographic Proximity Analyses of Existing Facilities 6-6
6.5 EJ PM2.5 and Ozone Exposure Impacts 6-11
6.5.1 Populations Predicted to Experience PM2 5 and Ozone Air Quality Changes 6-13
6.5.2 PM2 5 EJ Exposure Analysis 6-14
6.5.3 Ozone EJ Exposure Analysis 6-21
6.6 Qualitative Discussion of EJ PM2 5 Health Impacts 6-29
6.7 Qualitative Discussion of New Source EJ Impacts 6-30
6.8 Summary 6-30
6.9 References 6-33
7 Comparison of Benefits and Costs 7-1
7.1 Introduction 7-1
7.2 Methods 7-2
7.3 Results 7-3
8 Impacts of Proposed 111 (d) Standards on Existing Natural Gas-fired EGUs
and Third Phase of Proposed 111(b) Standards on New Natural Gas-fired EGUs . 8-1
8.1 Introduction 8-1
8.2 Methodology 8-2
8.2.1 111(d) Standards on Existing Natural Gas-Fired EGUs 8-5
8.2.2 Third Phase of 111(b) Standards on New Natural Gas-Fired EGUs 8-6
8.3 Estimated Regulatory Impacts 8-4
8.3.1 Emissions Reduction Assessment 8-5
8.3.2 Compliance Cost Assessment 8-6
8.3.3 Generation Mix and Compliance Outcomes 8-7
8.4 Climate Benefits Analysis 8-12
8.4.1 111(d) Standards on Existing Natural Gas-Fired EGUs 8-12
8.4.2 Third Phase of 111(b) Standards on New Natural Gas-Fired EGUs 8-17
8.5 Present Values and Equivalent Annualized Values of Costs and Climate Benefits 8-21
8.5.1 Compliance Costs 8-22
8.5.2 Climate Benefits 8-23
8.6 Limitations and Uncertainties 8-24
8.7 References 8-25
Appendix A: Air Quality Modeling A-l
A. 1 Air Quality Modeling Simulations A-2
A. 2 Applying Modeling Outputs to Create Spatial Fields A-9
A. 3 Scaling Factors Applied to Source Apportionment Tags A-16
A. 4 Air Quality Surface Results A-21
A. 5 Uncertainties and Limitations of the Air Quality Methodology A-29
A. 6 References A-3 0
Appendix B: Economy-wide Social Costs and Economic Impacts B-l
B. 1 Economy-Wide Modeling B-l
B.2 Overview of the SAGE CGE Model B-2
B. 3 Linking IPM PE Model to SAGE CGE Model B -6
B. 3.1 Overview of Linking Methodology B -7
B.3.2 Translating IPM Outputs into SAGE Inputs B-10
B.4 Results B-12
B.4.1 Economy-wide Social Costs B-12
B.4.2 Impacts on GDP B-14
B.4.3 Impacts on Output B-15
B.4.4 Output Price Impacts B-18
B.4.5 Labor Market Impacts B-19
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B. 4.6 Household Distributional Impacts B -22
B.5 Limitations to Analysis B-24
B.6 References B-25
Appendix C: Assessment of Potential Costs and Emissions Impacts of Proposed
New and Existing Source Standards Analyzed Separately C-l
C. 1 Modeling the Rules Independently C-l
C.2 Compliance Cost Assessment C-3
C.3 Emissions Reduction Assessment C-4
C.4 Impacts on Fuel Use and Generation Mix C-9
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TABLE OF TABLES
Table ES-1 Projected EGU Emissions and Emissions Changes for the Three Illustrative Scenarios for 2028, 2030,
and 2035, and 2040 ES-10
Table ES-2 Total National Compliance Cost Estimates for the Three Illustrative Scenarios (discounted to 2024,
billion 2019 dollars) ES-12
Table ES-3 Monetized Climate and Health Benefits for the Three Illustrative Scenarios, (discounted to 2024,
billion 2019 dollars) ES-15
Table ES-4 Summary of Certain Energy Market Impacts for the Illustrative Proposal Scenario Relative to the
Baseline ES-16
Table ES-5 Monetized Benefits, Costs, and Net Benefits of the Illustrative Scenarios (billions of 2019 dollars,
discounted to 2024) ES-21
Table ES-6 GHG Mitigation Measures for Existing NGCC Units under the Illustrative Proposal, More Stringent
and Less Stringent Scenarios ES-22
Table ES-7 GHG Mitigation Measures for New NGCC Units under the Illustrative Proposal, More Stringent and
Less Stringent Scenarios ES-23
Table ES-8 Estimated Changes in Power Sector Emissions from the Proposed 111(d) for Existing Natural Gas-
fired EGUs for the Three Illustrative Scenarios ES-24
Table ES-9 Estimated Changes in Power Sector Emissions from the Third Phase of the Proposed 111(b) for New
Natural Gas-fired EGUs for the Three Illustrative Scenarios ES-24
Table ES-10 Present Values and Equivalent Annualized Values of Compliance Cost Estimates for the Proposed
111(d) for Natural Gas-fired EGUs and Third Phase of the Proposed 111(b) for Natural Gas-fired
EGUs (discounted to 2024, billion 2019 dollars) ES-25
Table ES-11 Present Values and Equivalent Annualized Values of Monetized Climate Benefit Estimates for the
Proposed 111(d) for Natural Gas-fired EGUs and Third Phase of the Proposed 111(b) for Natural Gas-
fired EGUs (discounted to 2024, billion 2019 dollars) ES-25
Table 2-1 Total Net Summer Electricity Generating Capacity by Energy Source, 2010-21 and 2015-21 2-4
Table 2-2 Net Generation by Energy Source, 2010-21 and 2015-21 (Trillion kWh = TWh) 2-6
Table 2-3 Net Generation in 2015 and 2021 (Trillion kWh = TWh) 2-6
Table 2-4 Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Average Heat Rate in 2020... 2-8
Table 2-5 Total U.S. Electric Power Industry Retail Sales, 2010-21 and 2014-21 (billion kWh) 2-15
Table 3-1 Summary of GHG Mitigation Measures for Existing Sources by Source Category under the
Illustrative Proposal and More Stringent Scenarios 3-2
Table 3-2 Summary of GHG Mitigation Measures for Existing Sources by Source Category under the
Illustrative Less Stringent Scenario 3-3
Table 3-3 Summary of GHG Mitigation Measures for New Sources by Source Category under the Illustrative
Proposal, Less and More Stringent Scenarios 3-4
Table 3-4 Summary of State and Industry Annual Respondent Cost of Reporting and Recordkeeping
Requirements (million 2019 dollars) 3-7
Table 3-5 EGU Annual CO2 Emissions and Emissions Changes (million metric tons) for the Baseline and the
Illustrative Scenarios from 2028 through 2040 3-15
Table 3-6 EGU Annual Emissions and Emissions Changes for NOx, SO2, PM2 5, and Ozone NOx for the
Illustrative Scenarios for 2028 to 2040 3-16
Table 3-7 National Power Sector Compliance Cost Estimates (billions of 2019 dollars) for the Illustrative
Scenarios 3-17
Table 3-8 2028, 2030, 2035 and 2040 Projected U.S. Power Sector Coal Use for the Baseline and the Illustrative
Scenarios 3-21
Table 3-9 2028, 2030, 2035 and 2040 Projected U.S. Power Sector Natural Gas Use for the Baseline and the
Illustrative Scenarios 3-21
Table 3-10 2028, 2030, 2035 and 2040 Projected U.S. Power Sector Hydrogen Use for the Baseline and the
Illustrative Scenarios 3-22
Table 3-11 2028, 2030, 2035 and 2040 Projected Minemouth and Power Sector Delivered Coal Price (2019
dollars) for the Baseline and the Illustrative Scenarios 3-22
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Table 3-12 2028, 2030, 2035 and 2040 Projected Henry Hub and Power Sector Delivered Natural Gas Price
(2019 dollars) for the Baseline and the Illustrative Scenarios 3-23
Table 3-13 2028, 2030, 2035 and 2040 Projected U.S. Generation by Fuel Type for the Baseline and the
Illustrative Scenarios 3-24
Table 3-14 2028, 2030, 2035 and 2040 Projected U.S. Capacity by Fuel Type for the Baseline and the Illustrative
Scenarios 3-27
3-29
3-30
3-31
4-10
Table 3-15 Average Retail Electricity Price by Region for the Baseline and the Illustrative Scenarios, 2030
Table 3-16 Average Retail Electricity Price by Region for the Baseline and the Illustrative Scenarios, 2035
Table 3-17 Average Retail Electricity Price by Region for the Baseline and the Illustrative Scenarios, 2040
Table 4-1 Interim Social Cost of Carbon Values, 2028 to 2042 (2019 dollars per metric ton CO2)
Table 4-2 Annual CO2 Emissions Reductions (million metric tons) for the Illustrative Scenarios from 2028
through 2042 4-15
Table 4-3 Benefits of Reduced CO2 Emissions from the Illustrative Proposal Scenario, 2028 to 2042 (millions of
2019 dollars) 4-16
Table 4-4 Benefits of Reduced CO2 Emissions from the Illustrative Less Stringent Scenario, 2028 to 2042
(millions of 2019 dollars) 4-17
Table 4-5 Benefits of Reduced CO2 Emissions from the Illustrative More Stringent Scenario, 2028 to 2042
(millions of 2019 dollars) 4-18
Table 4-6 Health Effects of Ambient Ozone and PM2.5 and Climate Effects 4-24
Table 4-7 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the Illustrative
Scenarios for 2028 (95 percent confidence interval) 4-41
Table 4-8 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the Illustrative
Scenarios in 2030 (95 percent confidence interval) 4-42
Table 4-9 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the Illustrative
Scenarios in 2035 (95 percent confidence interval) 4-43
Table 4-10 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the Illustrative
Scenarios in 2040 (95 percent confidence interval) 4-44
Table 4-11 Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses for the Illustrative
Scenarios in 2028 (95 percent confidence interval) 4-45
Table 4-12 Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses for the Illustrative
Scenarios in 2030 (95 percent confidence interval) 4-46
Table 4-13 Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses for the Illustrative
Scenarios in 2035 (95 percent confidence interval) 4-47
Table 4-14 Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses for the Illustrative
Scenarios in 2040 (95 percent confidence interval) 4-48
Table 4-15 Estimated Discounted Economic Value of Avoided Ozone and PM2 5-Attributable Premature
Mortality and Illness for the Illustrative Scenarios in 2028 (95 percent confidence interval; millions of
2019 dollars) 4-49
Table 4-16 Estimated Discounted Economic Value of Avoided Ozone and PM2 5-Attributable Premature
Mortality and Illness for the Illustrative Scenarios in 2030 (95 percent confidence interval; millions of
2019 dollars) 4-50
Table 4-17 Estimated Discounted Economic Value of Avoided Ozone and PM2 5-Attributable Premature
Mortality and Illness for the Illustrative Scenarios in 2035 (95 percent confidence interval; millions of
2019 dollars) 4-51
Table 4-18 Estimated Discounted Economic Value of Avoided Ozone and PM2 5-Attributable Premature
Mortality and Illness for the Illustrative Scenarios in 2040 (95 percent confidence interval; millions of
2019 dollars) 4-52
Table 4-19 Estimated Discounted Economic Value of Avoided Ozone and PM2 5-Attributable Premature
Mortality and Illness for the Illustrative Scenarios in 2028, 2030, 2035 and 2040 (95 percent
confidence interval; millions of 2019 dollars) 4-53
Table 4-20 Stream of Human Health Benefits from 2028 through 2042: Monetized Benefits Quantified as Sum of
Long-Term Ozone Mortality and Illness and Long-Term PM2 5 Mortality and Illness for EGUs
(discounted at 3 percent; millions of 2019 dollars) 4-54
Table 4-21 Stream of Human Health Benefits from 2028 through 2042: Monetized Benefits Quantified as Sum of
Long-Term Ozone Mortality and Illness and Long-Term PM2 5 Mortality and Illness for EGUs
(discounted at 7 percent; millions of 2019 dollars) 4-55
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Table
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Table
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Table
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Table
4-25
Table
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Table
4-27
Table
4-28
Table
4-29
Table
5-1
Table
5-2
Table
5-3
Table
5-4
Table
5-5
Table
5-6
Table
6-1
Table
6-2
Table
6-3
Table
7-1
Table
7-2
Table
7-3
Table
7-4
Table
7-5
Table
7-6
Table
7-7
Table
8-1
Table
8-2
Table
8-3
Table
8-4
Table
8-5
Table
8-6
Table
8-7
Unqualified Health and Welfare Benefits Categories 4-56
Combined Monetized Climate Benefits and PM2 5 and 03-related Health Benefits for the Illustrative
Scenarios for 2028 (billions of 2019 dollars) 4-69
Combined Monetized Climate Benefits and PM2 5 and 03-related Health Benefits for the Illustrative
Scenarios for 2030 (billions of 2019 dollars) 4-70
Combined Monetized Climate Benefits and PM2 5 and 03-related Health Benefits for the Illustrative
Scenarios for 2035 (billions of 2019 dollars) 4-71
Combined Monetized Climate Benefits and PM2 5 and 03-related Health Benefits for the Illustrative
Scenarios for 2040 (billions of 2019 dollars) 4-72
Stream of Monetized Combined Climate Benefits and PM2 5 and Ch-related Health Benefits for the
Illustrative Proposal Scenario from 2024 through 2042 (billions of 2019 dollars) 4-73
Stream of Monetized Combined Climate Benefits and PM2 5 and Ch-related Health Benefits for the
Illustrative Less Stringent Scenario from 2024 through 2042 (billions of 2019 dollars) 4-74
Stream of Monetized Combined Climate Benefits and PM2 5 and Ch-related Health Benefits for the
Illustrative More Stringent Scenario from 2024 through 2042 (billions of 2019 dollars) 4-75
Summary of Certain Energy Market Impacts (percent change) 5-2
SBA Size Standards by NAICS Code 5-8
Historical NGCC and NGCT Additions (2017-present) 5-9
Projected Impact of the Proposed Rule on Small Entities in 2035 5-11
Changes in Labor Utilization: Construction-Related (number of job-years of employment in a single
year) 5-17
Changes in Labor Utilization: Recurring Non-Construction (number of job-years of employment in a
single year) 5-17
Proximity Demographic Assessment Results Within 10 km of Coal-Fired Units Greater than 25 MW
Affected by these Proposed Rulemakings 6-9
Proximity Demographic Assessment Results Within 50 km of Coal-Fired Units Greater than 25 MW
Affected by these Proposed Rulemakings 6-10
Demographic Populations Included in the Ozone and PM2 5 EJ Exposure Analysis 6-13
Monetized Benefits, Costs, and Net Benefits of the Three Illustrative Scenarios in 2028 (billion 2019
dollars) 7-4
Monetized Benefits, Costs, and Net Benefits of the Three Illustrative Scenarios in 2030 (billion 2019
dollars) 7-4
Monetized Benefits, Costs, and Net Benefits of the Three Illustrative Scenarios in 2035 (billion 2019
dollars) 7-5
Monetized Benefits, Costs, and Net Benefits of the Three Illustrative Scenarios in 2040 (billion 2019
dollars) 7-5
Illustrative Proposal Scenario: Present Values and Equivalent Annualized Values of Projected
Monetized Compliance Costs, Benefits, and Net Benefits for 2024 to 2042 (billion 2019 dollars)... 7-6
Illustrative Less Stringent Scenario: Present Values and Equivalent Annualized Values of Projected
Monetized Compliance Costs, Benefits, and Net Benefits for 2024 to 2042 (billion 2019 dollars)... 7-7
Illustrative More Stringent Scenario: Present Values and Equivalent Annualized Values of Projected
Monetized Compliance Costs, Benefits, and Net Benefits for 2024 to 2042 (billion 2019 dollars)... 7-8
GHG Mitigation Measures for Existing NGCC Units under the Illustrative Proposal, More Stringent
and Less Stringent Scenarios 8-1
GHG Mitigation Measures for New NGCC Units under the Illustrative Proposal, More Stringent and
Less Stringent Scenarios 8-2
Estimated Changes in Power Sector Emissions from Existing Source Standard under the Three
Illustrative Scenarios 8-5
Estimated Changes in Power Sector Emissions from New Source Standard under the Three Illustrative
Scenarios 8-6
Estimated Changes in Power Sector Costs from Existing Source Standard under the Three Illustrative
Scenarios (billion 2019 dollars) 8-6
Estimated Changes in Power Sector Costs from New Source Standard under the Three Illustrative
Scenarios (billion 2019 dollars) 8-7
Estimated Changes in Power Sector Generation from Existing Source Standard under the Three
Illustrative Scenarios 8-8
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Table 8-8 Estimated Changes in Power Sector Generation from New Source Standard under the Three
Illustrative Scenarios 8-11
Table 8-9 Estimated Changes in Power Sector Hydrogen Demand from New Source Standard under the Three
Illustrative Scenarios 8-12
Table 8-10 Annual CO2 Emissions Reductions (million metric tons) for the 111(d) Standards on Existing Natural
Gas-Fired EGUs Illustrative Scenarios from 2028 through 2042 8-13
Table 8-11 Range of Benefits of Reduced CO2 Emissions from the 111(d) Standards on Existing Natural Gas-
Fired EGUs Illustrative Proposal Scenario, 2028 to 2042 (millions of 2019 dollars) 8-15
Table 8-12 Range of Benefits of Reduced CO2 Emissions from the 111(d) Standards on Existing Natural Gas-
Fired EGUs Illustrative Less Stringent Scenario, 2028 to 2042 (millions of 2019 dollars) 8-16
Table 8-13 Range of Benefits of Reduced CO2 Emissions from the 111(d) Standards on Existing Natural Gas-
Fired EGUs Illustrative More Stringent Scenario, 2028 to 2042 (millions of 2019 dollars) 8-17
Table 8-14 Annual CO2 Emissions Reductions (million metric tons) for the 111(b) Standards on New Natural
Gas-Fired EGUs Illustrative Scenarios from 2028 through 2042 8-18
Table 8-15 Range of Benefits of Reduced CO2 Emissions from the 111(b) Standards on New Natural Gas-Fired
EGUs Illustrative Proposal Scenario, 2028 to 2042 (millions of 2019 dollars) 8-19
Table 8-16 Range of Benefits of Reduced CO2 Emissions from the 111(b) Standards on New Natural Gas-Fired
EGUs Illustrative Less Stringent Scenario, 2028 to 2042 (millions of 2019 dollars) 8-20
Table 8-17 Range of Benefits of Reduced CO2 Emissions from the 111(b) Standards on New Natural Gas-Fired
EGUs Illustrative More Stringent Scenario, 2028 to 2042 (millions of 2019 dollars) 8-21
Table 8-18 Present Values and Equivalent Annualized Values of Estimated Compliance Costs of Three
Illustrative Scenarios for 2028 to 2042, Calculated using 3 Percent Discount Rate (billion 2019
dollars) 8-22
Table 8-19 Present Values and Equivalent Annualized Values of Estimated Compliance Costs of Three
Illustrative Scenarios for 2028 to 2042, Calculated using 7 Percent Discount Rate (billion 2019
dollars) 8-23
Table 8-20 Present Values and Equivalent Annualized Values of Estimated Climate Benefits for the Three
Illustrative Scenarios for 2028 to 2042, Calculated using 3 Percent Discount Rate (billion 2019
dollars) 8-24
Table A-l 2026 Emissions Allocated to Each Modeled State-EGU Source Apportionment Tag A-4
Table A-2 Ozone Scaling Factors for EGU Tags in the Baseline and Illustrative Scenarios A-16
Table A-3 Nitrate Scaling Factors for EGU Tags in the Baseline and Illustrative Scenarios A-17
Table A-4 Sulfate Scaling Factors for EGU Tags in the Baseline and Illustrative Scenarios A-18
Table A-5 Primary PM2 5 Scaling Factors for EGU Tags in the Baseline and Illustrative Scenarios A-19
Table B-l SAGE Dimensional Details B-4
Table B-2 IPM Cost Outputs B-10
Table B-3 Social Costs (billions of 2019 dollars) B-13
Table C-l Summary of GHG Mitigation Measures for Existing Sources by Source Category under the Proposal
C-2
Table C-2 Summary of GHG Mitigation Measures for New Sources by Source Category under the Proposal..C-3
Table C-3 National Power Sector Compliance Cost Estimates for the Illustrative Scenarios (billions of 2019
dollars) C-4
Table C-4 EGU Annual CO2 Emissions and Emissions Changes (million metric tons) for the Baseline and the
Illustrative Scenarios from 2028 to 2045 C-6
Table C-5 EGU Annual Emissions and Emissions Changes for Annual NOx, Ozone Season (April to September)
NOx, SO2, and Direct PM2 5 for the Baseline and Illustrative Scenarios for 2028 to 2040 C-8
Table C-6 2028, 2030, 2035 and 2040 Projected U.S. Power Sector Coal Use for the Baseline and the Illustrative
Scenarios C-10
Table C-l 2028, 2030, 2035 and 2040 Projected Power Sector Natural Gas Use for the Baseline and the
Illustrative Scenarios C-ll
Table C-8 2028, 2030, 2035 and 2040 Projected Minemouth and Power Sector Delivered Coal Price (2019
dollars) for the Baseline and the Illustrative Scenarios C-ll
Table C-9 2028, 2030, 2035 and 2040 Projected Henry Hub and Power Sector Delivered Natural Gas Price
(2016 dollars) for the Baseline and the Illustrative Scenarios C-12
Table C-10 2028, 2030, 2035 and 2040 Projected U.S. Generation by Fuel Type for the Baseline and the
Illustrative Scenarios C-l3
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Table C-l 1 2028, 2030, 2035 and 2040 Projected U.S. Capacity by Fuel Type for the Baseline and the Illustrative
Scenarios C-l 5
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TABLE OF FIGURES
Figure 2-1 National Coal-fired Capacity (GW) by Age of EGU, 2021 2-5
Figure 2-2 Average Annual Capacity Factor by Energy Source 2-7
Figure 2-3 Cumulative Distribution in 2020 of Coal and Natural Gas Electricity Capacity and Generation, by Age
2-9
Figure 2-4 Fossil Fuel-Fired Electricity Generating Facilities, by Size 2-10
Figure 2-5 Selected Historical Mean LCOE Values 2-11
Figure 2-6 Real National Average Electricity Prices (including taxes) for Three Major End-Use Categories.. 2-17
Figure 2-7 Relative Real Prices of Fossil Fuels for Electricity Generation; Change in National Average Real
Price per MMBtu Delivered to EGU 2-18
Figure 2-8 Relative Growth of Electricity Generation, Population and Real GDP Since 2010 2-19
Figure 2-9 Relative Change of Real GDP, Population and Electricity Generation Intensity Since 2010 2-20
Figure 3-1 Electricity Market Module Regions 3-32
Figure 4-1 Frequency Distribution of SC-CO2 Estimates for 2030 4-11
Figure 4-2 Data Inputs and Outputs for the BenMAP-CE Model 4-28
Figure 6-1 Number of People Residing in the Contiguous U.S. Areas Improving or Not Changing (Blue) or
Worsening (Orange) in 2028, 2030, 2035, and 2040 for PM2 5 and Ozone and the National Average
Magnitude of Pollutant Concentration Changes (|ig/m3 and ppb) for the 3 Regulatory Options 6-14
Figure 6-2 Heat Map of the National Average PM2 5 Concentrations in the Baseline Across Demographic Groups
in 2028, 2030, 2035, and 2040 (ug/m:) 6-16
Figure 6-3 Heat Map of the Reductions in National Average PM2 5 Concentrations Due to the Three Illustrative
Scenarios Across Demographic Groups in 2028, 2030, 2035, and 2040 (ng/m3) 6-17
Figure 6-4 Map of the State Average PM2 5 Concentration Reductions (Blue) and Increases (Red) Due to the
Three Illustrative Scenarios Across Demographic Groups in 2028, 2030, 2035, and 2040 (|ig/m3) 6-19
Figure 6-5 Distributions of PM25 Concentration (|ig/m3) Changes Across Populations, Future Years, and
Regulatory Options 6-21
Figure 6-6 Heat Map of the National Average Ozone Concentrations in the Baseline Across Demographic
Groups in 2028, 2030, 2035, and 2040 (ppb) 6-24
Figure 6-7 Heat Map of Reductions (Green) and Increases (Red) in National Average Ozone Concentrations Due
to the Three Illustrative Scenarios Across Demographic Groups in 2028, 2030, 2035, and 2040 (ppb)
6-25
Figure 6-8 Heat Map of the State Average Ozone Concentrations Reductions (Green) and Increases (Red) Due to
the Three Illustrative Scenarios Across Demographic Groups in 2028, 2030, 2035. and 2040 (ppb).. 6-
27
Figure 6-9 Distributions of Ozone Concentration Changes (ppb) Across Populations, Future Years, and
Regulatory Options 6-29
Figure A-l Air Quality Modeling Domain A-3
Figure A-2 Maps of California EGU Tag contributions to a) April-September Seasonal Average MDA8 Ozone
(ppb); b) Annual Average PM2 5 Nitrate (|ig/m3): c) Annual Average PM2 5 sulfate (|ig/m3): d) Annual
Average PM2 5 Organic Aerosol (ng/m3) A-6
Figure A-3 Maps of Texas EGU Tag contributions to a) April-September Seasonal Average MDA8 Ozone (ppb);
b) Annual Average PM2 5 Nitrate (|ig/m3): c) Annual Average PM2 5 sulfate (|ig/m3): d) Annual
Average PM2 5 Organic Aerosol (ng/m3) A-7
Figure A-4 Maps of Iowa EGU Tag contributions to a) April-September Seasonal Average MDA8 Ozone (ppb);
b) Annual Average PM2 5 Nitrate (|ig/m3): c) Annual Average PM2 5 sulfate (|ig/m3): d) Annual
Average PM2 5 Organic Aerosol (ng/m3) A-8
Figure A-5 Maps of Ohio EGU Tag contributions to a) April-September Seasonal Average MDA8 Ozone (ppb);
b) Annual Average PM2 5 Nitrate (|ig/m3): c) Annual Average PM2 5 sulfate (|ig/m3): d) Annual
Average PM2 5 Organic Aerosol (ng/m3) A-9
Figure A-6 Maps of ASM-03 in 2028 A-22
Figure A-7 Maps of ASM-03 in 2030 A-23
Figure A-8 Maps of ASM-03 in 2035 A-24
Figure A-9 Maps of ASM-03 in 2040 A-25
x
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Figure A-10 Maps of PM2.5 in 2028 A-26
Figure A-l 1 Maps of PM2.5 in 2030 A-27
Figure A-12 Maps of PM2.5 in 2035 A-28
Figure A-13 Maps of PM2.5 in 2040 A-29
Figure B-l Depiction of the Circular Flow of the Economy B-3
Figure B-2 Hybrid Linkage Approach for IPM and SAGE B-8
Figure B-3 Percent Change in Real GDP and Components B-14
Figure B-4 Percent Change in Sectoral Output (Electricity, Coal, Natural Gas) B-16
Figure B-5 Percent Change in Sectoral Output (Rest of Economy) B-17
Figure B-6 Percent Change in Economy-wide Sectoral Output (All Sectors) B-18
Figure B-7 Percent Change in Real Output Prices B-19
Figure B-8 Percent Change in Economy-wide Labor Demand (All Sectors) B-21
Figure B-9 Percent Change in Labor Demand (Electricity, Coal, Natural Gas) B-21
Figure B-10 Percent Change in Labor Demand (Rest of Economy) B-22
Figure B-l 1 Distribution of General Equilibrium Social Costs B-23
XI
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EXECUTIVE SUMMARY
ES.l Introduction
In 2009, the EPA concluded that GHG emissions endanger our nation's public health and
welfare.1 Since that time, the evidence of the harms posed by GHG emissions has only grown
and Americans experience the destructive and worsening effects of climate change every day.
Fossil fuel-fired EGUs are the nation's largest stationary source of GHG emissions, representing
25 percent of the United States' total GHG emissions in 2020. At the same time, a range of cost-
effective technologies and approaches to reduce GHG emissions from these sources are available
to the power sector, and multiple projects are in various stages of operation and development—
including carbon capture and sequestration/storage (CCS) and co-firing with lower-GHG fuels.
Congress has also acted to provide funding and other incentives to encourage the deployment of
these technologies to achieve reductions in GHG emissions from the power sector.
In this notice, the EPA is proposing several actions under section 111 of the Clean Air
Act (CAA) to reduce the significant quantity of GHG emissions from new and existing fossil
fuel-fired EGUs by establishing new source performance standards (NSPS) and emission
guidelines that are based on available and cost-effective technologies that directly reduce GHG
emissions from these sources. Consistent with the statutory command of section 111, the
proposed NSPS and emission guidelines reflect the application of the best system of emission
reduction (BSER) that, taking into account costs, energy requirements, and other statutory
factors, is adequately demonstrated.
Specifically, the EPA is proposing to update and establish more protective NSPS for
GHG emissions from new and reconstructed fossil fuel-fired stationary combustion turbine
EGUs that are based on highly efficient generating practices, hydrogen co-firing, and CCS. The
EPA is also proposing to establish new emission guidelines for existing fossil fuel-fired steam
generating EGUs that reflect the application of CCS and the availability of natural gas co-firing.
The EPA is simultaneously proposing to repeal the Affordable Clean Energy (ACE) rule because
the emission guidelines established in ACE do not reflect the BSER for steam generating EGUs
and are inconsistent with section 111 of the CAA in other respects. To address GHG emissions
1 74 FR 66496 (December 15, 2009).
ES-1
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from existing fossil fuel-fired stationary combustion turbines, the EPA is proposing emission
guidelines for large and frequently used existing stationary combustion turbines. Further, the
EPA is soliciting comment on how the Agency should approach its legal obligation to establish
emission guidelines for the remaining existing fossil fuel-fired combustion turbines not covered
by this proposal, including smaller frequently used, and less frequently used, combustion
turbines.
Each of the NSPS and emission guidelines proposed here would ensure that EGUs reduce
their GHG emissions in a manner that is cost-effective and improves the emissions performance
of the sources, consistent with the applicable CAA requirements and caselaw. These proposed
standards and emission guidelines, if finalized, would significantly decrease GHG emissions
from fossil fuel-fired EGUs and the associated harms to human health and welfare. Further, the
EPA has designed these proposed standards and emission guidelines in a way that is compatible
with the nation's overall need for a reliable supply of affordable electricity.
In accordance with Executive Order (E.O). 12866 and 13563, the guidelines of OMB
Circular A-4 and EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2014), the
RIA analyzes the benefits and costs associated with the projected emissions reductions under the
proposed requirements, a less stringent set of requirements, and a more stringent set of
requirements to inform EPA and the public about these projected impacts. With respect to the
new source standard, the more stringent scenario differs from the proposal in that it assumes
imposition of the second phase of the NSPS in run year 2030, while the proposal and less
stringent scenarios assume imposition of the second phase of the NSPS in run year 2035. With
regards to the existing source standard, the proposal and more stringent scenarios assume all
long-term existing coal-fired steam generating units are subject to 90 percent CCS requirements
in 2030, while the less stringent scenario assumes that long-term existing coal-fired steam
generating units greater than 700 MW, and plants greater than 2,000 MW are subject to 90
percent CCS requirements, while those units less than 700 MW (and plants less than 2,000 MW)
are subject to 40 percent natural gas co-firing requirements. We evaluated the potential impacts
of the three illustrative scenarios using the present value (PV) of costs, benefits, and net benefits,
calculated for the years 2024 to 2042, discounted to 2024. In addition, the Agency presents the
assessment of costs, benefits, and net benefits for specific snapshot years, consistent with historic
practice. These snapshot years are 2028, 2030, 2035, and 2040.
ES-2
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ES.2 Regulatory Requirements
These actions include proposed BSER determinations and accompanying standards of
performance for GHG emissions from new and reconstructed fossil fuel-fired stationary
combustion turbines, proposed repeal of the ACE Rule, proposed BSER determinations and
emission guidelines for existing fossil fuel-fired steam generating units, proposed BSER
determinations and emission guidelines for large, frequently used existing fossil fuel-fired
stationary combustion turbines, and solicitation for comment on potential BSER options and
emission guidelines for existing fossil fuel-fired stationary combustion turbines not otherwise
covered by the proposal.
For new and reconstructed fossil fuel-fired combustion turbines, the EPA is proposing to
create three subcategories based on the function the combustion turbine serves: a low load
("peaking units") subcategory that consists of combustion turbines with a capacity factor of less
than 20 percent; an intermediate load subcategory for combustion turbines with a capacity factor
that ranges between 20 percent and a source-specific upper bound that is based on the design
efficiency of the combustion turbine; and a base load subcategory for combustion turbines that
operate above the upper-bound threshold for intermediate load turbines. This subcategorization
approach is similar to the current NSPS for these sources, which includes separate subcategories
for base load and non-base load units; however, the EPA is now proposing to subdivide the non-
base load subcategory into a low load subcategory and a separate intermediate load subcategory.
This revised approach to subcategories is consistent with the fact that utilities and power plant
operators are building new combustion turbines with plans to operate them at varying levels of
capacity, in coordination with existing and expected energy sources. These patterns of operation
are important for the type of controls that the EPA is proposing as the BSER for these turbines,
in terms of the feasibility of, emissions reductions that would be achieved by, and cost-
reasonableness of, those controls.
For the low load subcategory, the EPA is proposing that the BSER is the use of lower
emitting fuels (e.g., natural gas and distillate oil) with standards of performance ranging from
120 lb CCh/MMBtu to 160 lb CCh/MMBtu, depending on the type of fuel combusted.2 For the
2 In the 2015 NSPS, the EPA referred to clean fuels as fuels with a consistent chemical composition (i.e., uniform
fuels) that result in a consistent emission rate of 69 kilograms per gigajoule (kg/GJ) (160 lb C02/MMBtu). Fuels
ES-3
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intermediate load and base load subcategories, the EPA is proposing an approach in which the
BSER has multiple components: (1) highly efficient generation; and (2) depending on the
subcategory, use of CCS or co-firing low-GHG hydrogen.
These components of the BSER for the intermediate and base load subcategories form the
basis of a standard of performance that applies in multiple phases. That is, affected facilities—
which are facilities that commence construction or reconstruction after the date of publication in
the Federal Register of this proposed rulemaking—must meet the first phase of the standard of
performance, which is based exclusively on application of the first component of the BSER
(highly efficient generation), by the date the rule is promulgated. Affected sources in the
intermediate load and base load subcategories must also meet the second and in some cases third
and more stringent phases of the standard of performance, which are based on the continued
application of the first component of the BSER and the application of the second and in some
cases third component of the BSER. For base load units, the EPA is proposing two pathways as
potential BSER—(1) the use of CCS to achieve a 90 percent capture of GHG emissions by 2035
and (2) the co-firing of 30 percent (by volume) low-GHG hydrogen by 2032 and, ramping up to
96 percent by volume low-GHG hydrogen by 2038. These two BSER pathways both offer
significant opportunities to reduce GHG emissions but, may be available on slightly different
timescales.
More specifically, with respect to the first phase of the standards of performance, the
EPA is proposing that the BSER for both the intermediate load and base load subcategories
includes highly efficient generating technology {i.e., the most efficient available turbines). For
the intermediate load subcategory, the EPA is proposing that the BSER includes highly efficient
simple cycle combustion turbine technology with an associated first phase standard of 1,150 lb
CCh/MWh-gross. For the base load subcategory, the EPA is proposing that the BSER includes
highly efficient combined cycle technology with an associated first phase standard of 770 lb
CCh/MWh-gross for larger combustion turbine EGUs with a base load rating of 2,000 MMBtu/h
or more. For smaller base load combustion turbines (with a base load rating of less than 2,000
MMBtu/h), the proposed associated standard would range from 770 to 900 lb CCh/MWh-gross
in this category include natural gas and distillate oil. In this rulemaking, the EPA refers to these fuels as both
lower emitting fuels or uniform fuels.
ES-4
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depending on the specific base load rating of the combustion turbine. These standards would
apply immediately upon the effective date of the final rule.
With respect to the second phase of the standards of performance, for the intermediate
load subcategory, the EPA is proposing that the BSER includes co-firing 30 percent by volume
low-GHG hydrogen (unless otherwise noted, all co-firing hydrogen percentages are on a volume
basis) with an associated standard of 1,000 lb CCh/MWh-gross, compliance with which would be
required starting in 2032. For the base load subcategory, to elicit comment on both pathways, the
EPA is proposing to subcategorize further into base load units that are adopting the CCS
pathway and base load units that are adopting the low-GHG hydrogen co-firing pathway. For the
subcategory of base load units that are adopting the CCS pathway, the EPA is proposing that the
BSER includes the use of CCS with 90 percent capture of CO2 with an associated standard of 90
lb CCh/MWh-gross, compliance with which would be required starting in 2035. For the
subcategory of base load units that are adopting the low-GHG hydrogen co-firing pathway, the
EPA is proposing that the BSER includes co-firing 30 percent (by volume) low-GHG hydrogen
with an associated standard of 680 lb CCh/MWh-gross, compliance with which would be
required starting in 2032, and co-firing 96 percent (by volume) low-GHG hydrogen by 2038,
which corresponds to a standard of performance of 90 lb CCh/MWh-gross. In both cases, the
second (and sometimes third) phase standard of performance would be applicable to all
combustion turbines that were subject to the first phase standards of performance.
With respect to existing coal-fired steam generating units, the EPA is proposing to repeal
and replace the existing ACE Rule emission guidelines. The EPA recognizes that, since it
promulgated the ACE Rule, the costs of CCS have decreased due to technology advancements as
well as new policies including the expansion of the Internal Revenue Code section 45Q tax credit
for CCS in the Inflation Reduction Act (IRA); and the costs of natural gas co-firing have
decreased as well, due in large part to a decrease in the difference between coal and natural gas
prices. As a result, the EPA considered both CCS and natural gas co-firing as candidates for
BSER for existing coal-fired steam EGUs.
Based on the latest information available to the Agency on cost, emission reductions, and
other statutory criteria, the EPA is proposing that the BSER for existing coal-fired steam EGUs
that expect to operate in the long-term is CCS with 90 percent capture of CO2. The EPA has
ES-5
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determined that CCS satisfies the BSER criteria for these sources because it is adequately
demonstrated, achieves significant reductions in GHG emissions, and is highly cost-effective.
In response to industry stakeholder input descried in sections I.B.2 and X.C.3 of the
preamble, and recognizing that the cost effectiveness of controls depends on the unit's expected
operating time horizon, which dictates the amortization period for the capital costs of the
controls, the EPA believes it is appropriate to establish subcategories of existing steam EGUs
that are based on the operating horizon of the units. The EPA is proposing that for units that
expect to operate in the long-term (i.e., those that plan to operate past December 31, 2039), the
BSER is the use of CCS with 90 percent capture of CO2 with an associated degree of emission
limitation of an 88.4 percent reduction in emission rate (lb CCh/MWh-gross basis). As explained
in detail in this proposal, CCS with 90 percent capture of CO2 is adequately demonstrated, cost
reasonable, and achieves substantial emissions reductions from these units.
The EPA is proposing to define coal-fired steam generating units with medium-term
operating horizons as those that (1) operate after December 31, 2031, (2) have elected to commit
to permanently cease operations before January 1, 2040, (3) elect to make that commitment
federally enforceable and continuing by including it in the state plan, and (4) do not meet the
definition of near-term operating horizon units. For these medium-term operating horizon units,
the EPA is proposing that the BSER is co-firing 40 percent natural gas on a heat input basis with
an associated degree of emission limitation of a 16 percent reduction in emission rate (lb
CCh/MWh-gross basis). While this subcategory is based on a 10-year operating horizon (i.e.,
January 1, 2040), the EPA is specifically soliciting comment on the potential for a different
operating horizon between 8 and 10 years to define the threshold date between the definition of
medium-term and long-term coal-fired steam generating units (i.e., January 1, 2038 to January 1,
2040), given that the costs for CCS may be reasonable for units with amortization periods as
short as 8 years. For units with operating horizons that are imminent-term, i.e., those that (1)
have elected to commit to permanently cease operations before January 1, 2032, and (2) elect to
make that commitment federally enforceable and continuing by including it in the state plan, the
EPA is proposing that the BSER is routine methods of operation and maintenance with an
associated degree of emission limitation of no increase in emission rate (lb CCh/MWh-gross
basis). The EPA is proposing the same BSER determination for units in the near-term operating
horizon subcategory, i.e., units that (1) have elected to commit to permanently cease operations
ES-6
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by December 31, 2034, as well as to adopt an annual capacity factor limit of 20 percent, and (2)
elect to make both of these conditions federally enforceable by including them in the state plan.
The EPA is also soliciting comment on a potential BSER based on low levels of natural gas co-
firing for units in these last two subcategories.
The EPA is also proposing emission guidelines for existing natural gas-fired and oil-fired
steam generating units. Recognizing that virtually all of these units have limited operation, the
EPA is, in general, proposing that the BSER is routine methods of operation and maintenance
with an associated degree of emission limitation of no increase in emission rate (lb CCh/MWh-
gross).
The EPA is also proposing emission guidelines for large {i.e., greater than 300 MW),
frequently operated {i.e., with a capacity factor of greater than 50 percent), existing fossil fuel-
fired stationary combustion turbines. Because these existing combustion turbines are similar to
new stationary combustion turbines, the EPA is proposing a BSER that is similar to the BSER
for new base load combustion turbines. The EPA is not proposing a first phase efficiency-based
standard of performance; but the EPA is proposing that BSER for these units is based on either
the use of CCS by 2035 or co-firing of 30 percent (by volume) low-GHG hydrogen by 2032 and
co-firing 96 percent low-GHG hydrogen by 2038.
For the emission guidelines for existing fossil fuel-fired steam generating units and large,
frequently operated fossil fuel-fired combustion turbines, the EPA is also proposing state plan
requirements, including submittal timelines for state plans and methodologies for determining
presumptively approvable standards of performance consistent with BSER. This proposal also
addresses how states can implement the remaining useful life and other factors (RULOF)
provision of CAA section 111(d) and how states can conduct meaningful engagement with
impacted stakeholders. Finally, the EPA is proposing to allow states to include trading or
averaging in state plans so long as they demonstrate equivalent emissions reductions, and this
proposal discusses considerations related to the appropriateness of including such compliance
flexibilities.
ES-7
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ES.3 Baseline and Analysis Years
The impacts of proposed regulatory actions are evaluated relative to a modeled baseline
that represents expected behavior in the electricity sector under market and regulatory conditions
in the absence of a regulatory action. EPA frequently updates the power sector modeling baseline
to reflect the latest available electricity demand forecasts from the U.S. Energy Information
Administration (EIA) at the time the modeling was completed as well as expected costs and
availability of new and existing generating resources, fuels, emission control technologies, and
regulatory requirements. The electricity supply baseline includes the proposed Good Neighbor
Plan (GNP), the Revised Cross-State Air Pollution Rule (CSAPR) Update, CSAPR Update, and
CSAPR, as well as the 2012 Mercury and Air Toxics Standards. The power sector baseline also
includes the 2015 Effluent Limitation Guidelines (ELG) and the 2015 Coal Combustion
Residuals (CCR), and the recently finalized 2020 ELG and CCR rules. This version of the model
("EPA's post-IRA IPM 2022 reference case") also includes recent updates to state and federal
legislation affecting the power sector, including Public Law 117-169, 136 Stat. 1818 (August 16,
2022), commonly known as the Inflation Reduction Act of 2022 (IRA). The modeling
documentation, available in the docket, includes a summary of all legislation reflected in this
version of the model as well as a description of how that legislation is implemented in the model.
Also, see Section 3 for additional detail about the power sector baseline for this RIA.
This RIA evaluates the benefits, costs, and certain impacts of compliance with three
illustrative scenarios: the proposal, a less stringent scenario, and a more stringent scenario, which
assume both existing and new source GHG mitigation requirements. For details of the controls
modeled for each of the source categories under the three illustrative scenarios, please see
Section 3.2 of this RIA.
We evaluated the potential benefits, costs, and net benefits of the three illustrative
scenarios for the years 2024 to 2042 from the perspective of 2024, using both three percent and
seven percent discount rates. In addition, the Agency presents the assessment of costs, benefits,
and net benefits for specific snapshot years, consistent with historic practice. These snapshot
years are 2028, 2030, 2035, and 2040. The Agency believes that these specific years are each
representative of several surrounding years, which enables the analysis of costs and benefits over
the timeframe of 2024 to 2042. The year 2028 is the first year of detailed power sector modeling
ES-8
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for this RIA and approximates when the regulatory impacts of the proposed 111(b) new source
performance standards on the power sector will begin. However, because the Agency estimates
that some monitoring, reporting, and recordkeeping (MR&R) costs may be incurred in 2024, we
analyze compliance costs in years before 2028. Therefore, while MR&R costs analysis is
presented beginning in the year 2024, the detailed assessment of costs, emissions impacts, and
benefits begins in the year 2028. The analysis timeframe concludes in 2042, as this is the last
year that may be represented with the analysis conducted for the specific year of 2040. While the
results are described and presented in more detail later in this executive summary and throughout
the RIA, we present the high-level results of the analysis here.
The modeling of the illustrative proposal scenario that is discussed in Sections 3 through
7 of this RIA (and Sections 0.4 through 0.9 of the Executive Summary) includes all aspects of
the proposed 111(d) requirements for existing fossil fuel-fired steam generating units and most
aspects of the proposed 111(b) requirements for new and reconstructed stationary combustion
turbines. However, it does not reflect the proposed 111(d) requirements for existing stationary
combustion turbines or one additional component of the 111(b) requirements (for new base load
combustion turbines in the hydrogen co-firing subcategory, the third phase standard based on co-
firing 96 percent low-GHG hydrogen by 2038). For these additional measures, EPA performed a
spreadsheet-based analysis of regulatory impacts that is discussed in Section 8 of this RIA (and
in Section ES.10 of the Executive Summary).
ES.4 Emissions Impacts
The emissions impacts presented in this RIA are from years 2028, 2030, 2035, and 2040
and are based on Integrated Planning Model (IPM) projections.3 Table ES-1 presents the
estimated impact on power sector emissions in the contiguous U.S. resulting from compliance
with the proposed rules as modeled by the illustrative proposal scenario. The projections indicate
that the illustrative proposal scenario and less stringent scenario result in national emission
reductions of CO2, direct PM2.5, NOx, and SO2 throughout the year for each of the snapshot
years analyzed. The projections indicate that the more stringent scenario results in national
3 Section ES.4 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section ES. 10 for impact analysis of the proposed standards for existing natural gas-
fired EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
ES-9
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emissions reductions of CO2 and SO2 throughout the year for the snapshot years analyzed, but
national emission increases of NOx in 2028, both annually and during the ozone season under
the more stringent scenario. Under the more stringent scenario, hydrogen co-firing requirements
for new NGCC builds are in effect in the 2030 run year as compared to 2035 under the proposal
and less stringent scenarios. As a result, anticipating weaker economics for new NGCC builds,
there are 0.8 GW fewer NGCC additions and 3.3 GW greater NGCT additions projected relative
to the baseline. This in turn results in slightly higher NOx emissions in 2028. In 2030,
requirements on existing sources and new sources drive down total NOx emissions below
baseline levels. Under the illustrative proposal scenario CO2 emission reductions over the 2028
to 2042 timeframe are estimated to be 617 million metric tons. Under the less and more stringent
illustrative scenarios, cumulative CO2 emission reductions over the 2028 to 2042 timeframe are
estimated to be 578 million metric tons and 685 million metric tons, respectively.4
Table ES-1 Projected EGU Emissions and Emissions Changes for the Three Illustrative
Scenarios for 2028, 2030, and 2035, and 2040 a
CO2 (million
metric tons)
Annual NOx
Ozone Season
Annual SO2
Direct PM2.5
(thousand
short tons)
NOx (thousand
short tons)b
(thousand
short tons)
(thousand
short tons)
Proposal
2028
-10
-7
-3
-12
-1
2030
-89
-64
-22
-107
-6
2035
-37
-21
-7
-41
-1
2040
-24
-13
-4
-30
-1
Less Stringent
2028
-9
-7
-3
-9
-1
2030
-83
-61
-20
-99
-5
2035
-35
-20
-7
-38
-1
2040
-22
-12
-4
-27
-1
More Stringent
2028
0
3
1
-4
0
2030
-107
-61
-20
-114
-5
2035
-42
-22
-7
-41
-2
2040
-23
-13
-4
-30
-1
a This analysis is limited to the geographically contiguous lower 48 states.
b Ozone season is the May through September period in this analysis.
4 See Table 4-2 for annual CO2 emission reductions.
ES-10
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ES.5 Compliance Costs
The compliance cost estimates presented in this RIA are based on IPM projections, and
supplemented with cost estimates for MR&R.5 As described previously, this RIA evaluates three
illustrative scenarios: the proposal, a less stringent scenario, and a more stringent scenario. The
more stringent scenario differs from the proposal in that it assumes imposition of the second
phase of the NSPS requirements on new sources in run year 2030, while the proposal and less
stringent scenarios assume imposition of the second phase of the NSPS requirements in run year
2035.6 The proposal and more stringent scenarios assume all long-term existing coal-fired steam
generating units (i.e. units that do not have a firm retirement date prior to run year 2040) are
subject to 90 percent CCS requirements in 2030, while the less stringent scenario assumes that
long-term existing coal-fired steam generating units greater than 700 MW, and plants greater
than 2,000 MW are subject to 90 percent CCS requirements, while those units less than 700 MW
(and plants less than 2,000 MW) are subject to 40 percent natural gas co-firing requirements in
2030.
Table ES-2 below summarizes the present value (PV) and equivalent annualized value
(EAV) of the total national compliance cost estimates7 for the illustrative proposal scenario and
the less and more stringent scenarios. We present the PV of the costs over the 19-year period of
2024 to 2042. We also present the equivalent annualized value (EAV), which represents a flow
of constant annual values that, had they occurred annually, would yield a sum equivalent to the
PV. The EAV represents the value of a typical cost for each year of the analysis. Section 3
reports how annual power costs are projected to change over the time period of analysis.
IPM estimates compliance costs incurred by regulated firms, but because of the
availability of subsidy payments, there are additional real resource costs to the economy outside
of the regulated sector. IPM provides EPA's best estimate of the costs of the proposed rules to
the electricity sector and related energy sectors (i.e., natural gas, coal mining). To estimate the
5 Section ES.5 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section ES. 10 for impact analysis of the proposed standards for existing natural gas-
fired EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
6 Run year 2030 is mapped to calendar years 2029-2031, while run year 2035 is mapped to calendar years 2032-
2037.
7 Compliance costs refer to the difference between policy and baseline IPM projected capital, O&M, fuel,
transmission, and CO2 storage and transportation costs. Other costs are not accounted for. Please see Section 3.7
for further discussion of the differences between compliance costs and social costs.
ES-11
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social costs for the economy as a whole, EPA has used information from IPM as an input into the
Agency's computable general equilibrium model, SAGE. The economy-wide analysis is
considered a complement to the more detailed evaluation of sector costs produced by IPM. See
Section 5.2 and Appendix B for more discussion on estimates of private and social costs. EPA
requests comment on the SAGE analysis in section XIV(C) of the preamble to these proposed
rules.
Table ES-2 Total National Compliance Cost Estimates for the Three Illustrative
Scenarios (discounted to 2024, billion 2019 dollars)
3% Discount Rate
7% Discount Rate
PV
EAV
PV
EAV
Proposal
14
0.95
10
0.98
Less Stringent
13
0.93
10
0.96
More Stringent
10
0.70
7.5
0.73
Note: Values have been rounded to two significant figures.
Projected compliance costs are similar across the scenarios. Costs under the more
stringent scenario are projected to be lower than under the less-stringent scenario and the
proposal in 2030. This is due to the assumption that when the second phase of the NSPS is
active, hydrogen costs (represented exogenously in the modeling) are assumed to be $0.5/kg
rather than $l/kg otherwise. For details on the hydrogen modeling assumptions used in this
analysis, please see Section 3 of this RIA.8 Under the proposal and less stringent scenarios, the
second phase of the NSPS is assumed to be active in 2035, while under the more stringent
scenario, the second phase of the NSPS is assumed to be active in 2030. The lower input
hydrogen fuel price in 2030 under the more stringent scenario therefore drives total compliance
costs lower than under the other two scenarios. EPA solicits comments in section XIV(B) of the
preamble on its cost estimation generally.
ES.6 Benefits
The proposed rules are expected to reduce emissions CO2, NOx, PM2.5, and SO2 nationally.
This section reports the estimated monetized climate and health benefits associated with
8 EPA is continuing to evaluate the evolving literature on the economics of hydrogen, including the DOE's
Pathways to Commercial Liftoff: Clean Hydrogen report (available at: https://liftoff.energy.gov/wp-
content/uploads/2023/03/20230320-Liftoff-Clean-H2-vPUB-0329-update.pdf)
ES-12
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emission reductions for each of the three illustrative scenarios described in prior sections and
discusses other unquantified benefits.9
ES.6.1 Climate Benefits
Elevated concentrations of GHGs in the atmosphere have been warming the planet,
leading to changes in the Earth's climate including changes in the frequency and intensity of heat
waves, precipitation, and extreme weather events, rising seas, and retreating snow and ice. The
well-documented atmospheric changes due to anthropogenic GHG emissions are changing the
climate at a pace and in a way that threatens human health, society, and the natural environment.
Climate change touches nearly every aspect of public welfare in the U.S. with resulting
economic costs, including: changes in water supply and quality due to changes in drought and
extreme rainfall events; increased risk of storm surge and flooding in coastal areas and land loss
due to inundation; increases in peak electricity demand and risks to electricity infrastructure; and
the potential for significant agricultural disruptions and crop failures (though offset to some
extent by carbon fertilization).
There will be important climate benefits associated with the CO2 emissions reductions
expected from these proposed rules. Climate benefits from reducing emissions of CO2 are
monetized using estimates of the social cost of carbon (SC-CO2). See Section 4.2 of this RIA for
more discussion of the approach to monetization of the climate benefits associated with these
rules.
ES. 6.2 Health Benefits
These rules are expected to reduce national emissions of direct PM2.5, NOx, and SO2
throughout the year. Because NOx and SO2 are also precursors to secondary formation of
ambient PM2.5, reducing these emissions would reduce human exposure to ambient PM2.5
throughout the year and would reduce the incidence of PIVh.s-attributable health effects.
These proposed rules are expected to reduce ozone season NOx emissions. In the
presence of sunlight, NOx, and volatile organic compounds (VOCs) undergo chemical reactions
9 Section ES.6 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section ES. 10 for impact analysis of the proposed standards for existing natural gas-
fired EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
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in the atmosphere resulting in ozone formation. Reducing NOx emissions reduces human
exposure to ozone and the incidence of ozone-related health effects in most locations, though
ozone response to NOx emissions reductions depends on local conditions.
In this RIA, EPA estimates national-level health benefits resulting from the changes in
PM2.5 and ozone concentrations expected to occur with these proposed rules. The health effect
endpoints, effect estimates, and benefit unit-values, and how they were selected, are described in
the Technical Support Document (TSD) titled Estimating PM2.5- and Ozone-Attributable Health
Benefits (U.S. EPA, 2023). Our approach for updating the endpoints and to identify suitable
epidemiological studies, baseline incidence rates, population demographics, and valuation
estimates is summarized in Section 4.3.
ES. 6.3 Additional Unquantified Benefits
Data, time, and resource limitations prevented EPA from quantifying the estimated health
impacts or monetizing estimated benefits associated with direct exposure to hazardous air
pollutants (HAPs), NO2, and SO2, independent of the role NO2 and SO2 play as precursors to
PM2.5 and ozone. In addition, these limitations prevented quantification of welfare benefits
accrued due to reduced pollutant impacts on ecosystem and reductions in visibility impairment.
While all health benefits and welfare benefits were not able to be quantified, it does not imply
that there are not additional benefits associated with reductions in exposures to HAPs, ozone,
PM2.5, NO2, or SO2. For a qualitative description of these and potential water quality benefits,
please see Section 4.4 of this RIA.
ES.6.4 Total Climate and Health Benefits
Table ES-3 presents the total monetized climate and health benefits for the illustrative
proposal scenario and the more and less stringent scenarios.
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Table ES-3 Monetized Climate and Health Benefits for the Three Illustrative Scenarios,
(discounted to 2024, billion 2019 dollars)3
All Benefits Calculated using 3% Discount Rate
PM2.5 and 03-related
Climate Benefits b Health Benefitsc Total Benefits d e
PV
EAV
PV
EAV
PV
EAV
Proposal
30
2.1
68
4.8
98
6.9
Less Stringent
28
2.0
58
4.1
87
6.0
More Stringent
34
2.4
65
4.6
99
6.9
Climate Benefits Calculated using 3% Discount Rate,
Health Benefits Calculated using 7% Discount Rate
Climate Benefits b
PM2.5 and 03-related
Health Benefitsc
Total Benefits d e
PV
EAV
PV
EAV
PV
EAV
Proposal
30
2.1
44
4.3
74
6.4
Less Stringent
28
2.0
38
3.7
66
5.7
More Stringent
34
2.4
42
4.0
76
6.4
a Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
0 For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
e For discussions of the uncertainty associated with these health benefits estimates, see Section 4.3.8. See Section 4.2
for a discussion of the uncertainties associated with the climate benefit estimates.
ES.7 Economic Impacts
As a result of the compliance costs incurred by the regulated sector, these proposed
actions have economic and energy market implications. The energy impact estimates presented
here reflect EPA's illustrative analysis of the proposed rules.10 States are afforded flexibility to
implement the proposed rules, and thus the impacts could be different to the extent states make
different choices than those assumed in the illustrative analysis. Table ES-4 presents a variety of
energy market impact estimates for 2028, 2030, 2035, and 2040 for the illustrative proposal
scenario, relative to the baseline. These results are EPA's best estimate of possible compliance
10 Section ES.7 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section ES. 10 for impact analysis of the proposed standards for existing natural gas-
fired EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
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pathways with the policy. However, there are several key areas of uncertainty inherent in these
projections as outlined in Section 3.7.
Table ES-4 Summary of Certain Energy Market Impacts for the Illustrative Proposal
Scenario Relative to the Baseline
2028
2030
2035
2040
Retail electricity prices
-1%
2%
0%
0%
Average price of coal delivered to power sector
-1%
0%
2%
2%
Coal production for power sector use
-2%
-40%
-23%
-15%
Price of natural gas delivered to power sector
0%
9%
-2%
-3%
Price of average Henry Hub (spot)
0%
10%
-2%
-2%
Natural gas use for electricity generation
0%
8%
-1%
-2%
These and other energy market impacts are discussed more extensively in Section 3 of the
RIA. More broadly, changes in production in a directly regulated sector may have effects on
other markets when output from that sector - for these proposed rules, electricity - is used as an
input in the production of other goods. It may also affect upstream industries that supply goods
and services to the sector, along with labor and capital markets, as these suppliers alter
production processes in response to changes in factor prices. Changes in firm and household
behavior in response to the proposed rules could also interact with pre-existing distortions, such
as taxes, resulting in additional social costs. In addition, households may change their demand
for particular goods and services due to changes in the price of electricity and other final goods
prices. Economy-wide models - and, more specifically, computable general equilibrium (CGE)
models - are analytical tools that can be used to evaluate the broad impacts of a regulatory action.
A CGE-based approach to cost estimation concurrently considers the effect of a regulation across
all sectors in the economy, including interactions and feedbacks between them.
In 2015, EPA established a Science Advisory Board (SAB) panel to consider the
technical merits and challenges of using economy-wide models to evaluate costs, benefits, and
economic impacts in regulatory analysis. In its final report, the SAB recommended that EPA
begin to integrate CGE modeling into applicable regulatory analysis to offer a more
comprehensive assessment of the effects of air regulations (U.S. EPA Science Advisory Board,
2017). In response to the SAB's recommendations, EPA developed a new CGE model for the
U.S. economy called SAGE designed for use in regulatory analysis. A second SAB panel
ES-16
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performed a peer review of SAGE, and the review concluded in 2020 (U.S. EPA Science
Advisory Board, 2020).
EPA used SAGE to evaluate the economy-wide social costs and economic impacts of
these proposed rules. The annualized social costs estimated in SAGE are approximately 35
percent larger than the partial equilibrium private compliance costs (less taxes and transfers)
derived from IPM. This is consistent with general expectations based on the empirical literature
(e.g., Marten et al., 2019). However, the social cost estimate reflects the combined effect of the
proposed rules' requirements and interactions with IRA subsidies for specific technologies that
are expected to see increased use in response to the proposed rules. We are not able to identify
their relative roles at this time. A detailed discussion of the social costs and distributional
impacts of the proposed rules is contained in Appendix B of this RIA. Section XIV(C) of the
preamble to this proposal solicits comment on this economy-wide analysis presented in the RIA
appendix.
Environmental regulation may affect groups of workers differently, as changes in
abatement and other compliance activities cause labor and other resources to shift. An
employment impact analysis describes the characteristics of groups of workers potentially
affected by a regulation, as well as labor market conditions in affected occupations, industries,
and geographic areas. Employment impacts of these proposed actions are discussed more
extensively in Section 5 of the RIA.
ES.8 Environmental Justice Impacts
Environmental justice (EJ) concerns for each rulemaking are unique and should be
considered on a case-by-case basis, and EPA's EJ Technical Guidance (2015) states that "[t]he
analysis of potential EJ concerns for regulatory actions should address three questions:
1. Are there potential EJ concerns associated with environmental stressors affected by the
regulatory action for population groups of concern in the baseline?
2. Are there potential EJ concerns associated with environmental stressors affected by the
regulatory action for population groups of concern for the regulatory option(s) under
consideration?
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3. For the regulatory option(s) under consideration, are potential EJ concerns created or
mitigated compared to the baseline?"
To address these questions, EPA developed an analytical approach that considers the
purpose and specifics of the rulemaking, as well as the nature of known and potential exposures
and impacts.11 For the rule, we quantitatively evaluate 1) the proximity of affected facilities to
potentially vulnerable and/or overburdened populations for consideration of local pollutants
impacted by these rules but not modeled here (Section 6.4), and 2) the distribution of ozone and
PM2.5 concentrations in the baseline and changes due to the three illustrative scenarios across
different demographic groups on the basis of race, ethnicity, poverty status, employment status,
health insurance status, age, sex, educational attainment, and degree of linguistic isolation
(Section 6.5). While these analyses assess the distribution of non-climate impacts at more near-
term and local spatial scales, we also discuss potential EJ climate impacts from projected long-
term climate change (Section 6.3). Each of these analyses depends on mutually exclusive
assumptions, was performed to answer separate questions, and is associated with unique
limitations and uncertainties.
Baseline demographic proximity analyses provide information as to whether there may
be potential EJ concerns associated with environmental stressors, such as local HAP, emitted
from sources affected by the regulatory action for certain population groups of concern (Section
6.4). The baseline demographic proximity analyses examined the demographics of populations
living within 10 km and 50 km of the following three sets of sources: 1) all 140 coal plants with
units potentially subject to the proposed 111 rule, 2) three coal plants retiring by January 1, 2032,
with units potentially subject to the proposed 111 rules, and 3) 19 coal plants retiring between
January 1, 2032, to January 1, 2040, with units potentially subject to the proposed 111 rules. The
proximity analysis of the full population of potentially affected units greater than 25 MW
indicated that the demographic percentages of the population within 10 km and 50 km of the
facilities are relatively similar to the national averages. The proximity analysis of the 19 units
that will retire from January 1, 2032, to January 1, 2040 (a subset of the total 140 units) found
that the percent of the population within 10 km that is African American is higher than the
11 Section ES.8 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section ES. 10 for impact analysis of the proposed standards for existing natural gas-
fired EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
ES-18
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national average. The proximity analysis for the 3 units that will retire by January 1, 2032 (a
subset of the total 140 units) found that for both the 10 km and 50 km populations the percent of
the population that is American Indian for one facility is significantly above the national average,
the percent of the population that is Hispanic/Latino for another facility is substantially above the
national average, and all three facilities were well above the national average for both the percent
below the poverty level and the percent below two times the poverty level.
Because the pollution impacts that are the focus of these rules may occur downwind from
affected facilities, ozone and PM2.5 exposure analyses that evaluate demographic variables are
better able to evaluate any potentially disproportionate pollution impacts of this rulemaking. The
baseline PM2.5 and ozone exposure analyses respond to question 1 from EPA's EJ Technical
Guidance document more directly than the proximity analyses, as they evaluate a form of the
environmental stressor primarily affected by the regulatory action (Section 6.5). Baseline ozone
and PM2.5 exposure analyses show that certain populations, such as Hispanics, Asians, those
linguistically isolated, and those less educated may experience disproportionately higher ozone
and PM2.5 exposures as compared to the national average. Black populations may also experience
disproportionately higher PM2.5 concentrations than the reference group, and American Indian
populations and children may also experience disproportionately higher ozone concentrations
than the reference group. Therefore, there likely are potential EJ concerns associated with
environmental stressors affected by the regulatory action for population groups of concern in the
baseline (question 1).
Finally, we evaluate how the three illustrative scenarios of this proposed rulemaking are
expected to differentially impact demographic populations, informing questions 2 and 3 from
EPA's EJ Technical Guidance with regard to ozone and PM2.5 exposure changes. We infer that
baseline disparities in the ozone and PM2.5 concentration burdens are likely to remain after
implementation of the regulatory action or alternatives under consideration. This is due to the
small magnitude of the concentration changes associated with this rulemaking across population
demographic groups, relative to the magnitude of the baseline disparities (question 2). This EJ
assessment also suggests that this action is unlikely to mitigate or exacerbate PM2.5 exposures
disparities across populations of EJ concern analyzed. Regarding ozone exposures, while most
snapshot years for the illustrative scenarios analyzed will not likely mitigate or exacerbate ozone
exposure disparities for the population groups evaluated, ozone exposure disparities may be
ES-19
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exacerbated for some population groups analyzed in 2030 under all illustrative scenarios.
However, the extent to which disparities may be exacerbated is likely modest, due to the small
magnitude of the ozone concentration changes relative to baseline disparities across populations
(EJ question 3). Importantly, the action described in this proposal is expected to lower PM2.5 and
ozone in many areas, and thus mitigate some pre-existing health risks of air pollution across all
populations evaluated.
ES.9 Comparison of Benefits and Costs
In this RIA, the regulatory impacts are evaluated for the specific snapshot years of 2028,
2030, 2035, and 2040, and MR&R costs are estimated for all years in the 2024 to 2042
timeframe.12 Comparisons of benefits to costs for the snapshot years of 2028, 2030, 2035, and
2040 are presented in Section 7 of this RIA. Here we present the PV and EAV of costs, benefits,
and net benefits, calculated for the years 2024 to 2042 from the perspective of 2024, using both a
three percent and seven percent discount rate as directed by OMB's Circular A-4. All dollars are
in 2019 dollars. The compliance cost estimates are net of changes in renewable energy,
hydrogen, and CCS subsidies.
We also present the EAV, which represents a flow of constant annual values that, had
they occurred in each year from 2024 to 2042, would yield a sum equivalent to the PV. The EAV
represents the value of a typical cost or benefit for each year of the analysis, in contrast to the
year-specific estimates reported in the costs and benefits sections of this RIA.
The comparison of benefits and costs in PV and EAV terms for the illustrative proposal
scenario and less and more stringent scenarios can be found in Table ES-5. Estimates in the
tables are presented as rounded values.
12 Section ES.9 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section ES. 10 for impact analysis of the proposed standards for existing natural gas-
fired EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
ES-20
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Table ES-5 Monetized Benefits, Costs, and Net Benefits of the Illustrative Scenarios
(billions of 2019 dollars, discounted to 2024) a'b
All Values Calculated using 3% Discount Rate
PM2.5 and O3-
Climate related Health Compliance Net
Benefits b
Benefits
C
Costs
Benefits d
Regulatory Option
PV
EAV
PV
EAV
PV
EAV
PV EAV
Proposed
30
2.1
68
4.8
14
0.95
85 5.9
Less Stringent
28
2.0
58
4.1
13
0.93
73 5.1
More Stringent
34
2.4
65
4.6
10
0.70
89 6.2
Climate Benefits Calculated using 3% Discount Rate,
Compliance Costs and Health Benefits Calculated using 7% Discount Rate
PM2.5 and O3-
Climate related Health Compliance Net
Benefits b
Benefits
C
Costs
Benefits d
Regulatory Option
PV
EAV
PV
EAV
PV
EAV
PV EAV
Proposed
30
2.1
44
4.3
10
0.98
64 5.4
Less Stringent
28
2.0
38
3.7
10
0.96
56 4.7
More Stringent
34
2.4
42
4.0
7.5
0.73
68 5.7
a Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
0 The health benefits estimates use the larger of the two benefits estimates presented in Table 4-15 through Table
4-19. Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates.
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
As discussed in Section 4 of this RIA, the monetized benefits estimates provide an
incomplete overview of the beneficial impacts of the proposal. In particular, the monetized
climate benefits are incomplete and an underestimate as explained in Section 4.2. In addition,
important health, welfare, and water quality benefits anticipated under these proposed rules are
not quantified or monetized. EPA anticipates that taking non-monetized effects into account
would show the proposals to have greater benefit than the tables in this section reflect.
Simultaneously, the estimates of compliance costs used in the net benefits analysis may provide
an incomplete characterization of the true costs of the rule. The balance of unquantified benefits
and costs is ambiguous but is unlikely to change the result that the benefits of the proposals
exceed the costs by billions of dollars annually.
We also note that the RIA follows EPA's historic practice of using a technology-rich
partial equilibrium model of the electricity and related fuel sectors to estimate the incremental
costs of producing electricity under the requirements of proposed and final major EPA power
sector rules. In Appendix B of this RIA, EPA has also included an economy-wide analysis that
ES-21
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considers additional facets of the economic response to the proposed rules, including the full
resource requirements of the expected compliance pathways, some of which are paid for through
subsidies in the partial equilibrium analysis. The social cost estimates in the economy-wide
analysis and discussed in Appendix B are still far below the projected benefits of the proposed
rules.
ES.10 Proposed 111(d) Standards for Existing Natural Gas-Fired EGUs and Third Phase
of the Proposed 111(b) Standards for New Natural Gas-Fired EGUs
ES.10.1 Introduction
The existing source performance standards modeled using IPM did not include the
proposed requirements on existing natural gas-fired combined cycle (NGCC) units as
summarized in Table ES-6. To estimate the regulatory impacts of these proposed requirements,
EPA performed a spreadsheet-based analysis using the model output of each of the illustrative
scenarios described earlier to produce a range of possible outcomes. This analysis therefore does
not include any additional IPM modeling, and does not identify the least-cost compliance
pathways for affected sources given the standards modeled. As such, the results from this
analysis could differ from the compliance behavior that would be projected under incremental
IPM modeling. For details, please see Section 8.6.
Table ES-6 GHG Mitigation Measures for Existing NGCC Units under the Illustrative
Proposal, More Stringent and Less Stringent Scenarios
Affected EGUs
GHG Mitigation Measure
GHG Mitigation Measure
Natural Gas fired Combined
Cycle Units > 300 MW and
operating > 50% capacity factor
in run year 2035 with online
year of 2025 or earlier
Co-fire 30% by volume hydrogen in
run year 2035, and 96% by volume
hydrogen in run year 2040
CCS with 90 percent capture of CO2,
starting in run year 2035
The new source performance standards modeled using IPM also did not include
additional requirements on new NGCC units—specifically, the proposed requirements for new
base load combustion turbines in the hydrogen co-firing subcategory to comply with a third
phase standard based on co-firing 96 percent low-GHG hydrogen by run year 2040— as
summarized in Table ES-7. To estimate the impact of these proposed requirements, EPA
performed a spreadsheet-based analysis using the model output of each of the illustrative
ES-22
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scenarios to produce a range of possible outcomes as outlined in Section 8 of the RIA. As is the
case for the analysis of existing natural gas-fired combined cycle units, this analysis also does
not include any additional IPM modeling, and does not identify the least-cost compliance
pathways for affected sources given the standards modeled. As such, the results from this
analysis could differ from the compliance behavior that would be projected under incremental
IPM modeling. For details, please see Section 8.6.
Table ES-7 GHG Mitigation Measures for New NGCC Units under the Illustrative
Proposal, More Stringent and Less Stringent Scenarios
Affected EGUs
GHG Mitigation Measure
Natural Gas Combined Cycle Units with online year
after 2025 that operate at > 50% capacity factor
Co-fire 96% by volume hydrogen in run year 2040
onwards, or install CCS.
ES. 10.2 Emissions Impacts
Based on the analysis outlined above, EPA estimated the change in CO2 emissions from
the additional measures selected to the outcomes under the three illustrative scenarios (the IPM-
modeled aspects of the proposal and less and more stringent scenarios, for existing fossil-fuel
fired steam generating units and new and reconstructed stationary combustion turbines)). These
results are summarized in Table ES-8 and Table ES-9 below. Because this additional analysis
used the IPM outputs from the illustrative scenarios as its baseline, these results do not capture
the potential for interactive effects between the additional measures and the IPM-modeled
measures (e.g., the potential that establishing 111(d) requirements for existing natural gas-fired
EGUs could affect the compliance approaches undertaken by other EGUs or lead to different
shifts in the overall generation mix than those reflected in the IPM outputs). EPA did not
estimate changes in emissions of other non-CC>2 air pollutants.
Table ES-8 and Table ES-9 present CO2 change results for low and high ends of a range
based on different assumptions in how many model existing plants install CCS and how many
model new plants increase hydrogen co-firing.
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Table ES-8 Estimated Changes in Power Sector Emissions from the Proposed 111(d) for
Existing Natural Gas-fired EGUs for the Three Illustrative Scenarios
Annual CO2
Proposal
Less Stringent
More Stringent
(million
metric tons)
Low High
Low
High
Low
High
2028
0 0
0
0
0
0
2030
0 0
0
0
0
0
2035
-20 -37
-20
-37
-20
-37
2040
-19 -37
-19
-37
-19
-37
Table ES-9 Estimated Changes in Power Sector Emissions from the Third Phase of the
Proposed 111(b) for New Natural Gas-fired EGUs for the Three Illustrative Scenarios
Annual CO2
Proposal
Less Stringent
More Stringent
(million
metric tons)
Low High
Low
High
Low
High
2028
0 0
0
0
0
0
2030
0 0
0
0
0
0
2035
0 0
0
0
0
0
2040
-0.22 -2.5
-0.20
-2.5
-2.21
-4.2
ES.10.3 Cost Impacts
Table ES-10 summarizes the present value (PV) and equivalent annualized value (EAV)
of the total national compliance cost estimate for the existing gas standard and the third phase of
the new source standard under the illustrative proposal scenario, less stringent and more stringent
scenarios. These estimates are derived using the spreadsheet-based analysis just described and do
not include any additional IPM modeling.
Similar levels of projected costs are estimated under the proposal and less stringent
scenario, reflecting similar levels of existing and new gas operation under the illustrative
proposal and less stringent scenarios. Costs under the more stringent scenario (where the second
phase standards for new NGCC builds are in effect in the 2030 run year as compared to 2035)
are estimated to be higher than under the proposal and less stringent scenario, driven primarily
by higher levels of estimated new source hydrogen burn.
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Table ES-10 Present Values and Equivalent Annualized Values of Compliance Cost
Estimates for the Proposed 111(d) for Natural Gas-fired EGUs and Third Phase of the
Proposed 111(b) for Natural Gas-fired EGUs (discounted to 2024, billion 2019 dollars)
3% Discount Rate
7% Discount Rate
PV
EAV
PV
EAV
Low
High
Low
High
Low
High
Low
High
Proposal
5.7
10
0.40
0.70
3.5
6.2
0.34
0.60
Less Stringent
5.7
10
0.40
0.70
3.5
6.2
0.34
0.60
More Stringent
6.2
10
0.44
0.73
3.8
6.4
0.37
0.62
Note: Values have been rounded to two significant figures.
ES.10.4 Climate Benefits
As discussed in Section ES.6.1, there will be important climate benefits associated with
the estimated CO2 emissions reductions expected from these proposed rules. Climate benefits
from reducing emissions of CO2 are monetized using estimates of the social cost of carbon (SC-
CO2). See Section 4.2 of this RIA for more discussion of the approach to monetization of the
climate benefits associated with these rules. See Section 8.4 of this RIA for more discussion
about the specific estimated climate benefits associated with the proposed 111(d) for natural gas-
fired EGUs and the third phase of the proposed 111(b) for natural gas-fired EGUs.
Table ES-11 Present Values and Equivalent Annualized Values of Monetized Climate
Benefit Estimates for the Proposed 111(d) for Natural Gas-fired EGUs and Third Phase of
the Proposed 111(b) for Natural Gas-fired EGUs (discounted to 2024, billion 2019
dollars)a'b'c
3% Discount Rate
PV
EAV
Low
High
Low
High
Proposal
10
20
0.70
1.4
Less Stringent
10
20
0.71
1.4
More Stringent
11
20
0.74
1.4
a Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
0 Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
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ES.ll References
U.S. EPA. (2014). Guidelines for Preparing Economic Analyses. (EPA 240-R-10-001).
Washington DC: U.S. Environmental Protection Agency, Office of Policy, National
Center for Environmental Economics, https://www.epa.gov/environmental-
economics/guidelines-preparing-economic-analyses
U.S. EPA. (2015). Guidance on Considering Environmental Justice During the Development of
Regulatory Actions. https://www.epa.gov/sites/default/files/2015-
06/documents/considering-ej-in-rulemaking-guide-final.pdf
U.S. EPA. (2023). EstimatingPM2.5- and Ozone-Attributable Health Benefits. Research Triangle
Park, NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Health and Environmental Impact Division.
https://www.regulations.gov/docket/EPA-HQ-OAR-2018-0794
U.S. EPA Science Advisory Board. (2017). SAB Advice on the Use of Economy-Wide Models in
Evaluating the Social Costs, Benefits, and Economic Impacts of Air Regulations. (EPA-
SAB-17-012). Washington DC
U.S. EPA Science Advisory Board. (2020). Technical Review ofEPA's Computable General
Equilibrium Model, SAGE. (EPA-SAB-20-010). Washington DC
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1 INTRODUCTION AND BACKGROUND
1.1 Introduction
In 2009, the EPA concluded that GHG emissions endanger our nation's public health and
welfare.13 Since that time, the evidence of the harms posed by GHG emissions has only grown
and Americans experience the destructive and worsening effects of climate change every day.
Fossil fuel-fired EGUs are the nation's largest stationary source of GHG emissions, representing
25 percent of the United States' total GHG emissions in 2020. At the same time, a range of cost-
effective technologies and approaches to reduce GHG emissions from these sources are available
to the power sector, and multiple projects are in various stages of operation and development—
including carbon capture and sequestration/storage (CCS) and co-firing with lower-GHG fuels.
Congress has also acted to provide funding and other incentives to encourage the deployment of
these technologies to achieve reductions in GHG emissions from the power sector.
In this notice, the EPA is proposing several actions under section 111 of the Clean Air
Act (CAA) to reduce the significant quantity of GHG emissions from new and existing fossil
fuel-fired EGUs by establishing new source performance standards (NSPS) and emission
guidelines that are based on available and cost-effective technologies that directly reduce GHG
emissions from these sources. Consistent with the statutory command of section 111, the
proposed NSPS and emission guidelines reflect the application of the best system of emission
reduction (BSER) that, taking into account costs, energy requirements, and other statutory
factors, is adequately demonstrated for the purpose of improving the emissions performance of
the covered EGUs.
Specifically, the EPA is proposing to update and establish more protective NSPS for
GHG emissions from new and reconstructed fossil fuel-fired stationary combustion turbine
EGUs that are based on highly efficient generating practices, hydrogen co-firing, and CCS. The
EPA is also proposing to establish new emission guidelines for existing fossil fuel-fired steam
generating EGUs that reflect the application of CCS and the availability of natural gas co-firing.
The EPA is simultaneously proposing to repeal the Affordable Clean Energy (ACE) rule because
the emission guidelines established in ACE do not reflect the BSER for steam generating EGUs
13 74 FR 66496 (December 15, 2009).
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and are inconsistent with section 111 of the CAA in other respects. To address GHG emissions
from existing fossil fuel-fired stationary combustion turbines, the EPA is proposing emission
guidelines for large and frequently used existing stationary combustion turbines. Further, the
EPA is soliciting comment on how the Agency should approach its legal obligation to establish
emission guidelines for the remaining existing fossil fuel-fired combustion turbines not covered
by this proposal, including smaller frequently used existing fossil fuel-fired combustion turbine
EGUs and less frequently used existing fossil fuel-fired combustion turbines.
Each of the NSPS and emission guidelines proposed here would ensure that EGUs reduce
their GHG emissions in a manner that is cost-effective and improves the emissions performance
of the sources, consistent with the applicable CAA requirements and caselaw. These proposed
standards and emission guidelines, if finalized, would significantly decrease GHG emissions
from fossil fuel-fired EGUs and the associated harms to human health and welfare. Further, the
EPA has designed these proposed standards and emission guidelines in a way that is compatible
with the nation's overall need for a reliable supply of affordable electricity.
In accordance with Executive Order (E.O.) 12866 and 13563, the guidelines of OMB
Circular A-4 and EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2014), the
RIA analyzes the benefits and costs associated with the projected emissions reductions under the
proposed requirements, a less stringent set of requirements, and a more stringent set of
requirements to inform EPA and the public about these projected impacts. With respect to the
new source standard, the more stringent scenario differs from the proposal in that it assumes
imposition of the second phase of the NSPS in run year 2030, while the proposal and less
stringent scenarios assume imposition of the second phase of the NSPS in run year 2035. With
regards to the existing source standard, the proposal and more stringent scenarios assume all
long-term existing coal-fired steam generating units are subject to 90 percent CCS requirements
in 2030, while the less stringent scenario assumes that long-term existing coal-fired steam
generating units greater than 700 MW, and plants greater than 2,000 MW are subject to 90
percent CCS requirements, while those units less than 700 MW (and plants less than 2,000 MW)
are subject to 40 percent natural gas co-firing requirements. We evaluated the potential impacts
of the three illustrative scenarios using the present value (PV) of costs, benefits, and net benefits,
calculated for the years 2024 to 2042, discounted to 2024. In addition, the Agency presents the
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assessment of costs, benefits, and net benefits for specific snapshot years, consistent with historic
practice. These snapshot years are 2028, 2030, 2035, and 2040.
1.2 Legal and Economic Basis for Rulemaking
In this section, we summarize the statutory requirements in the CAA that serve as the
legal basis for the proposed rules and the economic theory that supports environmental
regulation as a mechanism to enhance social welfare. The CAA requires EPA to prescribe
regulations for new and existing sources of air pollution. In turn, those regulations attempt to
address negative externalities created when private entities fail to internalize the social costs of
air pollution.
1.2.1 Statutory Requirement
EPA's authority for and obligation to issue these proposed rules is CAA section 111,
which establishes mechanisms for controlling emissions of air pollutants from new and existing
stationary sources. This provision requires the EPA Administrator to promulgate a list of
categories of stationary sources that the Administrator, in his or her judgment, finds "causes, or
contributes significantly to, air pollution which may reasonably be anticipated to endanger public
health or welfare."14 EPA has listed more than 60 stationary source categories under this
provision.15 EPA has the authority to define the scope of the source categories, determine the
pollutants for which standards should be developed, and distinguish among classes, types, and
sizes within categories in establishing the standards.
Once EPA lists a source category, EPA must, under CAA section 111(b)(1)(B), establish
"standards of performance" for emissions of air pollutants from new sources (including modified
and reconstructed sources) in the source categories.16 These standards are known as new source
performance standards (NSPS), and they are national requirements that apply directly to the
sources subject to them.
When EPA establishes NSPS for sources in a source category under CAA section 111(b),
EPA is also required, under CAA section 111(d)(1), to prescribe regulations for states to submit
14 CAA §lll(b)(l)(A).
15 See 40 CFR 60 subparts Cb - OOOO.
16 CAA §111 (b)(1)(B), 111(a)(1).
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plans regulating existing sources in that source category for any air pollutant that, in general, is
not regulated under the CAA section 109 requirements for the NAAQS or regulated under the
CAA section 112 requirements for hazardous air pollutants (HAP). CAA section 11 l(d)'s
mechanism for regulating existing sources differs from the one that CAA section 111(b) provides
for new sources because CAA section 111(d) contemplates states submitting plans that establish
"standards of performance" for the affected sources and that contain other measures to
implement and enforce those standards.
"Standards of performance" are defined under CAA section 111(a)(1) as standards for
emissions that reflect the emission limitation achievable from the "best system of emission
reduction," considering costs and other factors, that "the Administrator determines has been
adequately demonstrated." CAA section 111(d)(1) grants the authority, in applying a standard of
performance, to take into account the source's remaining useful life and other factors.
Under CAA section 111(d), a state must submit its plan to EPA for approval, and EPA
must approve the state plan if it is "satisfactory."17 If a state does not submit a plan, or if EPA
does not approve a state's plan, then EPA must establish a plan for that state.18 Once a state
receives EPA's approval of its plan, the provisions in the plan become federally enforceable
against the entity responsible for noncompliance, in the same manner as the provisions of an
approved State Implementation Plan (SIP) under the Act. See section V of the preamble to the
proposed rules for more detailed statutory background and regulatory history for CAA Section
111.
1.2.1.1 Regulated Pollutant
In 2009, EPA concluded that GHG emissions endanger our nation's public health and
welfare.19 Since that time, the evidence of the harms posed by GHG emissions has only grown,
and Americans experience the effects of climate change every day.
17 CAA section 111(d)(2)(A).
18 CAA section 111(d)(2)(A).
19 74 FR 66496 (December 15, 2009).
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1.2.1.2 Definition of Affected Sources
These rules establish GHG mitigation measures on certain fossil fuel-fired electric
generating units. For details on the source categories and the mitigation measures considered
please see sections VII, X, and XI of the preamble.
1.2.2 The Need for Air Emissions Regulation
OMB Circular A-4 indicates that one of the reasons a regulation may be issued is to
address a market failure. The major types of market failure include externalities, market power,
and inadequate or asymmetric information. Correcting market failures is one reason for
regulation; it is not the only reason. Other possible justifications include improving the function
of government, correcting distributional unfairness, or securing privacy or personal freedom.
Environmental problems are classic examples of externalities - uncompensated benefits
or costs of one's action imposed on another party. For example, the smoke from a factory may
adversely affect the health of exposed individuals and soil the property in nearby neighborhoods.
For the proposed regulatory actions analyzed in this RIA, the good produced is electricity from
fossil fuel-fired EGUs. If these electricity producers pollute the atmosphere when generating
power, the social costs will not be borne exclusively by the polluting firm but rather by society as
a whole. Thus, the producer is imposing a negative externality, or a social cost of emissions, on
society. The equilibrium market price of electricity may fail to incorporate the full opportunity
cost to society of these products. Consequently, absent a regulation on emissions, producers may
not internalize the social cost of emissions and social costs will be higher as a result. The
proposed regulation will work towards addressing this market failure by causing affected
producers to more fully internalize the negative externality associated with GHG emissions from
electricity generation by new and existing fossil fuel-fired stationary combustion turbine EGUs
and existing fossil fuel-fired steam generating EGUs.
1.3 Overview of Regulatory Impact Analysis
1.3.1 Repeal of Affordable Clean Energy (ACE) Rule
Section IX of the preamble explains that EPA is proposing to repeal the Affordable Clean
Energy (ACE) Rule. The RIA for the ACE Rule presented the projected impacts of an illustrative
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policy scenario that modeled heat rate improvements (HRI) at coal-fired EGUs (U.S. EPA,
2019). In the ACE RIA, EPA projected the ACE Rule would have compliance costs in 2030 of
about $280 million and CO2 emissions reductions of about 11 million short tons in 2030.20
As explained in the preamble, EPA concludes based on new information including
experience implementing the ACE Rule that the suite of HRI set forth in the rule, at best, would
provide negligible CO2 reductions. The ACE Rule's projected benefits were premised in part on
a 2009 technical report by Sargent & Lundy that evaluated the effects of HRI technologies. In
2023, Sargent & Lundy issued an updated report which details that the HRI selected as the BSER
in the ACE Rule would bring fewer emissions reductions than estimated in 2009.21 The 2023
report concludes that, with few exceptions, HRI technologies are less effective at reducing CO2
emissions than assumed in 2009. Also, most sources had already optimized application of HRI,
and so there are fewer opportunities to reduce emissions than previously anticipated.
Additionally, for a subset of sources, HRI are likely to cause a rebound effect leading to an
increase in GHG emissions for those sources for the reasons explained in section X.D.5.a. of the
preamble. The estimate of the rebound effect was quite pronounced in the ACE Rule's own
analysis - the rule projected that it would increase CO2 emissions from power plants in 15 states
and the District of Columbia. Accordingly, EPA no longer believes that the suite of HRI the
ACE Rule selected as the BSER is an appropriate BSER for existing coal-fired EGUs.
Consequently, EPA has determined it is appropriate to repeal the ACE Rule and to
reevaluate whether other technologies constitute the BSER. EPA now concludes that different,
more effective technologies like co-firing of natural gas and CCS are now cost reasonable for
designated facilities with longer operating horizons. Since the ACE Rule was promulgated,
changes in the power industry, developments in the costs of controls, and new federal subsidies
have made these other more effective technologies more broadly available and less costly.
As noted in the ACE RIA, the ACE Rule itself required no specified degree of emission
limitation or standards of performance. States were given only general criteria to inform their
efforts to design standards for sources. After the ACE Rule was promulgated, early efforts at
implementation of the rule underscored that the rule did not include enough specificity to ensure
20 In comparison, the current proposal is projected to reduce 89 million metric tons of CO2 in 2030 (see Table 3-5).
21 See Heat Rate Improvement Method Costs and Limitations Memo, which is available in the docket for this action.
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GHG reductions consistent with the RIA. Because of these factors, the ACE Rule RIA results
should be treated as speculative at best. Note that even if we assumed the same degree of
effectiveness as was assumed in the ACE Rule RIA, the number of units that would be covered if
the ACE Rule were implemented today would be much lower because of declines in coal-fired
generation since the ACE Rule was promulgated as well as increases in projected retirements in
the coming years.22
Accordingly, based on reconsideration of the emissions impact of HRI and new
information gained from early implementation of the ACE Rule, among other factors, EPA
anticipates that the implementation of the ACE Rule would likely produce negligible, if any,
change in costs or emissions relative to a world without the rule. In addition, the proposed 111(b)
and 111(d) actions only occur after the repeal of the ACE Rule. As such, it is EPA's finding and
conclusion that there is likely to be no difference in the baseline between a world where ACE is
implemented and one where it is not.
1.3.2 Baseline and Analysis Years
The impacts of proposed regulatory actions are evaluated relative to a modeled baseline
that represents expected behavior in the electricity sector under market and regulatory conditions
in the absence of a regulatory action. EPA frequently updates the power sector modeling baseline
to reflect the latest available electricity demand forecasts from the U.S. Energy Information
Administration (EIA) as well as expected costs and availability of new and existing generating
resources, fuels, emission control technologies, and regulatory requirements. The baseline
includes the proposed Good Neighbor Plan (GNP), the Revised Cross-State Air Pollution Rule
(CSAPR) Update, CSAPR Update, and CSAPR, as well as the 2012 Mercury and Air Toxics
Standards. The power sector baseline also includes the 2015 Effluent Limitation Guidelines
(ELG) and the 2015 Coal Combustion Residuals (CCR), and the recently finalized 2020 ELG
and CCR rules. This version of the model ("EPA's post-IRA IPM 2022 reference case") also
includes recent updates to state and federal legislation affecting the power sector, including
Public Law 117-169, 136 Stat. 1818 (August 16, 2022), commonly known as the Inflation
Reduction Act of 2022 (IRA). The modeling documentation, available in the docket, includes a
22 For details on historical coal retirements, please see the Power Sector Trends - TSD available in the docket for
this rulemaking. For details on projected coal capacity under the baseline, please see Table 3-14.
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summary of all legislation reflected in this version of the model as well as a description of how
that legislation is implemented in the model. Also, see Section 3 for additional detail about the
power sector baseline for this RIA.
We evaluated the potential impacts of the three illustrative scenarios for the years 2024 to
2042 from the perspective of 2024, using both a three percent and seven percent discount rate. In
addition, the Agency presents the assessment of costs, benefits, and net benefits for specific
snapshot years, consistent with historic practice. These snapshot years are 2028, 2030, 2035, and
2040. The Agency believes that these specific years are each representative of several
surrounding years, which enables the analysis of costs and benefits over the timeframe of 2024 to
2042. The year 2028 is the first year of detailed power sector modeling for this RIA and
approximates when the regulatory impacts of the proposed 111(b) new source performance
standards on the power sector will begin. However, because the Agency estimates that some
monitoring, reporting, and recordkeeping (MR&R) costs may be incurred in 2024, we analyze
compliance costs in years before 2028. Therefore, while MR&R costs analysis is presented
beginning in the year 2024, the detailed assessment of costs, emissions impacts, and benefits
begins in the year 2028. The analysis timeframe concludes in 2042, as this is the last year that
may be represented with the analysis conducted for the specific year of 2040.
1.3.3 Best System of Emission Reduction (BSER)
These actions include proposed BSER determinations and accompanying standards of
performance for GHG emissions from new and reconstructed fossil fuel-fired stationary
combustion turbines, proposed repeal of the ACE Rule, proposed BSER determinations and
emission guidelines for existing fossil fuel-fired steam generating units, proposed BSER
determinations and emission guidelines for large, frequently used existing fossil fuel-fired
stationary combustion turbines, and solicitation for comment on potential BSER options and
emission guidelines for existing fossil fuel-fired stationary combustion turbines not otherwise
covered by the proposal.
For new and reconstructed fossil fuel-fired combustion turbines, the EPA is proposing to
create three subcategories based on the function the combustion turbine serves: a low load
("peaking units") subcategory that consists of combustion turbines with a capacity factor of less
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than 20 percent; an intermediate load subcategory for combustion turbines with a capacity factor
that ranges between 20 percent and a source-specific upper bound that is based on the design
efficiency of the combustion turbine; and a base load subcategory for combustion turbines that
operate above the upper-bound threshold for intermediate load turbines. This subcategorization
approach is similar to the current NSPS for these sources, which includes separate subcategories
for base load and non-base load units; however, the EPA is now proposing to subdivide the non-
base load subcategory into a low load subcategory and a separate intermediate load subcategory.
This revised approach to subcategories is consistent with the fact that utilities and power plant
operators are building new combustion turbines with plans to operate them at varying levels of
capacity, in coordination with existing and expected energy sources. These patterns of operation
are important for the type of controls that the EPA is proposing as the BSER for these turbines,
in terms of the feasibility of, emissions reductions that would be achieved by, and cost-
reasonableness of, those controls.
For the low load subcategory, the EPA is proposing that the BSER is the use of lower
emitting fuels (e.g., natural gas and distillate oil) with standards of performance ranging from
120 lb CCh/MMBtu to 160 lb CCh/MMBtu, depending on the type of fuel combusted.23 For the
intermediate load and base load subcategories, the EPA is proposing an approach in which the
BSER has multiple components: (1) highly efficient generation; and (2) depending on the
subcategory, use of CCS or co-firing low-GHG hydrogen.
These components of the BSER for the intermediate and base load subcategories form the
basis of a standard of performance that applies in multiple phases. That is, affected facilities—
which are facilities that commence construction or reconstruction after the date of publication in
the Federal Register of this proposed rulemaking—must meet the first phase of the standard of
performance, which is based exclusively on application of the first component of the BSER
(highly efficient generation), by the date the rule is promulgated. Affected sources in the
intermediate load and base load subcategories must also meet the second and in some cases third
and more stringent phases of the standard of performance, which are based on the continued
23 In the 2015 NSPS, the EPA referred to clean fuels as fuels with a consistent chemical composition (i.e., uniform
fuels) that result in a consistent emission rate of 69 kilograms per gigajoule (kg/GJ) (160 lb C02/MMBtu). Fuels
in this category include natural gas and distillate oil. In this rulemaking, the EPA refers to these fuels as both
lower emitting fuels or uniform fuels.
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application of the first component of the BSER and the application of the second and in some
cases third component of the BSER. For base load units, the EPA is proposing two pathways as
potential BSER—(1) the use of CCS to achieve a 90 percent capture of GHG emissions by 2035
and (2) the co-firing of 30 percent (by volume) low-GHG hydrogen by 2032 and, ramping up to
96 percent by volume low-GHG hydrogen by 2038. These two BSER pathways both offer
significant opportunities to reduce GHG emissions but, may be available on slightly different
timescales.
More specifically, with respect to the first phase of the standards of performance, the
EPA is proposing that the BSER for both the intermediate load and base load subcategories
includes highly efficient generating technology {i.e., the most efficient available turbines). For
the intermediate load subcategory, the EPA is proposing that the BSER includes highly efficient
simple cycle combustion turbine technology with an associated first phase standard of 1,150 lb
CCh/MWh-gross. For the base load subcategory, the EPA is proposing that the BSER includes
highly efficient combined cycle technology with an associated first phase standard of 770 lb
CCh/MWh-gross for larger combustion turbine EGUs with a base load rating of 2,000 MMBtu/h
or more. For smaller base load combustion turbines (with a base load rating of less than 2,000
MMBtu/h), the proposed associated standard would range from 770 to 900 lb CCh/MWh-gross
depending on the specific base load rating of the combustion turbine. These standards would
apply immediately upon the effective date of the final rule.
With respect to the second phase of the standards of performance, for the intermediate
load subcategory, the EPA is proposing that the BSER includes co-firing 30 percent by volume
low-GHG hydrogen (unless otherwise noted, all co-firing hydrogen percentages are on a volume
basis) with an associated standard of 1,000 lb CCh/MWh-gross, compliance with which would be
required starting in 2032. For the base load subcategory, to elicit comment on both pathways, the
EPA is proposing to subcategorize further into base load units that are adopting the CCS
pathway and base load units that are adopting the low-GHG hydrogen co-firing pathway. For the
subcategory of base load units that are adopting the CCS pathway, the EPA is proposing that the
BSER includes the use of CCS with 90 percent capture of CO2 with an associated standard of 90
lb CCh/MWh-gross, compliance with which would be required starting in 2035. For the
subcategory of base load units that are adopting the low-GHG hydrogen co-firing pathway, the
EPA is proposing that the BSER includes co-firing 30 percent (by volume) low-GHG hydrogen
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with an associated standard of 680 lb CCh/MWh-gross, compliance with which would be
required starting in 2032, and co-firing 96 percent (by volume) low-GHG hydrogen by 2038,
which corresponds to a standard of performance of 90 lb CCh/MWh-gross. In both cases, the
second (and sometimes third) phase standard of performance would be applicable to all
combustion turbines that were subject to the first phase standards of performance.
With respect to existing coal-fired steam generating units, the EPA is proposing to repeal
and replace the existing ACE Rule emission guidelines. The EPA recognizes that, since it
promulgated the ACE Rule, the costs of CCS have decreased due to technology advancements as
well as new policies including the expansion of the Internal Revenue Code section 45Q tax credit
for CCS in the Inflation Reduction Act (IRA); and the costs of natural gas co-firing have
decreased as well, due in large part to a decrease in the difference between coal and natural gas
prices. As a result, the EPA considered both CCS and natural gas co-firing as candidates for
BSER for existing coal-fired steam EGUs.
Based on the latest information available to the Agency on cost, emission reductions, and
other statutory criteria, the EPA is proposing that the BSER for existing coal-fired steam EGUs
that expect to operate in the long-term is CCS with 90 percent capture of CO2. The EPA has
determined that CCS satisfies the BSER criteria for these sources because it is adequately
demonstrated, achieves significant reductions in GHG emissions, and is highly cost-effective.
In response to industry stakeholder input descried in sections I.B.2 and X.C.3 of the
preamble, and recognizing that the cost effectiveness of controls depends on the unit's expected
operating time horizon, which dictates the amortization period for the capital costs of the
controls, the EPA believes it is appropriate to establish subcategories of existing steam EGUs
that are based on the operating horizon of the units. The EPA is proposing that for units that
expect to operate in the long-term (i.e., those that plan to operate past December 31, 2039), the
BSER is the use of CCS with 90 percent capture of CO2 with an associated degree of emission
limitation of an 88.4 percent reduction in emission rate (lb CCh/MWh-gross basis). As explained
in detail in this proposal, CCS with 90 percent capture of CO2 is adequately demonstrated, cost
reasonable, and achieves substantial emissions reductions from these units.
The EPA is proposing to define coal-fired steam generating units with medium-term
operating horizons as those that (1) operate after December 31, 2031, (2) have elected to commit
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to permanently cease operations before January 1, 2040, (3) elect to make that commitment
federally enforceable and continuing by including it in the state plan, and (4) do not meet the
definition of near-term operating horizon units. For these medium-term operating horizon units,
the EPA is proposing that the BSER is co-firing 40 percent natural gas on a heat input basis with
an associated degree of emission limitation of a 16 percent reduction in emission rate (lb
CCh/MWh-gross basis). While this subcategory is based on a 10-year operating horizon (i.e.,
January 1, 2040), the EPA is specifically soliciting comment on the potential for a different
operating horizon between 8 and 10 years to define the threshold date between the definition of
medium-term and long-term coal-fired steam generating units (i.e., January 1, 2038 to January 1,
2040), given that the costs for CCS may be reasonable for units with amortization periods as
short as 8 years. For units with operating horizons that are imminent-term, i.e., those that (1)
have elected to commit to permanently cease operations before January 1, 2032, and (2) elect to
make that commitment federally enforceable and continuing by including it in the state plan, the
EPA is proposing that the BSER is routine methods of operation and maintenance with an
associated degree of emission limitation of no increase in emission rate (lb CCh/MWh-gross
basis). The EPA is proposing the same BSER determination for units in the near-term operating
horizon subcategory, i.e., units that (1) have elected to commit to permanently cease operations
by December 31, 2034, as well as to adopt an annual capacity factor limit of 20 percent, and (2)
elect to make both of these conditions federally enforceable by including them in the state plan.
The EPA is also soliciting comment on a potential BSER based on low levels of natural gas co-
firing for units in these last two subcategories.
The EPA is also proposing emission guidelines for existing natural gas-fired and oil-fired
steam generating units. Recognizing that virtually all of these units have limited operation, the
EPA is, in general, proposing that the BSER is routine methods of operation and maintenance
with an associated degree of emission limitation of no increase in emission rate (lb CCh/MWh-
gross).
The EPA is also proposing emission guidelines for large (i.e., greater than 300 MW),
frequently operated (i.e., with a capacity factor of greater than 50 percent), existing fossil fuel-
fired stationary combustion turbines. Because these existing combustion turbines are similar to
new stationary combustion turbines, the EPA is proposing a BSER that is similar to the BSER
for new base load combustion turbines. The EPA is not proposing a first phase efficiency-based
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standard of performance; but the EPA is proposing that BSER for these units is based on either
the use of CCS by 2035 or co-firing of 30 percent (by volume) low-GHG hydrogen by 2032 and
co-firing 96 percent low-GHG hydrogen by 2038.
For the emission guidelines for existing fossil fuel-fired steam generating units and large,
frequently operated fossil fuel-fired combustion turbines, the EPA is also proposing state plan
requirements, including submittal timelines for state plans and methodologies for determining
presumptively approvable standards of performance consistent with BSER. This proposal also
addresses how states can implement the remaining useful life and other factors (RULOF)
provision of CAA section 111(d) and how states can conduct meaningful engagement with
impacted stakeholders. Finally, the EPA is proposing to allow states to include trading or
averaging in state plans so long as they demonstrate equivalent emissions reductions, and this
proposal discusses considerations related to the appropriateness of including such compliance
flexibilities.
For additional information on BSER in these actions, please see the preamble for these
actions. Related information can also be found in Technical Support Documents (TSDs)
available in the rulemaking docket.
1.3.4 Illustrative Scenarios
This RIA evaluates the benefits, costs and certain impacts of compliance with three
illustrative scenarios: the proposal, a less stringent scenario, and a more stringent scenario. The
modeling of the illustrative proposal scenario that is discussed in Sections 3 through 7 of this
RIA includes all aspects of the proposed 111(d) requirements for existing fossil fuel-fired steam
generating units and most aspects of the proposed 111(b) requirements for new and reconstructed
stationary combustion turbines. However, it does not reflect the proposed 111(d) requirements
for existing stationary combustion turbines or one additional component of the 111(b)
requirements (for new base load combustion turbines in the hydrogen co-firing subcategory, the
third phase standard based on co-firing 96 percent low-GHG hydrogen by 2038). For these
additional measures, EPA performed a spreadsheet-based analysis of regulatory impacts that is
discussed in Section 8 of this RIA.
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With respect to the new source standard, the more stringent scenario differs from the
proposal in that it assumes imposition of the second phase of the NSPS in run year 2030, while
the proposal and less stringent scenarios assume imposition of the second phase of the NSPS in
run year 2035. With regards to the existing source standard, the proposal and more stringent
scenarios assume all long-term existing coal-fired steam generating units are subject to 90
percent CCS requirements in 2030, while the less stringent scenario assumes that long-term
existing coal-fired steam generating units greater than 700 MW, and plants greater than 2,000
MW are subject to 90 percent CCS requirements, while those units less than 700 MW (and plants
less than 2,000 MW) are subject to 40 percent natural gas co-firing requirements.
The GHG mitigation measures in this RIA are illustrative since States are afforded
flexibility to implement the proposed rules, and thus the impacts could be different to the extent
states make different choices than those assumed in the illustrative analysis. Additionally, the
way that EGUs comply with the GHG mitigation measures may differ from the methods forecast
in the modeling for this RIA. See Section 3.4 for further discussion of the modeling approach
used in the analysis presented below. For details of the controls modeled for each of the existing
source categories starting in run year 2030 under the three illustrative scenarios please see
Section 3.2 of this document.
1.4 Organization of the Regulatory Impact Analysis
This RIA is organized into the following remaining sections:
• Section 2: Industry Profile. This section describes the electric power sector in detail.
• Section 3: Cost, Emissions, and Energy Impacts. This section summarizes the
projected compliance costs and other energy impacts associated with the regulatory
options.
• Section 4: Benefits Analysis. This section presents the projected climate benefits of CO2
emissions reductions, and the health and environmental benefits of reductions in
emissions of nitrogen oxides (NOx), fine particulate matter (PM2.5) and sulfur dioxide
(SO2). Potential benefits to drinking water quality and quantity are also discussed.
1-14
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• Section 5: Economic Impact Analysis. This section includes a discussion of potential
small entity, economic, and labor impacts.
• Section 6: Environmental Justice Impacts. This section includes an assessment of
potential impacts to potential EJ populations.
• Section 7: Comparison of Benefits and Costs. This section compares the total projected
benefits with total projected costs and summarizes the projected net benefits of the three
illustrative scenarios examined. The section also includes a discussion of potential
benefits that EPA is unable to quantify and monetize.
• Section 8: Impacts of Proposed 111(d) Standards on Existing Natural Gas-fired
EGUs and Third Phase of Proposed 111(b) Standards on New Natural Gas-fired
EGUs: This section summarizes the cost and emissions impact analysis of the proposed
standards for existing natural gas-fired EGUs and the third phase of the proposed
standards for new natural gas-fired EGUs.
• Appendix A: Air Quality Modeling. This section describes the air quality modeling
simulations, provides details on the methodology to apply the air quality modeling to
estimate ozone and PM2.5 impacts of the illustrative policy scenario and presents resulting
surfaces that represent air quality changes associated with the illustrative scenarios.
• Appendix B: Economy-wide Social Costs and Economic Impacts. This section
presents estimates of economy-wide social costs and economic impacts of these proposed
rules from a computable general equilibrium (CGE) model of the United States economy,
SAGE, as a complement to other analyses in this RIA.
• Appendix C: Assessment of Potential Costs and Emissions Impacts of Proposed New
and Existing Source Standards Analyzed Separately. This section summarizes the
projected compliance costs and other energy impacts associated with the imposition of
new source standards independently from existing source standards.
1.5 References
U.S. EPA. (2014). Guidelines for Preparing Economic Analyses. (EPA 240-R-10-001).
Washington DC: U.S. Environmental Protection Agency, Office of Policy, National
Center for Environmental Economics, https://www.epa.gov/environmental-
economics/guidelines-preparing-economic-analyses
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U. S. EPA. (2019). Regulatory Impact Analysis for the Repeal of the Clean Power Plan, and the
Emission Guidelines for Greenhouse Gas Emissions from Existing Electric Utility
Generating Units. (EPA-452/R-19-003). Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Health
and Environmental Impact Division, https://www.epa.gov/sites/production/files/2019-
06/documents/utilities_ria_final_cpp_repeal_and_ace_2019-06.pdf
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2 INDUSTRY PROFILE
2.1 Background
In the past decade, there have been substantial structural changes in both the mix of
generating capacity and in the share of electricity generation supplied by different types of
generation. These changes are the result of multiple factors in the power sector, including
replacements of older generating units with new units, changes in the electricity intensity of the
U.S. economy, growth and regional changes in the U.S. population, technological improvements
in electricity generation from both existing and new units, changes in the prices and availability
of different fuels, and substantial growth in electricity generation from renewable energy
sources. Many of these trends will likely continue to contribute to the evolution of the power
sector.24 The evolving economics of the power sector, specifically the increased natural gas
supply and subsequent relatively low natural gas prices, have resulted in more natural gas being
used to produce both base and peak load electricity. Additionally, rapid growth in the
deployment of wind and solar technologies has led to their now constituting a significant share of
generation. The combination of these factors has led to a decline in the share of electricity
generated from coal. This section presents data on the evolution of the power sector over the past
two decades from 2010 through 2021, as well as a focus on the period 2015 through 2021.
Projections of future power sector behavior and the impact of the proposed rules are discussed in
more detail in Section 3 of this RIA.
2.2 Power Sector Overview
The production and delivery of electricity to customers relies on of three distinct stages:
the generation, transmission, and distribution of electricity.
2.2.1 Generation
Electricity generation is the first process in the delivery of electricity to consumers. There
are two important aspects of electricity generation: capacity and net generation. Generating
Capacity refers to the maximum amount of production an EGU is capable of producing in a
24 For details on the evolution of EPA's power sector projections, please see archive of IPM outputs available at:
epa. gov/power-sector-modeling
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typical hour, typically measured in megawatts (MW) for individual units, or gigawatts (1 GW =
1,000 MW) for multiple EGUs. Electricity Generation refers to the amount of electricity actually
produced by an EGU over some period of time, measured in kilowatt-hours (kWh) or gigawatt-
hours (1 GWh = 1 million kWh). Net Generation is the amount of electricity that is available to
the grid from the EGU (i.e., excluding the amount of electricity generated but used within the
generating station for operations). Electricity generation is most often reported as the total annual
generation (or some other period, such as seasonal). In addition to producing electricity for sale
to the grid, EGUs perform other services important to reliable electricity supply, such as
providing backup generating capacity in the event of unexpected changes in demand or
unexpected changes in the availability of other generators. Other important services provided by
generators include facilitating the regulation of the voltage of supplied generation.
Individual EGUs are not used to generate electricity 100 percent of the time. Individual
EGUs are periodically not needed to meet the regular daily and seasonal fluctuations of
electricity demand. Units are also unavailable during routine and unanticipated outages for
maintenance. Furthermore, EGUs relying on renewable resources such as wind, sunlight and
surface water to generate electricity are routinely constrained by the availability of adequate
wind, sunlight, or water at different times of the day and season. These factors result in the share
of potential generating capacity being substantially different from the share of actual electricity
produced by each type of EGU in a given season or year.
Most of the existing capacity generates electricity by creating heat to create high pressure
steam that is released to rotate turbines which, in turn, create electricity. Natural gas combined
cycle (NGCC) units have two generating components operating from a single source of heat. The
first cycle is a gas-fired combustion turbine, which generates electricity directly from the heat of
burning natural gas. The second cycle reuses the waste heat from the first cycle to generate
steam, which is then used to generate electricity from a steam turbine. Other EGUs generate
electricity by using water or wind to rotate turbines, and a variety of other methods including
direct photovoltaic generation also make up a small, but growing, share of the overall electricity
supply. The most common generating capacity includes fossil-fuel-fired units, nuclear units, and
hydroelectric and other renewable sources (see Table 2-1). Table 2-1 also shows the comparison
between the generating capacity in 2010 to 2021 and 2015 to 2021.
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In 2021 the power sector comprised a total capacity25 of 1,179 GW, an increase of 140
GW (or 13 percent) from the capacity in 2010 (1,039 GW). The largest change over this period
was the decline of 107 GW of coal capacity, reflecting the retirement/rerating of over a third of
the coal fleet. This reduction in coal capacity was offset by increases in natural gas, solar, and
wind capacities of 85 GW, 61 GW, and 94 GW respectively. Substantial amounts of distributed
solar (33 GW) were also added.
These trends persist over the shorter 2015-21 period as well; total capacity in 2021 (1,179
GW) increased by 105 GW (or 10 percent). The largest change in capacity was driven by a
reduction of 70 GW of coal capacity. This was offset by a net increase of 52 GW of natural gas
capacity, an increase of 60 GW of wind, and an increase of 48 GW of solar. Additionally, 23
GW of distributed solar were also added over 2015-21.
25 This includes generating capacity at EGUs primarily operated to supply electricity to the grid and combined heat
and power facilities classified as Independent Power Producers (IPP) and excludes generating capacity at
commercial and industrial facilities that does not operate primarily as an EGU. Natural Gas information in this
section (unless otherwise stated) reflects data for all generating units using natural gas as the primary fossil heat
source. This includes Combined Cycle Combustion Turbine, Gas Turbine, steam, and miscellaneous (< 1
percent).
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Table 2-1 Total Net Summer Electricity Generating Capacity by Energy Source,
2010-21 and 2015-21
2010
2021
Change Between '10
and '21
Net
Net
Net
Net
Energy Source
Summer
% Total
Summer
Summer
% Total
Summer
Capacity
Capacity
Capacity
Capacity
Capacity
Capacity
(GW)
(GW)
(GW)
(GW)
Coal
317
30%
210
18%
-34%
-107
Natural Gas
407
39%
492
42%
21%
85
Nuclear
101
10%
96
8%
-6%
-6
Hydro
101
10%
103
9%
2%
2
Petroleum
56
5%
28
2%
-49%
-27
Wind
39
4%
133
11%
239%
94
Solar
1
0%
62
5%
7004%
61
Distributed Solar
0
0%
33
3%
33
Other Renewable
14
1%
15
1%
9%
1
Misc
4
0%
8
1%
129%
5
Total
1,039
100%
1,179
100%
13%
140
2015
2021
Change Between '15
and '21
Energy Source
Net
Summer
Capacity
(GW)
% Total
Capacity
Net
Summer
Capacity
(GW)
% Total
Capacity
%
Increase
Capacity
Change
(GW)
Coal
280
26%
210
18%
-25%
-70
Natural Gas
439
41%
492
42%
12%
52
Nuclear
99
9%
96
8%
-3%
-3
Hydro
102
10%
103
9%
1%
1
Petroleum
37
3%
28
2%
-23%
-9
Wind
73
7%
133
11%
83%
60
Solar
14
1%
62
5%
350%
48
Distributed Solar
10
1%
33
3%
238%
23
Other Renewable
17
2%
15
1%
-10%
-2
Misc
4
0%
8
1%
91%
4
Total
1,074
100%
1,179
100%
10%
105
Source: EIA. Electric Power Annual 2021, Table 4.2.A
The average age of coal-fired power plants that retired between 2015 and 2021 was over
50 years. Older power plants tend to become uneconomic over time as they become more costly
to maintain and operate, and as newer and more efficient alternative generating technologies are
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built. As a result, coal's share of total U.S. electricity generation has been declining for over a
decade, while generation from natural gas and renewables has increased significantly.26 As
shown in Figure 2-1 below, 65 percent of the coal fleet in 2021 had an average age of over 40
years.
100
89
90
80
70
60
50 46
40
30
I
10 5 H 5
¦ ¦ ¦ ¦
0-10yrs 10 - 20 yrs 20-30yrs 30-40yrs 40
Figure 2-1 National Coal-fired Capacity (GW) by Age of EGU, 2021
Source: NEEDS v6
In 2021, electric generating sources produced a net 4,157 TWh to meet national
electricity demand, which was around 1 percent higher than 2010. As presented in Table 2-2, 59
percent of electricity in 2021 was produced through the combustion of fossil fuels, primarily coal
and natural gas, with natural gas accounting for the largest single share. The total generation
share from fossil fuels in 2021 (60 percent) was 11 percent less than the share in 2010 (69
percent). Moreover, the share of fossil generation supplied by coal fell from 65 percent in 2010
to 36 percent by 2021, while the share of fossil generation supplied by natural gas rose from 35
percent to 64 percent over the same period. In absolute terms, coal generation declined by 51
percent, while natural gas generation increased by 60 percent. This reflects both the increase in
natural gas capacity during that period as well as an increase in the utilization of new and
existing gas EGUs during that period. The combination of wind and solar generation also grew
from 2 percent of the mix in 2010 to 13 percent in 2021.
26EIA, Today in Energy (April 17, 2017) available at https://www.eia.gov/todayinenergy/detail.php?id=30812
-50 yrs 50-60 yrs 60 +yrs
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Table 2-2 Net Generation by Energy Source, 2010 - 21 and 2015 - 21 (Trillion kWh =
TWh)
2010
2021
Change Between '10
and '21
Energy Source
Net
Generation
(TWh)
Fuel
Source
Share
Net
Generation
(TWh)
Fuel
Source
Share
%
Increase
Generation
Change
(TWh)
Coal
1,847
45%
898
22%
-51%
-949
Natural Gas
988
24%
1,579
38%
60%
592
Nuclear
807
20%
778
19%
-4%
-29
Hydro
255
6%
246
6%
-3%
-8
Petroleum
37
1%
19
0%
-48%
-18
Wind
95
2%
378
9%
300%
284
Solar
1
0%
115
3%
9410%
114
Distributed Solar
0
0%
49
1%
49
Other Renewable
71
2%
70
2%
-2%
-1
Misc
24
1%
24
1%
-3%
-1
Total
4,125
100%
4,157
100%
1%
32
Table 2-3 Net Generation in 2015 and 202]
(Trillion kWh = TWh)
2015
2021
Change Between '15
and '21
Energy Source
Net
Generation
(TWh)
Fuel
Source
Share
Net
Generation
(TWh)
Fuel
Source
Share
%
Increase
Generation
Change
(TWh)
Coal
1,352
33%
898
22%
-34%
-455
Natural Gas
1,335
33%
1,579
38%
18%
246
Nuclear
797
19%
778
19%
-2%
-19
Hydro
249
6%
252
6%
1%
2
Petroleum
28
1%
19
0%
-32%
-9
Wind
191
5%
378
9%
98%
187
Solar
25
1%
115
3%
363%
90
Distributed Solar
14
0%
49
1%
248%
35
Other Renewable
80
2%
70
2%
-12%
-9
Misc
27
1%
24
1%
-13%
-4
Total
4,092
100%
4,157
100%
2%
66
Source: EI A. Electric Power Annual 2021, Table 3. l.A and 3. l.B
Coal-fired and nuclear generating units have historically supplied "base load" electricity,
meaning that these units operate through most hours of the year and serve the portion of
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electricity load that is continually present. Although much of the coal fleet has historically
operated as base load, there can be notable differences in the design of various facilities (see
Table 2-3) which, along with relative fuel prices, can impact the operation of coal-fired power
plants. As one example of design variations, coal-fired units less than 100 megawatts (MW) in
size comprise 18 percent of the total number of coal-fired units, but only 2 percent of total coal-
fired capacity, and they tend to have higher heat rates. Gas-fired generation is generally better
able to vary output, is a primary option used to meet the variable portion of the electricity load
and has historically supplied "peak" and "intermediate" power, when there is increased demand
for electricity (for example, when businesses operate throughout the day or when people return
home from work and run appliances and heating/air-conditioning), versus late at night or very
early in the morning, when demand for electricity is reduced. Over the last decade, however, the
generally low price of natural gas and the growing age of the coal fleet has resulted in increasing
capacity factors for many gas-fired plants and decreasing capacity factors for many coal-fired
plants. As shown in Figure 2-2, average annual coal capacity factors have declined from 67
percent to 49 percent over the 2010 to 2021 period, indicating that a larger share of units are
operating in non-baseload fashion. Over the same period, natural gas combined cycle capacity
factors have risen from an annual average of 44 percent to 55 percent.
70%
< 20%
10%
0%
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
— — — Coal — — — Natural Gas
Figure 2-2 Average Annual Capacity Factor by Energy Source
Source: EI A. Electric Power Annual 2021, Table 4.8. A
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Table 2-4 also shows comparable data for the capacity and age distribution of natural gas
units. Compared with the fleet of coal EGUs, the natural gas fleet of EGUs is generally smaller
and newer. While 67 percent of the coal EGU fleet capacity is over 500 MW per unit, 75 percent
of the gas fleet is between 50 and 500 MW per unit.
Table 2-4 Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Average
Heat Rate in 2020
TT Avg. Net Total Net . TT .
ni lze i\0- o/0 0f ah Summer Summer % Total V^° ea
Units Units V& gC Capacity Capacity Capacity fBtlJ/|l.whl
(MW) (MW) (MW) (Btu/kWh)
COAL
0-24
31
6%
49
11
351
0%
11,379
25-49
32
6%
35
36
1,150
1%
11,541
50-99
24
5%
39
76
1,823
1%
11,649
100 - 149
36
7%
50
122
4,388
2%
11,167
150-249
61
12%
52
197
12,027
6%
10,910
250-499
132
26%
42
372
49,090
24%
10,700
500 - 749
138
27%
41
609
83,978
40%
10,315
750 - 999
50
10%
38
827
41,345
20%
10,135
1000 - 1500
11
2%
43
1,264
13,903
7%
9,834
Total Coal
515
100%
43
404
208,056
100%
10,718
NATURAL GAS
0-24
4,329
54%
31
5
21,626
4%
13,244
25-49
932
12%
26
41
38,089
8%
11,759
50-99
1,018
13%
27
71
72,744
15%
12,163
100 - 149
410
5%
23
126
51,567
10%
9,447
150-249
1,041
13%
18
179
186,494
37%
8,226
250-499
293
4%
21
332
97,244
19%
8,293
500 - 749
37
0%
38
592
21,910
4%
10,384
750 - 999
10
0%
46
828
8,278
2%
11,294
1000 - 1500
1
0%
0
1,060
1,060
0%
7,050
Total Gas
8,060
100%
28
62
499,012
100%
11,900
Source: National Electric Energy Data System (NEEDS) v.6
Note: The average heat rate reported is the mean of the heat rate of the units in each size category (as opposed to a
generation-weighted or capacity-weighted average heat rate.) A lower heat rate indicates a higher level of fuel
efficiency.
In terms of the age of the generating units, almost 50 percent of the total coal generating
capacity has been in service for more than 40 years, while nearly 50 percent of the natural gas
capacity has been in service less than 15 years. Figure 2-3 presents the cumulative age
distributions of the coal and gas fleets, highlighting the pronounced differences in the ages of the
fleets of these two types of fossil-fuel generating capacity. Figure 2-3 also includes the
distribution of generation, which is similar to the distribution of capacity.
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0 10 20 30 40 50 60 70
Age of EGU (years)
Gas Cap — — —Gas Gen ^'Coal Cap — — —Coal Gen
Figure 2-3 Cumulative Distribution in 2020 of Coal and Natural Gas Electricity
Capacity and Generation, by Age
Source: eGRID 2020 (January 2022 release from EPA eGRID website). Figure presents data from generators that
came online between 1950 and 2020 (inclusive); a 71-year period. Full eGRID data includes generators that came
online as far back as 1915. Full data from 1915 onward is used in calculating cumulative distributions; figure
truncation at 70 years is merely to improve visibility of diagram.
The locations of existing fossil units in EPA's National Electric Energy Data System
(NEEDS) v.6 are shown in Figure 2-4.
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Figure 2-4 Fossil Fuel-Fired Electricity Generating Facilities, by Size
Source: National Electric Energy Data System (NEEDS) v.6
Note: Tliis map displays fossil capacity at facilities in the NEEDS v.6 IPM frame. NEEDS v.6 reflects generating
capacity expected to be on-line at the end of 2023. This includes planned new builds already under construction and
planned retirements. In areas with a dense concentration of facilities, some facilities may be obscured.
The costs of renewable generation have fallen significantly due to technological
advances, improvements in performance, and local, state, and federal incentives such as the
recent extension of federal tax credits. According to Lazard, a financial advisoiy and asset
management firm, the current unsubsidized levelized cost of electricity for wind and solar energy
technologies is lower than the cost of technologies like coal, natural gas or nuclear, and in some
cases even lower than just the operating cost, which is expected to lead to ongoing and
significant deployment of renewable energy. Levelized cost of electricity is only one metric used
to compare the cost of different generating technologies. It contains a number of uncertainties
including utilization and regional factors.27 While this chart illustrates general trends, unit
specific build decisions will incorporate many other variables. These trends of declining costs
and cost projections for renewable resources are borne out by a range of other studies including
the NREL Annual Technology Baseline28, DOE's Land-Based Wind Market Report21, LBNL's
® Lazard, Levelized Cost of Energy Analysis-Version 15.0,2021. https://www.lazard.com/media/451905/lazards-
levelized-cost-of-energy-version-150-vf.pdf
28 Available at: https://atb.nrel.gov/
29 Available at: https://www.energy.gov/eere/wind/articles/land-based-wind-market-report-2022-edition
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Utility Scale solar report36, EIA's Annual Energy Outlook31, and DOE's 2022 Grid Energy
Storage Technology Cost and Performance Assessment,3-
Selected Historical Mean Unsubsidizcd LCOE Values'1)
Figure 2-5 Selected Historical Mean LCOE Values
Source: Lazard, Levelized Cost of Energy Analysis-Version 15.0. October 2021
The broad trends away from coal-fired generation and toward lower-emitting generation
are reflected in the recent actions and recently announced plans of many power plants across the
industry — spanning all types of companies in all locations. Furthermore, as detailed below,
many utilities have made commitments to move toward cleaner energy. Throughout the country,
utilities have included commitments towards cleaner energy in public releases, planning
documents, and integrated resource plans (IRPs). For strategic business reasons and driven by
the economics of different supply options, most major utilities plan to increase their renewable
energy holdings and continue reducing GHG emissions, regardless of what federal regulatory
requirements might exist. The Edison Electric Institute (EEI) has confirmed these developments:
"While the CPP was stayed by the Supreme Court in 2016, the power sector will have complied
with the final 2030 goals of the rule—in terms of gross emissions reductions—before the 2022
30 Available at: https://emp.lbl.gov/utility-scale-solar/
31 Available at: https://www.eia.gov/outlooks/aeo/pdf/electricity_generation.pdf
32 Available at: https://www.energy.gov/eere/analysis/2022-grid-energy-storage-teclinology-cost-and-perfonnance-
assessment
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start date included in that program."33 This trend is not unique to the largest owner-operators of
coal-fired generation; smaller utilities, public power, cooperatives, and municipal entities are
also contributing to these changes.
There are many recent examples of electric utilities that have publicly announced near-
and long-term emission reduction commitments. Here are but a few examples of emission
reduction targets of 80 percent or more (relative to 2005 levels) that have recently been
announced by major utilities that together serve roughly 40 million electric customers:
• Xcel Energy (with power plants that operate in MN, CO, MI, MN, NM, ND, SD,
TX, and WI): 85 percent reduction in CO2 emissions by 2030 and carbon-free by
2050. This includes a commitment to close or repower all remaining coal units by
2040.34
• DTE Energy (MI): 50 percent reduction in CO2 by 2028, 80 percent by 2040, and
carbon-free by 2050.35
• Ameren Energy (MO): 50 percent by 2030, 85 percent by 2040, and carbon-free by
2050.36
• Consumers Energy (MI): Carbon-free by 2040. This includes company retiring all
coal fire units by 2025.37
• Duke Energy: 50 percent reduction by 2030, carbon-free by 2050.38
• Allete Inc: 50 percent reduction by 2030, 80 percent reduction by 2035, carbon-free by
2050.39
• First Energy (FE): Carbon-free by 2050.40
• American Electric Power (AEP): 80 percent reduction by 2030 and carbon-free
33 EEI Comments on ACE, at 4 (Oct. 31, 2018)
34 Xcel Energy, Press Release, available at: https://investors.xcelenergy.com/news-market-information/press-
releases/press-release/2021/Xcel-Energy-Announces-2030-Clean-Energy-Plan-to-Reduce-Carbon-Emissions-
85/default.aspx
35 DTE Energy, Powering towards a net zero carbon future, available at: https://dtecleanenergy.com/pathway-to-net-
zero /
36 Ameren Missouri, 2021 Climate Report, available at: https://www.ameren. com/-/media/corporate-
site/files/environment/reports/climate-report-
tcfd.pdf?La=en&hash=B6CEB8301F0356B4E37B35176826FEEAFFEB5AlE%20
37 Consumers Energy, News Release, available at https://www.consumersenergy.com/news-releases/news-release-
details/2021/06/23/consumers-energy-announces-plan-to-end-coal-use-by-2025-lead-michigans-clean-energy-
transformation
38 Duke Energy, News Release, available at https://news.duke-energy.com/releases/duke-energy-expands-clean-
energy-action-
plan#:~:text=And%20it%20is%20on%20pace,approximately%207%2C500%20megawatts%20since%202010.
39 Allete Energy, New Release, available at: https://investor.allete.com/news-releases/news-release-
details/minnesota-power-announces-vision-100-percent-carbon-free-energy
40 First Energy, available at https://www.firstenergycorp.com/environmental.html
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by 2050 (from year 2000 levels).41
• Alliant Energy: 50 percent reduction by 2030 and carbon-free by 2050 and
retiring final coal fire plant by 2024.42
• Tennessee Valley Authority: 70 percent reduction by 2030, 80 percent reduction
by 2035, carbon-free by 2050.43
While EPA does not account for statements from utilities regarding their future plans that
are not technically legally enforceable in the economic modeling, the number and scale of these
announcements is significant on a systemic level. These statements are also part of long- term
planning processes that cannot be easily revoked, since there is considerable stakeholder
involvement, including by regulators, in the planning process. The direction in which these
companies have publicly stated they are moving is consistent across the sector and undergirded
by market fundamentals lending economic credibility to these commitments and confidence that
there is a high likelihood that most will be implemented.
2.2.2 Transmission
Transmission is the term used to describe the bulk transfer of electricity over a network
of high voltage lines, from electric generators to substations where power is stepped down for
local distribution. In the U.S. and Canada, there are three separate interconnected networks of
high voltage transmission lines,44 each operating synchronously. Within each of these
transmission networks, there are multiple areas where the operation of power plants is monitored
and controlled by regional organizations to ensure that electricity generation and load are kept in
balance. In some areas, the operation of the transmission system is under the control of a single
41 AEP, available at http://www.aepsustainability.com/environment/carbon/
42 Alliant Energy, available at
https://www.alliantenergy.com/cleanenergy/ourenergyvision/responsibilityreport/cleanenergyvisiongoals7utm_s
ource=WS&utm_campaign=Legacy&utm_medium=AboutAlliantEnergy/ResponsibilityReport/CleanEnergyVisi
onGoals
43 TVA, available at: https://www.tva.com/newsroom/press-releases/tva-charts-path-to-clean-energy-future
44 These three network interconnections are the Western Interconnection, comprising the western parts of both the
U.S. and Canada (approximately the area to the west of the Rocky Mountains), the Eastern Interconnection,
comprising the eastern parts of both the U.S. and Canada (except those part of eastern Canada that are in the
Quebec Interconnection), and the Texas Interconnection (which encompasses the portion of the Texas electricity
system commonly known as the Electric Reliability Council of Texas (ERCOT)). See map of all NERC
interconnections at
https://www.nerc.com/AboutNERC/keyplayers/PublishingImages/NERC%20Interconnections.pdf.
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regional operator;45 in others, individual utilities46 coordinate the operations of their generation,
transmission, and distribution systems to balance the system across their respective service
territories.
2.2.3 Distribution
Distribution of electricity involves networks of lower voltage lines and substations that
take the higher voltage power from the transmission system and step it down to lower voltage
levels to match the needs of customers. The transmission and distribution system is the classic
example of a natural monopoly, in part because it is not practical to have more than one set of
lines running from the electricity generating sources to substations or from substations to
residences and businesses.
Over the last few decades, several jurisdictions in the United States began restructuring
the power industry to separate transmission and distribution from generation, ownership, and
operation. Historically, vertically integrated utilities established much of the existing
transmission infrastructure. However, as parts of the country have restructured the industry,
transmission infrastructure has also been developed by transmission utilities, electric
cooperatives, and merchant transmission companies, among others. Distribution, also historically
developed by vertically integrated utilities, is now often managed by a number of utilities that
purchase and sell electricity, but do not generate it. Electricity restructuring has focused
primarily on efforts to reorganize the industry to encourage competition in the generation
segment of the industry, including ensuring open access of generation to the transmission and
distribution services needed to deliver power to consumers. In many states, such efforts have also
included separating generation assets from transmission and distribution assets to form distinct
economic entities. Transmission and distribution remain price-regulated throughout the country
based on the cost of service.
45 For example, PJM Interconnection, LLC.
46 For example, Los Angeles Department of Water and Power, Florida Power and Light.
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2.3 Sales, Expenses, and Prices
Electric generating sources provide electricity for ultimate commercial, industrial and
residential customers. Each of the three major ultimate categories consume roughly a quarter to a
third of the total electricity produced (see Table 2-5). 47 Some of these uses are highly variable,
such as heating and air conditioning in residential and commercial buildings, while others are
relatively constant, such as industrial processes that operate 24 hours a day. The distribution
between the end use categories changed very little between 2010 and 2020.
Table 2-5 Total U.S. Electric Power Industry Retail Sales, 2010-21 and 2014-21 (billion
kWh)
2010
2021
Sales/Direct
Use (Billion
kWh)
Share of Total
End Use
Sales/Direct
Use (Billion
kWh)
Share of Total
End Use
Residential
1,446
37%
1,470
37%
Sales
Commercial
Industrial
1,330
971
34%
25%
1,328
1,001
34%
25%
Transportation
8
0%
6
0%
Total
3,755
97%
3,806
96%
Direct Use
132
3%
139
Total End Use
3,887
100%
3,945
2015
2021
Sales/Direct
Use (Billion
kWh)
Share of Total
End Use
Sales/Direct
Use (Billion
kWh)
Share of Total
End Use
Residential
1,404
36%
1,470
37%
Sales
Commercial
Industrial
1,361
987
35%
25%
1,328
1,001
34%
25%
Transportation
8
0%
6
0%
Total
3,759
96%
3,806
96%
Direct Use
141
4%
139
Total End Use
3,900
100%
3,945
Source: Table 2.2, EIA Electric Power Annual, 2020, Electric Power Monthly March 2022.
Notes: Retail sales are not equal to net generation (Table 2-2) because net generation includes net imported
electricity and loss of electricity that occurs through transmission and distribution, along with data collection frame
differences and non-sampling error. Direct Use represents commercial and industrial facility use of onsite net
electricity generation; electricity sales or transfers to adjacent or co-located facilities; and barter transactions.
47 Transportation (primarily urban and regional electrical trains) is a fourth ultimate customer category which
accounts less than one percent of electricity consumption.
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2.3.1 Electricity Prices
Electricity prices vary substantially across the United States, differing both between the
ultimate customer categories and by state and region of the country. Electricity prices are
typically highest for residential and commercial customers because of the relatively high costs of
distributing electricity to individual homes and commercial establishments. The higher prices for
residential and commercial customers are the result of the extensive distribution network
reaching to virtually every building in every part of the country and the fact that generating
stations are increasingly located relatively far from population centers, increasing transmission
costs. Industrial customers generally pay the lowest average prices, reflecting both their
proximity to generating stations and the fact that industrial customers receive electricity at higher
voltages (which makes transmission more efficient and less expensive). Industrial customers
frequently pay variable prices for electricity, varying by the season and time of day, while
residential and commercial prices have historically been less variable. Overall, industrial
customer prices are usually considerably closer to the wholesale marginal cost of generating
electricity than residential and commercial prices.
On a state-by-state basis, all retail electricity prices vary considerably. In 2021, the
national average retail electricity price (all sectors) was 11.10 cents/kWh, with a range from 8.1
cents (Idaho) to 30.31 cents (Hawaii).48
The real year prices for 2010 through 2021 are shown in Figure 2-6. Average national
retail electricity prices decreased between 2010 and 2021 by 8 percent in real terms (2019
dollars), and 5 percent between 2015-21.49 The amount of decrease differed for the three major
end use categories (residential, commercial and industrial). National average industrial prices
decreased the most (7 percent), and residential prices decreased the least (4 percent) between
2015-21.
48 EIA State Electricity Profiles with Data for 2021 (http://www.eia.gov/electricity/state/)
49 All prices in this section are estimated as real 2019 prices adjusted using the GDP implicit price deflator unless
otherwise indicated.
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1A
Ol
a.
>
14
12
10
8
6
4
2
0
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
^^Residential Commercial Industrial — — — Total
Figure 2-6 Real National Average Electricity Prices (including taxes) for Three Major
End-Use Categories
Source: EIA. Electric Power Annual 2020 and 2021, Table 2.4.
2.3.2 Prices of Fossil Fuel Usedfor Generating Electricity
Another important factor in the changes in electricity prices are the changes in delivered
fuel prices50 for the three major fossil fuels used in electricity generation: coal, natural gas, and
petroleum products. Relative to real prices in 2014, the national average real price (in 2019
dollars) of coal delivered to EGUs in 2020 had decreased by 26 percent, while the real price of
natural gas decreased by 56 percent. The real price of delivered petroleum products also
decreased by 55 percent, and petroleum products declined as an EGU fuel (in 2020 petroleum
products generated 1 percent of electricity). The combined real delivered price of all fossil fuels
(weighted by heat input) in 2020 decreased by 39 percent over 2014 prices. Figure 2-7 shows the
relative changes in real price of all 3 fossil fuels between 2010 and 2021.
511 Fuel prices in this section are all presented in terms of price per MMBtu to make the prices comparable.
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40%
-80%
Coal Petroleum Natural Gas
Figure 2-7 Relative Real Prices of Fossil Fuels for Electricity Generation; Change in
National Average Real Price per MMBtu Delivered to EGU
Source: EI A. Electric Power Annual 2020 and 2021, Table 7.1.
2.3.3 Changes in Electricity Intensity of the U.S. Economy from 2010 to 2021
An important aspect of the changes in electricity generation (i.e., electricity demand)
between 2010 and 2021 is that while total net generation increased by 1 percent over that period,
the demand growth for generation was lower than both the population growth (7 percent) and
real GDP growth (24 percent). Figure 2-8 shows the growth of electricity generation, population,
and real GDP during this period.
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Figure 2-8 Relative Growth of Electricity Generation, Population and Real GDP Since
2010
Sources: Generation: U.S. EIA Electric Power Annual 2021 and 2020. Population: U.S. Census. Real GDP: 2022
Economic Report of the President, Table B-3.
Because demand for electricity generation grew more slowly than both the population
and GDP, the relative electric intensity of the U.S. economy improved (i.e., less electricity used
per person and per real dollar of output) during 2010 to 2021. On a per capita basis, real GDP per
capita grew by 16 percent between 2010 and 2021. At the same time electricity generation per
capita decreased by 6 percent. The combined effect of these two changes improved the overall
electricity generation efficiency in the U.S. market economy. Electricity generation per dollar of
real GDP decreased 19 percent. These relative changes are shown in Figure 2-9.
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25%
20%
-15% ¦ —
-20%
-25%
Real GDP / Capita Generation / Capita Generation / Real GDP
Figure 2-9 Relative Change of Real GDP, Population and Electricity Generation
Intensity Since 2010
Sources: Generation: U.S. EIA Electric Power Annual 2021 and 2020. Population: U.S. Census. Real GDP: 2022
Economic Report of the President, Table B-3.
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3 COMPLIANCE COSTS, EMISSIONS, AND ENERGY IMPACTS
3.1 Overview
This section reports the compliance costs, emissions, and energy analyses performed for
the proposed NSPS and proposed Emission Guidelines. Section 3 pertains to the analysis of the
proposed standards for new natural gas-fired EGUs and for existing coal-fired EGUs. Please see
Section 8 for impact analysis of the proposed standards for existing natural gas-fired EGUs and
the third phase of the proposed standards for new natural gas-fired EGUs. EPA used the
Integrated Planning Model (IPM)51 to conduct the electric generating units (EGU) analysis
discussed in this section. As explained in detail below, this section presents analysis for three
illustrative scenarios that differ in the level of EGU greenhouse gas (GHG) mitigation measures,
and timing thereof in the lower 48 states subject to this action. The analysis for EGUs in the
section includes effects from certain provisions of the Inflation Reduction Act (IRA) of 2022 in
the baseline. The analysis presented in this section reflects the combined effects of the proposals
on new and existing sources (with the exception of the proposed standards for existing natural
gas-fired EGUs and the third phase of the proposed standards for new natural gas-fired EGUs,
discussed in Section 8). The impacts of each action independently are presented in Appendix C.
The section is organized as follows: following a summary of the illustrative scenarios
analyzed and a summary of EPA's methodologies, we present estimates of compliance costs for
EGUs, as well as estimated impacts on emissions, generation, capacity, fuel use, fuel price, and
retail electricity price for select run years.52
3.2 Illustrative Scenarios
These rules establish GHG mitigation measures on certain fossil fuel-fired electric
generating units. The EGUs covered by these rules are existing fossil fuel-fired EGUs and fossil-
51 Information on IPM can be found at the following link: https://www.epa.gov/airmarkets/power-sector-modeling.
52 IPM uses model years to represent the full planning horizon being modeled. By mapping multiple calendar years
to a run year, the model size is kept manageable. IPM considers the costs in all years in the planning horizon
while reporting results only for model run years. For this analysis, IPM maps the calendar year 2028 to run year
2028, calendar years 2029-31 to run year 2030, calendar years 2032-37 to run year 2035, calendar years 2038-42
to run year 2040, calendar years 2043-47 to run year 2045 and calendar years 2048-52 to run year 2050. For
model details, please see Chapter 2 of the IPM documentation, available at:
https://www.epa.gov/airmarkets/power-sector-modeling
3-1
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fuel fired EGUs that commence construction or reconstruction after the publication of this
proposed regulation. For details on the source categories and the mitigation measures considered
please see sections VII, X and XI of the preamble.
This RIA evaluates the benefits, costs, and certain impacts of compliance with three
illustrative scenarios: the proposal, a less stringent scenario, and a more stringent scenario. To
the extent possible, EPA evaluated the 111(b) proposal for new natural-gas fired EGUs and
111(d) proposal for existing coal fired EGUs in combination to better analyze the interactive
effects of the proposals. For details of the controls modeled for each of the existing source
categories starting in run year 2030 under the three illustrative scenarios please see Table 3-1 and
Table 3-2 below.
Table 3-1 Summary of GHG Mitigation Measures for Existing Sources by Source
Category under the Illustrative Proposal and More Stringent Scenariosa'b'c'd
Affected EGUs
Subcategory Definition
GHG Mitigation
Measure
Long-term existing coal-fired
steam generating units
Coal-fired steam generating units without
committed retirement prior to 2040
CCS with 90 percent
capture of CO2, starting in
2030
Medium-term existing coal-
fired steam generating units
Coal-fired steam generating units with a
committed retirement by 2040 that are less than
500 MW, and that are not a near-term/low
utilization unit
Natural gas co-firing at 40
percent of the heat input
to the unit, starting in
2030
Near-term existing coal-fired
steam generating units
Coal-fired steam generating units with a
committed retirement prior to 2035 that operate
with annual capacity factors less than 20 percent
in 2030
Routine methods of
operation
Imminent-term existing coal-
fired steam generating units
Coal-fired steam generating units with a federally
enforceable retirement commitment prior to 2030
Routine methods of
operation
a All years shown in this table reflect IPM run years.
b Coal units that lack existing SCR controls must install these controls in addition to CCS to comply.
0 Coal-fired EGUs that convert entirely to burn natural gas are no longer subject to coal-fired EGU mitigation
measures outlined above.
d The modeling did not include GHG mitigation measure requirements on existing natural gas generation. These
requirements are analyzed separately in Section 8.
3-2
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Table 3-2 Summary of GHG Mitigation Measures for Existing Sources by Source
Category under the Illustrative Less Stringent Scenarioa'b'c'd
Affected EGUs
Subcategory Definition
GHG Mitigation
Measure
Long-term existing coal-fired
steam generating units > 700
MW
Coal-fired steam generating units > 700 MW, or
coal-fired steam generating plants > 2 GW,
without committed retirement prior to 2040
CCS with 90 percent
capture of CO2, starting in
2030
Long-term existing coal-fired
steam generating units < 700
MW
Coal-fired steam generating units < 700 MW
without committed retirement prior to 2040
Natural gas co-firing at 40
percent of the heat input
to the unit, starting in
2030
Near-term existing coal-fired
steam generating units
Coal-fired steam generating units with a
committed retirement prior to 2035 that operate
with annual capacity factors less than 20 percent
in 2030
Routine methods of
operation
Imminent-term existing coal-
fired steam generating units
Coal-fired steam generating units with a federally
enforceable retirement commitment prior to 2030
Routine methods of
operation
a All years shown in this table reflect IPM run years.
b Coal units that lack existing SCR controls must install these controls in addition to CCS to comply.
0 Coal-fired EGUs that convert entirely to burn natural gas are no longer subject to coal-fired EGU mitigation
measures outlined above.
d The modeling did not include GHG mitigation measure requirements on existing natural gas generation. These
requirements are analyzed separately in Section 8.
3-3
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Table 3-3 Summary of GHG Mitigation Measures for New Sources by Source Category
under the Illustrative Proposal, Less and More Stringent Scenariosa'b'c'd
Affected
EGUs
Subcategory
Definition
1st
Component
BSER
2nd
Component
BSER
Second Phase
Applicability:
Proposal and
Less Stringent
Scenario
Second Phase
Applicability:
More Stringent
Scenario
NGCC units that
Baseload
commence
30% by
Economic
construction after 2023
Efficient
volume
NGCC
Additions
and operate at an
annual capacity factor
of more than 50%
generation
hydrogen co-
firing or CCS
Intermediate
Load
Economic
NGCC
Additions
NGCC units that
commence
construction after 2023
Efficient
Efficient
and operate at an
annual capacity factor
of less than 50%
generation
generation
2035
2030
Intermediate
load
Economic
NGCT
Additions
NGCT units that
commence
construction after 2023
Efficient
48% by
volume
and operate at an
annual capacity factor
of more than 20%
generation
hydrogen co-
firing6
NGCT units that
Peaking
commence
Economic
construction after 2023
Efficient
Efficient
NGCT
Additions
and operate at an
annual capacity factor
of less than 20%
generation
generation
a All years shown in this table reflect IPM run years.
b Delivered hydrogen price is assumed to be $0.5/kg in years in which second phase of the NSPS is active, and $l/kg
in all other years.
0 NGCC unit additions that install CCS are no longer subject to the GHG mitigation measures outlined above.
d The modeling did not include certain elements of the new source performance standard. These requirements are
analyzed separately in Section 8.
e Efficient combustion turbines co-firing 30% low-GHG hydrogen are assumed to achieve the BSER-associated
intermediate load standard of 1,000 lb/MWh, as described in the preamble. However, the illustrative modeling
scenarios assume a higher level of co-firing (48%) to achieve the intermediate load standard. This discrepancy,
based on the use of an earlier assumption, is unlikely to significantly affect model projections.
The illustrative compliance outcomes in this RIA represent EGU behavior in response to
GHG mitigation measures applied to affected source categories in given IPM run years.53 This
53 IPM uses model years to represent the full planning horizon being modeled. By mapping multiple calendar years
to a run year, the model size is kept manageable. IPM considers the costs in all years in the planning horizon
while reporting results only for model run years. For this analysis, IPM maps the calendar year 2028 to run year
2028, calendar years 2029-31 to run year 2030, calendar years 2032-37 to run year 2035, calendar years 2038-42
to run year 2040, calendar years 2043-47 to run year 2045 and calendar years 2048-52 to run year 2050. For
model details, please see Chapter 2 of the IPM documentation, available at:
https://www.epa.gov/airmarkets/power-sector-modeling
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RIA analyzes the proposal, as well as a more and a less stringent scenario. The more stringent
scenario differs from the proposal in that it assumes imposition of the second phase of the NSPS
requirements on new sources in run year 2030, while the proposal and less stringent scenarios
assume imposition of second phase of the NSPS requirements in run year 2035. The proposal
and more stringent scenarios assume all long-term existing coal-fired steam generating units are
subject to 90 percent CCS requirements in 203054, while the less stringent scenario assumes that
long-term existing coal-fired steam generating units greater than 700 MW, and plants greater
than 2,000 MW are subject to 90 percent CCS requirements, while those less than 700 MW are
subject to 40 percent natural gas co-firing requirements.
The GHG mitigation measures in this RIA are illustrative since States are afforded
flexibility to implement the proposed rules, and thus the impacts could be different to the extent
states make different choices than those assumed in the illustrative analysis. Additionally, the
way that EGUs comply with the GHG mitigation measures may differ from the methods forecast
in the modeling for this RIA. See Section 3.4 for further discussion of the modeling approach
used in the analysis presented below.
3.3 Monitoring, Reporting, and Recordkeeping Costs
EPA projected monitoring, reporting and recordkeeping (MR&R) costs for both state
entities and affected EGUs for the years 2024 onwards. The MR&R cost estimates presented
below apply to the illustrative proposal scenario.
EPA estimates that industry will incur MR&R costs due to the Proposed New Source
Performance Standards for Greenhouse Gas Emissions from New, Modified, and Reconstructed
Fossil Fuel-Fired Electric Generating Units. More specifically, we estimate costs associated with
40 CFR Part 60, Subpart TTTTa, as described in the supporting statement found in the docket.
For purposes of RIA analysis, we assume that national costs in 2025 are approximately $13,000
in 2019 dollars, and then increase by approximately $61,000 in 2019 dollars each year thereafter
54 CCS costs used in this analysis are developed by Sargent & Lundy and are outlined in Chapter 6 of the IPM
documentation. These costs do not include the solvent acid or water washing costs. For details, please see:
https://www.epa.gov/power-sector-modeling.
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to reflect costs associated with additional respondents.55 We estimate that states will not incur
MR&R costs associated with the Proposed New Source Performance Standard.
EPA estimates that industry will not incur incremental MR&R costs due to the Proposed
Emission Guidelines for Greenhouse Gas Emissions from Existing Fossil Fuel-Fired Electric
Generating Units. This is because this action imposes no new MR&R burden on designated
facilities after final rule promulgation beyond what those facilities would already be subject to
under the authorities of 40 CFR parts 75 and 98. We estimate that states will incur MR&R costs
associated with these proposals. We estimate that this may affect 50 states, resulting in a total
national annual burden of approximately 104,000 hours of labor, or approximately $12 million in
2019 dollars. For detailed information, see the Information Collection Request Support
Statement for the Emission Guidelines for Greenhouse Gas Emissions from Existing Fossil Fuel-
Fired Electric Generating Units available in the docket for this action. For purposes of this
analysis, we estimate that these costs may begin as early as 2024 and continue through 2042.
55 For purposes of this analysis, we assume: (1) In 2019 dollars, costs in 2025 are approximately $13,000, based on
the TTTTa supporting statement in the docket; (2) Beginning in 2026, the costs per unit are approximately
$3,800 in 2019 dollars, which is the average cost per unit associated with subpart TTTT; (3) We assume 16
additional new respondents per year starting in 2026, which results in an additional cost of approximately
$61,000 each year in 2019 dollars.
3-6
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Table 3-4 Summary of State and Industry Annual Respondent Cost of Reporting and
Recordkeeping Requirements (million 2019 dollars)
Proposed NSPS for New, Modified,
Proposed EGs for Existing Fossil
and Reconstructed Fossil Fuel-Fired
Fuel-Fired Electric Generating
Total
Electric Generating Units
Units
Industry
State3
Industryb
State
Total
2024
-
-
-
12
12
2025
0.013
-
-
12
12
2026
0.075
-
-
12
12
2027
0.14
-
-
12
13
2028
0.20
-
-
12
13
2029
0.26
-
-
12
13
2030
0.32
-
-
12
13
2031
0.38
-
-
12
13
2032
0.44
-
-
12
13
2033
0.50
-
-
12
13
2034
0.57
-
-
12
13
2035
0.63
-
-
12
13
2036
0.69
-
-
12
13
2037
0.75
-
-
12
13
2038
0.81
-
-
12
13
2039
0.87
-
-
12
13
2040
0.94
-
-
12
13
2041
1.0
-
-
12
13
2042
1.1
-
-
12
13
a EPA estimates that states will not incur MR&R costs for the Proposed NSPS for New, Modified, and
Reconstructed Fossil Fuel-Fired Electric Generating Units.
b EPA estimates that industry will not incur MR&R costs for the Proposed EGs for Existing Fossil Fuel-Fired
Electric Generating Units.
3.4 Power Sector Modeling Framework
IPM is a state-of-the-art, peer-reviewed, dynamic linear programming model that can be
used to project power sector behavior under future business-as-usual conditions and to examine
prospective air pollution control policies throughout the contiguous United States for the entire
electric power system. EPA used IPM to project likely future electricity market conditions with
and without the proposed NSPS and Emissions Guidelines.
IPM, developed by the consultancy ICF, is a multi-regional, dynamic, deterministic linear
programming model of the contiguous U.S. electric power sector. It provides estimates of least
cost capacity expansion, electricity dispatch, and emissions control strategies while meeting
energy demand and environmental, transmission, dispatch, and reliability constraints.
EPA has used IPM for almost three decades to better understand power sector behavior
under future business-as-usual conditions and to evaluate the economic and emissions impacts of
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prospective environmental policies. The model is designed to reflect electricity markets as
accurately as possible. EPA uses the best available information from utilities, industry experts,
gas and coal market experts, financial institutions, and government statistics as the basis for the
detailed power sector modeling in IPM. The model documentation provides additional
information on the assumptions discussed here as well as all other model assumptions and
inputs.56
The model incorporates a detailed representation of the fossil-fuel supply system that is
used to estimate equilibrium fuel prices. The model uses natural gas fuel supply curves and
regional gas delivery costs (basis differentials) to simulate the fuel price associated with a given
level of gas consumption within the system. These inputs are derived using ICF's Gas Market
Model (GMM), a supply/demand equilibrium model of the North American gas market.57
IPM also endogenously models the partial equilibrium of coal supply and EGU coal
demand levels throughout the contiguous U.S., taking into account assumed non-power sector
demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal
transport network, which consists of over four thousand linkages representing rail, barge, and
truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough
bottom-up, mine-by-mine approach that depicts the coal choices and associated supply costs that
power plants would face if selecting that coal over the modeling time horizon. The IPM
documentation outlines the methods and data used to quantify the economically recoverable coal
reserves, characterize their cost, and build the 36 coal regions' supply curves.58
To estimate the annualized costs of additional capital investments in the power sector,
EPA uses a conventional and widely accepted approach that applies a capital recovery factor
(CRF) multiplier to capital investments and adds that to the annual incremental operating
expenses. The CRF is derived from estimates of the power sector's cost of capital (i.e., private
discount rate), the amount of insurance coverage required, local property taxes, and the life of
56 Detailed information and documentation of EPA's Baseline run using IPM (v6), including all the underlying
assumptions, data sources, and architecture parameters can be found on EPA's website at:
https://www.epa.gov/airmarkets/power-sector-modeling.
57 See Chapter 8 of EPA's Baseline run using IPM v6 documentation, available at:
https://www.epa.gov/airmarkets/power-sector-modeling
58 See Chapter 7 of the IPM documentation, available at: https://www.epa.gov/airmarkets/power-sector-modeling
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capital.59 It is important to note that there is no single CRF factor applied in the model; rather,
the CRF varies across technologies, book life of the capital investments, and regions in the
model in order to better simulate power sector decision-making.60
EPA has used IPM extensively over the past three decades to analyze options for reducing
power sector emissions. Previously, the model has been used to estimate the costs, emission
changes, and power sector impacts for the Clean Air Interstate Rule (U.S. EPA, 2005), the Cross-
State Air Pollution Rule (U.S. EPA, 201 la), the Mercury and Air Toxics Standards (U.S. EPA,
201 lb), the Clean Power Plan for Existing Power Plants (U.S. EPA, 2015b), the Cross-State Air
Pollution Update Rule (U.S. EPA, 2016), the Repeal of the Clean Power Plan, and the Emission
Guidelines for Greenhouse Gas Emissions from Existing Electric Utility Generating Units (U.S.
EPA, 2019), and the Revised Cross-State Air Pollution Update Rule (U.S. EPA, 2021), and the
Federal Good Neighbor Plan Addressing Regional Ozone Transport for the 2015 Ozone National
Ambient Air Quality Standards (U.S. EPA, 2023). EPA has also used IPM to estimate the air
pollution reductions and power sector impacts of water and waste regulations affecting EGUs,
including contributing to RIAs for the Cooling Water Intakes (316(b)) Rule (U.S. EPA, 2014a),
the Disposal of Coal Combustion Residuals from Electric Utilities rule (U.S. EPA, 2015c), the
Steam Electric Effluent Limitation Guidelines (U.S. EPA, 2015a), and the Steam Electric
Reconsideration Rule (U.S. EPA, 2020)
The model and EPA's input assumptions undergo periodic formal peer review. The
rulemaking process also provides opportunity for expert review and comment by a variety of
stakeholders, including owners and operators of capacity in the electricity sector that is
represented by the model, public interest groups, and other developers of U.S. electricity sector
models. The feedback that the Agency receives provides a highly detailed review of key input
assumptions, model representation, and modeling results. IPM has received extensive review by
energy and environmental modeling experts in a variety of contexts. For example, in October
2014 U.S. EPA commissioned a peer review of EPA Baseline version 5.13 using the Integrated
Planning Model.61 Additionally, and in the late 1990s, the Science Advisory Board reviewed
59 See Chapter 10 of the IPM documentation, available at: https://www.epa.gov/airmarkets/power-sector-modeling
60 Costs modeled in IPM reflect the costs faced by industry, and therefore are net of subsidies included in the IRA
61 See Response and Peer Review Report EPA Baseline run Version 5.13 Using IPM, available at:
https://www.epa.gov/airmarkets/response-and-peer-review-report-epa-base-case-version-513-using-ipm.
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IPM as part of the CAA Amendments Section 812 prospective studies.62 The Agency has also
used the model in a number of comparative modeling exercises sponsored by Stanford
University's Energy Modeling Forum over the past 20 years. IPM has also been employed by
states (e.g., for the Regional Greenhouse Gas Initiative, the Western Regional Air Partnership,
Ozone Transport Assessment Group), other Federal and state agencies, environmental groups,
and industry.
3.5 EPA's Power Sector Modeling of the Baseline Run and Three Illustrative Scenarios
The IPM "baseline" for any regulatory impact analysis is a business-as-usual scenario
that represents expected behavior in the electricity sector under market and regulatory conditions
in the absence of a regulatory action. As such, an IPM baseline represents an element of the
baseline for this RIA.63 EPA frequently updates the IPM baseline to reflect the latest available
electricity demand forecasts from the U.S. Energy Information Administration (EIA) as well as
expected costs and availability of new and existing generating resources, fuels, emission control
technologies, and regulatory requirements. The IPM baseline also includes power-sector related
provisions from the IRA.64
3.5.1 EPA's IPM Baseline Run \'6.21
For our analysis of the proposed NSPS, and the proposed Emissions Guidelines, EPA
used the post-IRA 2022 reference case version of IPM, as well as a companion updated database
of EGU units (the National Electricity Energy Data System or NEEDS 10-14-22) that is used in
EPA's modeling applications of IPM.65 The IPM Baseline includes the CSAPR, CSAPR Update,
the Revised CSAPR Update, and the proposed Good Neighbor Plan for 2015 Ozone NAAQS, as
well as the Mercury and Air Toxics Standards. The baseline also includes the 2015 Effluent
62 http://www2.epa.gov/clean-air-act-overview/benefits-and-costs-clean-air-act
63 As described in Chapter 5 of EPA's Guidelines for Preparing Economic Analyses, the baseline "should
incorporate assumptions about exogenous changes in the economy that may affect relevant benefits and costs
(e.g., changes in demographics, economic activity, consumer preferences, and technology), industry compliance
rates, other regulations promulgated by EPA or other government entities, and behavioral responses to the
proposed rule by firms and the public" (U.S. EPA, 2014b).
64 A wide variety of modeling teams have assessed baselines with IRA. The baseline estimated here is generally in
line with these other estimates. See Bistline, et al. (2023). "Power Sector Impacts of the Inflation Reduction Act
of 2022," In Preparation.
65 https://www.epa.gov/power-sector-modeling
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Limitation Guidelines (ELG) and the 2015 Coal Combustion Residuals (CCR), and the finalized
2020 ELG and CCR rules.66 Finalized in December 2021, the impacts of the 2023 and Later
Model Year Light-Duty Vehicle GHG Emissions Standards are also captured in the baseline; the
rule includes requirements for model years 2023 through 2026. The impacts of the Proposed
Standards of Performance for New, Reconstructed, and Modified Sources and Emissions
Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review are not captured in
the baseline.67 Additionally, the model was also updated to account for recent updates to state
and federal legislation affecting the power sector, including Public Law 117-169, 136 Stat. 1818
(August 16, 2022), commonly known as the Inflation Reduction Act of 2022 (IRA). The
Integrated Planning Model (IPM) Documentation includes a summary of all legislation reflected
in this version of the model as well as a description of how that legislation is implemented in the
model. The IPM documentation provides details on the provisions of the IRA that were
incorporated into this analysis, including provisions relating to tax subsidies for non-emitting
generation, energy storage, and CCS. The model runs for the main RIA analysis examine the
combined effects of the proposed NSPS, and the proposed Emissions Guidelines. Appendix C
examines the impact of the two rules independently. The analysis of power sector cost and
impacts presented in this section is based on a single IPM Baseline run, and represents
incremental impacts projected solely as a result of compliance with the GHG mitigation
measures presented in Table 3-1, Table 3-2, and Table 3-3.
3.5.2 Methodology for Evaluating the Illustrative Scenarios
To estimate the costs, benefits, and economic and energy market impacts of the proposed
NSPS, and the proposed Emissions Guidelines, EPA conducted quantitative analysis of the three
illustrative scenarios: the proposal and a more and a less stringent scenario. Details about these
illustrative scenarios as analyzed in this RIA, are provided above in Section 3.2.
Before undertaking power sector analysis to evaluate compliance with the illustrative
scenarios, EPA first considered available GHG mitigation strategies that could be implemented
66 For a full list of modeled policy parameters, please see:
https://www.epa.gov/airmarkets/power-sector-modeling
67 Available at: https://www.federalregister.gOv/documents/2021/l 1/15/2021-24202/standards-of-performance-for-
new-reconstructed-and-modified-sources-and-emissions-guidelines-for
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by the 2030 ran year. EPA considered the following GHG control strategies: Carbon Capture and
Storage (CCS), efficient generation practices, natural gas co-firing at existing coal-fired EGUs
and hydrogen co-firing at new combined cycle and combustion turbine EGUs. EPA then
developed subcategory definitions that assigned GHG mitigation measures to the appropriate
affected sources.68 This RIA projects the system-wide least-cost strategies for complying with the
assigned GHG mitigation measures. Least-cost compliance may lead to the application of
different control strategies at a given source, which is in keeping with the cost-saving
compliance flexibility afforded by this rulemaking.
While CCS at new and existing sources and co-firing natural gas at existing coal facilities
are captured endogenously within IPM v6.21, hydrogen co-firing at new gas EGUs is at present
represented exogenously, but alternative representations are likely to be considered in future
modeling.
By the next decade, costs for low-GHG hydrogen are expected to be competitive with
higher-GHG forms of hydrogen given declines due to learning and the IRC section 45 V
subsidies. Given the tax credits in IRC section 45V(b)(2)(D) of $3/kg H2 for hydrogen with GHG
emissions of less than 0.45 kg CChe/kg H2, and substantial DOE grant programs to drive down
costs of clean hydrogen, some entities project the delivered costs of electrolytic low-GHG
hydrogen to range from $l/kg H2 to $0/kg H2 or less.69'70'7172 These projections are more
optimistic than, but still comparable to, DOE projections of 2030 for delivered costs of
electrolytic low-GHG hydrogen in the range of $0.70/kg to $1.15/kg for power sector
applications, given R&D advancements and economies of scale.73 A growing number of studies
are demonstrating more efficient and less expensive techniques to produce low-GHG electrolytic
hydrogen; and, tax credits and market forces are expected to accelerate innovation and drive
68 For details, please see sections VII, VIII and X of the preamble.
69 "US green hydrogen costs to reach sub-zero under IRA: longer-term price impacts remain uncertain," S&P Global
Commodity Insights, September 29, 2022.
70 "DOE Funding Opportunity Targets Clean Hydrogen Technologies" American Public Power, January 31, 2023.
71
With the 45 V PTC, delivered costs of hydrogen are projected to fall in the range of $0.70/kg to $1.15/kg for power
sector applications.
72
"Treeprint: US Inflation Reduction Act - A tipping point in climate action," Credit Suisse, November 2022. See:
https://www.credit-suisse.com/treeprintusinflationreductionact
73 DOE Pathways to Commercial Liftoff: Clean Hydrogen, March 2023 See: https://liftoff.energy.gov/wp-
content/uploads/2023/03/20230320-Liftoff-Clean-H2-vPUB-0329-update.pdf
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down costs even further over the next decade.74 75 76 The combination of competitive pricing and
widespread net-zero commitments throughout the utility and merchant electricity generation
market has the potential to drive future hydrogen co-firing applications to be low-GHG
hydrogen.77 EPA is therefore soliciting comment on whether low-GHG hydrogen needs to be
defined as part of the BSER in this proposed rulemaking.
Hydrogen is an exogenous input to the model, represented as a fuel that is available at
affected sources at a delivered cost of $l/kg under the baseline, and at a delivered cost of $0.5/kg
in years when the second phase of the proposed NSPS is assumed to be active. These costs are
inclusive of $3/kg subsidies under the IRA. The second phase of the proposed NSPS is assumed
to provide investment certainty to produce hydrogen for use in power sector applications,
resulting in lower realized costs.78 These hydrogen subsidies, as well as subsidies for other
technologies such as renewables and CCS, are important factors in sector decision-making in the
baseline as well as under the illustrative scenarios modeled in this RIA. We also note the model
does not track upstream emissions associated with the production of the hydrogen (or any other
modeled fuels such as coal and natural gas), nor any incremental electricity demand associated
with its production. Under the illustrative Proposal scenario, incremental electricity demand from
hydrogen production in 2035 is estimated at about 108 TWh, or approximately 2 percent of the
total projected nationwide generation.
As noted in Section 5.2, IPM estimates compliance costs incurred by regulated firms, but
because of the availability of subsidy payments, there are also real resource costs to the economy
outside of the regulated sector. IPM provides EPA's best estimate of the costs of the proposed
rules to the electricity sector and related energy sectors (i.e., natural gas, coal mining). To
estimate the social costs for the economy as a whole, EPA has used information from IPM as an
input into the Agency's computable general equilibrium model, SAGE. The economy-wide
74 "Sound waves boost green hydrogen production," Power Engineering, January 4, 2023.
75 "Direct seawater electrolysis by adjusting the local reaction environment of a catalyst," Nature Energy, January
30, 2023.
76 Hydrogen from Next-generation Electrolyzers of Water (H2NEW) | H2NEW (energy.gov)
77 DOE Pathways to Commercial Liftoff: Clean Hydrogen, March 2023 See: https://liftoff.energy.gov/wp-
content/uploads/2023/03/20230320-Liftoff-Clean-H2-vPUB-0329-update.pdf
78 For details on the cost assumptions for hydrogen, please see the sections VII of the preamble.
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analysis is considered a complement to the more detailed evaluation of sector costs produced by
IPM.
The annualized social costs estimated in SAGE are approximately 35 percent larger than
the partial equilibrium private compliance costs (less taxes and transfers) derived from IPM. This
is consistent with general expectations based on the empirical literature (e.g., Marten et al.,
2019). However, the social cost estimate reflects the combined effect of the proposed rules'
requirements and interactions with IRA subsidies for specific technologies that are expected to
see increased use in response to the proposed rules. We are not able to identify their relative roles
at this time. See Section 5.2 and Appendix B for more discussion on estimates of private and
social costs. While the SAGE model has been peer reviewed by the EPA's Science Advisory
Board (SAB), this represents the first time it has been used in a regulatory context. As such, EPA
requests comment on its use in section XIV(C) of the preamble to these proposed rules.
3.5.3 Methodology for Estimating Compliance Costs
This section describes EPA's approach to quantify estimated compliance costs in the
power sector associated with the three illustrative scenarios, which include estimates projected
directly by the model, and costs estimated outside the model framework. The model projections
capture the costs associated with installation of GHG mitigation measures at affected sources as
well as the resulting effects on dispatch as the relative operating costs for units are affected.
Additionally, EPA estimates monitoring, reporting and recordkeeping (MR&R) costs for affected
EGUs for the timeframe of 2024 to 2042, and these costs are added to the estimated change in
the total system production cost projected by IPM.
3.6 Estimated Impacts of the Illustrative Scenarios
3.6.1 Emissions Reduction Assessment
As indicated in Section 3.2, the EGU CO2 emissions reductions are presented in this RIA
from 2028 through 2045 and are based on IPM projections. Table 3-4 presents the estimated
reduction in power sector CO2 emissions resulting from compliance with the evaluated
illustrative scenarios. The emission reductions follow an expected pattern: the less stringent
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alternative produces smaller emissions reductions than the proposal, and the more stringent
alternative results in more CO2 emissions reductions.
Table 3-5 EGU Annual CO2 Emissions and Emissions Changes (million metric tons)
for the Baseline and the Illustrative Scenarios from 2028 through 2040 79
Annual CO2
Total Emissions
Change from Baseline
(million
metric
tons)
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
2028
1,222
1,212
1,214
1,222
-10
-9
0
2030
972
882
889
865
-89
-83
-107
2035
608
572
573
566
-37
-35
-42
2040
481
458
459
459
-24
-22
-23
Within the compliance modeling, sources within each subcategory are subject to GHG
mitigation measures beginning in 2030. Since IPM is forward looking, investment decisions
prior to the start of the program are influenced by how those assets would fare under the policy
assumed. Hence, we see small reductions in 2028, prior to the imposition of the policy in 2030.
Emission reductions peak in 2030 across all scenarios, reflective of the start of the requirements
on existing coal-fired EGUs. Under the proposal and less stringent scenarios, the second phase of
the NSPS is assumed to begin in the 2035 run year, while the second phase of the NSPS is
assumed to begin in 2030 under the more stringent scenario. The impact of the IRA is to increase
the cost-competitiveness of low-emitting technology, with the result that emissions are projected
to fall significantly over the forecast period under the baseline. Hence reductions from the rules
are highest in 2030 relative to the baseline and also decline over time. For details on the EGU
emissions controls assumed in each of the illustrative scenarios, please see Table 3-1, Table 3-2,
and Table 3-3.
In addition to the annual CO2 reductions, there will also be reductions of other air
emissions associated with EGUs burning fossil fuels that result from compliance strategies to
reduce annual CO2 emissions. These other emissions include the annual total changes in
79 This analysis is limited to the geographically contiguous lower 48 states.
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emissions ofNOx, SO2, direct PM2.5, and ozone season NOx emissions changes. The emissions
reductions are presented in Table 3-6.
Table 3-6 EGU Annual Emissions and Emissions Changes for NOx, SO2, PM2.5, and
Ozone NOx for the Illustrative Scenarios for 2028 to 2040
Annual
NOx
Total Emissions
Change from Baseline
(Thousand
Tons)
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
2028
457
449
450
460
-7
-7
3
2030
368
304
307
306
-64
-61
-61
2035
214
193
194
192
-21
-20
-22
2040
162
149
150
149
-13
-12
-13
Ozone
Season
NOxa
Total Emissions
Change from Baseline
(Thousand
Tons)
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
2028
195
191
192
196
-3
-3
1
2030
163
142
143
143
-22
-20
-20
2035
104
97
97
97
-7
-7
-7
2040
80
76
76
76
-4
-4
-4
Annual
SO2
Total Emissions
Change from Baseline
(Thousand
Tons)
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
2028
394
382
385
390
-12
-9
-4
2030
282
175
183
169
-107
-99
-114
2035
130
89
92
89
-41
-38
-41
2040
89
59
62
59
-30
-27
-30
Direct
PM2S
Total Emissions
Change from Baseline
(Thousand
Tons)
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
2028
75
73
74
74
-1
-1
0
2030
66
60
60
61
-6
-5
-5
2035
47
45
45
45
-1
-1
-2
2040
38
38
38
38
-1
-1
-1
a Ozone season is the May through September period in this analysis.
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3.6.2 Compliance Cost Assessment
The estimates of the changes in the cost of supplying electricity for the illustrative
scenarios presented in Table 3-7.80 Since the rules are estimated to result in additional
recordkeeping, monitoring or reporting requirements, the costs associated with compliance,
monitoring, recordkeeping, and reporting requirements are included within the estimates in this
table.
Table 3-7 National Power Sector Compliance Cost Estimates (billions of 2019 dollars)
for the Illustrative Scenarios
Proposal
Less Stringent
More Stringent
2024 to 2042 (Annualized)
0.97
0.95
0.71
2024 to 2045 (Annualized)
0.88
0.86
0.68
2028 (Annual)
-0.21
-0.19
-0.07
2030 (Annual)
4.06
4.08
3.02
2035 (Annual)
0.28
0.23
0.20
2040 (Annual)
0.76
0.71
0.51
2045 (Annual)
-0.045
-0.053
0.384
"2024 to 2042 (Annualized)" reflects total estimated annual compliance costs levelized over the period 2024 through
2042 and discounted using a 3.76 real discount rate.81 This does not include compliance costs beyond 2042. "2024
to 2045 (Annualized)" reflects total estimated annual compliance costs levelized over the period 2024 through 2045
and discounted using a 3.76 real discount rate. This does not include compliance costs beyond 2045. "2028
(Annual)" through "2045 (Annual)" costs reflect annual estimates in each of those run years.82
There are several notable aspects of the results presented in Table 3-7. One notable result
in Table 3-7 is that the estimated annual compliance costs for the three scenarios are negative
(i.e., a cost reduction) in 2028, although these illustrative scenarios reduce C02 emissions as
shown in Table 3-5. While seemingly counterintuitive, estimating negative compliance costs in a
single year is possible given the assumption of perfect foresight. IPM's objective function is to
minimize the discounted present value (PV) of a stream of annual total cost of generation over a
80 Reported yearly costs reflect costs incurred in IPM run year mapped to respective calendar year. For details,
please see Chapter 2 of the IPM documentation.
81 This table reports compliance costs consistent with expected electricity sector economic conditions. The PV of
costs was calculated using a 3.76 percent real discount rate consistent with the rate used in IPM's objective
function for cost-minimization. The PV of costs was then used to calculate the levelized annual value over a 19-
year period (2024 to 2042) and a 21-year period (2024 to 2045) using the 3.76 percent rate as well. Table 0-2
reports the PV of the annual stream of costs from 2024 to 2042 using 3 percent and 7 percent consistent with
OMB guidance.
82 Cost estimates include financing charges on capital expenditures that would reflect a transfer and would not
typically be considered part of total social costs.
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multi-decadal time period.83 Under the baseline, the proposed GNP rule results in installation of
SCR controls in the 2028 run year on some coal-fired EGUs that currently lack them. Under the
scenarios modeled, a subset of these facilities retires rather than retrofit, since they would face
additional requirements under the GHG regulations modeled. This in turn results in lower capital
costs in the first run year and is balanced by higher costs in later years. Costs peak in 2030 across
all scenarios, reflecting the date of imposition of the proposed Emission Guidelines for coal-fired
steam generating units. Costs under the more stringent scenario are projected to be lower than
under the less-stringent scenario and the proposal in 2030. This is due to the assumption (as
discussed in Section 3.5.2: Methodology for Evaluating the Illustrative Scenarios) that when the
second phase of the NSPS is active, hydrogen costs (represented exogenously in the modeling)
are assumed to be $0.5/kg rather than $l/kg otherwise. Under the proposal and less stringent
scenarios, the second phase of the NSPS is assumed to be active in 2035, while under the more
stringent scenario, the second phase of the NSPS is assumed to be active in 2030. The lower
input fuel price in 2030 under the more stringent scenario therefore drives total compliance costs
lower than under the other two scenarios. In 2035, costs are similar across all scenarios,
reflecting similar hydrogen price assumptions and similar compliance outcomes under the
modeled policies. In general, costs decline over the forecast period.
In addition to evaluating annual compliance cost impacts, EPA believes that a full
understanding of these three illustrative scenarios benefits from an evaluation of annualized costs
over the 2028 to 2045 timeframe. Starting with the estimated annual cost time series, it is
possible to estimate the net present value of that stream, and then estimate a levelized annual cost
associated with compliance with each illustrative scenario.84 For this analysis we first calculated
the PV of the stream of costs from 2024 through 204585 using a 3.76 percent discount rate. In
this cost annualization, we use a 3.76 percent discount rate, which is consistent with the rate used
in IPM's objective function for minimizing the PV of the stream of total costs of electricity
generation. This discount rate is meant to capture the observed equilibrium market rate at which
83 For more information, please see Chapter 2 of the IPM documentation.
84 The XNPV() function in Microsoft Excel for Windows 365 was used to calculate the PV of the variable stream of
costs, and the PMT() function in Microsoft Excel for Windows 365 is used to calculate the level annualized cost
from the estimated PV.
85 Consistent with the relationship between IPM run years and calendar years, EPA assigned run year compliance
cost estimates to all calendar years mapped to that run year. For more information, see Chapter 7 of the IPM
Documentation.
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investors are willing to sacrifice present consumption for future consumption and is based on a
Weighted Average Cost of Capital (WACC).86 After calculating the PV of the cost streams, the
same 3.76 percent discount rate and 2024 to 2045 time period are used to calculate the levelized
annual (i.e., annualized) cost estimates shown in Table 3-7.87 The same approach was used to
develop the annualized cost estimates for the 2024 to 2042 timeframe. Additionally, note that the
2028 to 2042 and 2028 to 2045 equivalent annualized compliance cost estimates have the
expected relationship to each other; the annualized costs are lowest for the more stringent
alternative (driven by the assumption of earlier lower cost hydrogen availability).
3.6.3 Impacts on Fuel Use, Prices and Generation Mix
The proposed NSPS, and the proposed Emissions Guidelines are expected to result in
significant GHG emissions reductions. The rules are also expected to have some impacts to the
economics of the power sector. Consideration of these potential impacts is an important
component of assessing the relative impact of the illustrative scenarios. In this section we discuss
the estimated changes in fuel use, fuel prices, generation by fuel type, capacity by fuel type, and
retail electricity prices for the 2028, 2030, 2035 and 2040 IPM model run years.
Table 3-8 and Table 3-9 present the percentage changes in national coal and natural gas
usage by EGUs in the 2028, 2030, 2035, and 2040 run years. These fuel use estimates reflect
some power companies choosing to shift to natural gas and renewables from coal in 2030 rather
than implement available cost-reasonable controls as a result of the imposition of GHG
mitigation measures under the proposed Emissions Guidelines for coal-fired steam generating
units. Under the proposal and less stringent scenario, in the 2035 run year, natural gas
consumption increases are less than in the 2030 run year, reflective of the imposition of the
second phase of the NSPS. Under the more stringent scenario, the second phase of the NSPS is
assumed to be active in 2030, which reduces the total amount of increase in gas consumption in
that year relative to the other scenarios. By 2040, total coal and gas consumption are at similar
86 The IPM Baseline run documentation (Appendix B.4.1 Introduction to Discount Rate Calculations) states "The
real discount rate for all expenditures (capital, fuel, variable operations and maintenance, and fixed operations
and maintenance costs) in the EPA Platform v6 is 3.76 percent."
87 The PMT() function in Microsoft Excel for Windows 365 is used to calculate the level annualized cost from the
estimated PV.
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levels across the three scenarios, reflecting similar GHG mitigation measure imposition at
similar source categories by that year.
To put these reductions into context, under the Baseline, power sector coal consumption is
projected to decrease from 252 million tons in 2028 to 176 million tons in 2030 (15 percent
annually between 2028-2030), and to 80 million tons in 2035 (11 percent annually between
2030-2035). Under the proposal, coal consumption is projected to decrease from 246 million
tons in 2028 to 105 million tons in 2028 (29 percent annually between 2028-2030), and 62
million tons in 2035 (8 percent annually between 2030-2035). Between 2015 and 2020, annual
coal consumption in the electric power sector fell between 8 and 19 percent annually.88
Table 3-10 presents the projected hydrogen power sector consumption under the Baseline
and the Illustrative Scenarios.
88 U.S. EIA Monthly Energy Review, Table 6.2, January 2022.
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Table 3-8 2028, 2030, 2035 and 2040 Projected U.S. Power Sector Coal Use for the
Baseline and the Illustrative Scenarios
Million Tons
Percent Change from Baseline
Year
Baseline
„ , Less
Proposal .
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
Appalachia
48
48 48
50
-2%
0%
2%
Interior
51
49 49
50
-4%
-4%
-1%
Waste Coal
2028
4
4 4
4
0%
0%
0%
West
148
145 145
147
-2%
-2%
-1%
Total
252
246 247
251
-2%
-2%
0%
Appalachia
28
19 21
19
-31%
-27%
-34%
Interior
37
31 30
31
-17%
-17%
-16%
Waste Coal
2030
4
3 3
3
-32%
-33%
-30%
West
107
52 56
50
-51%
-47%
-53%
Total
176
105 110
103
-40%
-38%
-42%
Appalachia
11
10 10
10
-8%
-4%
-8%
Interior
20
21 22
21
9%
10%
6%
Waste Coal
2035
2
0 0
0
-83%
-85%
-79%
West
48
30 30
31
-37%
-36%
-36%
Total
80
62 63
62
-23%
-22%
-23%
Appalachia
6
7 8
7
34%
36%
19%
Interior
16
19 20
19
25%
26%
25%
Waste Coal
2040
2
0 0
0
-100%
-100%
-100%
West
39
26 26
26
-33%
-33%
-32%
Total
62
53 53
52
-15%
-14%
-15%
Table 3-9 2028, 2030, 2035 and 2040 Projected U.S. Power Sector Natural Gas Use for
the Baseline and the Illustrative Scenarios
Trillion Cubic Feet
Percent Change from Baseline
Year
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More Stringent
2028
12.5
12.6
12.6
12.6
0%
0%
0%
2030
12.6
13.6
13.5
13.3
8%
7%
5%
2035
9.9
9.9
9.8
9.7
-1%
-1%
-2%
2040
8.1
7.9
7.9
7.9
-2%
-2%
-2%
3-21
-------
Table 3-10 2028, 2030, 2035 and 2040 Projected U.S. Power Sector Hydrogen Use for the
Baseline and the Illustrative Scenarios
Trillion Btu
Year
Baseline
Proposal
Less Stringent
More Stringent
2028
0
0
0
0
2030
0
3
3
531
2035
0
294
295
458
2040
0
347
345
388
Table 3-11 and Table 3-12 present the projected coal and natural gas prices in 2030, 2035
and 2040, as well as the percent change from the baseline projected due to the illustrative
scenarios. In 2030, gas prices are higher, which is reflective of higher gas consumption as a
result of the imposition of the proposed Emission Guidelines for coal-fired steam generating
units. In 2035, the second phase of the NSPS is assumed to be active, resulting in less gas
consumption and lower prices. Under the more stringent scenario, the second phase of the NSPS
is assumed to be active in 2030, resulting in smaller increases in gas consumption in that year
relative to the other scenarios and consequently smaller increases in natural gas prices.
Table 3-11 2028, 2030, 2035 and 2040 Projected Minemouth and Power Sector Delivered
Coal Price (2019 dollars) for the Baseline and the Illustrative Scenarios
$/MMBtu
Percent Change from Baseline
,, , Less More
Baseline Proposal . ,,, .
Stringent Stringent
„ , Less More
roposa Stringent Stringent
Minemouth 1.16 1.16 1.16 1.16
2028
Delivered 1.59 1.58 1.58 1.59
0% 0% 0%
-1% -1% 0%
Minemouth 1.17 1.27 1.26 1.27
2030
Delivered 1.47 1.47 1.48 1.46
8% 8% 8%
0% 1% 0%
Minemouth 1.34 1.41 1.41 1.41
2035
Delivered 1.38 1.40 1.40 1.39
5% 5% 5%
2% 2% 1%
Minemouth 1.42 1.49 1.49 1.49
2040
Delivered 1.42 1.45 1.45 1.45
5% 4% 4%
2% 2% 1%
3-22
-------
Table 3-12 2028, 2030, 2035 and 2040 Projected Henry Hub and Power Sector Delivered
Natural Gas Price (2019 dollars) for the Baseline and the Illustrative Scenarios
$/MMBtu
Percent Change from Baseline
,, , Less More
Baseline Proposal ^ .
Stringent Stringent
„ , Less More
roposa Stringent Stringent
HemyHub 3.0 3.0 3.0 3.0
2028
Delivered 3.0 3.0 3.0 3.0
0% 0% 0%
0% 0% 0%
Hemy Hub 2.4 2.6 2.6 2.6
2030
Delivered 2.5 2.8 2.8 2.7
10% 10% 7%
9% 9% 5%
HemyHub 1.9 1.8 1.8 1.8
2035
Delivered 2.1 2.0 2.0 2.0
-2% -2% -2%
-2% -2% -3%
Hemy Hub 2.0 2.0 2.0 2.0
2040
Delivered 2.2 2.1 2.1 2.1
-2% -2% -2%
-3% -2% -3%
Table 3-13 presents the projected percentage changes in the amount of electricity
generation in 2028, 2030, 2035 and 2040 by fuel type. Consistent with the fuel use projections
and emissions trends above, EPA projects an overall shift from coal to gas and renewables under
the baseline, and these trends persist under the illustrative scenarios analyzed. The projected
impacts are highest in 2030 reflecting the imposition of the proposed Emissions Guidelines and
are smaller thereafter. 45(q) is available for 12 years within the modeling, after which point units
no longer receive tax credits and must dispatch based on unsubsidized operating costs.
3-23
-------
Table 3-13 2028, 2030, 2035 and 2040 Projected U.S. Generation by Fuel Type for the
Baseline and the Illustrative Scenarios
Generation (TWh)
Year
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
Coal
484
472
474
482
-2%
-2%
0%
Coal & CCS
0
0
0
0
-
-
-
Nat. Gas co-firing
0
0
0
0
-
-
-
Nat. Gas
1,773
1,783
1,781
1,773
1%
0%
0%
H2 co-firing
0
0
0
0
-
-
-
Nat. Gas & CCS
2028
0
0
0
0
-
-
-
Nuclear
765
765
765
765
0%
0%
0%
Hydro
294
294
294
294
0%
0%
0%
Non-Hydro RE
964
966
966
966
0%
0%
0%
Oil/Gas Steam
30
30
30
30
0%
-1%
1%
Other
30
30
30
30
0%
0%
0%
Grand Total
4,341
4,341
4,341
4,341
0%
0%
0%
Coal
243
80
78
78
-67%
-68%
-68%
Coal & CCS
66
85
84
85
29%
28%
28%
Nat. Gas co-firing
0
5
28
5
-
-
-
Nat. Gas
1,722
1,846
1,836
1,715
7%
7%
0%
H2 co-firing
0
2
2
134
-
-
-
Nat. Gas & CCS
2030
50
31
31
26
-37%
-38%
-48%
Nuclear
734
734
734
734
0%
0%
0%
Hydro
303
303
303
302
0%
0%
0%
Non-Hydro RE
1,269
1,278
1,277
1,273
1%
1%
0%
Oil/Gas Steam
33
50
41
59
52%
25%
79%
Other
29
29
29
29
0%
0%
0%
Grand Total
4,447
4,442
4,443
4,439
0%
0%
0%
Coal
44
0
1
0
-100%
-99%
-100%
Coal & CCS
75
85
85
85
13%
13%
12%
Nat. Gas co-firing
0
1
5
1
-
-
-
Nat. Gas
1,325
1,290
1,288
1,234
-3%
-3%
-7%
H2 co-firing
0
70
70
133
-
-
-
Nat. Gas & CCS
2035
77
60
59
56
-22%
-23%
-27%
Nuclear
660
660
660
660
0%
0%
0%
Hydro
329
328
328
329
0%
0%
0%
Non-Hydro RE
2,180
2,186
2,187
2,181
0%
0%
0%
Oil/Gas Steam
16
18
17
19
13%
1%
17%
Other
29
29
29
29
0%
0%
0%
Grand Total
4,736
4,728
4,728
4,728
0%
0%
0%
Percent Change from Baseline
3-24
-------
Coal
24
0
1
0
-99%
-98%
-99%
Coal & CCS
55
65
65
64
19%
19%
17%
Nat. Gas co-firing
0
0
2
0
-
-
-
Nat. Gas
1,087
1,044
1,043
1,006
-4%
-4%
-7%
H2 co-firing
0
75
75
122
-
-
-
Nat. Gas & CCS
2040
77
54
54
52
-29%
-30%
-33%
Nuclear
616
616
616
616
0%
0%
0%
Hydro
346
346
346
346
0%
0%
0%
Non-Hydro RE
2,826
2,818
2,818
2,814
0%
0%
0%
Oil/Gas Steam
3
3
3
3
-3%
-19%
1%
Other
28
28
28
28
0%
0%
0%
Grand Total
5,061
5,050
5,050
5,051
0%
0%
0%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind. Oil/Gas steam
category includes coal to gas conversions.
Table 3-14 presents the projected percentage changes in the amount of generating capacity
in 2028, 2030, 2035 and 2040 by primary fuel type. In 2030, the proposed Emissions Guidelines
is assumed to be in effect under all three scenarios. Under the proposal, 45 GW of coal-fired
EGUs have committed retirements by 2035 and operate at an annual capacity factor of 20
percent or less in 2030, and as such are subject to the near-term existing coal-fired steam
generating units subcategory. One GW of coal-fired EGUs have committed to retirement by
2040 are subject to the medium-term existing coal-fired steam generating units and are subject to
40 percent natural gas co-firing requirement. 12 GW of coal-fired EGUs who plan to operate past
2040 are subject to the long-term existing coal-fired steam generating unit subcategory and, as
such, install CCS (reflecting 3 GW incremental to the baseline). Finally, 21 GW of coal-fired
EGUs undertake coal to gas conversion (9 GW incremental to the baseline).
Under the baseline, total coal retirements between 2023 and 2035 are projected to be 104
GW (or 15 GW annually). Under the proposed rules, total coal retirements between 2023 and
2035 are projected to be 126 GW (or 18 GW annually). This is compared to an average recent
historical retirement rate of 11 GW per year from 2015 - 2020.89
By 2030 the proposal is projected to result in an additional 1 GW of coal retirements, by
2035 an incremental 23 GW of coal retirements and by 2040 an incremental 18 GW of coal
retirements relative to the baseline. These compliance decisions reflect EGU operators making
89 See EIA's Today in Energy: https://www.eia.gov/todayinenergy/detail.php?id=50838.
3-25
-------
least-cost decisions on how to achieve efficient compliance with the rules while maintaining
sufficient generating capacity to ensure grid reliability.90
An incremental 2 GW of renewable capacity additions is projected by 2035 (consisting
primarily of solar capacity builds) in the illustrative proposal scenario. Under the proposal, 25
GW of economic NGCC additions occur by 2035 (300 MW incremental to the baseline), and 43
GW of economic NGCT additions occur by 2035 (23 GW incremental to the baseline). These
builds partially reflect early action, i.e., builds that would otherwise have occurred later in the
forecast period under the baseline. Of these units, 6 GW of NGCCs and 5 GW of NGCT
additions co-fire hydrogen in 2035.
Under the baseline, the reduction in generation from natural-gas and coal fired facilities is
greater than the reduction in their capacities over time. Hence thermal resources tend to be
operated less frequently over time, due to the increase in low-emitting generation. These trends
persist under the illustrative scenarios.
90 For further discussion of how the rule is anticipated to integrate into the ongoing power sector transition while not
impacting resource adequacy, see section XIV(F) of the preamble, and the Resource Adequacy Assessment TSD
included in the docket.
3-26
-------
Table 3-14 2028, 2030, 2035 and 2040 Projected U.S. Capacity by Fuel Type for the
Baseline and the Illustrative Scenarios
Capacity (GW)
Year
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
Coal
100
99
99
99
-2%
-1%
-1%
Coal & CCS
0
0
0
0
-
-
-
Nat. Gas co-firing
0
0
0
0
-
-
-
Nat. Gas
463
467
466
466
1%
1%
1%
H2 co-firing
0
0
0
0
-
-
-
Nat. Gas & CCS
2028
0
0
0
0
-
-
-
Nuclear
96
96
96
96
0%
0%
0%
Hydro
102
102
102
102
0%
0%
0%
Non-Hydro RE
315
316
316
316
0%
0%
0%
Oil/Gas Steam
63
63
63
63
0%
0%
1%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,146
1,149
1,149
1,149
0%
0%
0%
Coal
60
46
44
44
-23%
-26%
-26%
Coal & CCS
9
12
12
12
30%
29%
29%
Nat. Gas co-firing
0
1
6
1
-
-
-
Nat. Gas
454
460
460
445
1%
1%
-2%
H2 co-firing
0
0
0
19
-
-
-
Nat. Gas & CCS
2030
7
4
4
3
-37%
-38%
-48%
Nuclear
92
92
92
92
0%
0%
0%
Hydro
104
104
104
104
0%
0%
0%
Non-Hydro RE
403
405
405
404
0%
0%
0%
Oil/Gas Steam
60
69
67
69
15%
10%
14%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,196
1,200
1,200
1,200
0%
0%
0%
Coal
33
0
1
0
-99%
-97%
-99%
Coal & CCS
11
12
12
12
13%
13%
13%
Nat. Gas co-firing
0
1
6
1
-
-
-
Nat. Gas
460
476
473
469
4%
3%
2%
H2 co-firing
0
11
11
20
-
-
-
Nat. Gas & CCS
2035
10
8
8
8
-22%
-23%
-27%
Nuclear
84
84
84
84
0%
0%
0%
Hydro
108
108
108
108
0%
0%
0%
Non-Hydro RE
668
670
670
668
0%
0%
0%
Oil/Gas Steam
59
67
64
67
13%
8%
14%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,439
1,443
1,443
1,443
0%
0%
0%
Percent Change from Baseline
3-27
-------
Coal
28
0
1
0
-99%
-97%
-99%
Coal & CCS
8
9
9
9
20%
19%
18%
Nat. Gas co-firing
0
0
6
0
-
-
-
Nat. Gas
503
512
509
506
2%
1%
1%
H2 co-firing
0
13
13
20
-
-
-
Nat. Gas & CCS
2040
10
8
8
8
-22%
-23%
-27%
Nuclear
79
79
79
79
0%
0%
0%
Hydro
110
110
110
110
0%
0%
0%
Non-Hydro RE
868
867
867
865
0%
0%
0%
Oil/Gas Steam
59
67
64
67
14%
8%
14%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,672
1,672
1,672
1,671
0%
0%
0%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind
EPA estimated the change in the retail price of electricity (2019 dollars) using the Retail
Price Model (RPM).91 The RPM was developed by ICF for EPA and uses the IPM estimates of
changes in the cost of generating electricity to estimate the changes in average retail electricity
prices. The prices are average prices over consumer classes (i.e., consumer, commercial, and
industrial) and regions, weighted by the amount of electricity used by each class and in each
region. The RPM combines the IPM annual cost estimates in each of the 64 IPM regions with
EIA electricity market data for each of the 25 electricity supply regions in the electricity market
module of the National Energy Modeling System (NEMS).92
Table 3-15, Table 3-16, and Table 3-17 present the projected percentage changes in the
retail price of electricity for the three illustrative scenarios in 2030, 2035 and 2040, respectively.
Consistent with other projected impacts presented above, average retail electricity prices at both
the national and regional level are projected to experience the largest impacts in 2030. National
electricity rates are projected to increase 2 percent above baseline levels in 2030, or an increase
of 2 mills/kWh (2019 dollars). In 2035, EPA estimates that these rules will result in a 0.24
percent increase in national average retail electricity price, or by about 0.22 mills/kWh (2019
91 See documentation available at: https://www.epa.gov/airmarkets/retail-price-model
92 See documentation available at:
https://www.eia.gov/outlooks/aeo/nems/documentation/electricity /pdf/m068(2020).pdf
3-28
-------
dollars). In 2040, EPA estimates that these rules will result in a 0.08 percent increase in national
average retail electricity price, or by about 0.07 mills/kWh.
Table 3-15 Average Retail Electricity Price by Region for the Baseline and the
Illustrative Scenarios, 2030
All Sector
2030 Average Retail Electricity Price
(2019 mills/kWh)
Percent Change from Baseline
Region
Baseline
Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
TRE
78
81
81
80
3%
3%
2%
FRCC
89
90
90
90
2%
2%
1%
MISW
80
82
82
82
2%
2%
1%
MISC
89
92
92
91
3%
3%
2%
MISE
97
100
100
102
4%
4%
5%
MISS
89
91
91
91
2%
2%
2%
ISNE
147
148
148
148
1%
1%
1%
NYCW
202
205
205
205
1%
1%
1%
NYUP
122
124
124
123
2%
2%
1%
PJME
102
105
105
105
4%
4%
4%
PJMW
94
97
97
98
3%
3%
4%
PJMC
78
82
82
83
5%
5%
6%
PJMD
72
75
74
75
3%
3%
4%
SRCA
97
98
98
98
1%
1%
1%
SRSE
90
92
92
91
2%
2%
1%
SRCE
105
106
106
105
1%
1%
0%
SPPS
69
69
69
69
0%
1%
-1%
SPPC
80
81
81
81
1%
1%
1%
SPPN
60
64
64
64
8%
8%
8%
SRSG
83
84
84
84
1%
1%
1%
CANO
155
155
156
155
0%
0%
0%
CASO
187
187
187
186
0%
0%
0%
NWPP
74
75
75
75
1%
1%
2%
RMRG
86
88
88
89
2%
2%
3%
BASN
88
88
88
88
-1%
0%
-1%
NATIONAL
97
99
99
99
2%
2%
2%
3-29
-------
Table 3-16 Average Retail Electricity Price by Region for the Baseline and the
Illustrative Scenarios, 2035
All Sector
2035 Average Retail Electricity Price
(2019 mills/kWh)
Percent Change from Baseline
Region
Baseline Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
TRE
68
68
68
68
0%
0%
0%
FRCC
81
81
81
81
0%
0%
0%
MISW
80
81
81
81
0%
0%
0%
MISC
80
80
80
80
0%
0%
0%
MISE
89
89
89
89
0%
0%
0%
MISS
84
85
85
84
0%
0%
0%
ISNE
150
151
151
151
0%
0%
0%
NYCW
187
188
188
188
0%
0%
1%
NYUP
107
107
107
107
0%
0%
1%
PJME
105
106
106
106
1%
1%
1%
PJMW
82
83
83
83
1%
1%
1%
PJMC
82
85
85
84
3%
3%
3%
PJMD
73
74
74
74
0%
0%
1%
SRCA
93
93
93
93
0%
0%
0%
SRSE
114
113
113
113
0%
0%
0%
SRCE
69
69
69
69
0%
0%
0%
SPPS
70
71
71
71
0%
0%
0%
SPPC
68
68
68
68
0%
0%
0%
SPPN
63
65
65
65
4%
4%
4%
SRSG
94
93
93
92
-1%
-1%
-2%
CANO
151
150
150
150
0%
0%
-1%
CASO
178
178
178
178
0%
0%
0%
NWPP
80
80
80
80
0%
0%
0%
RMRG
92
91
91
91
0%
0%
-1%
BASN
78
80
80
80
2%
2%
2%
NATIONAL
93
93
93
93
0%
0%
0%
3-30
-------
Table 3-17 Average Retail Electricity Price by Region for the Baseline and the
Illustrative Scenarios, 2040
All Sector
2040 Average Retail Electricity Price
(2019 mills/kWh)
Percent Change from Baseline
Region
Baseline Proposal
Less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
TRE
68
68
68
68
0%
0%
0%
FRCC
86
86
86
86
0%
0%
0%
MISW
90
90
90
90
0%
0%
0%
MISC
68
69
69
69
0%
0%
0%
MISE
89
90
90
90
0%
0%
0%
MISS
79
79
79
79
0%
0%
0%
ISNE
150
150
150
150
0%
0%
0%
NYCW
203
203
203
203
0%
0%
0%
NYUP
119
119
119
119
0%
0%
0%
PJME
110
110
110
110
0%
0%
0%
PJMW
80
81
81
81
0%
0%
1%
PJMC
75
76
76
76
1%
1%
1%
PJMD
75
75
75
75
0%
0%
0%
SRCA
120
120
120
120
0%
0%
0%
SRSE
77
77
77
77
0%
0%
0%
SRCE
83
83
83
83
0%
0%
0%
SPPS
60
60
60
60
0%
0%
0%
SPPC
72
73
73
73
0%
0%
0%
SPPN
70
70
70
70
0%
0%
0%
SRSG
86
86
86
86
0%
0%
0%
CANO
151
151
151
150
0%
0%
0%
CASO
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-2%
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84
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79
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BASN
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NATIONAL
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Figure 3-1 Electricity Market Module Regions
Source: EIA (http://www.eia. gov/forecasts/aeo/pdf/nerc_map.pdf)
3.7 Limitations
EPA's modeling is based on expert judgment of various input assumptions for variables
whose outcomes are uncertain. As a general matter, the Agency reviews the best available
information from engineering studies of air pollution controls and new capacity construction
costs to support a reasonable modeling framework for analyzing the cost, emission changes, and
other impacts of regulatory actions for EGUs. The annualized cost of the rules for EGUs, as
quantified here, is EPA's best assessment of the cost of implementing the rules for the power
sector. These costs are generated from rigorous economic modeling of anticipated changes in the
power sector due to implementation of the rule.
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EPA's modeling did not include impacts of the proposed 111(d) standards on existing
natural gas-fired EGUs or some elements of the proposed 111(b)93 standards on new natural gas-
fired EGUs. These requirements are analyzed separately in Section 8 of this RIA.
There are several key areas of uncertainty related to the electric power sector that are worth
noting, including:
• Electric demand: The analysis includes an assumption for future electric demand. This is
based on AEO 2021 reference case with incremental demand from EPA's OTAQ's on the books
rules that are not captured in AEO 2021 reference case projections.94 To the extent electric
demand is higher or lower, it may increase/decrease the projected future composition of the fleet.
• Natural gas supply and demand: The recent run up in fuel costs is assumed to abate by
the first run year in this analysis (2028). Large increases in supply over the last few years, and
relatively low prices, are represented in the analysis for subsequent run years. To the extent
prices are higher or lower, it would influence the use of natural gas for electricity generation and
overall competitiveness of other EGUs (e.g., coal and nuclear units).
• Longer-term planning by utilities: Many utilities have announced long-term clean energy
and/or climate commitments, with a phasing out of large amounts of coal capacity by 2030 and
continuing through 2050. These announcements, some of which are not legally binding, are not
necessarily reflected in the baseline, and may alter the amount of coal capacity projected in the
baseline that would be covered under this rule.
• Inflation Reduction Act (IRA): The IRA was passed in August of 2022. In order to
illustrate the impact of the IRA on this rulemaking, EPA included a baseline that incorporates
key provisions of the IRA as well as imposing the proposed rules as modeled in this RIA on that
93 Specifically, the requirement for new gas-fired capacity operating at greater than 50 percent annual capacity factor
in run year 2040 to increase Hydrogen co-firing to 96 percent by volume or convert to CCS was not modeled.
94 For details, see chapter 3 of the IPM documentation available at: https://www.epa.gov/power-sector-modeling
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baseline. However, additional effects of the IRA beyond those modeled in this RIA could result
in a change in projected system compliance costs and emissions outcomes.95
• Hydrogen production: Currently, hydrogen is an exogenous input to the model,
represented as a fuel that is available at affected sources at a delivered cost of $l/kg under the
baseline, and at a delivered cost of $0.5/kg in years when the second phase of the NSPS is
assumed to be active. The model does not track any upstream emissions96 associated with the
production of the hydrogen, nor any incremental electricity demand associated with its
production.97 The incorporation of these effects could change the amount of hydrogen selected as
a compliance measure. The model also does not account for any possible increases in NOx
emission rates at higher levels of hydrogen blending.98 For details on hydrogen modeling
assumptions, please see Section 3.5.2.
The baseline includes modeling to capture the finalized 2020 Effluent Limitation
Guidelines (ELG), and it also incorporates information provided by owners of affected facilities
to state permitting authorities in October 2021 that indicate their likely compliance pathway,
including retirement by 2028. Potential future incorporation of this information may result in
additional coal plant retirements in an updated baseline scenario, which could affect modeled
costs and benefits of the rules depending on the extent that these retirements occur before
compliance deadlines for this action. Similarly, the baseline accounts for the effect of expected
compliance methods for the 2020 CCR Rule. However, plants may adopt compliance methods
that are different than those represented in the baseline.
The impact of the Proposed Standards of Performance for New, Reconstructed, and
Modified Sources and Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector
95 For details of IRA representation in this analysis please see IPM documentation, available at: g
https://www.epa.gov/power-sector-modeling
96 IPM does not track upstream emissions for any modeled fuels.
97 Potential impacts associated with hydrogen production and utilization are discussed in preamble Sections
VII(F)(3), and XIV(E)(3). These include water use in hydrogen production, combustibility, and potential
increased NOx emissions from combustion of higher percentages of hydrogen in natural gas blends. Analysis in
this RIA does not assess these potential impacts, nor the potential impacts of hydrogen gas release on climate or
air quality through atmospheric chemical reactions.
98 For details on the possible increases in NOx emission rates at higher levels of hydrogen blending, please see the
Hydrogen in Combustion Turbine Electricity Generating Units TSD, available in the docket for this rulemaking.
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Climate Review" are also not included in this analysis. Inclusion of these standards would likely
increase the price of natural gas modestly as a result of limitations on the usage of reciprocating
internal combustion engines in the pipeline transportation of natural gas. All else equal, inclusion
of this program would likely result in a modest increase in the total cost of compliance for this
rule.
These are key uncertainties that may affect the overall composition of electric power
generation fleet and could thus have an effect on the estimated costs and impacts of this action.
However, these uncertainties would largely affect the modeling of the baseline and illustrative
scenarios similarly, and therefore, the impact on the incremental projections (reflecting the
potential costs/benefits of the regulatory alternatives) would be more limited and are not likely to
result in notable changes to the assessment of the proposed NSPS and Emissions Guidelines
found in this section. While it is important to recognize these key areas of uncertainty, they do
not change EPA's overall confidence in the estimated impacts of the illustrative regulatory
alternatives presented in this section. EPA continues to monitor industry developments and
makes appropriate updates to the modeling platforms in order to reflect the best and most current
data available.
3.8 References
Bistline, J., Mehrota, N., & Wolfram, C. (2023). Economic Implications of the Climate
Provisions of the Inflation Reduction Act. Brookings Papers on Economic Activity.
U.S. EPA. (2005). Regulatory Impact Analysis for the Final Clean Air Interstate Rule. Research
Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Health and Environmental Impact Division.
https://www.epa.gov/sites/default/files/2020-07/documents/transport_ria_final-clean-air-
interstate-rule_2005-03 .pdf
U.S. EPA. (201 la). Regulatory Impact Analysis for the Federal Implementation Plans to Reduce
Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of
SIP Approvals for 22 States. Research Triangle Park, NC: U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact
Division, https://www3.epa.gov/ttn/ecas/docs/ria/transport_ria_final-csapr_2011-06.pdf
99 Available at: https://www.federalregister.gOv/documents/2021/l 1/15/2021-24202/standards-of-performance-for-
new-reconstructed-and-modified-sources-and-emissions-guidelines-for
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U. S. EPA. (20 lib). Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards.
(EPA-452/R-11-011). Research Triangle Park, NC: U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact
Division. http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf
U.S. EPA. (2014a). Economic Analysis for the Final Section 316(b) Existing Facilities Rule.
(EPA-821-R-14-001). Washington DC: U.S. Environmental Protection Agency.
https://www.epa.gov/sites/default/files/2015-05/documents/cooling-water_phase-
4_economics_2014.pdf
U.S. EPA. (2014b). Guidelines for Preparing Economic Analyses. (EPA 240-R-10-001).
Washington DC: U.S. Environmental Protection Agency, Office of Policy, National
Center for Environmental Economics, https://www.epa.gov/environmental-
economics/guidelines-preparing-economic-analyses
U.S. EPA. (2015 a). Benefit and Cost Analysis for the Effluent Limitations Guidelines and
Standards for the Steam Electric Power Generating Point Source Category. (EPA-821-
R-15-005). Washington DC: U.S. Environmental Protection Agency.
https://www.epa.gov/sites/default/files/2015-10/documents/steam-electric_benefit-cost-
analysis_09-29-2015.pdf
U.S. EPA. (2015b). Regulatory Impact Analysis for the Clean Power Plan Final Rule. (EPA-
452/R-l5-003). Research Triangle Park, NC: U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Health and Environmental Impact
Division, https://www.epa.gov/sites/default/files/2020-07/documents/utilities_ria_final-
clean-power-plan-existing-units_2015-08.pdf
U.S. EPA. (2015c). Regulatory Impact Analysis: EPA's 2015 RCRA Final Rule Regulating Coal
Combustion Residual (CCR) Landfills and Surface Impoundments At Coal-Fired Electric
Utility Power Plants. (EPA-821-R-20-003). Washington DC: U.S. Environmental
Protection Agency. https://www.regulations.gov/document/EPA-HQ-RCRA-2009-0640-
12034
U.S. EPA. (2016). Regulatory Impact Analysis of the Cross-State Air Pollution Rule (CSAPR)
Update for the 2008 National Ambient Air Quality Standards for Ground-Level Ozone.
(EPA-452/R-16-004). Research Triangle Park, NC: U.S. Environmental Protection
Agency, Office of Air Quality Planning and Standards, Health and Environmental Impact
Division, https://www.epa.gov/sites/default/files/2020-07/documents/transport_ria_final-
csapr-update_2016-09.pdf
U. S. EPA. (2019). Regulatory Impact Analysis for the Repeal of the Clean Power Plan, and the
Emission Guidelines for Greenhouse Gas Emissions from Existing Electric Utility
Generating Units. (EPA-452/R-19-003). Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Health
and Environmental Impact Division, https://www.epa.gov/sites/production/files/2019-
06/documents/utilities_ria_final_cpp_repeal_and_ace_2019-06.pdf
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U.S. EPA. (2020). Benefit and Cost Analysis for Revisions to the Effluent Limitations Guidelines
and Standards for the Steam Electric Power Generating Point Source Category. (EPA-
821-R-20-003). Washington DC: U.S. Environmental Protection Agency.
https://www.epa.gov/sites/default/files/2020-
08/documents/steam_electric_elg_2020_final_reconsideration_rule_benefit_and_cost_an
alysis.pdf
U. S. EPA. (2021). Regulatory Impact Analysis for the Final Revised Cross-State Air Pollution
Rule (CSAPR) Update for the 2008 Ozone NAAQS. (EPA-452/R-21-002). Research
Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Health and Environmental Impact Division.
https://www.epa.gov/sites/default/files/2021-
03/documents/revised_csapr_update_ria_final.pdf
U.S. EPA. (2023). Regulatory Impact Analysis for the Final Federal Good Neighbor Plan
Addressing Regional Ozone Transport for the 2015 Ozone National Ambient Air Quality
Standards. (EPA-452/R-23-001). Research Triangle Park, NC: U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Health and
Environmental Impact Division, https://www.epa.gov/system/files/documents/2023-
03/SAN%208670%20Federal%20Good%20Neighbor%20Plan%2020230315%20RIA_Fi
nal.pdf
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4.1 Introduction
4 BENEFITS ANALYSIS
The proposed rules are expected to reduce emissions of carbon dioxide (CO2), nitrogen
oxides (NOx), fine particulate matter (PM2.5), and sulfur dioxide (SO2) nationally. This section
reports the estimated monetized climate and health benefits associated with emission reductions
for each of the three illustrative scenarios described in prior sections and discusses other
unquantified benefits.100
The section describes the methods used to estimate the climate benefits from reductions
of CO2 emissions. This analysis uses estimates of the social cost of greenhouse gases to monetize
the estimated changes in CO2 emissions expected to occur over 2028 through 2042 for the
illustrative scenarios. In principle, SC-GHG includes the value of all climate change impacts
(both negative and positive), including (but not limited to) changes in net agricultural
productivity, human health effects, property damage from increased flood risk and natural
disasters, disruption of energy systems, risk of conflict, environmental migration, and the value
of ecosystem services. The SC-GHG therefore, reflects the societal value of reducing emissions
of the gas in question by one metric ton and is the theoretically appropriate value to use in
conducting benefit-cost analyses of policies that affect GHG emissions.
This section also describes the methods used to estimate the benefits to human health of
reducing concentrations of ozone and PM2.5 from EGUs. This analysis uses methodology for
determining air quality changes that has been used in the RIAs from multiple previous proposed
and final rules (EPA, 2020; U.S. EPA, 2019b, 2020a, 2021, 2022c). The approach involves two
major steps: (1) developing spatial fields of air quality across the U.S. for baseline and three
illustrative scenarios for 2028, 2030, 2035 and 2040 using nationwide photochemical modeling
and related analyses; and (2) using these spatial fields in BenMAP-CE to quantify the benefits
under each scenario and each year as compared to the baseline in that year. Health benefit
analyses were also run for each year between 2028 and 2042, using the model surfaces for 2028,
100 Section 4 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing coal-
fired EGUs. Please see Section 8 for impact analysis of the proposed standards for existing natural gas-fired
EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
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2030, 2035 and 2040 as described in Section 4.3.1, but accounting for the change in population
size in each year, income growth and baseline mortality incidence rates at five-year increments.
Specifically, the analysis quantifies health benefits resulting from changes in ozone and PM2.5
concentrations in 2028, 2030, 2035 and 2040 for each of the three illustrative scenarios (i.e.,
proposal, less stringent scenario, and more stringent scenario). The methods for quantifying the
number and value of air pollution-attributable premature deaths and illnesses are described in the
Technical Support Document (TSD) titled Estimating PM2.5- and Ozone-Attributable Health
Benefits (U.S. EPA, 2023b) and further referred to as the Health Benefits TSD in this RIA.
Though the proposed rules are likely to also yield positive benefits associated with
reducing pollutants other than CO2, ozone, and PM2.5, time, resource, and data limitations
prevented us from characterizing the value of those reductions. Specifically, in this RIA, EPA
does not monetize health benefits of reducing direct exposure to NO2, SO2 or hazardous air
pollutants nor ecosystem effects and visibility impairment associated with changes in air quality.
In addition, this RIA does not include monetized benefits from reductions in pollutants in other
media, such as water effluents. We qualitatively discuss these unquantified benefits in this
section. This RIA also does not quantify impacts of the CCS and hydrogen compliance
technologies beyond the direct compliance cost and emissions impacts reflected in Section 3,
which is discussed in more detail in Sections 3.7 and 6.2.
4.2 Climate Benefits
We estimate the social benefits of CO2 reductions expected to occur as a result of the
illustrative scenarios using estimates of the social cost of greenhouse gases (SC-GHG),
specifically using the social cost of carbon (SC-CO2). The SC-GHG is the monetary value of the
net harm to society associated with a marginal increase in GHG emissions in a given year, or the
benefit of avoiding that increase. In principle, SC-GHG includes the value of all climate change
impacts (both negative and positive), including (but not limited to) changes in net agricultural
productivity, human health effects, property damage from increased flood risk and natural
disasters, disruption of energy systems, risk of conflict, environmental migration, and the value
of ecosystem services. The SC-GHG therefore, reflects the societal value of reducing emissions
of the gas in question by one metric ton and is the theoretically appropriate value to use in
conducting benefit-cost analyses of policies that affect GHG emissions. In practice, data and
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modeling limitations naturally restrain the ability of SC-GHG estimates to include all the
important physical, ecological, and economic impacts of climate change, such that the estimates
are a partial accounting of climate change impacts and will therefore, tend to be underestimates
of the marginal benefits of abatement. EPA and other Federal agencies began regularly
incorporating SC-GHG estimates in their benefit-cost analyses conducted under Executive Order
(E.O.) 12866101 since 2008, following a Ninth Circuit Court of Appeals remand of a rule for
failing to monetize the benefits of reducing CO2 emissions in that rulemaking process.
In 2017, the National Academies of Sciences, Engineering, and Medicine published a
report that provides a roadmap for how to update SC-GHG estimates used in Federal analyses
going forward to ensure that they reflect advances in the scientific literature (National
Academies, 2017). The National Academies' report recommended specific criteria for future SC-
GHG updates, a modeling framework to satisfy the specified criteria, and both near-term updates
and longer-term research needs pertaining to various components of the estimation process. The
research community has made considerable progress in developing new data and methods that
help to advance various components of the SC-GHG estimation process in response to the
National Academies' recommendations.
In a first-day executive order (E.O. 13990), Protecting Public Health and the
Environment and Restoring Science to Tackle the Climate Crisis, President Biden called for a
renewed focus on updating estimates of the social cost of greenhouse gases (SC-GHG) to reflect
the latest science, noting that "it is essential that agencies capture the full benefits of reducing
greenhouse gas emissions as accurately as possible." Important steps have been taken to begin to
fulfill this directive of E.O. 13990. In February 2021, the Interagency Working Group on the SC-
GHG (IWG) released a technical support document (hereinafter the "February 2021 SC-GHG
TSD") that provided a set of IWG recommended SC-GHG estimates while work on a more
comprehensive update is underway to reflect recent scientific advances relevant to SC-GHG
estimation (IWG, 2021). In addition, as discussed further below, EPA has developed a draft
101 Presidents since the 1970s have issued executive orders requiring agencies to conduct analysis of the economic
consequences of regulations as part of the rulemaking development process. E.O. 12866, released in 1993 and
still in effect today, requires that for all economically significant regulatory actions, an agency provide an
assessment of the potential costs and benefits of the regulatory action, and that this assessment include a
quantification of benefits and costs to the extent feasible. For purposes of this action, monetized climate benefits
are presented for purposes of providing a complete benefit-cost analysis under E.O. 12866 and other relevant
executive orders. The estimates of the monetized benefits play no part in the record basis for this action.
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updated SC-GHG methodology within a sensitivity analysis in the regulatory impact analysis of
EPA's November 2022 supplemental proposal for oil and natural gas emissions standards that is
currently undergoing external peer review and a public comment process.102
EPA has applied the IWG's recommended interim SC-GHG estimates in the Agency's
regulatory benefit-cost analyses published since the release of the February 2021 SC-GHG TSD
and is likewise using them in this RIA. We have evaluated the SC-GHG estimates in the
February 2021 SC-GHG TSD and have determined that these estimates are appropriate for use in
estimating the social benefits of GHG reductions expected to occur as a result of the illustrative
scenarios. These SC-GHG estimates are interim values developed for use in benefit-cost analyses
until updated estimates of the impacts of climate change can be developed based on the best
available science and economics. After considering the SC-GHG TSD, and the issues and studies
discussed therein, EPA finds that these estimates, while likely an underestimate, are the best
currently available SC-GHG estimates until revised estimates have been developed reflecting the
latest, peer-reviewed science.
The SC-GHG estimates presented in the February 2021 SC-GHG TSD and used in this
RIA were developed over many years, using a transparent process, peer-reviewed
methodologies, the best science available at the time of that process, and with input from the
public. Specifically, in 2009, an interagency working group (IWG) that included EPA and other
executive branch agencies and offices was established to develop estimates relying on the best
available science for agencies to use. The IWG published SC- CO2 estimates in 2010 that were
developed from an ensemble of three widely cited integrated assessment models (IAMs) that
estimate global climate damages using highly aggregated representations of climate processes
and the global economy combined into a single modeling framework. The three IAMs were run
using a common set of input assumptions in each model for future population, economic, and
CO2 emissions growth, as well as equilibrium climate sensitivity (ECS)—a measure of the
globally averaged temperature response to increased atmospheric CO2 concentrations. These
estimates were updated in 2013 based on new versions of each IAM (Anthoff and Tol, 2013a,
2013b; Hope, 2013; Nordhaus, 2010).103 In August 2016 the IWG published estimates of the
102 See https://www.epa.gov/environmental-economics/scghg
103 Dynamic Integrated Climate and Economy (DICE), Climate Framework for Uncertainty, Negotiation, and
Distribution (FUND), and Policy Analysis of the Greenhouse Gas Effect (PAGE) 2009
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social cost of methane (SC-CH4) and nitrous oxide (SC-N2O) using methodologies that are
consistent with the methodology underlying the SC-CO2 estimates. The modeling approach that
extends the IWG SC-CO2 methodology to non-C02 GHGs has undergone multiple stages of peer
review. The SC-CH4 and SC-N2O estimates were developed by Marten et al. (2015) and
underwent a standard double-blind peer review process prior to journal publication. These
estimates were applied in regulatory impact analyses of EPA proposed rulemakings with CH4
and N2O emissions impacts. EPA also sought additional external peer review of technical issues
associated with its application to regulatory analysis. Following the completion of the
independent external peer review of the application of the Marten et al. (2015) estimates, EPA
began using the estimates in the primary benefit-cost analysis calculations and tables for a
number of proposed rulemakings in 2015 (U.S. EPA, 2015b, 2015d). EPA considered and
responded to public comments received for the proposed rulemakings before using the estimates
in final regulatory analyses in 2016.104 In 2015, as part of the response to public comments
received to a 2013 solicitation for comments on the SC-CO2 estimates, the IWG announced a
National Academies of Sciences, Engineering, and Medicine review of the SC-CO2 estimates to
offer advice on how to approach future updates to ensure that the estimates continue to reflect the
best available science and methodologies. In January 2017, the National Academies released
their final report, Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon
Dioxide, and recommended specific criteria for future updates to the SC-GHG estimates, a
modeling framework to satisfy the specified criteria, and both near-term updates and longer-term
research needs pertaining to various components of the estimation process (National Academies,
2017). Shortly thereafter, in March 2017, President Trump issued Executive Order 13783, which
disbanded the IWG, withdrew the previous TSDs, and directed agencies to ensure SC-GHG
estimates used in regulatory analyses are consistent with the guidance contained in OMB's
Circular A-4, "including with respect to the consideration of domestic versus international
impacts and the consideration of appropriate discount rates" (E.O. 13783, Section 5(c)). Benefit-
cost analyses following E.O. 13783 used SC-GHG estimates that attempted to focus on the
specific share of climate change damages in the U.S. as captured by the models (which did not
104 The SC-CH4 and SC-N20 estimates were first used in sensitivity analysis for the Proposed Rulemaking for
Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles-
Phase 2 (U.S. EPA and U.S. DOT, 2015).
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reflect many pathways by which climate impacts affect the welfare of U.S. citizens and
residents) and were calculated using two discount rates recommended by Circular A-4, 3 percent
and 7 percent.105 All other methodological decisions and model versions used in SC-GHG
calculations remained the same as those used by the IWG in 2010 and 2013, respectively.
On January 20, 2021, President Biden issued Executive Order 13990, which re-
established an IWG and directed it to develop an update of the social cost of carbon and other
greenhouse gas estimates that reflect the best available science and the recommendations of the
National Academies. In February 2021, the IWG recommended the interim use of the most
recent SC-GHG estimates developed by the IWG prior to the group being disbanded in 2017,
adjusted for inflation (IWG, 2021). As discussed in the February 2021 SC-GHG TSD, the IWG's
selection of these interim estimates reflected the immediate need to have SC-GHG estimates
available for agencies to use in regulatory benefit-cost analyses and other applications that were
developed using a transparent process, peer reviewed methodologies, and the science available at
the time of that process.
As noted above, EPA participated in the IWG but has also independently evaluated the
interim SC-GHG estimates published in the February 2021 SC-GHG TSD and determined they
are appropriate to use here to estimate climate benefits. EPA and other agencies intend to
undertake a fuller update of the SC-GHG estimates that takes into consideration the advice of the
National Academies (2017) and other recent scientific literature. EPA has also evaluated the
supporting rationale of the February 2021 SC-GHG TSD, including the studies and
methodological issues discussed therein, and concludes that it agrees with the rationale for these
estimates presented in the TSD and summarized below.
In particular, the IWG found that the SC-GHG estimates used under E.O. 13783 fail to
reflect the full impact of GHG emissions in multiple ways. First, the IWG concluded that those
estimates fail to capture many climate impacts that can affect the welfare of U.S. citizens and
105 EPA regulatory analyses under E.O. 13783 included sensitivity analyses based on global SC-GHG values and
using a lower discount rate of 2.5 percent. OMB Circular A-4 (OMB, 2003) recognizes that special
considerations arise when applying discount rates if intergenerational effects are important. In the IWG's 2015
Response to Comments, OMB—as a co-chair of the IWG—made clear that "Circular A-4 is a living document,"
that "the use of 7 percent is not considered appropriate for intergenerational discounting," and that "[t]here is
wide support for this view in the academic literature, and it is recognized in Circular A-4 itself." OMB, as part of
the IWG, similarly repeatedly confirmed that "a focus on global SCC estimates in [regulatory impact analyses] is
appropriate" (IWG, 2015)
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residents. Examples of affected interests include direct effects on U.S. citizens and assets located
abroad, international trade, and tourism, and spillover pathways such as economic and political
destabilization and global migration that can lead to adverse impacts on U.S. national security,
public health, and humanitarian concerns. Those impacts are better captured within global
measures of the social cost of greenhouse gases.
In addition, assessing the benefits of U.S. GHG mitigation activities requires
consideration of how those actions may affect mitigation activities by other countries, as those
international mitigation actions will provide a benefit to U.S. citizens and residents by mitigating
climate impacts that affect U.S. citizens and residents. A wide range of scientific and economic
experts have emphasized the issue of reciprocity as support for considering global damages of
GHG emissions. Using a global estimate of damages in U.S. analyses of regulatory actions
allows the U.S. to continue to actively encourage other nations, including emerging major
economies, to take significant steps to reduce emissions. The only way to achieve an efficient
allocation of resources for emissions reduction on a global basis—and so benefit the U.S. and its
citizens—is for all countries to base their policies on global estimates of damages.
As a member of the IWG involved in the development of the February 2021 SC-GHG
TSD, EPA agrees with this assessment and, therefore, in this RIA EPA centers attention on a
global measure of SC-GHG. This approach is the same as that taken in EPA regulatory analyses
over 2009 through 2016. A robust estimate of climate damages to U.S. citizens and residents that
accounts for the myriad of ways that global climate change reduces the net welfare of U.S.
populations does not currently exist in the literature. As explained in the February 2021 SC-GHG
TSD, existing estimates are both incomplete and an underestimate of total damages that accrue to
the citizens and residents of the U.S. because they do not fully capture the regional interactions
and spillovers discussed above, nor do they include all of the important physical, ecological, and
economic impacts of climate change recognized in the climate change literature, as discussed
further below. EPA, as a member of the IWG, will continue to review developments in the
literature, including more robust methodologies for estimating the magnitude of the various
damages to U.S. populations from climate impacts and reciprocal international mitigation
activities, and explore ways to better inform the public of the full range of carbon impacts.
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Second, the IWG concluded that the use of the social rate of return on capital (7 percent
under current OMB Circular A-4 guidance) to discount the future benefits of reducing GHG
emissions inappropriately underestimates the impacts of climate change for the purposes of
estimating the SC-GHG. Consistent with the findings of the National Academies (2017) and the
economic literature, the IWG continued to conclude that the consumption rate of interest is the
theoretically appropriate discount rate in an intergenerational context, and recommended that
discount rate uncertainty and relevant aspects of intergenerational ethical considerations be
accounted for in selecting future discount rates (IWG, 2010, 2013, 2016a, 2016b). Furthermore,
the damage estimates developed for use in the SC-GHG are estimated in consumption-equivalent
terms, and so an application of OMB Circular A-4's guidance for regulatory analysis would then
use the consumption discount rate to calculate the SC-GHG. EPA agrees with this assessment
and will continue to follow developments in the literature pertaining to this issue. EPA also notes
that while OMB Circular A-4, as published in 2003, recommends using 3 percent and 7 percent
discount rates as "default" values, Circular A-4 also reminds agencies that "different regulations
may call for different emphases in the analysis, depending on the nature and complexity of the
regulatory issues and the sensitivity of the benefit and cost estimates to the key assumptions." On
discounting, Circular A-4 recognizes that "special ethical considerations arise when comparing
benefits and costs across generations," and Circular A-4 acknowledges that analyses may
appropriately "discount future costs and consumption benefits.. .at a lower rate than for
intragenerational analysis." In the 2015 Response to Comments on the Social Cost of Carbon for
Regulatory Impact Analysis, OMB, EPA, and the other IWG members recognized that "Circular
A-4 is a living document" and "the use of 7 percent is not considered appropriate for
intergenerational discounting. There is wide support for this view in the academic literature, and
it is recognized in Circular A-4 itself." Thus, EPA concludes that a 7 percent discount rate is not
appropriate to apply to value the social cost of greenhouse gases in the analysis presented in this
proposal. In this analysis, to calculate the present and annualized values of climate benefits, EPA
uses the same discount rate as the rate used to discount the value of damages from future GHG
emissions, for internal consistency. That approach to discounting follows the same approach that
the February 2021 SC-GHG TSD recommends "to ensure internal consistency—i.e., future
damages from climate change using the SC-GHG at 2.5 percent should be discounted to the base
year of the analysis using the same 2.5 percent rate." EPA has also consulted the National
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Academies' 2017 recommendations on how SC-GHG estimates can "be combined in RIAs with
other cost and benefits estimates that may use different discount rates." The National Academies
reviewed "several options," including "presenting all discount rate combinations of other costs
and benefits with [SC-GHG] estimates."
While the IWG works to assess how best to incorporate the latest, peer reviewed science
to develop an updated set of SC-GHG estimates, it recommended the interim estimates to be the
most recent estimates developed by the IWG prior to the group being disbanded in 2017. The
estimates rely on the same models and harmonized inputs and are calculated using a range of
discount rates. As explained in the February 2021 SC-GHG TSD, the IWG has concluded that it
is appropriate for agencies to revert to the same set of four values drawn from the SC-GHG
distributions based on three discount rates as were used in regulatory analyses between 2010 and
2016 and subject to public comment. For each discount rate, the IWG combined the distributions
across models and socioeconomic emissions scenarios (applying equal weight to each) and then
selected a set of four values for use in agency analyses: an average value resulting from the
model runs for each of three discount rates (2.5 percent, 3 percent, and 5 percent), plus a fourth
value, selected as the 95th percentile of estimates based on a 3 percent discount rate. The fourth
value was included to represent the extensive evidence in the scientific and economic literature
of the potential for lower-probability, higher-impact outcomes from climate change, which
would be particularly harmful to society and thus relevant to the public and policymakers.
Absent formal inclusion of risk aversion in the modeling, considering values above the mean in a
right skewed distribution with long tails acknowledges society's preference for avoiding risk
when high consequence outcomes are possible. As explained in the February 2021 SC-GHG
TSD, this update reflects the immediate need to have an operational SC-GHG that was
developed using a transparent process, peer-reviewed methodologies, and the science available at
the time of that process. Those estimates were subject to public comment in the context of
dozens of proposed rulemakings as well as in a dedicated public comment period in 2013.
Table 4-1 summarizes the interim SC-CO2 estimates for the years 2028-2042. These
estimates are reported in 2020 dollars in the IWG's 2021 SC-GHG TSD but are otherwise
identical to those presented in the IWG's 2016 TSD (IWG, 2016b) . For purposes of capturing
uncertainty around the SC-CO2 estimates in analyses, the February 2021 SC-GHG TSD
emphasizes the importance of considering all four of the SC-CO2 values. The SC-CO2 increases
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over time within the models (i.e., the societal harm from one metric ton emitted in 2030 is higher
than the harm caused by one metric ton emitted in 2025) because future emissions produce larger
incremental damages as physical and economic systems become more stressed in response to
greater climatic change, and because GDP is growing over time and many damage categories are
modeled as proportional to GDP.
Table 4-1 Interim Social Cost of Carbon Values, 2028 to 2042 (2019 dollars per metric
ton CO2)
Discount Rate and Statistic
Emissions Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
$18
$59
$86
$178
2029
$19
$60
$87
$181
2030
$19
$61
$88
$184
2031
$20
$62
$90
$188
2032
$20
$63
$91
$192
2033
$21
$64
$92
$196
2034
$21
$66
$94
$200
2035
$22
$67
$95
$203
2036
$23
$68
$96
$207
2037
$23
$69
$98
$211
2038
$24
$70
$99
$215
2039
$24
$71
$101
$218
2040
$25
$72
$102
$222
2041
$26
$73
$103
$226
2042
$26
$75
$105
$229
Note: The 2028 to 2042 SC-CO2 values are identical to those reported in the February 2021 SC-GHG TSD (IWG,
2021) adjusted to 2019 dollars using the annual GDP Implicit Price Deflator values in the U.S. Bureau of Economic
Analysis' (BEA) NIPA Table 1.1.9 (U.S. BEA, 2022). This table displays the values rounded to the nearest dollar;
the annual unrounded values used in the calculations in this analysis are available on OMB's website:
https://www.whitehouse.gOv/omb/information-regulatory-affairs/regulatory-matters/#scghgs.
There are a number of limitations and uncertainties associated with the SC-CO2 estimates
presented in Table 4-1. Some uncertainties are captured within the analysis, while other areas of
uncertainty have not yet been quantified in a way that can be modeled. Figure 4-1 presents the
quantified sources of uncertainty in the form of frequency distributions for the SC-CO2 estimates
for emissions in 2030 (in 2020$). The distribution of the SC-CO2 estimate reflects uncertainty in
key model parameters such as the equilibrium climate sensitivity, as well as uncertainty in other
parameters set by the original model developers. To highlight the difference between the impact
of the discount rate and other quantified sources of uncertainty, the bars below the frequency
distributions provide a symmetric representation of quantified variability in the SC-CO2
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estimates for each discount rate. As illustrated by the figure, the assumed discount rate plays a
critical role in the ultimate estimate of the SC-CO2. This is because CO2 emissions today
continue to impact society far out into the future, so with a higher discount rate, costs that accrue
to future generations are weighted less, resulting in a lower estimate. As discussed in the
February 2021 SC-GHG TSD, there are other sources of uncertainty that have not yet been
quantified and are thus not reflected in these estimates.
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Social Cost of Carbon in 2030 [2019$ / metric ton C02]
Figure 4-1 Frequency Distribution of SC-CO2 Estimates for 2030106
The interim SC-GHG estimates presented in Table 4-1 have a number of other
limitations. First, the current scientific and economic understanding of discounting approaches
suggests discount rates appropriate for intergenerational analysis in the context of climate change
are likely to be less than 3 percent, near 2 percent or lower (IWG, 2021). Second, the IAMs used
to produce these interim estimates do not include all of the important physical, ecological, and
1116 Although the distributions and numbers are based on the full set of model results (150,000 estimates for each
discount rate and gas), for display purposes the horizontal axis is truncated with 0.39 to 0.83 percent of the
estimates falling below the lowest bin displayed and 0.26 to 3.39 percent of the estimates falling above the
highest bin displayed, depending on the discount rate and GHG.
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economic impacts of climate change recognized in the climate change literature and the science
underlying their "damage functions" - i.e., the core parts of the IAMs that map global mean
temperature changes and other physical impacts of climate change into economic (both market
and nonmarket) damages - lags behind the most recent research. For example, limitations
include the incomplete treatment of catastrophic and non-catastrophic impacts in the integrated
assessment models, their incomplete treatment of adaptation and technological change, the
incomplete way in which inter-regional and intersectoral linkages are modeled, uncertainty in the
extrapolation of damages to high temperatures, and inadequate representation of the relationship
between the discount rate and uncertainty in economic growth over long time horizons.
Likewise, the socioeconomic and emissions scenarios used as inputs to the models do not reflect
new information from the last decade of scenario generation or the full range of projections.
The modeling limitations do not all work in the same direction in terms of their influence
on the SC-GHG estimates. However, as discussed in the February 2021 SC-GHG TSD, the IWG
has recommended that, taken together, the limitations suggest that the SC-GHG estimates used in
these proposed rules likely underestimate the damages from GHG emissions. EPA concurs that
the values used in this rulemaking conservatively underestimate the rule's climate benefits. In
particular, the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report,
which was the most current IPCC assessment available at the time when the IWG decision over
the ECS input was made, concluded that SC-CO2 estimates "very likely.. .underestimate the
damage costs" due to omitted impacts (IPCC, 2007). Since then, the peer-reviewed literature has
continued to support this conclusion, as noted in the IPCC's Fifth Assessment report and other
recent scientific assessments (IPCC, 2014, 2018, 2019a, 2019b; National Academies, 2016;
National Academy of Sciences, 2019; USGCRP, 2016, 2018).
These assessments confirm and strengthen the science, updating projections of future
climate change and documenting and attributing ongoing changes. For example, sea level rise
projections from the IPCC's Fourth Assessment report ranged from 18 to 59 centimeters by the
2090s relative to 1980-1999, while excluding any dynamic changes in ice sheets due to the
limited understanding of those processes at the time. A decade later, the Fourth National Climate
Assessment projected a substantially larger sea level rise of 30 to 130 centimeters by the end of
the century relative to 2000, while not ruling out even more extreme outcomes (USGCRP, 2018).
EPA has reviewed and considered the limitations of the models used to estimate the interim SC-
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GHG estimates and concurs with the February 2021 SC-GHG TSD's assessment that, taken
together, the limitations suggest that the interim SC-GHG estimates likely underestimate the
damages from GHG emissions.
The February 2021 SC-GHG TSD briefly previews some of the recent advances in the
scientific and economic literature that the IWG is actively following and that could provide
guidance on, or methodologies for, addressing some of the limitations with the interim SC-GHG
estimates. The IWG is currently working on a comprehensive update of the SC-GHG estimates
taking into consideration recommendations from the National Academies of Sciences,
Engineering and Medicine, recent scientific literature, public comments received on the February
2021 SC-GHG TSD and other input from experts and diverse stakeholder groups (National
Academies, 2017). While that process continues EPA is continuously reviewing developments in
the scientific literature on the SC-GHG, including more robust methodologies for estimating
damages from emissions, and looking for opportunities to further improve SC-GHG estimation
going forward. Most recently, EPA presented a draft set of updated SC-GHG estimates within a
sensitivity analysis in the regulatory impact analysis of EPA's November 2022 supplemental
proposal for oil and gas standards that that aims to incorporate recent advances in the climate
science and economics literature. Specifically, the draft updated methodology incorporates new
literature and research consistent with the National Academies near-term recommendations on
socioeconomic and emissions inputs, climate modeling components, discounting approaches, and
treatment of uncertainty, and an enhanced representation of how physical impacts of climate
change translate to economic damages in the modeling framework based on the best and readily
adaptable damage functions available in the peer reviewed literature. EPA solicited public
comment on the sensitivity analysis and the accompanying draft technical report, which explains
the methodology underlying the new set of estimates, in the docket for the proposed oil and
natural gas rule. EPA is also conducting an external peer review of this technical report. More
information about this process and public comment opportunities is available on EPA's
website.107 The agency is in the process of reviewing public comments on the updated estimates
within the oil and natural gas rulemaking docket as well as the recommendations of the external
peer reviewers. EPA remains committed to using the best available science in its analyses. Thus,
107 See https://www.epa.gov/environmental-economics/scghg
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if EPA's updated SC-GHG methodology is finalized before these rules are finalized, EPA
intends to present monetized climate benefits using the updated SC-GHG estimates in the final
RIA.
Table 4-3 through Table 4-5 show the estimated monetary value of the estimated changes
in CO2 emissions expected to occur over 2028 through 2042 for the illustrative scenarios. EPA
estimated the dollar value of the GHG-related effects for each analysis year between 2028 and
2042 by applying the SC-GHG estimates presented in Table 4-1 to the estimated changes in
GHG emissions in the corresponding year as shown in Table 4-2. EPA then calculated the
present value (PV) and equivalent annualized value (EAV) of benefits from the perspective of
2024 by discounting each year-specific value to the year 2024 using the same discount rate used
to calculate the SC-GHG.108
108 According to OMB's Circular A-4 (OMB, 2003), an "analysis should focus on benefits and costs that accrue to
citizens and residents of the United States", and international effects should be reported, but separately. Circular
A-4 also reminds analysts that "[different regulations may call for different emphases in the analysis, depending
on the nature and complexity of the regulatory issues." To correctly assess the total climate damages to U.S.
citizens and residents, an analysis should account for all the ways climate impacts affect the welfare of U.S.
citizens and residents, including how U.S. GHG mitigation activities affect mitigation activities by other
countries, and spillover effects from climate action elsewhere. The SC-GHG estimates used in regulatory
analysis under revoked EO 13783 were a limited approximation of some of the U.S. specific climate damages
from GHG emissions. These estimates range from $8 per metric ton CO2 for emissions occurring in 2024 to $10
per metric ton CO2 for emissions occurring in 2042. Applying the same estimates (based on a 3 percent discount
rate) to the GHG emissions reduction expected under this proposed rule would yield benefits from climate
impacts within U.S borders of $81 million in 2028, increasing to $239 million in 2042 for CO2. However, as
discussed at length in the IWG's February 2021 SC-GHG TSD, these estimates are an underestimate of the
benefits of GHG mitigation accruing to U.S. citizens and residents, as well as being subject to a considerable
degree of uncertainty due to the manner in which they are derived. In particular, as discussed in this analysis,
EPA concurs with the assessment in the February 2021 SC-GHG TSD that the estimates developed under
revoked E.O. 13783 did not capture significant regional interactions, spillovers, and other effects and so are
incomplete underestimates. As the U.S. Government Accountability Office (GAO) concluded in a June 2020
report examining the SC-GHG estimates developed under E.O. 13783, the models "were not premised or
calibrated to provide estimates of the social cost of carbon based on domestic damages" p.29 (U.S. GAO, 2020).
Further, the report noted that the National Academies found that country-specific social costs of carbon estimates
were "limited by existing methodologies, which focus primarily on global estimates and do not model all
relevant interactions among regions" p.26 (U.S. GAO, 2020). It is also important to note that the SC-GHG
estimates developed under E.O. 13783 were never peer reviewed, and when their use in a specific regulatory
action was challenged, the U.S. District Court for the Northern District of California determined that use of those
values had been "soundly rejected by economists as improper and unsupported by science," and that the values
themselves omitted key damages to U.S. citizens and residents including to supply chains, U.S. assets and
companies, and geopolitical security. The Court found that by omitting such impacts, those estimates "fail[ed] to
consider.. .important aspect[s] of the problem" and departed from the "best science available" as reflected in the
global estimates. California v. Bernhardt, 472 F. Supp. 3d 573, 613-14 (N.D. Cal. 2020). EPA continues to center
attention in this analysis on the global measures of the SC-GHG as the appropriate estimates given the flaws in
the U.S. specific estimates, and as necessary for all countries to use to achieve an efficient allocation of resources
for emissions reduction on a global basis, and so benefit the U.S. and its citizens.
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Table 4-2 Annual CO2 Emissions Reductions (million metric tons) for the Illustrative
Scenarios from 2028 through 2042
Million Metric Tons of CO2
Emissions Year
Proposal Scenario
Less Stringent Scenario
More Stringent Scenario
2028
10.1
8.7
0.5
2029
89.2
82.6
106.7
2030
89.2
82.6
106.7
2031
89.2
82.6
106.7
2032
36.7
35.2
41.8
2033
36.7
35.2
41.8
2034
36.7
35.2
41.8
2035
36.7
35.2
41.8
2036
36.7
35.2
41.8
2037
36.7
35.2
41.8
2038
23.7
22.0
22.8
2039
23.7
22.0
22.8
2040
23.7
22.0
22.8
2041
23.7
22.0
22.8
2042
23.7
22.0
22.8
Total
616.8
577.9
685.3
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Table 4-3 Benefits of Reduced CO2 Emissions from the Illustrative Proposal Scenario,
2028 to 2042 (millions of 2019 dollars)3
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
$180
$600
$870
$1,800
2029
$1,700
$5,400
$7,800
$16,000
2030
$1,700
$5,400
$7,900
$16,000
2031
$1,800
$5,500
$8,000
$17,000
2032
$750
$2,300
$3,300
$7,100
2033
$770
$2,400
$3,400
$7,200
2034
$790
$2,400
$3,400
$7,300
2035
$810
$2,500
$3,500
$7,500
2036
$830
$2,500
$3,500
$7,600
2037
$850
$2,500
$3,600
$7,800
2038
$560
$1,700
$2,400
$5,100
2039
$580
$1,700
$2,400
$5,200
2040
$590
$1,700
$2,400
$5,300
2041
$610
$1,700
$2,400
$5,300
2042
$620
$1,800
$2,500
$5,400
PV
$8,200
$30,000
$45,000
$92,000
EAV
$680
$2,100
$3,000
$6,400
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th
percentile at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits
calculated using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon,
Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of
climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted
when discounting intergenerational impacts.
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Table 4-4 Benefits of Reduced CO2 Emissions from the Illustrative Less Stringent
Scenario, 2028 to 2042 (millions of 2019 dollars)b
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
$160
$510
$750
$1,500
2029
$1,500
$5,000
$7,200
$15,000
2030
$1,600
$5,000
$7,300
$15,000
2031
$1,600
$5,100
$7,400
$16,000
2032
$710
$2,200
$3,200
$6,800
2033
$740
$2,300
$3,300
$6,900
2034
$760
$2,300
$3,300
$7,000
2035
$780
$2,400
$3,400
$7,200
2036
$800
$2,400
$3,400
$7,300
2037
$820
$2,400
$3,400
$7,400
2038
$520
$1,500
$2,200
$4,700
2039
$540
$1,600
$2,200
$4,800
2040
$550
$1,600
$2,200
$4,900
2041
$560
$1,600
$2,300
$5,000
2042
$580
$1,600
$2,300
$5,000
PV
$7,700
$28,000
$43,000
$86,000
EAV
$640
$2,000
$2,800
$6,000
b Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th
percentile at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits
calculated using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon,
Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of
climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted
when discounting intergenerational impacts.
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Table 4-5 Benefits of Reduced CO2 Emissions from the Illustrative More Stringent
Scenario, 2028 to 2042 (millions of 2019 dollars)0
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
$9.0
$29
$43
$88
2029
$2,000
$6,400
$9,300
$19,000
2030
$2,000
$6,500
$9,400
$20,000
2031
$2,100
$6,600
$9,600
$20,000
2032
$850
$2,600
$3,800
$8,000
2033
$870
$2,700
$3,900
$8,200
2034
$900
$2,700
$3,900
$8,300
2035
$920
$2,800
$4,000
$8,500
2036
$940
$2,800
$4,000
$8,700
2037
$970
$2,900
$4,100
$8,800
2038
$540
$1,600
$2,300
$4,900
2039
$550
$1,600
$2,300
$5,000
2040
$570
$1,600
$2,300
$5,100
2041
$580
$1,700
$2,400
$5,100
2042
$600
$1,700
$2,400
$5,200
PV
$9,100
$34,000
$51,000
$100,000
EAV
$760
$2,400
$3,400
$7,100
0 Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th
percentile at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits
calculated using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon,
Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of
climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted
when discounting intergenerational impacts.
4.3 Human Health Benefits
Estimating the health benefits of reductions in ozone and PM2.5 exposure begins with
estimating the change in exposure for each individual and then estimating the change in each
individual's risks for health outcomes affected by exposure. The benefit of the reduction in each
health risk is based on the exposed individual's willingness to pay (WTP) for the risk change,
assuming that each outcome is independent of one another. The greater the magnitude of the risk
reduction from a given change in concentration, the greater the individual's WTP, all else equal.
The social benefit of the change in health risks equals the sum of the individual WTP estimates
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across all of the affected individuals residing in the U.S.109 We conduct this analysis by adapting
primary research—specifically, air pollution epidemiology studies and economic value studies—
from similar contexts. This approach is sometimes referred to as "benefits transfer." Below we
describe the procedure we follow for: (1) developing spatial fields of air quality for baseline and
three illustrative scenarios (2) selecting air pollution health endpoints to quantify; (3) calculating
counts of air pollution effects using a health impact function; (4) specifying the health impact
function with concentration-response parameters drawn from the epidemiological literature to
calculate the economic value of the health impacts. We estimate the quantity and economic value
of air pollution-related effects using a "damage-function." This approach quantifies counts of air
pollution-attributable cases of adverse health outcomes and assigns dollar values to those counts,
while assuming that each outcome is independent of one another.
As structured, the proposed rules would affect the distribution of ozone and PM2.5
concentrations in much of the U.S. This RIA estimates avoided ozone- and PM2.5-related health
impacts that are distinct from those reported in the RIAs for both ozone and PM NAAQS (U.S.
EPA, 2012, 2015c, 2022d). The ozone and PM NAAQS RIAs illustrate, but do not predict, the
benefits and costs of strategies that States may choose to enact when implementing a revised
NAAQS; these costs and benefits are illustrative and cannot be added to the costs and benefits of
policies that prescribe specific emission control measures. This RIA estimates the benefits (and
costs) of specific emissions control measures. The benefit estimates are based on these modeled
changes in PM2.5 and summer season average ozone concentrations for each of the years 2028,
2030, 2035 and 2040.
4.3.1 Air Quality Modeling Methodology
The proposed rules influence the level of pollutants emitted in the atmosphere that
adversely affect human health, including directly emitted PM2.5, as well as SO2 and NOx, which
are both precursors to ambient PM2.5. NOx emissions are also a precursor to ambient ground-
level ozone. EPA used air quality modeling to estimate changes in ozone and PM2.5
109 This RIA also reports the change in the sum of the risk, or the change in the total incidence, of a health outcome
across the population. If the benefit per unit of risk is invariant across individuals, the total expected change in
the incidence of the health outcome across the population can be multiplied by the benefit per unit of risk to
estimate the social benefit of the total expected change in the incidence of the health outcome.
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concentrations that may occur as a result of the three illustrative scenarios for the proposed rules
relative to the baseline.
As described in the Air Quality Modeling Appendix (Appendix A), gridded spatial fields
of ozone and PM2.5 concentrations representing the baseline and three illustrative scenarios were
derived from CAMx source apportionment modeling in combination with NOx, SO2, and
primary PM2.5 EGU emissions obtained from the outputs of the IPM runs described in Section 3
of this RIA. While the air quality modeling includes all inventoried pollution sources in the
contiguous U.S., contributions from all sources other than EGUs are held constant at projected
2026 levels in this analysis, and the only changes quantified between the baseline and three
illustrative scenarios are those associated with the projected impacts of the proposed rules on
EGU emissions. EPA prepared gridded spatial fields of air quality for the baseline and the three
illustrative scenarios for two health-impact metrics: annual mean PM2.5 and April through
September seasonal average 8-hour daily maximum (MDA8) ozone (AS-M03). These ozone and
PM2.5 gridded spatial fields cover all locations in the contiguous U.S. and were used as inputs to
BenMAP-CE which, in turn, was used to quantify the benefits from this proposed rule.
The basic methodology for determining air quality changes is the same as that used in the
RIAs from multiple previous rules (EPA, 2020; U.S. EPA, 2019b, 2020a, 2021, 2022c). The Air
Quality Modeling Appendix (Appendix A) provides additional details on the air quality
modeling and the methodologies EPA used to develop gridded spatial fields of summertime
ozone and annual PM2.5 concentrations. The appendix also provides figures showing the
geographical distribution of air quality changes in the illustrative scenarios relative to the
baseline.
4.3.2 Selecting Air Pollution Health Endpoints to Quantify
As a first step in quantifying ozone and PM2.5-related human health impacts, the Agency
consults the Integrated Science Assessment for Ozone and Related Photochemical Oxidants
(Ozone ISA) (U.S. EPA, 2020c), the Integrated Science Assessment for Particulate Matter (PM
ISA) (U.S. EPA, 2019a), and the Supplement to the ISA for Particulate Matter (U.S. EPA,
2022f). These documents synthesize the toxicological, clinical, and epidemiological evidence to
determine whether PM is causally related to an array of adverse human health outcomes
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associated with either acute (i.e., hours or days-long) or chronic (i.e., years-long) exposure; for
each outcome, the ISA reports this relationship to be causal, likely to be causal, suggestive of a
causal relationship, inadequate to infer a causal relationship or not likely to be a causal
relationship. Historically, the Agency estimates the incidence of air pollution effects for those
health endpoints that the ISA classified as either causal or likely-to-be-causal. The analysis also
accounts for recommendations from the Science Advisory Board (U.S. EPA Science Advisory
Board, 2019, 2020a). When updating each health endpoint EPA considered: (1) the extent to
which there exists a causal relationship between that pollutant and the adverse effect; (2) whether
suitable epidemiologic studies exist to support quantifying health impacts; (3) and whether
robust economic approaches are available for estimating the value of the impact of reducing
human exposure to the pollutant. Our approach for updating the endpoints and to identify
suitable epidemiologic studies, baseline incidence rates, population demographics, and valuation
estimates is summarized below. The Health Benefits TSD (U.S. EPA, 2023b) fully describes the
Agency's approach for quantifying the number and value of estimated air pollution-related
impacts. In this document the reader can find the rationale for selecting health endpoints to
quantify; the demographic, health and economic data used; modeling assumptions; and our
techniques for quantifying uncertainty110.
In brief, the ISA for ozone found short-term (less than one month) exposures to ozone to
be causally related to respiratory effects, a "likely to be causal" relationship with metabolic
effects and a "suggestive of, but not sufficient to infer, a causal relationship" for central nervous
system effects, cardiovascular effects, and total mortality. The ISA reported that long-term
exposures (one month or longer) to ozone are "likely to be causal" for respiratory effects
including respiratory mortality, and a "suggestive of, but not sufficient to infer, a causal
relationship" for cardiovascular effects, reproductive effects, central nervous system effects,
metabolic effects, and total mortality. The PM ISA found short-term exposure to PM2.5 to be
causally related to cardiovascular effects and mortality (i.e., premature death), respiratory effects
as likely-to-be-causally related, and a suggestive relationship for metabolic effects and nervous
system effects. The ISA identified cardiovascular effects and total mortality as being causally
related to long-term exposure to PM2.5. A likely-to-be-causal relationship was determined
110 The analysis was completed using BenMAP-CE version 1.5.8, which is a variant of the current publicly available
version.
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between long-term PM2.5 exposures and respiratory effects, nervous system effects, and cancer
effects; and the evidence was suggestive of a causal relationship for male and female
reproduction and fertility effects, pregnancy and birth outcomes, and metabolic effects. Table 4-6
reports the ozone and PM2.5-related human health impacts effects we quantified and those we did
not quantify in this RIA. The list of benefit categories not quantified is not exhaustive. And,
among the effects quantified, it might not have been possible to quantify completely either the
full range of human health impacts or economic values. Section 4.4 and Table 4-22 below report
other omitted health and environmental benefits expected from the emissions and effluent
changes as a result of this proposal, such as health effects associated with NO2 and SO2, and any
welfare effects such as acidification and nutrient enrichment.
Consistent with economic theory, the willingness-to-pay (WTP) for reductions in
exposure to environmental hazards will depend on the expected impact of those reductions on
human health and other outcomes. All else equal, WTP is expected to be higher when there is
stronger evidence of a causal relationship between exposure to the contaminant and changes in a
health outcome (McGartland et al., 2017). For example, in the case where there is no evidence of
a potential relationship the WTP would be expected to be zero and the effect should be excluded
from the analysis. Alternatively, when there is some evidence of a relationship between exposure
and the health outcome, but that evidence is insufficient to definitively conclude that there is a
causal relationship, individuals may have a positive WTP for a reduction in exposure to that
hazard (Kivi and Shogren, 2010; U.S. EPA Science Advisory Board, 2020b). Lastly, the WTP
for reductions in exposure to pollutants with strong evidence of a relationship between exposure
and effect are likely positive and larger than for endpoints where evidence is weak, all else equal.
Unfortunately, the economic literature currently lacks a settled approach for accounting for how
WTP may vary with uncertainty about causal relationships.
Given this challenge, the Agency draws its assessment of the strength of evidence on the
relationship between exposure to PM2.5 or ozone and potential health endpoints from the ISAs
that are developed for the NAAQS process as discussed above. The focus on categories
identified as having a "causal" or "likely to be causal" relationship with the pollutant of interest
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is to estimate the pollutant-attributable human health benefits in which we are most confident.111
All else equal, this approach may underestimate the benefits of PM2.5 and ozone exposure
reductions as individuals may be WTP to avoid specific risks where the evidence is insufficient
to conclude they are "likely to be caus[ed]" by exposure to these pollutants.112 At the same time,
WTP may be lower for those health outcomes for which causality has not been definitively
established. This approach treats relationships with ISA causality determinations of "likely to be
causal" as if they were known to be causal, and therefore benefits could be overestimated. Table
4-6 reports the effects we quantified and those we did not quantify in this RIA. The list of benefit
categories not quantified is not exhaustive. The table below omits welfare effects such as
acidification and nutrient enrichment.
111 This decision criterion for selecting health effects to quantify and monetize PM2 5 and ozone is only applicable to
estimating the benefits of exposure of these two pollutants. This is also the approach used for identifying the
unqualified benefit categories for criteria pollutants. This decision criterion may not be applicable or suitable
for quantifying and monetizing health and ecological effects of other pollutants. The approach used to determine
whether there is sufficient evidence of a relationship between an endpoint affected by non-criteria pollutants, and
consequently a positive WTP for reductions in those pollutants, for other unqualified benefits described in this
section can be found in the source documentation for each of these pollutants (see relevant sections below). The
conceptual framework for estimating benefits when there is uncertainty in the causal relationship between a
hazard and the endpoints it potentially affects described here applies to these other pollutants.
112 EPA includes risk estimates for an example health endpoint with a causality determination of "suggestive, but not
sufficient to infer" that is associated with a potentially substantial economic value in the quantitative uncertainty
characterization (Health Benefits TSD section 6.2.3).
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Table 4-6 Health Effects of Ambient Ozone and PM2.5 and Climate Effects
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Premature
mortality from
Adult premature mortality based on cohort study
estimates and expert elicitation estimates (age 65-99
or age 30-99)
~
~
PM ISA
exposure to PM2 5
Infant mortality (age <1)
~
PM ISA
Heart attacks (age >18)
~
PM ISA
Hospital admissions—cardiovascular (ages 65-99)
~
S
PM ISA
Emergency department visits— cardiovascular (age
0-99)
~
V
PM ISA
Hospital admissions—respiratory (ages 0-18 and 65-
99)
~
S
PM ISA
Emergency room visits—respiratory (all ages)
~
~
PM ISA
Cardiac arrest (ages 0-99; excludes initial hospital
and/or emergency department visits)
~
PM ISA
Stroke (ages 65-99)
~
PM ISA
Asthma onset (ages 0-17)
~
V
PM ISA
Asthma symptoms/exacerbation (6-17)
~
V
PM ISA
Lung cancer (ages 30-99)
~
V
PM ISA
Nonfatal
Allergic rhinitis (hay fever) symptoms (ages 3-17)
~
~
PM ISA
morbidity from
Lost work days (age 18-65)
~
~
PM ISA
exposure to PM2 5
Minor restricted-activity days (age 18-65)
~
~
PM ISA
Hospital admissions—Alzheimer's disease (ages 65-
99)
~
~
PM ISA
Hospital admissions—Parkinson's disease (ages 65-
99)
~
PM ISA
Other cardiovascular effects (e.g., other ages)
—
—
PM ISA2
Other respiratory effects (e.g., pulmonary function,
non-asthma ER visits, non-bronchitis chronic
diseases, other ages and populations)
—
—
PM ISA2
Other nervous system effects (e.g., autism, cognitive
decline, dementia)
—
—
PM ISA2
Metabolic effects (e.g., diabetes)
—
—
PM ISA2
Reproductive and developmental effects (e.g., low
birth weight, pre-term births, etc.)
—
—
PM ISA2
Cancer, mutagenicity, and genotoxicity effects
—
—
PM ISA2
Mortality from
Premature respiratory mortality based on short-term
study estimates (0-99)
~
~
Ozone ISA
exposure to ozone
Premature respiratory mortality based on long-term
study estimates (age 30-99)
~
~
Ozone ISA
Hospital admissions—respiratory (ages 0-99)
~
V
Ozone ISA
Emergency department visits—respiratory (ages 0-
99)
~
V
Ozone ISA
Asthma onset (0-17)
~
~
Ozone ISA
Nonfatal
morbidity from
exposure to ozone
Asthma symptoms/exacerbation (asthmatics age 2-
17)
~
~
Ozone ISA
Allergic rhinitis (hay fever) symptoms (ages 3-17)
~
~
Ozone ISA
Minor restricted-activity days (age 18-65)
~
~
Ozone ISA
School absence days (age 5-17)
~
~
Ozone ISA
Decreased outdoor worker productivity (age 18-65)
—
—
Ozone ISA2
Metabolic effects (e.g., diabetes)
—
—
Ozone ISA2
Other respiratory effects (e.g., premature aging of
lungs)
—
—
Ozone ISA2
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Cardiovascular and nervous system effects
—
—
Ozone ISA2
Reproductive and developmental effects
—
—
Ozone ISA2
Climate impacts from carbon dioxide (CO2)
—
¦/
Section 5.2
Climate
Effects
Other climate impacts (e.g., ozone, black carbon,
aerosols, other impacts)
—
—
IPCC,
Ozone ISA,
PM ISA
1 Valuation estimate excludes initial hospital and/or emergency department visits.
2 Not quantified due to data availability limitations and/or because current evidence is only suggestive of causality.
4.3.3 Calculating Counts of Air Pollution Effects Using the Health Impact Function
We use the environmental Benefits Mapping and Analysis Program—Community
Edition (BenMAP-CE) software program to quantify counts of premature deaths and illnesses
attributable to photochemical modeled changes in annual mean PM2.5 and summer season
average ozone concentrations for the years 2028, 2030, 2035, and 2040 using health impact
functions (Sacks et al., 2020). A health impact function combines information regarding: the
concentration-response relationship between air quality changes and the risk of a given adverse
outcome; the population exposed to the air quality change; the baseline rate of death or disease in
that population; and the air pollution concentration to which the population is exposed.
BenMAP quantifies counts of attributable effects using health impact functions, which
combine information regarding the: concentration-response relationship between air quality
changes and the risk of a given adverse outcome; population exposed to the air quality change;
baseline rate of death or disease in that population; and air pollution concentration to which the
population is exposed.
The following provides an example of a health impact function, in this case for PM2.5
mortality risk. We estimate counts of PM2.5-related total deaths (yij) during each year i among
adults aged 18 and older (a) in each county in the contiguous U.S. j (j=l,.. ,,J where J is the total
number of counties) as
yij= Ea yija
yija = moija x(ep-ACij-l) x pija3 Eq[l]
where moija is the baseline total mortality rate for adults aged a=18-99 in county j in year
i stratified in 10-year age groups, P is the risk coefficient for total mortality for adults associated
with annual average PM2.5 exposure, Cij is the annual mean PM2.5 concentration in county j in
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year i, and Pija is the number of county adult residents aged a=18-99 in county j in year i
stratified into 5-year age groups.113
The BenMAP-CE tool is pre-loaded with projected population from the Woods & Poole
company; cause-specific and age-stratified death rates from the Centers for Disease Control and
Prevention, projected to future years; recent-year baseline rates of hospital admissions,
emergency department visits and other morbidity outcomes from the Healthcare Cost and
Utilization Program and other sources; concentration-response parameters from the published
epidemiologic literature cited in the Integrated Science Assessments for fine particles and
ground-level ozone; and cost of illness or willingness to pay economic unit values for each
endpoint.
To assess economic value in a damage-function framework, the changes in environmental
quality must be translated into effects on people or on the things that people value. In some
cases, the changes in environmental quality can be directly valued. In other cases, such as for
changes in ozone and PM, a health and welfare impact analysis must first be conducted to
convert air quality changes into effects that can be assigned dollar values.
We note at the outset that EPA rarely has the time or resources to perform extensive new
research to measure directly either the health outcomes or their values for regulatory analyses.
Thus, similar to Kiinzli et al. (2000) and other, more recent health impact analyses, our estimates
are based on the best available methods of benefits transfer. Benefits transfer adapts primary
research from similar contexts to obtain the most accurate measure of benefits for the
environmental quality change under analysis. Adjustments are made for the level of
environmental quality change, the socio-demographic and economic characteristics of the
affected population, and other factors to improve the accuracy and robustness of benefits
estimates.
113 In this illustrative example, the air quality is resolved at the county level. For this RIA, we simulate air quality
concentrations at 12 km grid resolution. The BenMAP-CE tool assigns the rates of baseline death and disease
stored at the county level to the grid cell level using an area-weighted algorithm. This approach is described in
greater detail in the appendices to the BenMAP-CE user manual.
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4.3.4 Calculating the Economic Valuation of Health Impacts
After quantifying the change in adverse health impacts, the final step is to estimate the
economic value of these avoided impacts. The appropriate economic value for a change in a
health effect depends on whether the health effect is viewed ex ante (before the effect has
occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air
pollution generally lower the risk of future adverse health effects by a small amount for a large
population. The appropriate economic measure is therefore ex ante WTP for changes in risk.
However, epidemiological studies generally provide estimates of the relative risks of a particular
health effect avoided due to a reduction in air pollution. A convenient way to use these data in a
consistent framework is to convert probabilities to units of avoided statistical incidences. This
measure is calculated by dividing individual WTP for a risk reduction by the related observed
change in risk. For example, suppose a regulation reduces the risk of premature mortality from 2
in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is
$1000, then the WTP for an avoided statistical premature mortality amounts to $10 million
($1000/0.0001 change in risk). Hence, this value is population-normalized, as it accounts for the
size of the population and the percentage of that population experiencing the risk. The same type
of calculation can produce values for statistical incidences of other health endpoints.
For some health effects, such as hospital admissions, WTP estimates are generally not
available. In these cases, we instead use the cost of treating or mitigating the effect to
economically value the health impact. For example, for the valuation of hospital admissions, we
use the avoided medical costs as an estimate of the value of avoiding the health effects causing
the admission. These cost-of-illness (COI) estimates generally (although not in every case)
understate the true value of reductions in risk of a health effect. They tend to reflect the direct
expenditures related to treatment but not the value of avoided pain and suffering from the health
effect.
4.3.5 Benefits Analysis Data Inputs
In Figure 4-2, we summarize the key data inputs to the health impact and economic
valuation estimates, which were calculated using BenMAP-CE model version 1.5.1 (Sacks et al.,
2020). In the sections below we summarize the data sources for each of these inputs, including
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demographic projections, incidence and prevalence rates, effect coefficients, and economic
valuation.
Figure 4-2 Data Inputs and Outputs for the BenMAP-CE Model
4.3.5.1 Demographic Data
Quantified and monetized human health impacts depend on the demographic
characteristics of the population, including age, location, and income. We use projections based
on economic forecasting models developed by Woods & Poole, Inc. (2015). The Woods & Poole
database contains county-level projections of population by age, sex, and race to 2060, relative to
a baseline using the 2010 Census data. Projections in each county are determined simultaneously
with every other county in the U.S. to consider patterns of economic growth and migration. The
sum of growth in county-level populations is constrained to equal a previously determined
national population growth, based on Bureau of Census estimates (Hollmann et al., 2000).
According to Woods & Poole, linking county-level growth projections together and constraining
the projected population to a national-level total growth avoids potential errors introduced by
forecasting each county independently (for example, the projected sum of county-level
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populations cannot exceed the national total). County projections are developed in a four-stage
process:
First, national-level variables such as income, employment, and populations are
forecasted.
Second, employment projections are made for 179 economic areas defined by the
Bureau of Economic Analysis (U.S. BEA, 2004), using an "export-base"
approach, which relies on linking industrial-sector production of non-locally
consumed production items, such as outputs from mining, agriculture, and
manufacturing with the national economy. The export-based approach requires
estimation of demand equations or calculation of historical growth rates for output
and employment by sector.
Third, population is projected for each economic area based on net migration rates
derived from employment opportunities and following a cohort-component
method based on fertility and mortality in each area.
Fourth, employment and population projections are repeated for counties, using
the economic region totals as bounds. The age, sex, and race distributions for each
region or county are determined by aging the population by single year by sex and
race for each year through 2060 based on historical rates of mortality, fertility,
and migration.
4.3.5.2 Baseline Incidence and Prevalence Estimates
Epidemiological studies of the association between pollution levels and adverse health
effects generally provide a direct estimate of the relationship of air quality changes to the relative
risk of a health effect, rather than estimating the absolute number of avoided cases. For example,
a typical result might be that a 5 |ig/m3 decrease in daily PM2.5 levels is associated with a
decrease in hospital admissions of 3 percent. A baseline incidence rate, necessary to convert this
relative change into a number of cases, is the estimate of the number of cases of the health effect
per year in the assessment location, as it corresponds to baseline pollutant levels in that location.
To derive the total baseline incidence per year, this rate must be multiplied by the corresponding
population number. For example, if the baseline incidence rate is the number of cases per year
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per million people, that number must be multiplied by the millions of people in the total
population.
The Health Benefits TSD (U.S. EPA, 2023b) (Table 12) summarizes the sources of
baseline incidence rates and reports average incidence rates for the endpoints included in the
analysis. For both baseline incidence and prevalence data, we used age-specific rates where
available. We applied concentration-response functions to individual age groups and then
summed over the relevant age range to provide an estimate of total population benefits. National-
level incidence rates were used for most morbidity endpoints, whereas county-level data are
available for premature mortality. Whenever possible, the national rates used are national
averages, because these data are most applicable to a national assessment of benefits. For some
studies, however, the only available incidence information comes from the studies themselves; in
these cases, incidence in the study population is assumed to represent typical incidence at the
national level.
We projected mortality rates such that future mortality rates are consistent with our
projections of population growth (U.S. EPA, 2023b). To perform this calculation, we began first
with an average of 2007-2016 cause-specific mortality rates. Using Census Bureau projected
national-level annual mortality rates stratified by age range, we projected these mortality rates to
2060 in 5-year increments (U.S. Census Bureau). Further information regarding this procedure
may be found in the Health Benefits TSD and the appendices to the BenMAP user manual (U.S.
EPA, 2022a, 2023b).
The baseline incidence rates for hospital admissions and emergency department visits
reflect the revised rates first applied in the Revised Cross-State Air Pollution Rule Update (U.S.
EPA, 2021). In addition, we revised the baseline incidence rates for acute myocardial infarction.
These revised rates are more recent than the rates they replace and more accurately represent the
rates at which populations of different ages, and in different locations, visit the hospital and
emergency department for air pollution-related illnesses (AHRQ, 2016). Lastly, these rates
reflect unscheduled hospital admissions only, which represents a conservative assumption that
most air pollution-related visits are likely to be unscheduled. If air pollution-related hospital
admissions are scheduled, this assumption would underestimate these benefits.
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4.3.5.3 Effect Coefficients
Our approach for selecting and parametrizing effect coefficients for the benefits analysis
is described fully in the Health Benefits TSD. Because of the substantial economic value
associated with estimated counts of PM2.5-attributable deaths, we describe our rationale for
selecting among long-term exposure epidemiologic studies below; a detailed description of all
remaining endpoints may be found in the Health Benefits TSD.
A substantial body of published scientific literature documents the association between
PM2.5 concentrations and the risk of premature death (U.S. EPA, 2019a, 2022f). This body of
literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA,
completed as part of this review of the PM standards and reviewed by the Clean Air Scientific
Advisory Committee (CASAC) (U.S. EPA Science Advisory Board, 2022) concluded that there
is a causal relationship between mortality and both long-term and short-term exposure to PM2.5
based on the full body of scientific evidence (U.S. EPA, 2019a, 2022f). The size of the mortality
effect estimates from epidemiologic studies, the serious nature of the effect itself, and the high
monetary value ascribed to prolonging life make mortality risk reduction the most significant
health endpoint quantified in this analysis.
EPA selects hazard ratios from cohort studies to estimate counts of PM-related premature
death, following a systematic approach detailed in the Health Benefits TSD accompanying this
RIA that is generally consistent with previous RIAs (e.g., (EPA, 2020; U.S. EPA, 2019b, 2020a,
2021, 2022c)). Briefly, clinically significant epidemiologic studies of health endpoints for which
ISAs report strong evidence are evaluated using established minimum and preferred criteria for
identifying studies and hazard ratios best characterizing risk. Further discussion of the cohort
studies and hazard ratios for quantifying ozone- and PM2.5-attributable premature death can be
found below in Sections 4.3.6 and 4.3.7.
4.3.6 Quantifying Cases of Ozone-Attributable Premature Death
Mortality risk reductions account for the majority of monetized ozone-related and PM2.5-
related benefits. For this reason, this subsection and the following provide a brief background of
the scientific assessments that underly the quantification of these mortality risks and identifies
the risk studies used to quantify them in this RIA, for ozone and PM2.5, respectively. As noted
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above, the Health Benefits TSD describes fully the Agency's approach for quantifying the
number and value of ozone and PM2.5 air pollution-related impacts, including additional
discussion of how the Agency selected the risk studies used to quantify them in this RIA. The
Health Benefits TSD also includes additional discussion of the assessments that support
quantification of these mortality risk than provide here.
In 2008, the National Academies of Science (NRC, 2008) issued a series of
recommendations to EPA regarding the procedure for quantifying and valuing ozone-related
mortality due to short-term exposures. Chief among these was that "... short-term exposure to
ambient ozone is likely to contribute to premature deaths" and the committee recommended that
"ozone-related mortality be included in future estimates of the health benefits of reducing ozone
exposures..." The NAS also recommended that".. .the greatest emphasis be placed on the
multicity and [National Mortality and Morbidity Air Pollution Studies (NMMAPS)] ... studies
without exclusion of the meta-analyses" (NRC, 2008). Prior to the 2015 Ozone NAAQS RIA,
the Agency estimated ozone-attributable premature deaths using an NMMAPS-based analysis of
total mortality (Bell et al., 2004), two multi-city studies of cardiopulmonary and total mortality
(Huang et al., 2005; Schwartz, 2005)In 2008, the National Academies of Science (NRC, 2008)
issued a series of recommendations to EPA regarding the procedure for quantifying and valuing
ozone-related mortality due to short-term exposures. Chief among these was that"... short-term
exposure to ambient ozone is likely to contribute to premature deaths" and the committee
recommended that "ozone-related mortality be included in future estimates of the health benefits
of reducing ozone exposures..The NAS also recommended that".. .the greatest emphasis be
placed on the multicity and [National Mortality and Morbidity Air Pollution Studies
(NMMAPS)] ... studies without exclusion of the meta-analyses" (NRC, 2008). Prior to the 2015
Ozone NAAQS RIA, the Agency estimated ozone-attributable premature deaths using an
NMMAPS-based analysis of total mortality (Bell et al., 2004), two multi-city studies of
cardiopulmonary and total mortality (Huang et al., 2005; Schwartz, 2005) and effect estimates
from three meta-analyses of non-accidental mortality (Bell et al., 2005; Ito et al., 2005; Levy et
al., 2005). Beginning with the 2015 Ozone NAAQS RIA, the Agency began quantifying ozone-
attributable premature deaths using two newer multi-city studies of non-accidental mortality
(Smith et al., 2009; Zanobetti and Schwartz, 2008) and one long-term cohort study of respiratory
mortality (Jerrett et al., 2009). The 2020 Ozone ISA included changes to the causality
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relationship determinations between short-term exposures and total mortality, as well as
including more recent epidemiologic analyses of long-term exposure effects on respiratory
mortality (U.S. EPA, 2020c). Beginning with the RCU analysis we use two estimates of ozone-
attributable respiratory deaths from short-term exposures are estimated using the risk estimate
parameters from Zanobetti and Schwartz (2008) and Katsouyanni et al. (2009). Ozone-
attributable respiratory deaths from long-term exposures are estimated using Turner et al. (2016).
Due to time and resource limitations, we were unable to reflect the warm season defined by
Zanobetti and Schwartz (2008) as June-August. Instead, we apply this risk estimate to our
standard warm season of May-September.(Smith et al., 2009; Zanobetti and Schwartz, 2008) and
one long-term cohort study of respiratory mortality (Jerrett et al., 2009). The 2020 Ozone ISA
included changes to the causality relationship determinations between short-term exposures and
total mortality, as well as including more recent epidemiologic analyses of long-term exposure
effects on respiratory mortality (U.S. EPA, 2020c). Beginning with the RCU analysis we use two
estimates of ozone-attributable respiratory deaths from short-term exposures are estimated using
the risk estimate parameters from Zanobetti and Schwartz (2008) and Katsouyanni et al. (2009).
Ozone-attributable respiratory deaths from long-term exposures are estimated using Turner et al.
(2016). Due to time and resource limitations, we were unable to reflect the warm season defined
by Zanobetti and Schwartz (2008) as June-August. Instead, we apply this risk estimate to our
standard warm season of May-September.
4.3.7 Quantifying Cases of PM2 5-A ttributable Premature Death
The PM ISA, which was reviewed by the Clean Air Scientific Advisory Committee of
EPA's Science Advisory Board (SAB-CASAC), concluded that there is a causal relationship
between mortality and both long-term and short-term exposure to PM2.5 based on the entire body
of scientific evidence (U.S. EPA, 2022e; U.S. EPA Science Advisory Board, 2019, 2022). The
PM ISA also concluded that the scientific literature supports the use of a no-threshold log-linear
model to portray the PM-mortality concentration-response relationship while recognizing
potential uncertainty about the exact shape of the concentration-response relationship. The 2019
PM ISA, which informed the setting of the 2020 PM NAAQS, reviewed available studies that
examined the potential for a population-level threshold to exist in the concentration-response
relationship. Based on such studies, the ISA concluded that the evidence supports the use of a
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"no-threshold" model and that "little evidence was observed to suggest that a threshold exists"
(U.S. EPA, 2009a) (pp. 2-25 to 2-26). Consistent with this evidence, the Agency historically has
estimated health impacts above and below the prevailing NAAQS (U.S. EPA, 2010, 201 la,
2011b, 2012, 2015a, 2015b, 2015c, 2016b).
Following this systematic approach led to the identification of three studies best
characterizing the risk of premature death associated with long-term exposure to PM2.5 in the
U.S. (Pope et al., 2019; Turner et al., 2016; Wu et al., 2020). The PM ISA, Supplement to the
ISA, and 2022 Policy Assessment also identified these three studies as providing key evidence of
the association between long-term PM2.5 exposure and mortality (U.S. EPA, 2019a, 2022b,
2022f). These studies used data from three U.S. cohorts: (1) an analysis of Medicare
beneficiaries (Medicare); (2) the American Cancer Society (ACS); and (3) the National Health
Interview Survey (NHIS). As premature mortality typically constitutes the vast majority of
monetized benefits in a PM2.5 benefits assessment, quantifying effects using risk estimates
reported from multiple long-term exposure studies using different cohorts helps account for
uncertainty in the estimated number of PM-related premature deaths. Below we summarize the
three identified studies and hazard ratios and then describe our rationale for quantifying
premature PM-attributable deaths using two of these studies.
Wu et al. (2020) evaluated the relationship between long-term PM2.5 exposure and all-
cause mortality in more than 68.5 million Medicare enrollees (over the age of 64), using
Medicare claims data from 2000-2016 representing over 573 million person-years of follow up
and over 27 million deaths. This cohort included over 20 percent of the U.S. population and was,
at the time of publishing, the largest air pollution study cohort to date. The authors modeled
PM2.5 exposure at a 12 km grid resolution using a hybrid ensemble-based prediction model that
combined three machine learning models and relied on satellite data, land-use information,
weather variables, chemical transport model simulation outputs, and monitor data. Wu et al.,
2020 fit five different statistical models: a Cox proportional hazards model, a Poisson regression
model, and three causal inference approaches (GPS estimation, GPS matching, and GPS
weighting). All five statistical approaches provided consistent results; we report the results of the
Cox proportional hazards model here. The authors adjusted for numerous individual-level and
community-level confounders, and sensitivity analyses suggest that the results are robust to
unmeasured confounding bias. In a single-pollutant model, the coefficient and standard error for
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PM2.5 are estimated from the hazard ratio (1.066) and 95 percent confidence interval (1.058-
1.074) associated with a change in annual mean PM2.5 exposure of 10.0 |ig/m3 (Wu et al., 2020,
Table S3, Main analysis, 2000-2016 Cohort, Cox PH). We use a risk estimate from this study in
place of the risk estimate from Di et al. (2017). These two epidemiologic studies share many
attributes, including the Medicare cohort and statistical model used to characterize population
exposure to PM2.5. As compared to Di et al. (2017), Wu et al. (2020) includes a longer follow-up
period and reflects more recent PM2.5 concentrations.
Pope et al. (2019) examined the relationship between long-term PM2.5 exposure and all-
cause mortality in a cohort of 1,599,329 U.S. adults (aged 18-84 years) who were interviewed in
the National Health Interview Surveys (NHIS) between 1986 and 2014 and linked to the
National Death Index (NDI) through 2015. The authors also constructed a sub-cohort of 635,539
adults from the full cohort for whom body mass index (BMI) and smoking status data were
available. The authors employed a hybrid modeling technique to estimate annual-average PM2.5
concentrations derived from regulatory monitoring data and constructed in a universal kriging
framework using geographic variables including land use, population, and satellite estimates.
Pope et al. (2019) assigned annual-average PM2.5 exposure from 1999-2015 to each individual by
census tract and used complex (accounting for NHIS's sample design) and simple Cox
proportional hazards models for the full cohort and the sub-cohort. We select the Hazard Ratio
calculated using the complex model for the sub-cohort, which controls for individual-level
covariates including age, sex, race-ethnicity, inflation-adjusted income, education level, marital
status, rural versus urban, region, survey year, BMI, and smoking status. In a single-pollutant
model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.12) and
95 percent confidence interval (1.08-1.15) associated with a change in annual mean PM2.5
exposure of 10.0 |ig/m3 (Pope et al., 2019) (Table 2, Subcohort). This study exhibits two key
strengths that makes it particularly well suited for a benefits analysis: (1) it includes a long
follow-up period with recent (and thus relatively low) PM2.5 concentrations; (2) the NHIS cohort
is representative of the U.S. population, especially with respect to the distribution of individuals
by race, ethnicity, income, and education.
EPA has historically used estimated Hazard Ratios from extended analyses of the ACS
cohort (Krewski et al., 2009; Pope et al., 2002; Pope et al., 1995) to estimate PM-related risk of
premature death. More recent ACS analyses (Pope et al., 2015; Turner et al., 2016):
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extended the follow-up period of the ACS CSP-II to 22 years (1982-2004),
evaluated 669,046 participants over 12,662,562 person-years of follow up and
237,201 observed deaths, and
applied a more advanced exposure estimation approach than had previously been
used when analyzing the ACS cohort, combining the geostatistical Bayesian Maximum Entropy
framework with national-level land use regression models.
The total mortality hazard ratio best estimating risk from these ACS cohort studies was
based on a random-effects Cox proportional hazard model incorporating multiple individual and
ecological covariates (relative risk =1.06, 95 percent confidence intervals 1.04-1.08 per 10|ig/m3
increase in PM2.5) from Turner et al., 2016. The relative risk estimate is identical to a risk
estimate drawn from earlier ACS analysis of all-cause long-term exposure PM2.5-attributable
mortality (Krewski et al., 2009). However, as the ACS hazard ratio is quite similar to the
Medicare estimate of (1.066, 1.058-1.074), especially when considering the broader age range
(>29 vs >64), only the Wu et al. (2020) and Pope et al. (2019) are included in the main benefits
assessments, with Wu et al. (2020) representing results from both the Medicare and ACS
cohorts.
4.3.8 Characterizing Uncertainty in the Estimated Benefits
In any complex analysis using estimated parameters and inputs from numerous models,
there are likely to be many sources of uncertainty. This analysis is no exception. The Health
Benefits TSD details our approach to characterizing uncertainty in both quantitative and
qualitative terms (U.S. EPA, 2023b). That Health Benefits TSD describes the sources of
uncertainty associated with key input parameters including emissions inventories, air quality data
from models (with their associated parameters and inputs), population data, population estimates,
health effect estimates from epidemiology studies, economic data for monetizing benefits, and
assumptions regarding the future state of the country (i.e., regulations, technology, and human
behavior). Each of these inputs is uncertain and affects the size and distribution of the estimated
benefits. When the uncertainties from each stage of the analysis are compounded, even small
uncertainties can have large effects on the total quantified benefits.
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To characterize uncertainty and variability into this assessment, we incorporate three
quantitative analyses described below and in greater detail within the Health Benefits TSD
(Section 7.1):
1. A Monte Carlo assessment that accounts for random sampling error and between study
variability in the epidemiological and economic valuation studies;
2. The quantification of PM-related mortality using alternative PM2.5 mortality effect
estimates drawn from two long-term cohort studies; and
3. Presentation of 95th percentile confidence interval around each risk estimate.
Quantitative characterization of other sources of PM2.5 uncertainties are discussed only in
Section 7.1 of the Health Benefits TSD:
1. For adult all-cause mortality:
a. The distributions of air quality concentrations experienced by the original cohort
population (Health Benefits TSD Section 7.1.2.1);
b. Methods of estimating and assigning exposures in epidemiologic studies (Health
Benefits TSD Section 7.1.2.2);
c. Confounding by ozone (Health Benefits TSD Section 7.1.2.3); and
d. The statistical technique used to generate hazard ratios in the epidemiologic study
(Health Benefits TSD Section 7.1.2.4).
2. Plausible alternative risk estimates for asthma onset in children (Health Benefits TSD
Section 7.1.3), cardiovascular hospital admissions (Health Benefits TSD Section 7.1.4,), and
respiratory hospital admissions (Health Benefits TSD Section 7.1.5);
3. Effect modification of PM2.5-attributable health effects in at-risk populations (Health
Benefits TSD Section 7.1.6).
Quantitative consideration of baseline incidence rates and economic valuation estimates
are provided in Section 7.3 and 7.4 of the TSD, respectively. Qualitative discussions of various
sources of uncertainty can be found in Section 7.5 of the TSD.
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4.3.8.1 Monte Carlo Assessment
Similar to other recent RIAs, we used Monte Carlo methods for characterizing random
sampling error associated with the concentration response functions from epidemiological
studies and random effects modeling to characterize both sampling error and variability across
the economic valuation functions. The Monte Carlo simulation in the BenMAP-CE software
randomly samples from a distribution of incidence and valuation estimates to characterize the
effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to
generate confidence intervals around the estimated health impact and monetized benefits. The
reported standard errors in the epidemiological studies determined the distributions for individual
effect estimates for endpoints estimated using a single study. For endpoints estimated using a
pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and
the variance across studies. The confidence intervals around the monetized benefits incorporate
the epidemiology standard errors as well as the distribution of the valuation function. These
confidence intervals do not reflect other sources of uncertainty inherent within the estimates,
such as baseline incidence rates, populations exposed, and transferability of the effect estimate to
diverse locations. As a result, the reported confidence intervals and range of estimates give an
incomplete picture about the overall uncertainty in the benefits estimates.
4.3.8.2 Sources of Uncertainty Treated Qualitatively
Although we strive to incorporate as many quantitative assessments of uncertainty as
possible, there are several aspects we are only able to address qualitatively. These attributes are
summarized below and described more fully in the Health Benefits TSD.
Key assumptions underlying the estimates for premature mortality, which account for
over 98 percent of the total monetized benefits in this analysis, include the following:
1. We assume that all fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality. This is an important assumption, because PM2.5
varies considerably in composition across sources, but the scientific evidence is not yet sufficient
to allow differentiation of effect estimates by particle type. The PM ISA, which was reviewed by
CASAC, concluded that "across exposure durations and health effects categories ... the evidence
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does not indicate that any one source or component is consistently more strongly related with
health effects than PM2.5 mass" (U.S. EPA, 2019a).
2. We assume that the health impact function for fine particles is log-linear down to
the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits
from reducing fine particles in areas with varied concentrations of PM2.5, including both regions
that are in attainment with the fine particle standard and those that do not meet the standard
down to the lowest modeled concentrations. The PM ISA concluded that "the majority of
evidence continues to indicate a linear, no-threshold concentration-response relationship for
long-term exposure to PM2.5 and total (nonaccidental) mortality" (U.S. EPA, 2019a).
3. We assume that there is a "cessation" lag between the change in PM exposures
and the total realization of changes in mortality effects. Specifically, we assume that some of the
incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion over
the 20 years following exposure based on the advice of the SAB-HES (U.S. EPA Science
Advisory Board, 2004), which affects the valuation of mortality benefits at different discount
rates. Similarly, we assume there is a cessation lag between the change in PM exposures and
both the development and diagnosis of lung cancer.
4. Uncertainties associated with the IPM projections used to derive the inputs for the
air quality modeling in this analysis are outlined in Section 3.8. IPM is a system-wide least-cost
optimization model that projects EGU behavior across the geographically contiguous U.S., and
projects one possible combination of compliance outcomes under a given policy scenario. The
GHG mitigation measures in this RIA are illustrative since States are afforded flexibility to
implement the proposed rules, and thus the impacts could be different to the extent states make
different choices than those assumed in the illustrative analysis. Additionally, the way that EGUs
comply with the GHG mitigation measures may differ from the methods forecast in the modeling
for this RIA.
5. Uncertainties associated with applying air quality modeling to create ozone and
PM2.5 surfaces are discussed in Appendix A.
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4.3.9 Estimated Number and Economic Value of Health Benefits
Table 4-7 through Table 4-14 report the estimated number of reduced premature deaths
and illnesses in each year relative to the baseline along with the 95 percent confidence interval.
Table 4-7 through Table 4-10 report the ozone-related health benefits for each scenario year, and
Table 4-11 through Table 4-14 report the PM-related health benefits for each scenario year. The
number of reduced estimated deaths and illnesses from the three illustrative scenarios are
calculated from the sum of individual reduced mortality and illness risk across the population.
Table 4-15 through Table 4-18 report the estimated economic value of avoided premature
deaths and illness in each year relative to the baseline along with the 95 percent confidence
interval. Table 4-19 summarizes the monetized benefits for all illustrative scenarios and the four
analysis years. We also report the stream of benefits from 2028 through 2042 for the proposal,
more- and less- stringent alternatives, using the monetized sums of long-term ozone and PM2.5
mortality and morbidity impacts (Table 4-15 through Table 4-19).114 When estimating the value
of improved air quality over a multi-year time horizon, the analysis applies population growth
and income growth projections for each future year through 2042 and estimates of baseline
mortality incidence rates at five-year increments.
Table 4-15 through Table 4-18 include two estimates for each scenario. These estimates
were quantified using two different epidemiological estimates for the mortality impact of ozone
and two different epidemiological estimates for the mortality impact of PM, as well as their
sum. For ozone, one estimate reflects the impacts associated with short-term exposure on
mortality impacts while the other reflects long-term exposure on mortality. For PM, one estimate
reflects impacts associated mortality estimated based on Pope et al. (2019), while the other
reflects impacts associated with mortality estimated based on Wu et al. (2020). These estimates
should not be thought of as representing low and high bounds.
114 EPA continues to refine its approach for estimating and reporting PM-related effects at lower concentrations. The
Agency acknowledges the additional uncertainty associated with effects estimated at these lower levels and seeks
to develop quantitative approaches for reflecting this uncertainty in the estimated PM benefits.
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Table 4-7 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and
Illnesses for the Illustrative Scenarios for 2028 (95 percent confidence interval)3
Proposal
Less Stringent
More Stringent
Avoided premature respiratory mortalities
Long-
term
exposure
Turner et al. (2016)b
21
(14 to 27)
16
(11 to 20)
-38
(-26 to -49)
Short-
term
exposure
Katsouyanni et al. (2009)b c and
Zanobetti et al. (2008)° pooled
0.94
(0.38 to 1.5)
0.7
(0.28 to 1.1)
-1.7
(-2.7 to -0.69)
Morbidity
effects
Long-
Asthma onsetd
180
(160 to 210)
150
(130 to 170)
-290
(-250 to -330)
term
exposure
Allergic rhinitis symptomsf
1,000
(540 to 1,500)
830
(440 to 1,200)
-1,700
(-880 to -2,400)
Hospital admissions—respiratory0
2.3
(-0.61 to 5.2)
1.7
(-0.43 to 3.7)
-5
(1.3 to 11)
ED visits—respiratory6
65
(18 to 140)
52
(14 to 110)
-82
(-170 to -23)
Short-
term
Asthma symptoms
33,000
(-4,100 to 70,000)
27,000
(-3,300 to
56,000)
-54,000
(-110,000 to
6,700)
exposure -
Minor restricted-activity days0 6
16,000
(6,200 to 25,000)
12,000
(4,900 to
20,000)
-25,000
(-10,000 to -
40,000)
School absence days
12,000
(-1,700 to 25,000)
9,400
(-1,300 to
20,000)
-19,000
(2,700 to -
40,000)
a Values rounded to two significant figures.
b Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
0 Converted ozone risk estimate metric from maximum daily 1-hour average (MDA1) to maximum daily 8-hour
average (MDA8).
d Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
e Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
f Converted ozone risk estimate metric from daily 24-hour average (DA24) to MDA8.
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Table 4-8 Estimated Avoided Ozone-Related Premature Respiratory Mortalities
and Illnesses for the Illustrative Scenarios in 2030 (95 percent confidence interval)"
Proposal
Less Stringent
More Stringent
Avoided premature respiratory mortalities
Long-term
exposure
Turner et al. (2016)b
95
(66 to 120)
83
(58 to 110)
60
(41 to 77)
Short-term
exposure
Katsouyanni et al.
(2009)bc and Zanobetti
et al. (2008)° pooled
4.3
(1.7 to 6.8)
3.8
(1.5 to 5.9)
2.7
(1.1 to 4.3)
Morbidity effects
Long-term
exposure
Asthma onsetd
560
(480 to 630)
480
(410 to 540)
320
(280 to 370)
Allergic rhinitis
symptomsf
3,300
(1,700 to
4,800)
2,800
(1,500 to
4,100)
1,900
(990 to
2,700)
Hospital admissions—
respiratory0
11
(-3.0 to 25)
9.9
(-2.6 to 22)
6.9
(-1.8 to 15)
ED visits—respiratory6
180
(49 to 370)
150
(42 to 320)
82
(23 to 170)
Short-term
Asthma symptoms
110,000 (-13,000 to
220,000)
91,000 (-11,000 to
190,000)
62,000 (-7,600 to
130,000)
exposure
Minor restricted-activity
days0,6
46,000
(18,000 to
72,000)
39,000
(16,000 to 62,000)
24,000
(9,400 to 37,000)
School absence days
38,000
(-5,300 to 79,000)
32,000
(-4,500 to 67,000)
22,000
(-3,100 to 45,000)
a Values rounded to two significant figures.
b Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
0 Converted ozone risk estimate metric from MDA1 to MDA8.
d Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
e Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
f Converted ozone risk estimate metric from DA24 to MDA8.
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Table 4-9 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and
Illnesses for the Illustrative Scenarios in 2035 (95 percent confidence interval)3
Proposal Less Stringent More Stringent
Avoided premature respiratory mortalities
Long-term
exposure
Turner et al. (2016)b
26
(18 to 33)
18
(12 to 23)
23
(16 to 30)
Short-
Katsouyanni et al.
term
exposure
(2009)bc and Zanobetti
et al. (2008)° pooled
1.2
(0.47 to 1.8)
0.79
(0.32 to 1.3)
1.1
(0.43 to 1.7)
Morbidity effects
Long-term
Asthma onsetd
180
(150 to 200)
130
(110 to 150)
160
(130 to 180)
exposure
Allergic rhinitis
1,100
750
910
symptomsf
(550 to 1,500)
(400 to 1,100)
(480 to 1,300)
Hospital admissions—
respiratory0
3.1
(-0.81 to 6.8)
2.0
(-0.52 to 4.5)
2.8
(-0.73 to 6.2)
ED visits—respiratory6
63
(17 to 130)
45
(12 to 94)
55
(15 to 120)
Short-
term
Asthma symptoms
33,000 (-4,100 to
69,000)
24,000 (-2,900 to
50,000)
29,000 (-3,600 to
60,000)
exposure
Minor restricted-
activity days0,6
16,000
(6,300 to
25,000)
11,000
(4,500 to
18,000)
14
,000
(5,400 to 21,000)
12,000
School absence days
(-1,700 to
8,600
10,000
25,000)
(-1,200 to 18,000)
(-1,500 to 22,000)
a Values rounded to two significant figures.
b Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
0 Converted ozone risk estimate metric from MDA1 to MDA8.
d Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
e Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
f Converted ozone risk estimate metric from DA24 to MDA8.
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Table 4-10 Estimated Avoided Ozone-Related Premature Respiratory Mortalities and
Illnesses for the Illustrative Scenarios in 2040 (95 percent confidence interval)a'b
Proposal Less Stringent More Stringent
Avoided premature respiratory mortalities
Long-term
exposure
Turner et al. (2016)b
0.26
(0.18 to 0.33)
-7.0
(-4.8 to -9.1)
1.8
(1.2 to 2.3)
Short-term
exposure
Katsouyanni et al.
(2009)bc and Zanobetti
et al. (2008)° pooled
0.012
(0.0049 to 0.019)
-0.32
(-0.50 to -0.13)
0.081
(0.033 to 0.13)
Morbidity effects
Asthma onsetd
21
-26
26
Long-term
(18 to 24)
(-22 to -30)
(22 to 29)
exposure
Allergic rhinitis
symptomsf
120
(64 to 180)
-160
(-82 to -230)
150
(78 to 220)
Hospital admissions—
respiratory0
-0.021
(0.0054 to -0.046)
-1.0
(0.26 to -2.2)
0.18
(-0.047 to 0.39)
ED visits—respiratory6
8.3
(2.3 to 17)
-8.0
(-17 to -2.2)
8.4
(2.3 to 18)
Short-term
Asthma symptoms
3,900 (-480 to 8,100)
-4,900 (-10,000 to
600)
4,800 (-590 to
10,000)
exposure
Minor restricted-
1,400
(550 to 2,200)
-2,800
(-1,100 to
-4,400)
1,600
(640 to 2,500)
activity days0,6
School absence days
1,400
(-190 to 2,900)
-1,800
(250 to -3,800)
1,700
(-240 to 3,600)
a Values rounded to two significant figures.
b Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
0 Converted ozone risk estimate metric from MDA1 to MDA8.
d Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
e Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
f Converted ozone risk estimate metric from DA24 to MDA8.
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Table 4-11 Estimated Avoided PM-Related Premature Respiratory Mortalities and
Illnesses for the Illustrative Scenarios in 2028 (95 percent confidence interval)
Avoided Mortality
Proposal
Less Stringent
More Stringent
(Pope et al., 2019) (adult
mortality ages 18-99 years)
130
(92 to 160)
93
(66 to 120)
0.86
(0.62 to 1.1)
(Wu et al., 2020) (adult mortality
ages 65-99 years)
61
(53 to 68)
44
(38 to 49)
0.090
(0.080 to 0.10)
(Woodruff et al., 2008) (infant
mortality)
0.16
(-0.10 to 0.42)
0.12
(-0.075 to 0.31)
0.012
(-0.0073 to 0.030)
Avoided Morbidity
Hospital admissions—
cardiovascular (age >18)
8.9
(6.5 to 11)
6.4
(4.6 to 8.1)
-0.068
(-0.049 to -0.086)
Hospital admissions—respiratory
1.3
(0.049 to 2.5)
0.91
(0.034 to 1.7)
-0.036
(-0.0013 to -0.068)
ED visits-cardiovascular
19
(-7.3 to 44)
14
(-5.2 to 32)
0.35
(-0.13 to 0.81)
ED visits—respiratory
40
(7.9 to 84)
29
(5.7 to 61)
1.9
(0.38 to 4.0)
Acute Myocardial Infarction
2.0
(1.2 to 2.8)
1.4
(0.82 to 2.0)
-0.062
(-0.036 to -0.087)
Cardiac arrest
0.99
(-0.40 to 2.2)
0.72
(-0.29 to 1.6)
0.033
(-0.013 to 0.074)
Hospital admissions-
Alzheimer's Disease
28
(21 to 35)
19
(14 to 24)
-2.4
(-1.8 to -3.0)
Hospital admissions-
Parkinson's Disease
3.9
(2.0 to 5.8)
2.7
(1.4 to 4.0)
-0.15
(-0.078 to -0.23)
Stroke
3.9
(1.0 to 6.7)
2.8
(0.73 to 4.8)
0.12
(0.032 to 0.21)
Lung cancer
4.4
(1.3 to 7.4)
3.2
(0.98 to 5.4)
0.13
(0.039 to 0.21)
Hay Fever/Rhinitis
1,000
(250 to 1,800)
760
(180 to 1,300)
61
(15 to 110)
Asthma Onset
160
(150 to 170)
120
(110 to 120)
11
(10 to 11)
Asthma symptoms - Albuterol
use
22,000
(-11,000 to 53,000)
16,000
(-7,800 to 39,000)
1,300
(-620 to 3,100)
Lost work days
7,700
(6,500 to 8,900)
5,700
(4,800 to 6,600)
340
(290 to 390)
Minor restricted-activity daysd f
45,000
(37,000 to 54,000)
33,000
(27,000 to 40,000)
2,000
(1,600 to 2,300)
Note: Values rounded to two significant figures.
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Table 4-12 Estimated Avoided PM-Related Premature Respiratory Mortalities and
Illnesses for the Illustrative Scenarios in 2030 (95 percent confidence interval)
Avoided Mortality
Proposal
Less Stringent
More Stringent
(Pope et al., 2019) (adult
mortality ages 18-99 years)
1,200
(880 to 1,600)
1,100
(790 to 1,400)
1,200
(860 to 1,500)
(Wu et al., 2020) (adult mortality
590
530
580
ages 65-99 years)
(520 to 650)
(470 to 590)
(510 to 640)
(Woodruff et al., 2008) (infant
1.4
1.3
1.4
mortality)
(-0.89 to 3.6)
(-0.81 to 3.3)
(-0.88 to 3.6)
Avoided Morbidity
Hospital admissions—
85
77
84
cardiovascular (age >18)
(62 to 110)
(56 to 98)
(61 to 110)
Hospital admissions—respiratory
14
12
13
(0.52 to 26)
(0.47 to 24)
(0.51 to 26)
ED visits-cardiovascular
180
160
170
(-68 to 410)
(-62 to 370)
(-67 to 400)
ED visits—respiratory
340
310
330
(67 to 710)
(61 to 650)
(66 to 700)
Acute Myocardial Infarction
20
18
20
(12 to 28)
(10 to 25)
(11 to 28)
Cardiac arrest
8.9
8.1
8.7
(-3.6 to 20)
(-3.3 to 18)
(-3.5 to 20)
Hospital admissions-
320
290
320
Alzheimer's Disease
(240 to 400)
(220 to 360)
(240 to 400)
Hospital admissions-
39
35
38
Parkinson's Disease
(20 to 57)
(18 to 52)
(19 to 56)
Stroke
36
33
35
(9.3 to 62)
(8.5 to 56)
(9.1 to 60)
Lung cancer
41
37
40
(12 to 68)
(11 to 61)
(12 to 66)
Hay Fever/Rhinitis
8,900
8,100
8,600
(2,100 to 15,000)
(2,000 to 14,000)
(2,100 to 15,000)
Asthma Onset
1,400
1,200
1,300
(1,300 to 1,400)
(1,200 to 1,300)
(1,300 to 1,400)
Asthma symptoms - Albuterol
use
190,000
170,000
(-84,000 to 420,000)
180,000
(-92,000 to 460,000)
(-89,000 to 450,000)
Lost work days
66,000
(55,000 to 76,000)
60,000
(50,000 to 69,000)
63,000
(53,000 to 73,000)
Minor restricted-activity daysd f
390,000
(310,000 to 460,000)
350,000
(290,000 to 420,000)
370,000
(300,000 to 440,000)
Note: Values rounded to two significant figures.
4-46
-------
Table 4-13 Estimated Avoided PM-Related Premature Respiratory Mortalities and
Illnesses for the Illustrative Scenarios in 2035 (95 percent confidence interval)
Avoided Mortality
Proposal
Less Stringent
More Stringent
(Pope et al., 2019) (adult
mortality ages 18-99 years)
400
(280 to 510)
340
(240 to 430)
390
(280 to 500)
(Wu et al., 2020) (adult mortality
190
170
190
ages 65-99 years)
(170 to 220)
(150 to 190)
(170 to 220)
(Woodruff et al., 2008) (infant
0.42
0.36
0.42
mortality)
(-0.27 to 1.1)
(-0.23 to 0.94)
(-0.27 to 1.1)
Avoided Morbidity
Hospital admissions—
29
24
28
cardiovascular (age >18)
(21 to 36)
(18 to 31)
(20 to 36)
Hospital admissions—respiratory
4.5
3.8
4.4
(0.17 to 8.6)
(0.14 to 7.3)
(0.17 to 8.5)
ED visits-cardiovascular
59
51
59
(-23 to 140)
(-20 to 120)
(-23 to 140)
ED visits—respiratory
120
100
120
(23 to 240)
(20 to 210)
(23 to 240)
Acute Myocardial Infarction
6.3
5.3
6.3
(3.7 to 8.9)
(3.1 to 7.5)
(3.7 to 8.8)
Cardiac arrest
2.9
2.5
2.8
(-1.2 to 6.5)
(-1.0 to 5.6)
(-1.2 to 6.4)
Hospital admissions-
99
83
100
Alzheimer's Disease
(74 to 120)
(62 to 100)
(74 to 120)
Hospital admissions-
12
11
12
Parkinson's Disease
(6.3 to 18)
(5.4 to 16)
(6.2 to 18)
Stroke
12
10
12
(3.1 to 20)
(2.6 to 17)
(3.0 to 20)
Lung cancer
14
12
14
(4.2 to 23)
(3.6 to 20)
(4.1 to 23)
Hay Fever/Rhinitis
2,800
2,400
2,700
(680 to 4,900)
(590 to 4,200)
(660 to 4,800)
Asthma Onset
430
370
420
(410 to 450)
(360 to 390)
(400 to 440)
Asthma symptoms - Albuterol
use
59,000
(-29,000 to 140,000)
51,000
(-25,000 to 120,000)
57,000
(-28,000 to 140,000)
Lost work days
21,000
(18,000 to 24,000)
18,000
(15,000 to 21,000)
20,000
(17,000 to 23,000)
Minor restricted-activity daysd f
120,000
(99,000 to 140,000)
110,000
(86,000 to 120,000)
120,000
(97,000 to 140,000)
Note: Values rounded to two significant figures.
4-47
-------
Table 4-14 Estimated Avoided PM-Related Premature Respiratory Mortalities and
Illnesses for the Illustrative Scenarios in 2040 (95 percent confidence interval)
Avoided Mortality
Proposal
Less Stringent
More Stringent
(Pope et al., 2019) (adult
mortality ages 18-99 years)
320
(230 to 400)
240
(170 to 300)
330
(240 to 430)
(Wu et al., 2020) (adult mortality
160
120
170
ages 65-99 years)
(140 to 180)
(100 to 130)
(150 to 190)
(Woodruff et al., 2008) (infant
0.31
0.24
0.33
mortality)
(-0.20 to 0.80)
(-0.15 to 0.62)
(-0.21 to 0.84)
Avoided Morbidity
Hospital admissions—
24
17
25
cardiovascular (age >18)
(17 to 30)
(12 to 22)
(18 to 32)
Hospital admissions—respiratory
3.7
2.7
3.9
(0.14 to 7.1)
(0.10 to 5.1)
(0.15 to 7.5)
ED visits-cardiovascular
49
36
51
(-19 to 110)
(-14 to 85)
(-20 to 120)
ED visits—respiratory
95
72
99
(19 to 200)
(14 to 150)
(19 to 210)
Acute Myocardial Infarction
5.1
3.7
5.5
(3.0 to 7.2)
(2.1 to 5.2)
(3.2 to 7.7)
Cardiac arrest
2.3
1.7
2.5
(-0.95 to 5.3)
(-0.70 to 3.9)
(-1.0 to 5.6)
Hospital admissions-
24
17
25
Alzheimer's Disease
(17 to 30)
(12 to 22)
(18 to 32)
Hospital admissions-
9.5
7.1
10
Parkinson's Disease
(4.8 to 14)
(3.6 to 11)
(5.1 to 15)
Stroke
9.5
7.0
10
(2.5 to 16)
(1.8 to 12)
(2.6 to 17)
Lung cancer
12
8.6
12
(3.5 to 19)
(2.6 to 14)
(3.7 to 20)
Hay Fever/Rhinitis
2,200
1,700
2,400
(540 to 3,900)
(410 to 2,900)
(570 to 4,100)
Asthma Onset
340
260
360
(330 to 360)
(250 to 270)
(350 to 380)
Asthma symptoms - Albuterol
use
47,000
(-23,000 to 110,000)
35,000
(-17,000 to 85,000)
50,000
(-24,000 to 120,000)
Lost work days
17,000
(14,000 to 20,000)
13,000
(11,000 to 15,000)
18,000
(15,000 to 21,000)
Minor restricted-activity daysd f
100,000
(81,000 to 120,000)
75,000
(61,000 to 88,000)
110,000
(86,000 to 130,000)
Note: Values rounded to two significant figures.
4-48
-------
Table 4-15 Estimated Discounted Economic Value of Avoided Ozone and PM2.5-
Attributable Premature Mortality and Illness for the Illustrative Scenarios in 2028 (95
percent confidence interval; millions of 2C
)19 dollars)3
Disc.
Rate
Pollutant
Ozone Benefits
PM Benefits
Ozone plus PM Benefits
3%
Proposal
$32 ($8.2 j $240 ($27
nnn
to $66) to $620)
$650 $1,400
($69 to and ($130 to
$1,700) $3,700)
$680 $1,600
($77 to and ($160
$l,800)b to $4,300)°
Less
Stringent
$25 ($6.6 $180 ($21
to $51) to $470)
$470
rtsn t a $990 ($96
So, »«*»>
$490 $1,200
($56 to and ($120 to
$l,300)b $3,100)°
More
Stringent
-$430 -$53
(-$1,100 and (-$110
to -$47) to -$13)
$1.9 $10
($0.61 and ($1.3 to
to $4) $26)
-$420 -$51
(-$1,100 and (-$110 to-
to -$21)b $9.3)°
7%
Proposal
$28 ($5.3 j $210 ($22
nnn
to 62) to $560)
$590 $1,200
($60 to and ($120 to
$1,500) $3,300)
$610 $1,400
($66 to and ($140 to
$l,600)b $3,900)°
Less
Stringent
$22 ($4.2 j $160 ($17
nun
to $48) to $420)
$420
* , $890 ($85
$1,100) 'O*2'400'
$440 $1,000
($48 to and ($100 to
$l,200)b $2,800)°
More
Stringent
-$380 -$48
(-$1,000 and (-$110
to -$39) to -$8.6)
$1.6 $8.9
($0.41 to and ($1.1 to
$3.5) $23)
-$370 -$46
(-$1,000 and (-$110 to
to -$16)b -$5)°
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should
not be summed.
b Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al.
(2020) long-term PM2 5 exposure mortality risk estimate.
0 Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2019) long-term PM2 5
exposure mortality risk estimate.
4-49
-------
Table 4-16 Estimated Discounted Economic Value of Avoided Ozone and PM2.5-
Attributable Premature Mortality and Illness for the Illustrative Scenarios in 2030 (95
percent confidence interval; millions of 2019 dollars)3
Disc.
Rate
Pollutant
Ozone Benefits
PM Benefits
Ozone plus PM Benefits
3%
Proposal
$120 $1,100
($26 to and ($110 to
$250) $2,800)
$6,300 $13,000
($660 to and ($1,300 to
$17,000) $35,000)
$6,500 $14,000
($690 to and ($1,400 to
$17,000)b $38,000)°
Less
Stringent
$100 $930
($23 to and ($98 to
$220) $2,500)
$5,800 $12,000
($600 to and ($1,100 to
$15,000) $32,000)
$5,900 $13,000
($620 to and ($1,200 to
$15,000)b $34,000)°
More
Stringent
$69 $670
($15 to and ($70 to
$150) $1,800)
$6,300 $13,000
($650 to and ($1,200 to
$16,000) $35,000)
$6,300 $14,000
($670 to and ($1,300 to
$17,000)b $36,000)°
7%
Proposal
$100 $960
($17 to and ($94 to
240) $2,500)
$5,700 $12,000
($580 to and ($1,100 to
$15,000) $31,000)
$5,800 $13,000
($600 to and ($1,200 to
$15,000)b $34,000)°
Less
Stringent
$91 $840
($15 to and ($83 to
$210) $2,200)
$5,200 $11,000
($530 to and ($1,000 to
$14,000) $29,000)
$5,300 $12,000
($540 to and ($1,100 to
$14,000)b $31,000)°
More
Stringent
$63 $600
($10 to and ($59 to
$150) $1,600)
$5,600 $12,000
($570 to and ($1,100 to
$15,000) $32,000)
$5,700 $12,000
($580 to and ($1,200 to
$15,000)b $33,000)°
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should not
be summed.
b Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al.
(2020) long-term PM2 5 exposure mortality risk estimate.
0 Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2019) long-term PM2 5
exposure mortality risk estimate.
4-50
-------
Table 4-17 Estimated Discounted Economic Value of Avoided Ozone and PM2.5-
Attributable Premature Mortality and Illness for the Illustrative Scenarios in 2035 (95
percent confidence interval; millions of 2019 dollars)3
Disc.
Rate
Pollutant
Ozone Benefits
PM Benefits
Ozone plus PM Benefits
3%
Proposal
$35 $300
($8.3 to and ($32 to
$75) $790)
$2,200 $4,400
($220 to and ($420 to
$5,700) $12,000)
$2,200 $4,700
($230 to and ($450 to
$5,700)b $13,000)°
Less
Stringent
$24 $210
($5.9 to and ($22 to
$52) $540)
$1,800 $3,700
($190 to and ($360 to
$4,800) $10,000)
$1,900 $3,900
($200 to and ($380 to
$4,900)b $11,000)C
More
Stringent
$31 $270
($7.3 and ($29 to
to $67) $720)
$2,100 $4,300
($220 to and ($420 to
$5,600) $12,000)
$2,200 $4,600
($230 to and ($450 to
$5,700)b $12,000)°
7%
Proposal
$31 $270
($5.4 to and ($27 to
71) $710)
$1,900 $3,900
($200 to and ($370 to
$5,100) $11,000)
$2,000 $4,200
($200 to and ($400 to
$5,200)b $11,000)°
Less
Stringent
$22 $180
($3.8 to and ($19 to
$50) $490)
$1,700 $3,300
($170 to and ($320 to
$4,300) $9,000)
$1,700 $3,500
($170 to and ($340 to
$4,400)b $9,500)°
More
Stringent
$28 $240
($4.8 to and ($24 to
$64) $650)
$1,900 $3,900
($190 to and ($370 to
$5,100) $10,000)
$2,000 $4,100
($200 to and ($390 to
$5,100)b $11,000)°
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should not
be summed.
b Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al.
(2020) long-term PM2 5 exposure mortality risk estimate.
0 Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2019) long-term PM2 5
exposure mortality risk estimate.
4-51
-------
Table 4-18 Estimated Discounted Economic Value of Avoided Ozone and PM2.5-
Attributable Premature Mortality and Illness for the Illustrative Scenarios in 2040 (95
percent confidence interval; millions of 2019 dollars)3
Disc.
Rate
Pollutant
Ozone Benefits
PM Benefits
Ozone plus PM Benefits
3%
Proposal
$2.5
($0.85
to $4.4)
and
$5.3
($1.1 to
$12)
$1,800
($190 to
$4,700)
and
$3,600
($340 to
$9,600)
$1,800
($190 to
$4,700)b
and
$3,600
($340 to
$9,600)°
Less
Stringent
-$81
(-$220
to -$8)
and
-$7
(-$1.4 to
-$16)
$1,300
($140 to
$3,500)
and
$2,700
($260 to
$7,100)
$1,300
($120 to
$3,500)b
and
$2,600
($40 to
$7,100)°
More
Stringent
$3.9
($1.1
to $7.8)
and
$23
($2.8 to
$59)
$1,900
($200 to
$5,000)
and
$3,800
($360 to
$10,000)
$1,900
($200 to
$5,000)b
and
$3,800
($370 to
$10,000)°
7%
Proposal
$2.2
($0.51
to 3.9)
and
$4.6
($0.73 to
$11)
$1,600
($160 to
$4,200)
and
$3,200
($300 to
$8,600)
$1,600
($160 to
$4,200)b
and
$3,200
($310 to
$8,600)°
Less
Stringent
-$6.5
(-$16 to
-$0.95)
and
-$73
(-$190 to
-$6.9)
$1,200
($120 to
$3,100)
and
$2,400
($230 to
$6,400)
$1,200
($110 to
$3,100)b
and
$2,300
($33 to
$6,400)°
More
Stringent
$3.5
($0.68
to $72)
and
$20
($2.2 to
$53)
$1,700
($170 to
$4,500)
and
$3,400
($320 to
$9,100)
$1,700
($170 to
$4,500)b
and
$3,400
($320 to
$9,200)°
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should not
be summed.
b Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al.
(2020) long-term PM2 5 exposure mortality risk estimate.
0 Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2019) long-term PM2 5
exposure mortality risk estimate.
4-52
-------
Table 4-19 Estimated Discounted Economic Value of Avoided Ozone and PM2.5-
Attributable Premature Mortality and Illness for the Illustrative Scenarios in 2028, 2030,
2035 and 2040 (95 percent confidence interval; millions of 2019 dollars)a'b
3% Discount Rate
7% Discount Rate
Ozone
Ozone
Ozone
PM
plus PM
Ozone
PM
plus PM
Benefits
Benefits
Benefits
Benefits
Benefits
Benefits
$32 and
$650 and
$680 and
$28 and
$590 and
$610 and
Proposal
$240
$1400
$1600
$210
$1,200
$1,400
2028
$25 and
$470 and
$490 and
$22 and
$420 and
$440 and
Less Stringent
$180
$990
$1200
$160
$890
$1,000
-$430 and
$1.9 and
-$420 and
-$380 and
$1.6 and
-$370 and
More Stringent
-$53
$10
-$51
-$48
$8.9
-$46
$120 and
$6,300 and
$6,500 and
$100 and
$5,700 and
$5,800 and
Proposal
$1,100
$13,000
$14,000
$960
$12,000
$13,000
2030
$100 and
$5,800 and
$5,900 and
$91 and
$5,200 and
$5,300 and
Less Stringent
$930
$12,000
$13,000
$840
$11,000
$12,000
$69 and
$6,300 and
$6,300 and
$63 and
$5,600 and
$5,700 and
More Stringent
$670
$13,000
$14,000
$600
$12,000
$12,000
$35 and
$2,200 and
$2,200 and
$31 and
$1,900 and
$2,000 and
Proposal
$300
$4,400
$4,700
$270
$3,900
$4,200
2035
$24 and
$1,800 and
$1,900 and
$22 and
$1,700 and
$1,700 and
Less Stringent
$210
$3,700
$3,900
$180
$3,300
$3,500
$31 and
$2,100 and
$2,200 and
$28 and
$1,900 and
$2,000 and
More Stringent
$270
$4,300
$4,600
$240
$3,900
$4,100
$2.5 and
$1,800 and
$1,800 and
$2.2 and
$1,600 and
$1,600 and
Proposal
$5.3
$3,600
$3,600
$4.6
$3200
$3,200
2040
-$7 and
$1,300 and
$1,300 and
-$6.5 and -
$1,200 and
$1,200 and
Less Stringent
-$81
$2,700
$2,600
$73
$2,400
$2,300
$3.9 and
$1,900 and
$1,900 and
$3.5 and
$1,700 and
$1,700 and
More Stringent
$23
$3,800
$3,800
$20
$3,400
$3,400
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should
not be summed.
b Values are the monetized benefits of the mortality and illnesses included in Tables 4-7 through 4-14.
4-53
-------
Table 4-20 Stream of Human Health Benefits from 2028 through 2042: Monetized
Benefits Quantified as Sum of Long-Term Ozone Mortality and Illness and Long-Term
PM2.5 Mortality and Illness for EGUs (discounted at 3 percent; millions of 2019 dollars)3
Proposal
Less Stringent
More Stringent
2028*
$1,600
$1,200
($420)
2029
$14,000
$13,000
$13,000
2030*
$14,000
$13,000
$14,000
2031
$14,000
$13,000
$14,000
2032
$4,300
$3,600
$4,300
2033
$4,400
$3,700
$4,400
2034
$4,500
$3,800
$4,500
2035*
$4,700
$3,900
$4,600
2036
$4,800
$4,000
$4,700
2037
$4,900
$4,100
$4,500
2038
$3,400
$2,500
$3,600
2039
$3,500
$2,500
$3,700
2040*
$3,600
$2,600
$3,800
2041
$3,600
$2,600
$3,900
2042
$3,700
$2,700
$3,900
PV
$68,000
$58,000
$65,000
EAV
$4,800
$4,100
$4,600
*Year in which air quality models were run. Benefits for all other years were extrapolated from years with model-
based air quality estimates. Benefits calculated as value of avoided: PM2 5-attributable deaths (quantified using a
concentration-response relationship from the Pope et al. 2019 study); Ozone-attributable deaths (quantified using a
concentration-response relationship from the Turner et al. 2016 study); and PM2 5 and ozone-related morbidity
effects.
a For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
4-54
-------
Table 4-21 Stream of Human Health Benefits from 2028 through 2042: Monetized
Benefits Quantified as Sum of Long-Term Ozone Mortality and Illness and Long-Term
PM2.5 Mortality and Illness for EGUs (discounted at 7 percent; millions of 2019 dollars)3
Proposal
Less Stringent
More Stringent
2028*
$1,400
$1,000
($370)
2029
$12,000
$11,000
$12,000
2030*
$13,000
$12,000
$12,000
2031
$13,000
$12,000
$12,000
2032
$3,900
$3,300
$3,800
2033
$4,000
$3,400
$3,900
2034
$4,100
$3,400
$4,000
2035*
$4,200
$3,500
$4,100
2036
$4,300
$3,600
$4,200
2037
$4,400
$3,700
$4,100
2038
$3,100
$2,200
$3,300
2039
$3,100
$2,300
$3,300
2040*
$3,200
$2,300
$3,400
2041
$3,300
$2,400
$3,500
2042
$3,300
$2,400
$3,500
PV
$44,000
$38,000
$42,000
EAV
$4,300
$3,700
$4,000
*Year in which air quality models were run. Benefits for all other years were extrapolated from years with model-
based air quality estimates. Benefits calculated as value of avoided: PM2 5-attributable deaths (quantified using a
concentration-response relationship from the Pope et al. 2019 study); Ozone-attributable deaths (quantified using a
concentration-response relationship from the Turner et al. 2016 study); and PM2 5 and ozone-related morbidity
effects.
a For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
4.4 Additional Unquantified Benefits
Data, time, and resource limitations prevented EPA from quantifying the estimated health
impacts or monetizing estimated benefits associated with incremental changes in direct exposure
to NO2 and SO2, independent of the role NO2 and SO2 play as precursors to PM2.5 and ozone, as
well as ecosystem effects, and visibility impairment that might result from emissions changes
associated with compliance with the proposed requirements. While all health benefits and
welfare benefits were not quantified, it does not imply that there are not additional benefits
associated with reductions in human exposures to NO2 or SO2 and ecosystem exposure to air
pollutants potentially resulting from emissions changes under this rule. In this section, we
provide a qualitative description of these and water quality benefits, which are listed in Table
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4-22. Note also that some pollutants from U.S. EGUs, such as NO2, SO2, and particulate matter,
can be transported downwind into foreign countries, in particular Canada and Mexico. Therefore,
reduced pollution from U.S. EGUs can lead to public health and welfare benefits in foreign
countries. EPA is currently unable to quantify or monetize these effects.
Table 4-22 Unquantified Health and Welfare Benefits Categories
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Improved Human Health
Asthma hospital admissions
—
—
NO2 ISA1
Chronic lung disease hospital admissions
—
—
NO2 ISA1
Respiratory emergency department visits
—
—
NO2 ISA1
Reduced incidence of
morbidity from exposure
to NO2
Asthma exacerbation
—
—
NO2 ISA1
Acute respiratory symptoms
—
—
NO2 ISA1
Premature mortality
—
—
NO2 ISA1'2'3
Other respiratory effects (e.g., airway
hyperresponsiveness and inflammation, lung
function, other ages and populations)
—
—
NO2 ISA2'3
Reduced incidence of
mortality and morbidity
through drinking water
from reduced effluent
discharges.
Bladder, colon, and rectal cancer from
halogenated disinfection byproducts
exposure.
—
—
SEELGBCA4
Reproductive and developmental effects
from halogenated disinfection byproducts
exposure.
—
—
SEELGBCA4
Neurological and cognitive effects to
children from lead exposure from fish
consumption (including need for specialized
education).
—
—
SEELGBCA4
Possible cardiovascular disease from lead
exposure
—
—
SEELGBCA4
Reduced incidence of
morbidity and mortality
from toxics through fish
consumption from reduced
effluent discharges.
Neurological and cognitive effects from in
in-utero mercury exposure from maternal
fish consumption
—
—
SEELGBCA4
Skin and gastrointestinal cancer incidence
from arsenic exposure
—
—
SEELGBCA4
Cancer and non-cancer incidence from
exposure to toxic pollutants (lead, cadmium,
thallium, hexavalent chromium etc.
—
—
SEELGBCA4
Neurological, alopecia, gastrointestinal
effects, reproductive and developmental
damage from short-term thallium exposure.
Reduced incidence of
morbidity and mortality
from recreational water
exposure from reduced
effluent discharges.
Cancer and Non-Cancer incidence from
exposure to toxic pollutants (methyl-
mercury, selenium, and thallium.)
—
—
SEELGBCA4
Improved Environment
Reduced visibility
Visibility in Class 1 areas
—
—
PM ISA1
impairment
Visibility in residential areas
—
—
PM ISA1
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Reduced effects on
materials
Household soiling
—
—
PMISA1,2
Materials damage (e.g., corrosion, increased
wear)
—
—
PM ISA2
Reduced effects from PM
deposition (metals and
organics)
Effects on individual organisms and
ecosystems
—
—
PMISA2
Visible foliar injury on vegetation
—
—
Ozone ISA1
Reduced vegetation growth and reproduction
—
—
Ozone ISA1
Yield and quality of commercial forest
products and crops
—
—
Ozone ISA1
Damage to urban ornamental plants
—
—
Ozone ISA2
Reduced vegetation and
ecosystem effects from
Carbon sequestration in terrestrial
ecosystems
—
—
Ozone ISA1
exposure to ozone
Recreational demand associated with forest
aesthetics
—
—
Ozone ISA2
Other non-use effects
Ozone ISA2
Ecosystem functions (e.g., water cycling,
biogeochemical cycles, net primary
productivity, leaf-gas exchange, community
composition)
—
—
Ozone ISA2
Recreational fishing
—
—
NOx SOx ISA1
Tree mortality and decline
—
—
NOx SOx ISA2
Reduced effects from acid
deposition
Commercial fishing and forestry effects
—
—
NOx SOx ISA2
Recreational demand in terrestrial and
aquatic ecosystems
—
—
NOx SOx ISA2
Other non-use effects
NOx SOx ISA2
Ecosystem functions (e.g., biogeochemical
cycles)
—
—
NOx SOx ISA2
Species composition and biodiversity in
terrestrial and estuarine ecosystems
—
—
NOx SOx ISA2
Reduced effects from
nutrient enrichment from
deposition.
Coastal eutrophication
—
—
NOx SOx ISA2
Recreational demand in terrestrial and
estuarine ecosystems
—
—
NOx SOx ISA2
Other non-use effects
NOx SOx ISA2
Ecosystem functions (e.g., biogeochemical
cycles, fire regulation)
—
—
NOx SOx ISA2
Reduced vegetation effects
from ambient exposure to
SO2 and NOx
Injury to vegetation from SO2 exposure
—
—
NOx SOx ISA2
Injury to vegetation from NOx exposure
—
—
NOx SOx ISA2
Improved water aesthetics
from reduced effluent
Improvements in water clarity, color, odor in
residential, commercial and recreational
SEELGBCA4
discharges.
settings.
Protection of Threatened and Endangered
(T&E) species from changes in habitat and
potential population effects.
—
—
SEELGBCA4
Effects on aquatic
organisms and other
wildlife from reduced
effluent discharges
Other non-use effects
—
—
SEELGBCA4
Changes in sediment contamination on
benthic communities and potential for re-
entrainment.
—
—
SEELGBCA4
Quality of recreational fishing and other
recreational use values.
—
—
SEELGBCA4
Commercial fishing yields and harvest
quality.
—
—
SEELGBCA4
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Reduced water treatment
costs from reduced
effluent discharges
Reduced drinking, irrigation, and other
agricultural use water treatment costs.
—
—
SE ELG BCA4
Increased storage availability in reservoirs
—
—
SE ELG BCA4
Reduced sedimentation
from effluent discharges
Improved functionality of navigable
waterways
—
—
SE ELG BCA4
Decreased cost of dredging
—
—
SE ELG BCA4
Benefits of reduced water
withdrawal
Benefits from effects aquatic and riparian
species from additional water availability.
—
—
SE ELG BCA4
Increased water availability in reservoirs
increasing hydropower supply, recreation,
and other services.
—
—
SE ELG BCA4
1 We assess these benefits qualitatively due to data and resource limitations for this RIA.
2 We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.
3 We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant
concerns over the strength of the association.
4 Benefit and Cost Analysis (BCA) for Revisions to the Effluent Limitations Guidelines (ELG) and Standards for the Steam
Electric (SE) Power Generating Point Source Category.
4.4.1 Hazardous Air Pollutant Impacts
4.4.1.1 Mercury Air Pollutant Impacts
The proposed rules are expected to reduce fossil-fired EGU generation and
consequentially is expected to lead to reduced HAP emissions. HAP emitted from EGUs can
cause premature mortality from heart attacks, cancer, and neurodevelopmental delays in children,
and detrimentally affect economically vital ecosystems used for recreational and commercial
purposes. Further, these public health effects have been particularly pronounced for certain
segments of the American population that are especially vulnerable (e.g., subsistence fishers and
their children) to impacts from EGU HAP emissions.
The proposed rules are expected to reduce emissions of mercury. Mercury is a persistent,
bioaccumulative toxic metal that is emitted from power plants in three forms: gaseous elemental
mercury (HgO), oxidized mercury compounds (Hg+2), and particle-bound mercury (HgP).
Elemental mercury does not quickly deposit or chemically react in the atmosphere, resulting in
residence times that are long enough to contribute to global scale deposition. Oxidized mercury
and HgP deposit quickly from the atmosphere impacting local and regional areas in proximity to
sources. MeHg is formed by microbial action in the top layers of sediment and soils, after
mercury has precipitated from the air and deposited into waterbodies or land. Once formed,
MeHg is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger
predatory fish may have MeHg concentrations many times, typically on the order of one million
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times, that of the concentrations in the freshwater body in which they live. MeHg can adversely
impact ecosystems and wildlife. The projected reductions in mercury are expected to reduce the
bioconcentration of MeHg in fish. Subsistence fishing is associated with vulnerable populations,
including minorities and those of low socioeconomic status. Further reductions in mercury
emissions from lignite-fired facilities could help address exposure inequities for the subsistence
fisher sub-population.
Human exposure to MeHg is known to have several adverse neurodevelopmental
impacts, such as IQ loss measured by performance on neurobehavioral tests, particularly on tests
of attention, fine motor-function, language, and visual spatial ability. In addition, evidence in
humans and animals suggests that MeHg can have adverse effects on both the developing and the
adult cardiovascular system, including fatal and non-fatal ischemic heart disease (IHD). Further,
nephrotoxicity, immunotoxicity, reproductive effects (impaired fertility), and developmental
effects have been observed with MeHg exposure in animal studies disease (Agency for Toxic
Substances and Disease Registry, 2022). MeHg has some genotoxic activity and is capable of
causing chromosomal damage in a number of experimental systems. EPA has classified MeHg as
a "possible" human carcinogen.
4.4.1.2 Metal HAP
The projected reductions in emissions of non-mercury metal HAP are expected to reduce
exposure to carcinogens, such as nickel, arsenic, and hexavalent chromium, in the surrounding
areas. U.S. EGUs are the largest source of selenium (Se) emissions and a major source of
metallic HAP emissions including arsenic (As), chromium (Cr), nickel (Ni), and cobalt (Co).
Additionally, U.S. EGUs emit cadmium (Cd), beryllium (Be), lead (Pb), and manganese (Mn).
These emissions include metal HAPs that are persistent and bioaccumulative (Cd, As, and Pb)
and others have the potential to cause cancer (Ni, Cr, Cd, Be, Co, and Pb). PM controls are
expected to reduce metal HAP emissions and therefore reduce the potential for adverse effects
from metal HAP exposure.
Exposure to these metal HAP, depending on exposure duration and levels of exposures, is
associated with a variety of adverse health effects. These adverse health effects may include
chronic health disorders (e.g., irritation of the lung, skin, and mucus membranes; decreased
pulmonary function, pneumonia, or lung damage; detrimental effects on the central nervous
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system; damage to the kidneys; and alimentary effects such as nausea and vomiting). As of 2023,
three of the key metal HAP emitted by EGUs (As, Cr, and Ni) have been classified as human
carcinogens, while two others (Cd, and Se) are classified as probable human carcinogens.
4.4.2 NO2 Health Benefits
In addition to being a precursor to PM2.5 and ozone, NOx emissions are also linked to a
variety of adverse health effects associated with direct exposure. This analysis only quantifies
and monetizes the ozone PM2.5 benefits associated with the reductions in NOx emissions and
does not quantify the impacts of changing direct exposure to NO2. Following a comprehensive
review of health evidence from epidemiologic and laboratory studies, the Integrated Science
Assessment for Oxides of Nitrogen —Health Criteria (NOx ISA) concluded that there is a likely
causal relationship between respiratory health effects and short-term exposure to NO2 (U.S. EPA,
2016a). These epidemiologic and experimental studies encompass a number of endpoints
including emergency department visits and hospitalizations, respiratory symptoms, airway
hyperresponsiveness, airway inflammation, and lung function. The NOx ISA also concluded that
the relationship between short-term NO2 exposure and premature mortality was "suggestive but
not sufficient to infer a causal relationship," because it is difficult to attribute the mortality risk
effects to NO2 alone. Although the NOx ISA stated that studies consistently reported a
relationship between NO2 exposure and mortality, the effect was generally smaller than that for
other pollutants such as PM.
4.4.3 SO2 Health Benefits
In addition to being a precursor to PM2.5, SO2 emissions are also linked to a variety of
adverse health effects associated with direct exposure. This analysis only quantifies and
monetizes the PM2.5 benefits associated with the reductions in SO2 emissions and does not
quantify the impacts of changing direct exposure to SO2. Following an extensive evaluation of
health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment
for Oxides of Sulfur —Health Criteria (SO2 ISA) ISA concluded that there is a causal
relationship between respiratory health effects and short-term exposure to SO2 (U.S. EPA, 2017).
The immediate effect of SO2 on the respiratory system in humans is bronchoconstriction.
Asthmatics are more sensitive to the effects of SO2 likely resulting from pre-existing
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inflammation associated with this disease. A clear concentration-response relationship has been
demonstrated in laboratory studies following exposures to SO2 at concentrations between 20 and
100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely
affected. Based on our review of this information, we identified three short-term morbidity
endpoints that the SO2 ISA identified as a "causal relationship": asthma exacerbation,
respiratory-related emergency department visits, and respiratory-related hospitalizations. The
differing evidence and associated strength of the evidence for these different effects is described
in detail in the SO2 ISA. The SO2 ISA also concluded that the relationship between short-term
SO2 exposure and premature mortality was "suggestive of a causal relationship" because it is
difficult to attribute the mortality risk effects to SO2 alone. Although the SO2 ISA stated that
studies are generally consistent in reporting a relationship between SO2 exposure and mortality,
there was a lack of robustness of the observed associations to adjustment for other pollutants.
4.4.4 Ozone Welfare Benefits
Exposure to ozone has been associated with a wide array of vegetation and ecosystem
effects in the published literature (U.S. EPA, 2020c). Sensitivity to ozone is highly variable
across species, with over 65 plant species identified as "ozone-sensitive", many of which occur
in state and national parks and forests. These effects include those that damage or impair the
intended use of the plant or ecosystem. Such effects can include reduced growth and/or biomass
production in sensitive plant species, including forest trees, reduced yield and quality of crops,
visible foliar injury, species composition shift, and changes in ecosystems and associated
ecosystem services. See Section F of the Ozone Transport Policy Analysis Proposed Rule TSD
(U.S. EPA, 2022g) for a summary of an assessment of risk of ozone-related growth impacts on
selected forest tree species.
4.4.5 NO2 and SO2 Welfare Benefits
As described in the Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides
of Sulfur and Particulate Matter Ecological Criteria (NOx/SOx/PM ISA), NOx and SO2
emissions also contribute to a variety of adverse welfare effects, including those associated with
acidic deposition, visibility impairment, and nutrient enrichment (U.S. EPA, 2020b). Deposition
of nitrogen and sulfur causes acidification, which can cause a loss of biodiversity of fishes,
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zooplankton, and macro invertebrates in aquatic ecosystems, as well as a decline in sensitive tree
species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial
ecosystems. In the northeastern U.S., the surface waters affected by acidification are a source of
food for some recreational and subsistence fishermen and for other consumers and support
several cultural services, including aesthetic and educational services and recreational fishing.
Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum
toxicity, which can cause reduced root growth, restricting the ability of the plant to take up water
and nutrients. These direct effects can, in turn, increase the sensitivity of these plants to stresses,
such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of
canopy trees. Terrestrial acidification affects several important ecological services, including
declines in habitat for threatened and endangered species (cultural), declines in forest aesthetics
(cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and
reductions in water retention (cultural and regulating) (U.S. EPA, 2008).
Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment.
In estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of
estuaries can disrupt an important source of food production, particularly fish and shellfish
production, and a variety of cultural ecosystem services, including water-based recreational and
aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and
number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets
the balance between native and nonnative plants, changing the ability of an area to support
biodiversity. When the composition of species changes, then fire frequency and intensity can
also change, as nonnative grasses fuel more frequent and more intense wildfires (U.S. EPA,
2008).
4.4.6 Visibility Impairment Benefits
Reducing ambient PM2.5 levels would improve levels of visibility in the U.S. because
suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA,
2009b). Fine particles with significant light-extinction efficiencies include sulfates, nitrates,
organic carbon, elemental carbon, and soil (Sisler, 1996).Fine particles with significant light-
extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil
(Sisler, 1996). Visibility has direct significance to people's enjoyment of daily activities and
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their overall sense of wellbeing. Good visibility increases the quality of life where individuals
live and work, and where they engage in recreational activities. Particulate sulfate is the
dominant source of regional haze in the eastern U.S. and particulate nitrate is an important
contributor to light extinction in California and the upper Midwestern U.S., particularly during
winter (U.S. EPA, 2009b). Previous analyses show that visibility benefits can be a significant
welfare benefit category. In this analysis we did not quantify visibility-related benefits and did
not determine whether the emission reductions associated with the final emission guidelines
would be likely to have a significant impact on visibility in urban areas or Class I areas (U.S.
EPA, 2012).
Reductions in emissions of direct PM2.5, SO2, and NO2 will improve the level of visibility
throughout the United States because primary and secondary PM2.5 impairs visibility by
scattering and absorbing light (U.S. EPA, 2009b). Visibility is also referred to as visual air
quality (VAQ), and it directly affects people's enjoyment of a variety of daily activities (U.S.
EPA, 2009b). Good visibility increases quality of life where individuals live and work, and
where they travel for recreational activities, including sites of unique public value, such as the
Great Smoky Mountains National Park (U.S. EPA, 2009b).
4.4.7 Water Quality and Availability Benefits
As described in Section 3, operators are expected to increase generation from lower-
emitting resources in the baseline, and these proposed rules are expected to continue this trend.
Operators may increase generation at some subset of fossil fuel units, particularly those that
install CCS. As described in Section 3, incremental adoption of CCS and hydrogen technologies
are expected under this rulemaking, and as noted in preamble sections VII(F)(3), X(D)(1), and
XIV(E)(3), these technologies have water demands and may have implications for water
availability.
At coal units that decrease generation, there are several negative health, ecological, and
productivity effects associated with water effluent and intake that will be avoided. The impacts
of coal generation on water quality and availability are qualitatively described below. For
additional discussion of these impacts and welfare implications, see U.S. EPA (2020a) and U.S.
EPA (2023a). Coal units that increase generation, particularly those that install CCS, may have
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associated water quality disbenefits if there is increased effluent related to wet-flue gas
desulfurization (FGD) controls and bottom ash (BA) transport. However, this concern would be
mitigated with the finalization of the 2023 Proposed Supplemental Effluent Limitations
Guidelines and Standards for the Steam Electric Power Generating Point Source Category,
which proposes zero-discharge effluent limitations for FGD wastewater and BA transport
water.115 Also, the proposed effluent limitation guidelines propose new numeric limits to
combustion residual leachate, which addresses concerns that FGD waste increases leachate of
mercury.
4.4.7.1 Potential Water Quality Benefits of Reduced Coal-Fired Power Generation
Discharges of wastewater from coal-fired power plants contain toxic and bioaccumulative
pollutants (e.g., selenium, mercury, arsenic, nickel), halogen compounds (containing bromide,
chloride, or iodide), nutrients, and total dissolved solids (TDS), which can cause human health
and environmental harm through surface water and fish tissue contamination. Pollutants in coal
combustion wastewater are of particular concern because they can occur in large quantities (i.e.,
total pounds) and at high concentrations (i.e., exceeding drinking water Maximum Contaminant
Levels (MCLs)) in discharges and leachate to groundwater and surface waters. These potential
beneficial effects follow directly from reductions in pollutant loadings to receiving waters, and
indirectly from other changes in plant operations. The potential benefits come in the form of
reduced morbidity, mortality, and on environmental quality and economic activities; reduction in
water use, which provides benefits in the form of increased availability of surface water and
groundwater; and reductions in the use of surface impoundments to manage Coal Combustion
Residual wastes, with benefits in the form of avoided cleanup and other costs associated with
impoundment releases.
Discharges of wastewater from coal-fired power plants affect human health risk by
changing exposure to pollutants in water via two principal exposure pathways: (1) treated water
sourced from surface waters affected by coal-fired power plant discharges and (2) fish and
shellfish taken from waterways affected by coal-fired power plant discharges. The human health
115 https://www.epa.gov/eg/steam-electric-power-generating-effluent-guidelines-2023-proposed-rule
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benefits from surface water quality improvements may include drinking water benefits, fish
consumption benefits, and other complimentary measures.
In addition, corresponding surface water quality changes can affect the ecological
condition and recreation use effects. EPA expects the ecological impacts from reduced coal-fired
power plant discharges could include habitat changes for fresh- and saltwater plants,
invertebrates, fish, and amphibians, as well as terrestrial wildlife and birds that prey on aquatic
organisms exposed to pollutants from coal combustion. The change in pollutant loadings has the
potential to result in changes in ecosystem productivity in waterways and the health of resident
species, including threatened and endangered (T&E) species. Loadings from coal-fired power
generation have the potential to impact the general health of fish and invertebrate populations,
their propagation to waters, and fisheries for both commercial and recreational purposes.
Changes in water quality also have the potential to impact recreational activities such as
swimming, boating, fishing, and water skiing.
Potential economic productivity effects may stem from changes in the quality of public
drinking water supplies and irrigation water; changes in sediment deposition in reservoirs and
navigational waterways; and changes in tourism, commercial fish harvests, and property values.
4.4.7.2 Drinking Water
Pollutants discharged by coal-fired power plants to surface waters may affect the quality
of water used for public drinking supplies. In turn these impacts to public water supplies have the
potential to affect the costs of drinking water treatment (e.g., filtration and chemical treatment)
by changing eutrophication levels and pollutant concentrations in source waters. Eutrophication
is one of the main causes of taste and odor impairment in drinking water, which has a major
negative impact on public perceptions of drinking water safety. Additional treatment to address
foul tastes and odors can significantly increase the cost of public water supply.
Although public drinking water supplies are subject to legally enforceable maximum
contaminant levels (MCLs), established by EPA, pollutants discharged from coal-fired power
plants, particularly episodic releases, may not be removed adequately during treatment at a
drinking water treatment plant exposing consumers to these contaminants through ingestion,
inhalation, and skin absorption. The constituents found in the power plant discharge may also
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interact with drinking water treatment processes and contribute to the formation of disinfection
byproducts that can have adverse human health impacts.
4.4.7.3 Fish Consumption
Recreational and subsistence fishers (and their household members) who consume fish
caught in the reaches downstream of coal-fired power plants may be affected by changes in
pollutant concentrations in fish tissue. See U.S. EPA (2020a) and U.S. EPA (2023a) for a
demonstration of the changes in risk to human health from exposure to contaminated fish tissue.
This document describes the neurological effects to children ages 0 to 7 from exposure to lead;
the neurological effects to infants from in-utero exposure to mercury; the incidence of skin
cancer from exposure to arsenic; and the reduced risk of other cancer and non-cancer toxic
effects.
4.4.7.4 Changes in Surface Water Quality
Reduced coal-fired power plant discharges may affect the value of ecosystem services
provided by surface waters through changes in the habitats or ecosystems (aquatic and
terrestrial). Society values changes in ecosystem services by a number of mechanisms, including
increased frequency of use and improved quality of the habitat for recreational activities (e.g.,
fishing, swimming, and boating). Individuals also value the protection of habitats and species
that may reside in waters that receive water discharges from coal-plants, even when those
individuals do not use or anticipate future use of such waters for recreational or other purposes,
resulting in nonuse values.
4.4.7.5 Impacts on Threatened and Endangered Species
For T&E species, even minor changes to reproductive rates and mortality levels may
represent a substantial portion of annual population variation. Therefore, changing the discharge
of coal-fired power plant pollutants to aquatic habitats has the potential to impact the
survivability of some T&E species living in these habitats. The economic value for these T&E
species primarily comes from the nonuse values people hold for the survivorship of both
individual organisms and species survival.
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4.4.7.6 Changes in Sediment Contamination
Water effluent discharges from coal-fired power plants can also contaminate waterbody
sediments. For example, sediment adsorption of arsenic, selenium, and other pollutants found in
water discharges can result in accumulation of contaminated sediment on stream and lake beds,
posing a particular threat to benthic (i.e., bottom-dwelling) organisms. These pollutants can later
be re-released into the water column and enter organisms at different trophic levels.
Concentrations of selenium and other pollutants in fish tissue of organisms of lower trophic
levels can bio-magnify through higher trophic levels, posing a threat to the food chain at large
(Ruhletal., 2012).
4.4.7.7 Reservoir Capacity and Sedimentation Changes in Navigational Waterways
Reservoirs serve many functions, including storage of drinking and irrigation water
supplies, flood control, hydropower supply, and recreation. Streams can carry sediment into
reservoirs, where it can settle and cause buildup of sediment layers over time, reducing reservoir
capacity (Graf et al., 2010, 2011) and the useful life of reservoirs unless measures such as
dredging are taken to reclaim capacity (Hargrove et al., 2010; Miranda, 2017). Likewise,
navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are prone to
reduced functionality due to sediment build-up, which can reduce the navigable depth and width
of the waterway (Ribaudo and Johansson, 2006). For many navigable waters, periodic dredging
is necessary to remove sediment and keep them passable. Dredging of reservoirs and navigable
waterways can be costly. EPA expects that changes in suspended solids effluent discharge from
coal-fired power plants could reduce sediment loadings to surface waters decreasing reservoir
and navigable waterway maintenance costs by changing the frequency or volume of dredging
activity. (Hargrove et al., 2010; Miranda, 2017). Likewise, navigable waterways, including
rivers, lakes, bays, shipping channels and harbors, are prone to reduced functionality due to
sediment build-up, which can reduce the navigable depth and width of the waterway (Ribaudo
and Johansson, 2006). For many navigable waters, periodic dredging is necessary to remove
sediment and keep them passable. Dredging of reservoirs and navigable waterways can be costly.
EPA expects that changes in suspended solids effluent discharge from coal-fired power plants
could reduce sediment loadings to surface waters decreasing reservoir and navigable waterway
maintenance costs by changing the frequency or volume of dredging activity. (Graf et al., 2010,
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2011) and the useful life of reservoirs unless measures such as dredging are taken to reclaim
capacity (Hargrove et al., 2010; Miranda, 2017). Likewise, navigable waterways, including
rivers, lakes, bays, shipping channels and harbors, are prone to reduced functionality due to
sediment build-up, which can reduce the navigable depth and width of the waterway (Ribaudo
and Johansson, 2006). For many navigable waters, periodic dredging is necessary to remove
sediment and keep them passable. Dredging of reservoirs and navigable waterways can be costly.
EPA expects that changes in suspended solids effluent discharge from coal-fired power plants
could reduce sediment loadings to surface waters decreasing reservoir and navigable waterway
maintenance costs by changing the frequency or volume of dredging activity. (Hargrove et al.,
2010; Miranda, 2017). Likewise, navigable waterways, including rivers, lakes, bays, shipping
channels and harbors, are prone to reduced functionality due to sediment build-up, which can
reduce the navigable depth and width of the waterway (Ribaudo and Johansson, 2006). For many
navigable waters, periodic dredging is necessary to remove sediment and keep them passable.
Dredging of reservoirs and navigable waterways can be costly. EPA expects that changes in
suspended solids effluent discharge from coal-fired power plants could reduce sediment loadings
to surface waters decreasing reservoir and navigable waterway maintenance costs by changing
the frequency or volume of dredging activity.
4.4.7.8 Changes in Water Withdrawals
A reduction in water withdrawals from coal-fired power plants may benefit aquatic and
riparian species downstream of the power plant intake through the provision of additional water
resources in the face of drying conditions and increased rainfall variability. Reductions in water
withdraws will also lower the number of aquatic organisms impinged and entrained by the power
plant's water filtration and cooling systems.
4.5 Total Monetized Benefits
Table 4-23 through Table 4-26 present the combined monetized climate benefits and
PM2.5 and ozone-related health benefits for the three illustrative scenarios for the four snapshot
years analyzed. Table 4-27 through Table 4-29 present the stream of annual monetized combined
climate benefits and PM2.5 and ozone-related health benefits for the three illustrative scenarios,
4-68
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as well as the present values (PVs) and equivalent annualized values (EAVs), calculated for the
2024 to 2042 timeframe.
Table 4-23 Combined Monetized Climate Benefits and PM2.5 and 03-related Health
Benefits for the Illustrative Scenarios for 2028 (billions of 2019 dollars)
Climate Benefits and PM2.5 and 03-
related Health Benefitsb
(Discount Rate Applied to Health
Benefits)
Climate Benefits
SC-CO2 Discount Rate and Statistic
Only3
3%
7%
Proposal
5% (average)
0.18
1.8
1.6
3% (average)
0.60
2.2
2.0
2.5% (average)
0.87
2.5
2.3
3% (95th percentile)
1.8
3.4
3.2
Less Stringent
5% (average)
0.16
1.3
1.2
3% (average)
0.51
1.7
1.6
2.5% (average)
0.75
1.9
1.8
3% (95th percentile)
1.5
2.7
2.6
More Stringent
5% (average)
0.0090
-0.41
-0.36
3% (average)
0.029
-0.39
-0.34
2.5% (average)
0.043
-0.37
-0.33
3% (95th percentile)
0.088
-0.33
-0.29
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3
percent discount rate).
b For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
4-69
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Table 4-24 Combined Monetized Climate Benefits and PM2.5 and 03-related Health
Benefits for the Illustrative Scenarios for 2030 (billions of 2019 dollars)
Climate Benefits and PM2.5 and 03-
related Health Benefitsb
(Discount Rate Applied to Health
Benefits)
Climate Benefits
SC-CO2 Discount Rate and Statistic
Only3
3%
7%
Proposal
5% (average)
1.7
16
14
3% (average)
5.4
20
18
2.5% (average)
7.9
22
21
3% (95th percentile)
16
31
29
Less Stringent
5% (average)
1.6
14
13
3% (average)
5.0
18
17
2.5% (average)
7.3
20
19
3% (95th percentile)
15
28
27
More Stringent
5% (average)
2.0
16
14
3% (average)
6.5
20
19
2.5% (average)
9.4
23
22
3% (95th percentile)
20
33
32
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3
percent discount rate).
b For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
4-70
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Table 4-25 Combined Monetized Climate Benefits and PM2.5 and 03-related Health
Benefits for the Illustrative Scenarios for 2035 (billions of 2019 dollars)3
Climate Benefits and PM2.5 and 03-
related Health Benefits
(Discount Rate Applied to Health
Benefits)
Climate Benefits
SC-CO2 Discount Rate and Statistic
Only3
3%
7%
Proposal
5% (average)
0.81
5.5
5.0
3% (average)
2.5
7.1
6.6
2.5% (average)
3.5
8.2
7.7
3% (95th percentile)
7.5
12
12
Less Stringent
5% (average)
0.78
4.7
4.3
3% (average)
2.4
6.3
5.9
2.5% (average)
3.4
7.3
6.9
3% (95th percentile)
7.2
11
11
More Stringent
5% (average)
0.92
5.5
5.1
3% (average)
2.8
7.4
6.9
2.5% (average)
4.0
8.6
8.1
3% (95th percentile)
8.5
13
13
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3
percent discount rate).
b For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
4-71
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Table 4-26 Combined Monetized Climate Benefits and PM2.5 and 03-related Health
Benefits for the Illustrative Scenarios for 2040 (billions of 2019 dollars)
Climate Benefits and PM2.5 and 03-
related Health Benefits3
(Discount Rate Applied to Health
Benefits)
Climate Benefits
SC-CO2 Discount Rate and Statistic
Only3
3%
7%
Proposal
5% (average)
0.59
4.2
3.8
3% (average)
1.7
5.3
4.9
2.5% (average)
2.4
6.0
5.6
3% (95th percentile)
5.3
8.8
8.5
Less Stringent
5% (average)
0.55
3.1
2.9
3% (average)
1.6
4.2
3.9
2.5% (average)
2.2
4.8
4.6
3% (95th percentile)
4.9
7.5
7.2
More Stringent
5% (average)
0.57
4.4
4.0
3% (average)
1.6
5.4
5.1
2.5% (average)
2.3
6.1
5.7
3% (95th percentile)
5.1
8.8
8.5
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3
percent discount rate).
a For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
4-72
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Table 4-27 Stream of Monetized Combined Climate Benefits and PM2.5 and Ch-related
Health Benefits for the Illustrative Proposal Scenario from 2024 through 2042 (billions of
2019 dollars)3
Values Calculated
Values Calculated
Year
using 3% Discount Rate
using 7% Discount Rate
PM2.5 and
Climate
PM2.5 and
03-related
Benefits
03-related
Climate
Health
Total
(discounted
Health
Total
Benefits
Benefits b
Benefits
at 3%) b
Benefits
Benefits
2024
-
-
-
-
-
-
2025
-
-
-
-
-
-
2026
-
-
-
-
-
-
2027
-
-
-
-
-
-
2028
0.60
1.6
2.2
0.60
1.4
2.0
2029
5.4
14
19
5.4
12
18
2030
5.4
14
20
5.4
13
18
2031
5.5
14
20
5.5
13
19
2032
2.3
4.3
6.6
2.3
3.9
6.2
2033
2.4
4.4
6.8
2.4
4.0
6.4
2034
2.4
4.5
6.9
2.4
4.1
6.5
2035
2.5
4.7
7.1
2.5
4.2
6.6
2036
2.5
4.8
7.3
2.5
4.3
6.8
2037
2.5
4.9
7.4
2.5
4.4
6.9
2038
1.7
3.4
5.1
1.7
3.1
4.7
2039
1.7
3.5
5.2
1.7
3.1
4.8
2040
1.7
3.6
5.3
1.7
3.2
4.9
2041
1.7
3.6
5.4
1.7
3.3
5.0
2042
1.8
3.7
5.5
1.8
3.3
5.1
PVd
30
68
98
30
44
74
EAVd
2.1
4.8
6.9
2.1
4.3
6.4
a Emissions impacts are not estimated for the years 2024 to 2027. As a result, the first year of benefits analysis is
2028.
b For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
0 For 7 percent PV and EAV calculations, climate benefits are discounted at 3 percent.
d The PV and EAV values in this table are for the timeframe of 2024 to 2042, not 2028 to 2042.
4-73
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Table 4-28 Stream of Monetized Combined Climate Benefits and PM2.5 and Ch-related
Health Benefits for the Illustrative Less Stringent Scenario from 2024 through 2042
(billions of 2019 dollars)3
Year
Values Calculated
using 3% Discount Rate
Values Calculated
using 7% Discount Rate
Climate
Benefits
PM2.5 and
03-related
Health
Benefits b
Total
Benefits
Climate
Benefits
(discounted
at 3%)c
PM2.5 and
03-related
Health
Benefits
Total
Benefits
2024
-
-
-
-
-
-
2025
-
-
-
-
-
-
2026
-
-
-
-
-
-
2027
-
-
-
-
-
-
2028
0.51
1.2
1.7
0.51
1.0
1.6
2029
5.0
13
17
5.0
11
16
2030
5.0
13
18
5.0
12
17
2031
5.1
13
18
5.1
12
17
2032
2.2
3.6
5.9
2.2
3.3
5.5
2033
2.3
3.7
6.0
2.3
3.4
5.6
2034
2.3
3.8
6.1
2.3
3.4
5.8
2035
2.4
3.9
6.3
2.4
3.5
5.9
2036
2.4
4.0
6.4
2.4
3.6
6.0
2037
2.4
4.1
6.5
2.4
3.7
6.1
2038
1.5
2.5
4.0
1.5
2.2
3.8
2039
1.6
2.5
4.1
1.6
2.3
3.8
2040
1.6
2.6
4.2
1.6
2.3
3.9
2041
1.6
2.6
4.2
1.6
2.4
4.0
2042
1.6
2.7
4.3
1.6
2.4
4.0
PVd
28
58
87
28
38
66
EAVd
2.0
4.1
6.0
2.0
3.7
5.7
a Emissions impacts are not estimated for the years 2024 to 2027. As a result, the first year of benefits analysis is
2028.
b For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
0 For 7 percent PV and EAV calculations, climate benefits are discounted at 3 percent.
d The PV and EAV values in this table are for the timeframe of 2024 to 2042, not 2028 to 2042.
4-74
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Table 4-29 Stream of Monetized Combined Climate Benefits and PM2.5 and Ch-related
Health Benefits for the Illustrative More Stringent Scenario from 2024 through 2042
(billions of 2019 dollars)3
Year
Values Calculated
using 3% Discount Rate
Values Calculated
using 7% Discount Rate
Climate
Benefits
PM2.5 and
03-related
Health
Benefits b
Total
Benefits
Climate
Benefits
(discounted
at 3%)c
PM2.5 and
03-related
Health
Benefits
Total
Benefits
2024
-
-
-
-
-
-
2025
-
-
-
-
-
-
2026
-
-
-
-
-
-
2027
-
-
-
-
-
-
2028
0.029
-0.42
-0.39
0.029
-0.37
-0.34
2029
6.4
13
20
6.4
12
18
2030
6.5
14
20
6.5
12
19
2031
6.6
14
20
6.6
12
19
2032
2.6
4.3
6.9
2.6
3.8
6.5
2033
2.7
4.4
7.1
2.7
3.9
6.6
2034
2.7
4.5
7.2
2.7
4.0
6.8
2035
2.8
4.6
7.4
2.8
4.1
6.9
2036
2.8
4.7
7.5
2.8
4.2
7.1
2037
2.9
4.5
7.4
2.9
4.1
7.0
2038
1.6
3.6
5.2
1.6
3.3
4.9
2039
1.6
3.7
5.3
1.6
3.3
5.0
2040
1.6
3.8
5.4
1.6
3.4
5.1
2041
1.7
3.9
5.5
1.7
3.5
5.1
2042
1.7
3.9
5.6
1.7
3.5
5.2
PVd
34
65
99
34
42
76
EAVd
2.4
4.6
6.9
2.4
4.0
6.4
a Emissions impacts are not estimated for the years 2024 to 2027. As a result, the first year of benefits analysis is
2028.
b For simplicity of presentation, the estimated value of the health benefits reported here are the larger of the two
benefits estimates presented in Table 4-15 through Table 4-19. Monetized benefits include those related to public
health associated with reductions in PM2 5 and ozone concentrations. The health benefits are associated with several
point estimates.
0 For 7 percent PV and EAV calculations, climate benefits are discounted at 3 percent.
d The PV and EAV values in this table are for the timeframe of 2024 to 2042, not 2028 to 2042.
4-75
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5 ECONOMIC IMPACT ANALYSIS
This section discusses potential energy and economic impacts, impacts on small entities,
and labor impacts associated with this proposed rulemaking.116 For additional discussion of
impacts on fuel use and electricity prices, see Section 3.
5.1 Energy Market Impacts
The energy sector impacts presented in Section 3 of this RIA include potential changes in
the prices for electricity, natural gas, and coal resulting from the proposed requirements. This
section addresses the impact of these potential changes on other markets and discusses some of
the determinants of the magnitude of these potential impacts. We refer to these changes as
secondary market impacts. Under these proposed emission guidelines for existing fossil-fuel
fired steam generating units, coal-fired EGUs are not directly required to use any of the measures
that EPA determines constitute BSER. Rather, CAA section 111(d) allows each state in applying
standards of performance based on the BSER candidate technologies to take into account
remaining useful life and other factors. Given the flexibility afforded states in implementing the
emission guidelines under 111(d) and the flexibilities coal-fired EGUs have in complying with
the subsequent, state-established emission standards, the potential economic impacts of the
illustrative scenarios reported in this RIA are necessarily illustrative of actions that states and
affected EGUs may take. The implementation approaches adopted by the states, and the
strategies adopted by affected EGUs, will ultimately drive the magnitude and timing of
secondary impacts from changes in the price of electricity, and the demand for inputs by the
electricity sector, on other markets that use and produce these inputs.
To estimate the impacts of the proposed rules, EPA modeled an illustrative proposal
scenario, as described in Section 1 and Section 3. This section provides a quantitative assessment
of the energy price impacts for the illustrative proposal scenario and qualitative assessment of the
factors that will in part determine the timing and magnitude of potential effects in other markets.
116 Section 5 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing coal-
fired EGUs. Please see Section 8 for impact analysis of the proposed standards for existing natural gas-fired
EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
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Table 5-1 summarizes projected changes in energy prices and fuel use resulting from the
illustrative proposal scenario.
Table 5-1 Summary of Certain Energy Market Impacts (percent change)
2028
2030
2035
2040
Retail electricity prices
-1%
2%
0%
0%
Average price of coal delivered to the power sector
-1%
0%
2%
2%
Coal production for power sector use
-2%
-40%
-23%
-15%
Price of natural gas delivered to power sector
0%
9%
-2%
-3%
Price of average Henry Hub (spot)
0%
10%
-2%
-2%
Natural gas use for electricity generation
0%
8%
-1%
-2%
Note: Positive values indicate increases relative to the baseline.
To provide some historical context to Table 5-1, we present below recent trends observed
over the last decade (2011 to 2021) for the energy market impacts listed:117
• The annual percent change in real electricity price over this period has been from -2.4
percent to 1.8 percent and averaged -0.8 percent.
• The percent change to the real annual price of coal for electricity generation has ranged
from -7.3 percent to 3.1 percent over the past decade and averaged -3 percent.
• The percent change to annual coal use for electricity plants has ranged from -19 percent
to 15 percent over the past decade and averaged -5.4 percent.
• The percent change to the average cost of natural gas for electricity generation has
ranged from -36 percent to 108 percent over the past decade and averaged 3.6 percent.
• The percent change to annual natural gas use for electricity plants has ranged from -33.2
percent to 35.9 percent over the past decade and averaged -3.3 percent.
Overall, these projected changes are within the range of recent historical changes.
The projected energy market and electricity retail rate impacts of the proposed rules are
discussed more extensively in Section 3, which also presents projections of power sector
generation and capacity changes by technology and fuel type. The change in wholesale energy
prices and the changes in power generation were forecasted using IPM. The change in retail
electricity prices reported in Chapter 3 is a national average across residential, commercial, and
117 EIA. Electric Power Annual 2021 and 2022, available at: https://www.eia.gov/electricity/annual/
5-2
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industrial consumers. The change in electricity retail prices and bills were forecasted using
outputs of IPM.
5.2 Social Costs
As discussed in EPA's Guidelines for Preparing Economic Analyses, social costs are the
total economic burden of a regulatory action guidelines (U.S. EPA, 2014). This burden is the
sum of all opportunity costs incurred due to the regulatory action, where an opportunity cost is
the value lost to society of any goods and services that will not be produced and consumed
because of reallocating some resources towards pollution mitigation. Estimates of social costs
may be compared to the social benefits expected because of a regulation to assess its net impact
on society. The social costs of these rules will not necessarily be equal to the expenditures by the
electricity sector and other affected industries to comply with the proposed requirements. As
described is Section 3 above, these compliance costs are primarily calculated using the
IPM. Table 3-7 above presents the total annual estimated compliance costs for EGUs for 2024 to
2042.
The compliance cost estimates from IPM for the proposed rules are the change in
expenditures by the power sector to achieve and maintain compliance under each alternative. The
production cost changes include changes in fuel expenditures. IPM solves for the least-cost
approach to meet new regulatory requirements in the electricity sector with highly detailed
information on electricity generation and air pollution control technologies and primary energy
sector market conditions (coal and natural gas) while meeting fixed electricity demands,
regulatory requirements, resource adequacy, and other constraints. However, potential effects
outside of the electricity, coal and natural gas sectors are not evaluated within IPM. The
estimated compliance costs do not equal social costs because they do not include a complete
accounting of transfers and effects in other sectors of the economy.
More broadly, changes in production in a directly regulated sector may have effects on
other markets when output from that sector - for this rule electricity - is used as an input in the
production of other goods. It may also affect upstream industries that supply goods and services
to the sector, along with labor and capital markets, as these suppliers alter production processes
in response to changes in factor prices. In addition, households may change their demand for
5-3
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particular goods and services due to changes in the price of electricity and other final goods
prices.
Changes in the behavior of firms and households in response to the proposed rules could
also interact with pre-existing distortions in the economy, such as taxes, resulting in additional
social costs. In addition, the IRA provides investment, production, and fuel subsidies (i.e.,
ITC/PTC, 45Q and 45V) that are targeted to specific technologies EPA expects will be adopted
to comply with regulatory requirements of these proposed rules. When modeling compliance
with the proposed rules, IPM attempts to account for IRA subsidies in private technology
adoption decisions in the electricity sector. See the IPM Documentation and Section 3 for further
discussion of IRA representation in IPM, fuel and technology cost assumptions, and related
uncertainties. While IPM estimates compliance costs incurred by the regulated firms, subsidy
payments also represent real resource costs to the economy outside of the regulated sector. Thus,
an economy-wide modeling approach would be necessary to account for changes in subsidy
payments and associated social costs.
Economy-wide models—and, more specifically, computable general equilibrium (CGE)
models—are analytical tools that can be used to evaluate the broad impacts of a regulatory
action. A CGE-based approach to cost estimation concurrently considers the effect of a
regulation across all sectors in the economy. In 2015, EPA established a Science Advisory Board
(SAB) panel to consider the technical merits and challenges of using economy-wide models to
evaluate costs, benefits, and economic impacts in regulatory analysis. In its final report, the SAB
recommended that EPA begin to integrate CGE modeling into applicable regulatory analysis to
offer a more comprehensive assessment of the effects of air regulations (U.S. EPA Science
Advisory Board, 2017). In response to the SAB's recommendations, EPA developed a new CGE
model called SAGE designed for use in regulatory analysis. A second SAB panel performed a
peer review of SAGE, and the review concluded in 2020 (U.S. EPA Science Advisory Board,
2020). EPA used SAGE to evaluate potential economy-wide impacts of these proposed rules
while accounting for IRA subsidies to technologies being used for compliance. This analysis is
presented in Appendix B of the RIA. In section XIV(C) of the preamble to this proposed rule,
EPA solicits comment on the SAGE analysis presented in appendix B.
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5.3 Small Entity Analysis
5.3.1 Overview
For the proposed rules, EPA performed a small entity screening analysis for impacts on
all affected EGUs by comparing compliance costs to historic revenues at the ultimate parent
company level. This is known as the cost-to-revenue or cost-to-sales test, or the "sales test." The
sales test is an impact methodology EPA employs in analyzing entity impacts as opposed to a
"profits test," in which annualized compliance costs are calculated as a share of profits. The sales
test is frequently used because revenues or sales data are commonly available for entities
impacted by EPA regulations, and profits data normally made available are often not the true
profit earned by firms because of accounting and tax considerations. Also, the use of a sales test
for estimating small business impacts for a rulemaking is consistent with guidance offered by
EPA on compliance with the Regulatory Flexibility Act (RFA)"8 and is consistent with guidance
published by the U.S. Small Business Administration's (SBA) Office of Advocacy that suggests
that cost as a percentage of total revenues is a metric for evaluating cost increases on small
entities in relation to increases on large entities.119
5.3.2 EGU Small Entity Analysis and Results
This section presents the methodology and results for estimating the impact of the New
Source Performance Standards for Greenhouse Gas Emissions from New, Modified, and
Reconstructed Fossil Fuel-Fired Electric Generating Units on small EGU entities in 2035 based
on the following endpoints:
• annual economic impacts of the proposal on small entities, and
• ratio of small entity impacts to revenues from electricity generation.
This rule would affect the buildout and operation of future NGCC and NGCT additions.
Costs are projected to peak in 2035, which is consistent with the imposition of the second phase
118 The RFA compliance guidance to EPA rule writers can be found at
119 See U.S. SBA Office of Advocacy. (2017). A Guide For Government Agencies: How To Comply With The
Regulatory Flexibility Act. Available at: https://advocacy.sba.gOv/2017/08/31/a-guide-for-government-agencies-
how-to-comply-with-the-regulatory-flexibility-act
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of the NSPS requirements on new NGCC builds, and as such, the analysis focuses on this year.
While IPM can provide important information about the future operation and addition of natural
gas capacity over the analysis period, the model does not project actions taken by individual
firms. Hence, as a proxy for the future gas capacity built by small entities EPA assumed that the
same small entities identified using the process outlined below would continue to build the same
share of future capacity additions projected by IPM over the forecast period. EPA reviewed
historical data and planned builds since 2017 to determine the universe of NGCC and NGCT
additions as outlined in EPA National Electric Energy Data System (NEEDS) v.6 database.
Based on these criteria, EPA identified a total of 53 GW of NGCC and 7 GW of NGCT
built since 2017. Next, we determined power plant ownership information, including the name of
associated owning entities, ownership shares, and each entity's type of ownership. Ownership
information for these assets was obtained primarily using data from Ventyx120, supplemented by
research using S&P121 and publicly available data.
Majority owners of power plants with affected EGUs were categorized as one of the
seven ownership types.122 These ownership types are:
1. Investor-Owned Utility (IOU): Investor-owned assets (e.g., a marketer, independent
power producer, financial entity) and electric companies owned by stockholders, etc.
2. Cooperative (Co-Op): Non-profit, customer-owned electric companies that generate
and/or distribute electric power.
3. Municipal: A municipal utility, responsible for power supply and distribution in a small
region, such as a city.
4. Sub-division: Political subdivision utility is a county, municipality, school district,
hospital district, or any other political subdivision that is not classified as a municipality
under state law.
5. Private: Similar to an investor-owned utility, however, ownership shares are not openly
traded on the stock markets.
6. State: Utility owned by the state.
7. Federal: Utility owned by the federal government.
120 The Ventyx Energy Velocity Suite database consists of detailed ownership and corporate affiliation information
at the EGU level. For more information, see: www.ventyx.com.
121 The S&P database consists of detailed ownership and corporate affiliation information at the EGU level. For
more information, see: www.capitaliq.spglobal.com
122 Throughout this analysis, EPA refers to the owner with the largest ownership share as the "majority owner" even
when the ownership share is less than 51 percent.
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Next, EPA used the D&B Hoover's online database, the Ventyx database, and the S&P
database to identify the ultimate owners of power plant owners identified in the NEEDS
database. This was necessary, as many majority owners of power plants (listed in Ventyx) are
themselves owned by other ultimate parent entities (listed in D&B Hoover's).123 In these cases,
the ultimate parent entity was identified via D&B Hoover's, whether domestically or
internationally owned.
EPA followed SB A size standards to determine which non-government ultimate parent
entities should be considered small entities in this analysis. These SBA size standards are
specific to each industry, each having a threshold level of either employees, revenue, or assets
below which an entity is considered small.124 SBA guidelines list all industries, along with their
associated North American Industry Classification System (NAICS) code125 and SBA size
standard. Therefore, it was necessary to identify the specific NAICS code associated with each
ultimate parent entity in order to understand the appropriate size standard to apply. Data from
D&B Hoover's was used to identify the NAICS codes for most of the ultimate parent entities. In
many cases, an entity that is a majority owner of a power plant is itself owned by an ultimate
parent entity with a primary business other than electric power generation. Therefore, it was
necessary to consider SBA entity size guidelines for the range of NAICS codes listed in Table
5-2. This table represents the range of NAICS codes and areas of primary business of ultimate
parent entities that are majority owners of potentially affected EGUs in the historical record.
123 The D&B Hoover's online platform includes company records that can contain NAICS codes, number of
employees, revenues, and assets. For more information, see: https://www.dnb.com/products/marketing-
sales/dnb-hoovers.html.
124 SBA's table of size standards can be located here: https://www.sba.gov/document/support-table-size-standards.
125 North American Industry Classification System can be accessed at the following link:
https://www.census.gov/naics/
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Table 5-2 SBA Size Standards by NAICS Code
NAICS Codes
NAICS U.S. Industry Title
Size
Standards
(millions of
dollars)
Size
Standards
(number of
employees)
221111
Hydroelectric Power Generation
500
221112
Fossil Fuel Electric Power Generation
750
221113
Nuclear Electric Power Generation
750
221114
Solar Electric Power Generation
250
221115
Wind Electric Power Generation
250
221116
Geothermal Electric Power Generation
250
221117
Biomass Electric Power Generation
250
221118
Other Electric Power Generation
250
221121
Electric Bulk Power Transmission and Control
500
221122
Electric Power Distribution
1,000
221210
Natural Gas Distribution
1,000
221310
Water Supply and Irrigation Systems
$41.0
221320
Sewage Treatment Facilities
$35.0
221330
Steam and Air-Conditioning Supply
$30.0
Note: Based on size standards effective at the time EPA conducted this analysis (SBA size standards, effective
December 19, 2022. Available at the following link: https://www.sba.gov/document/support--table-size-standards).
Source: SBA, 2022
EPA compared the relevant entity size criterion for each ultimate parent entity to the SBA
size standard noted in Table 5-2. We used the following data sources and methodology to
estimate the relevant size criterion values for each ultimate parent entity:
1. Employment, Revenue, and Assets: EPA used the D&B Hoover's database as the
primary source for information on ultimate parent entity employee numbers, revenue, and
assets.126 In parallel, EPA also considered estimated revenues from affected EGUs based
on analysis of IPM estimates for the baseline for 2035. EPA assumed that the ultimate
parent entity revenue was the larger of the two revenue estimates. In limited instances,
supplemental research was also conducted to estimate an ultimate parent entity's number
of employees, revenue, or assets.
2. Population: Municipal entities are defined as small if they serve populations of less than
50,000.127 EPA primarily relied on data from the Ventyx database and the U.S. Census
Bureau to inform this determination.
126 Estimates of sales were used in lieu of revenue estimates when revenue data was unavailable.
127 The Regulatory Flexibility Act defines a small government jurisdiction as the government of a city, county,
town, township, village, school district, or special district with a population of less than 50,000
(5 U.S.C. section 601(5)). For the purposes of the RFA, States and tribal governments are not
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Ultimate parent entities for which the relevant measure is less than the SBA size standard
were identified as small entities and carried forward in this analysis. Using this analysis, EPA
identified 8 percent of the NGCC and 10 percent of the NGCT additions over the historical
period were attributed to small entities as summarized in Table 5-3 below.
Table 5-3 Historical NGCC and NGCT Additions (2017-present)
Capacity Type
Total Additions
(GW)
Total Additions by Small
Entities (GW)
Share of Small Entities to
Total Build (%)
NGCC
52.8
4.4
8%
NGCT
7.2
0.7
10%
In 2035, a new NGCC addition can comply with the proposed rule by implementing
efficiency improvements (if it operates at an annual capacity factor of below 50 percent), co-
firing hydrogen, or installing CCS. A new NGCT addition can comply with the proposed rule
through implementing efficiency improvements (if it operates at an annual capacity factor of
below 20 percent) or co-firing hydrogen. The chosen compliance strategy will be primarily a
function of the unit's marginal control costs and its position relative to the marginal control costs
of other units.
To attempt to account for each potential control strategy, EPA estimates compliance costs
as follows:
CCompliance A COperating+Retrofit A CFuel A R
where C represents a component of cost as labeled128, and A R represents the change in revenues,
calculated as the difference in value of electricity generation between the baseline case and the
rule in in 2035 for projected NGCC and NGCT additions (calculated separately), when the
second phase of the NSPS is assumed to be active under the proposal.
Realistically, compliance choices and market conditions can combine such that an entity
may actually experience a reduction in any of the individual components of cost. Under the rule,
considered small governments. EPA's Final Guidance for EPA Rulewriters: Regulatory Flexibility Act is located
here: https://www.epa.gov/sites/default/files/2015-06/documents/guidance-regflexact.pdf.
128 Retrofit costs include the costs of installation of CCS.
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some units will generate less electricity (and thus revenues), and this impact will be lessened on
these entities by the projected increase in electricity prices under the rule. On the other hand,
those units increasing generation levels will see an increase in electricity revenues and as a
result, lower net compliance costs. If entities are able to increase revenue more than an increase
in fuel cost and other operating costs, ultimately, they will have negative net compliance costs
(or increased profit). Because this analysis evaluates the total costs along each of the compliance
strategies laid out above for each entity, it inevitably captures gains such as those described. As a
result, what we describe as cost is a measure of the net economic impact of the rule on small
entities.
For this analysis, EPA used IPM output to estimate costs based on the parameters above,
at the unit level. These impacts were then summed for each small entity, adjusting for ownership
share. Net impact estimates were based on the following: operating and retrofit costs, and the
change in fuel costs or electricity generation revenues under the proposed rule relative to the
baseline. These individual components of compliance costs were estimated as follows:
1. Operating and retrofit costs (A Coperamg+Retrofu): The change in operating and retrofit
costs under the proposed rule was estimated by taking the difference in projected FOM,
VOM and retrofit capital expenditures between the IPM estimates for the proposed rule
and the baseline for the NGCT and NGCC additions projected by the model.
2. Fuel costs (A CFuei): The change in fuel expenditures under the proposed rule was
estimated by taking the difference in projected fuel expenditures between the IPM
estimates for the proposed rule and the baseline for the NGCT and NGCC additions
projected by the model.
3. Revenue: To estimate the value of electricity generated, the projected level of electricity
generation is multiplied by the regional wholesale electricity price ($/MWh) projected by
IPM, and the accredited capacity multiplied by the projected regional capacity price
projected by IPM for the NGCT and NGCC additions projected by the model. The
difference between this value under the baseline and the proposed rule constitutes the
estimated change in revenue.
Once the costs of the rule were calculated in the manner described above, the costs
attributed to small entities were calculated by multiplying the total costs to the share of the
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historical build attributed to small entities. These costs were then shared to individual entities
using the ratio of their build to total small entity additions in the historical dataset.
Under the compliance modeling for the proposal, NGCT additions and dispatch are
higher as a result of reductions in existing coal-fired EGU capacity and generation. As a result,
economic NGCT additions experience negative compliance costs in 2035. Under the compliance
modeling for the proposal, economic NGCC additions dispatch at lower levels relative to the
baseline when the second phase of the NSPS is active. As such, they experience positive
compliance costs.
As indicated above, the use of a sales test for estimating small business impacts for a
rulemaking is consistent with guidance offered by EPA on compliance with the RFA and is
consistent with guidance published by the SBA's Office of Advocacy that suggests that cost as a
percentage of total revenues is a metric for evaluating cost increases on small entities in relation
to increases on large entities. The potential impacts, including compliance costs, of the proposed
rule on NGCCs owned by small entities are summarized in Table 5-4. All costs are presented in
2019 dollars. EPA estimated the annual net compliance cost to small entities to be approximately
$13 million in 2035.
Table 5-4 Projected Impact of the Proposed Rule on Small Entities in 2035
EGU
Total Net
Number of Small Entities with
Ownership
Number of Potentially
Compliance Cost
Compliance Costs >=1% of Generation
Type
Affected Entities
($2019 millions)
Revenues
Municipal
0
0
0
Private
6
11
0
Co-op
1
2
0
Total
7
13
0
Source: IPM analysis
EPA assessed the economic and financial impacts of the rule using the ratio of
compliance costs to the value of revenues from electricity generation, focusing in particular on
entities for which this measure is greater than 1 percent. Of the 7 entities that own NGCC units
considered in this analysis, none are projected to experience compliance costs greater than or
equal to 1 percent of generation revenues in 2035.
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5.4 Labor Impacts
This section discusses potential employment impacts of this regulation. As economic
activity shifts in response to a regulation, typically there will be a mix of declines and gains in
employment in different parts of the economy over time and across regions. To present a
complete picture, an employment impact analysis will describe the potential positive and
negative changes in employment levels. There are significant challenges when trying to evaluate
the employment effects of an environmental regulation due to a wide variety of other economic
changes that can affect employment, including the impact of the coronavirus pandemic on labor
markets and the state of the macroeconomy generally. Considering these challenges, we look to
the economics literature to provide a constructive framework and empirical evidence. We focus
on impacts on labor demand related to compliance behavior. Environmental regulation may also
affect labor supply through changes in worker health and productivity (Zivin and Neidell, 2018).
Economic theory of labor demand indicates that employers affected by environmental
regulation may increase their demand for some types of labor, decrease demand for other types,
or for still other types, not change their demand at all (Berman and Bui, 2001; Deschenes, 2018;
Morgenstern et al., 2002). To study labor demand impacts empirically, a growing literature has
compared employment levels at facilities subject to an environmental regulation to employment
levels at similar facilities not subject to that environmental regulation; some studies find no
employment effects, and others find significant differences. For example, see Berman and Bui
(2001), Curtis (2018, 2020), Deschenes (2018), Ferris et al. (2014), Greenstone (2002), and
Morgenstern et al. (2002), Greenstone (2002).
A variety of conditions can affect employment impacts of environmental regulation,
including baseline labor market conditions and employer and worker characteristics such as
occupation and industry. Changes in employment may also occur in different sectors related to
the regulated industry, both upstream and downstream, or in sectors producing substitute or
complimentary products. We focus our labor impacts analysis primarily on the directly regulated
facilities, with an extension to other EGUs and related fuel markets.
This section discusses and projects potential employment impacts for the utility power,
coal and natural gas production sectors that may result from the proposed rule. EPA has a long
history of analyzing the potential impacts of air pollution regulations on changes in the amount
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of labor needed in the power generation sector and closely related sectors. The analysis
conducted for this RIA builds upon the approaches used in the past and takes advantage of newly
available data to improve the assumptions and methodology.129
The results presented in this section are based on a methodology that estimates
employment impacts based on differences in projections between two modeling scenarios: the
baseline scenario, and a scenario that represents the implementation of the rule. The estimated
employment difference between these scenarios can be interpreted as the incremental effect of
the rule. As discussed in Section 3, there is uncertainty related to the future baseline projections.
Note that there is also uncertainty related to the employment factors applied in this analysis,
particularly factors informing job-years related to relatively new technologies, such as energy
storage, on which there is limited data to base assumptions.
Like previous analyses, this analysis represents an evaluation of "first-order employment
impacts" using a partial equilibrium modeling approach. It includes some of the potential ripple
effects of these impacts on the broader economy. These potential ripple effects include the
secondary job impacts in both upstream and downstream sectors. While the analysis includes
impacts on upstream sectors including coal, natural gas, and uranium, it does not analyze impacts
on other fuel sectors, nor does it analyze potential impacts related to transmission or distribution.
This approach excludes the economy-wide employment effects of changes to energy markets
(such as higher or lower forecasted electricity prices). This approach also excludes labor impacts
that are sometimes reflected in a benefits analysis for an environmental policy, such as increased
productivity from a healthier workforce and reduced absenteeism due to fewer sick days of
employees and dependent family members (e.g., children).
5.4.1 Overview of Methodology
The methodology includes the following two general approaches, based on the available
data. The first approach utilizes the rich employment data that is available for several types of
generation technologies in the 2020 U.S. Energy and Employment Report.130 Detailed
employment inventory data is available regarding recent employment related to coal, hydro,
129 For a detailed overview of this methodology, including all underlying assumptions, see the U.S. EPA
Methodology for Power Sector-Specific Employment Analysis, available in the docket.
130 https://www.usenergyjobs.org/
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natural gas, geothermal, wind, and solar generation technologies. The data enables the creation
of technology-specific factors that can be applied to model projections of capacity (reported in
megawatts, or MW) and generation (reported in megawatt-hours, or MWh) in order to estimate
impacts on employment. Since employment data is only available in aggregate by fuel type, it is
necessary to disaggregate by labor type in order to differentiate between types of jobs or tasks for
categories of workers. For example, some types of employment remain constant throughout the
year and are largely a function of the size of a generator, e.g., fixed operation and maintenance
activities, while others are variable and are related to the amount of electricity produced by the
generator, e.g., variable operation and maintenance activities. The approach can be summarized
in three basic steps:
• Quantify the total number of employees by fuel type in a given year;
• Estimate total fixed operating & maintenance (FOM), variable operating & maintenance
(VOM), and capital expenditures by fuel type in that year; and
• Disaggregate total employees into three expenditure-based groups and develop factors for
each group (FTE/MWh, FTE/MW-year, FTE/MW new capacity).
For employment related to electric power generation other than coal, hydro, natural gas,
geothermal, wind and solar, as well as employment required by pollution control technologies,
detailed employment data is not available. Thus, EPA implements a second approach that utilizes
information available in the U.S. Economic Census. These data are used to estimate labor
impacts using labor intensity ratios. These factors provide a relationship between employment
and economic output and are used to estimate employment impacts related to construction and
operation of pollution control retrofits, as well as some types of electric generation technologies.
For a detailed overview of this methodology, including all underlying assumptions and
the types of employment represented by this analysis, see the U.S. EPA Methodology for Power
Sector-Specific Employment Analysis, available in the docket.
5.4.2 Overview of Power Sector Employment
In this section we focus on employment related to electric power generation, as well as
coal and natural gas extraction because these are the segments of the power sector with available
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data that are relevant to the projected impacts of the rule. Other segments not discussed here
include the extraction or production of other fuels (e.g., hydrogen), energy efficiency, and
transmission, distribution, and storage. The statistics presented here are based on the 2020
USEER, which reports data from 2019.131
In 2019, the electric power generation sector employed nearly 900,000 people. Relative to
2018, this sector grew by over 2 percent, despite job losses related to nuclear and coal generation
which were offset by increases in employment related to other generating technologies, including
natural gas, solar, and wind. The largest component of total 2019 employment in this sector is
construction (33 percent). Other components of the electric power generation workforce include
utility workers (20 percent), professional and business service employees (20 percent),
manufacturing (13 percent), wholesale trade (8 percent), and other (5 percent). In 2019, jobs
related to solar and wind generation represent 31 percent and 14 percent of total jobs,
respectively, and jobs related to coal generation represent 10 percent of total employment.
In addition to generation-related employment we also look at employment related to coal
and natural gas in the electric power sector. In 2019, the coal industry employed about 75,000
workers. Mining and extraction jobs represent the vast majority of total coal-related employment
in 2019 (74 percent). The natural gas fuel sector employed about 276,000 employees in 2019.
About 60 percent of those jobs were related to mining and extraction.
5.4.3 Projected Sectoral Employment Changes due to the Proposed Rule
Electric generating units subject to these proposed rules will use various GHG mitigation
measures to comply. Under the modeling of the proposal, 16 GW of coal and gas capacity is
estimated to install CCS (similar to the baseline), 1 GW of coal-fired EGUs are projected to co-
fire natural gas, and 21 GW of coal-fired capacity undertake coal to gas conversion (9 GW
incremental to the baseline) in 2030. By 2030, the proposal is projected to result in an additional
1 GW of coal retirements, by 2035 an incremental 23 GW of coal retirements and by 2040, an
incremental 18 GW of coal retirements relative to the baseline. Under the proposal in 2035 the
131 While 2020 data is available in the 2021 version of this report, this section of the RIA utilizes 2019 data because
this year does not reflect any short-term trends related to the COVID-19 pandemic. The annual report is available
at: https://www.usenergyjobs.org/.
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modeling projects an incremental 1 GW of NGCC, and an incremental 23 GW of NGCT
additions relative to the baseline. Eleven GW of natural gas capacity is projected to co-fire with
hydrogen by 2035. Two GW of incremental wind and solar additions and are also projected to
occur relative to the baseline.
Based on these power sector modeling projections, we estimate an increase of over 9,000
construction-related job-years related to the installation of new pollution controls under the rule
in 2030. In 2035, we estimate a decrease in construction-related job-years associated with
pollution controls because some of those controls are projected to be built earlier under the rule,
and some of that controlled capacity is projected to retire. We estimate a decrease of
approximately 44,800 job-years in 2028 related to the construction of new capacity in that year,
and an increase of approximately 51,100 construction-related job-years in 2030. In 2035 and
2040, we estimate an increase of 9,300 construction-related job-years and a decrease of 17,300
construction-related job-years, respectively. The relatively large near-term decrease followed by
a relatively large increase and subsequent increase and decrease results primarily from relatively
small temporal changes in the projected deployment of battery storage capacity in the modeling.
The employment factors related to battery storage are relatively high, and, as a relatively new
technology on which there is limited data to base assumptions, these factors are uncertain. The
projected decrease in battery storage is related to the proposed new source standard, which is
projected to generally result in a large increase in new NGCT capacity and a small decrease in
new storage capacity. Without including battery storage in the total estimate, we would estimate
increases in 2028, 2030, and 2035 of 12,000, 600, and 43,000 job-years, respectively, related to
the construction of new capacity in those years, and a decrease of 19,000 job-years in 2040.
Construction-related job-year changes are one-time impacts, occurring during each year
of the multi-year periods during which construction of new capacity is completed. Construction-
related figures in Table 5-5 represent a point estimate of incremental changes in construction
jobs for each year (for a three-year construction projection, this table presents one-third of the
total j obs for that proj ect).
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Table 5-5 Changes in Labor Utilization: Construction-Related (number of job-years of
employment in a single year)
2028
2030
2035
2040
New Pollution Controls
-300
9,300
-3,300
100
New Capacity
-44,800
51,100
9,300
-17,300
Notes: A large share of the construction-related job years is attributable to construction of energy storage, a
relatively new technology on which there is limited data to base labor assumptions.
We also estimate changes in the number of job-years related to recurring non-
construction employment. Recurring employment changes are job-years associated with annual
recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly
built generating capacity creates a recurring stream of positive job-years, while retiring
generating capacity, as well as avoided capacity builds, create a stream of negative job-years.
The rule is projected to result, generally, in a replacement of relatively labor-intensive coal
capacity with less labor-intensive capacity, which results in an overall decrease of non-
construction jobs over 2030 to 2040. The total net estimated decrease in recurring employment is
about 25,000 job-years over 2028 to 2040, which is a small percentage of total 2019 power
sector employment reported in the 2020 USEER (approximately 900,000 generation-related jobs,
75,000 coal-related jobs, and 276,000 natural gas-related jobs). Table 5-6 provide detailed
estimates of recurring non-construction employment changes.
Table 5-6 Changes in Labor Utilization: Recurring Non-Construction (number of job-
years of employment in a single year)
2028
2030
2035
2040
Pollution Controls
<100
-300
<100
<100
Existing Capacity
-500
-18,000
-8,000
-6,000
New Capacity
1,100
1,500
4,400
3,300
Fuels (Coal, Natural Gas, Uranium)
-200
-700
-900
-600
Coal
-400
-3,200
-800
-100
Natural Gas
100
2,600
-100
-500
Uranium
<100
<100
<100
<100
Note: "<100" denotes an increase or decrease of less than 100 job-years; Numbers may not sum due to rounding
5.4.4 Conclusions
Generally, there are significant challenges when trying to evaluate the employment effects
due to an environmental regulation from employment effects due to a wide variety of other
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economic changes, including the impact of the coronavirus pandemic, on labor markets and the
state of the macroeconomy generally. For EGUs, this proposed rule may result in increases and
decreases over time of construction-related jobs related to the installation of new pollution
controls and construction of new capacity. The rule is also projected to result, generally, in a
replacement of relatively labor-intensive coal capacity with less labor-intensive capacity, which
results in an overall decrease of non-construction jobs.
It is important to note that this analysis does not include any estimates of the employment
gains likely to result from the expected increase in hydrogen production, distribution, or use at
EGUs. Furthermore, this analysis does not estimate the employment gains likely to result from
the expected development and construction of new transmission and distribution capacity
throughout the U.S.
Speaking generally, a variety of federal programs are available to invest in communities
potentially affected by coal mine and coal power plant closures. An initial report by The
Interagency Working Group on Coal and Power Plant Communities and Economic
Revitalization (April 2021) identifies funding available to invest in such "energy communities"
through existing programs from agencies including Department of Energy, Department of
Treasury, Department of Labor and others.132 The Inflation Reduction Act also provides
numerous incentives to encourage investment in communities affected by coal mine and coal
power plant closures and, more broadly, communities whose economies are more-reliant on
fossil fuels.133
5.5 References
Berman, E., & Bui, L. T. M. (2001). Environmental regulation and labor demand: evidence from
the South Coast Air Basin. Journal of Public Economics, 79(2), 265-295.
doi: https ://doi. org/10.1016/S0047-2727(99)00101 -2
132 See "Initial Report to the President on Empowering Workers Through Revitalizing Energy Communities" April
2021 at https://energycommunities.gov/wp-content/uploads/2021/11/Initial-Report-on-Energy-
Communities_Apr2021 .pdf
133 For more details see Congressional Research Service. "Inflation Reduction Act of 2022 (IRA): Provisions
Related to Climate Change" October 3, 2022 at https://crsreports.congress.gOv/product/pdf/R/R47262
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Curtis, E. M. (2018). Who Loses under Cap-and-Trade Programs? The Labor Market Effects of
the NOx Budget Trading Program. The Review of Economics and Statistics, 700(1), 151-
166. doi: 10.1162/REST_a_00680
Curtis, E. M. (2020). Reevaluating the ozone nonattainment standards: Evidence from the 2004
expansion. Journal of Environmental Economics and Management, 99, 102261.
doi: 10.1016/j.jeem.2019.102261
Deschenes, O. (2018). Environmental regulations and labor markets. IZA World of Labor, 22.
doi:10.15185/izawol.22.v2
Ferris, A. E., Shadbegian, R. J., & Wolverton, A. (2014). The Effect of Environmental
Regulation on Power Sector Employment: Phase I of the Title IV S02 Trading Program.
Journal of the Association of Environmental and Resource Economists, 7(4), 521-553.
doi: 10.1086/679301
Greenstone, M. (2002). The Impacts of Environmental Regulations on Industrial Activity:
Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of
Manufactures. Journal of Political Economy, 770(6), 1175-1219. doi: 10.1086/342808
Morgenstern, R. D., Pizer, W. A., & Shih, J.-S. (2002). Jobs Versus the Environment: An
Industry-Level Perspective. Journal of Environmental Economics and Management,
43(3), 412-436. doi:https://doi.org/10.1006/jeem.2001.1191
U.S. EPA. (2014). Guidelines for Preparing Economic Analyses. (EPA 240-R-10-001).
Washington DC: U.S. Environmental Protection Agency, Office of Policy, National
Center for Environmental Economics, https://www.epa.gov/environmental-
economics/guidelines-preparing-economic-analyses
U.S. EPA Science Advisory Board. (2017). SAB Advice on the Use of Economy-Wide Models in
Evaluating the Social Costs, Benefits, and Economic Impacts of Air Regulations. (EPA-
SAB-17-012). Washington DC
U.S. EPA Science Advisory Board. (2020). Technical Review of EPA's Computable General
Equilibrium Model, SAGE. (EPA-SAB-20-010). Washington DC
Zivin, J. G., & Neidell, M. (2018). Air pollution's hidden impacts. Science, 359(6371), 39-40.
doi: doi: 10.1126/science. aap7711
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6 ENVIRONMENTAL JUSTICE IMPACTS
6.1 Introduction
E.O. 12898 directs EPA to "achiev[e] environmental justice (EJ) by identifying and
addressing, as appropriate, disproportionately high and adverse human health or environmental
effects" (59 FR 7629, February 16, 1994), termed disproportionate impacts in this section.
Additionally, E.O. 13985 was signed to advance racial equity and support underserved
communities through Federal government actions (86 FR 7009, January 20, 2021). EPA defines
EJ as the fair treatment and meaningful involvement of all people regardless of race, color,
national origin, or income with respect to the development, implementation, and enforcement of
environmental laws, regulations, and policies. EPA further defines the term fair treatment to
mean that "no group of people should bear a disproportionate burden of environmental harms
and risks, including those resulting from the negative environmental consequences of industrial,
governmental, and commercial operations or programs and policies."134 Meaningful involvement
means that: (1) potentially affected populations have an appropriate opportunity to participate in
decisions about a proposed activity that will affect their environment and/or health; (2) the
public's contribution can influence the regulatory Agency's decision; (3) the concerns of all
participants involved will be considered in the decision-making process; and (4) the rule-writers
and decision-makers seek out and facilitate the involvement of those potentially affected.
The term "disproportionate impacts" refers to differences in impacts or risks that are
extensive enough that they may merit Agency action.135 In general, the determination of whether
a disproportionate impact exists is ultimately a policy judgment which, while informed by
analysis, is the responsibility of the decision-maker. The terms "difference" or "differential"
indicate an analytically discernible distinction in impacts or risks across population groups. It is
the role of the analyst to assess and present differences in anticipated impacts across population
groups of concern for both the baseline and proposed regulatory options, using the best available
information (both quantitative and qualitative) to inform the decision-maker and the public.
134 See, e.g., "Environmental Justice." Epa.gov, U.S. Environmental Protection Agency, 4 Mar. 2021,
https://www.epa.gov/environmentaljustice.
135 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-regulatory-
analysis.
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A regulatory action may involve potential EJ concerns if it could: (1) create new
disproportionate impacts on minority populations, low-income populations, and/or Indigenous
peoples; (2) exacerbate existing disproportionate impacts on minority populations, low-income
populations, and/or Indigenous peoples; or (3) present opportunities to address existing
disproportionate impacts on minority populations, low-income populations, and/or Indigenous
peoples through the action under development.
The Presidential Memorandum on Modernizing Regulatory Review (86 FR 7223;
January 20, 2021) calls for procedures to "take into account the distributional consequences of
regulations, including as part of a quantitative or qualitative analysis of the costs and benefits of
regulations, to ensure that regulatory initiatives appropriately benefit, and do not inappropriately
burden disadvantaged, vulnerable, or marginalized communities." Under E.O. 13563, federal
agencies may consider equity, human dignity, fairness, and distributional considerations, where
appropriate and permitted by law. For purposes of analyzing regulatory impacts, EPA relies upon
its June 2016 "Technical Guidance for Assessing Environmental Justice in Regulatory
Analysis,"136 which provides recommendations that encourage analysts to conduct the highest
quality analysis feasible, recognizing that data limitations, time, resource constraints, and
analytical challenges will vary by media and circumstance.
A reasonable starting point for assessing the need for a more detailed EJ analysis is to
review the available evidence from the published literature and from community input on what
factors may make population groups of concern more vulnerable to adverse effects (e.g.,
underlying risk factors that may contribute to higher exposures and/or impacts). It is also
important to evaluate the data and methods available for conducting an EJ analysis. EJ analyses
can be grouped into two types, both of which are informative, but not always feasible for a given
rulemaking:
1. Baseline: Describes the current (pre-control) distribution of exposures and risk,
identifying potential disparities.
136 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-regulatory-
analysis.
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2. Policy: Describes the distribution of exposures and risk after the regulatory option(s)
have been applied (post-control), identifying how potential disparities change in response
to the rulemaking.
EPA's 2016 Technical Guidance does not prescribe or recommend a specific approach or
methodology for conducting EJ analyses, though a key consideration is consistency with the
assumptions underlying other parts of the regulatory analysis when evaluating the baseline and
regulatory options.
6.2 Analyzing EJ Impacts in This Proposal
In addition to the benefits assessment (Section 4), EPA considers potential EJ concerns of
these proposed rulemakings.137 A potential EJ concern is defined as "the actual or potential lack
of fair treatment or meaningful involvement of minority populations, low-income populations,
tribes, and Indigenous peoples in the development, implementation and enforcement of
environmental laws, regulations and policies."138 For analytical purposes, this concept refers
more specifically to "disproportionate impacts on minority populations, low-income populations,
and/or Indigenous peoples that may exist prior to or that may be created by the proposed
regulatory actions." Although EJ concerns for each rulemaking are unique and should be
considered on a case-by-case basis, EPA's EJ Technical Guidance states that "[t]he analysis of
potential EJ concerns for regulatory actions should address three questions:
1. Are there potential EJ concerns associated with environmental stressors affected by the
regulatory actions for population groups of concern in the baseline?
2. Are there potential EJ concerns associated with environmental stressors affected by the
regulatory actions for population groups of concern for the regulatory option(s) under
consideration?
137 Section 6 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing coal-
fired EGUs. Please see Section 8 for impact analysis of the proposed standards for existing natural gas-fired
EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
138 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-regulatory-
analysis.
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3. For the regulatory option(s) under consideration, are potential EJ concerns created [,
exacerbated,] or mitigated compared to the baseline?"
To address these questions, EPA developed an analytical approach that considers the
purpose and specifics of the rulemakings, as well as the nature of known and potential exposures
across various demographic groups. As the proposed rules are focused on climate impacts
resulting from emission reductions directly targeted in this rulemaking, we begin with a
qualitative discussion in Section 6.3. Insight into near-source pollutant emission changes
associated with existing units is provided by demographic proximity analyses, including
concerns related to specific control technologies such as CCS, although proximity analyses for
new units are not feasible as their locations are unknown (Section 6.4).139 PM2.5 and ozone
concentration changes due to this action are also quantitatively evaluated with respect to EJ
impacts (Section 6.5). Potential PM2.5 EJ health impacts (i.e., mortality impacts) and potential
impacts of new sources are discussed qualitatively, based on other recent national quantitative
analyses (Section 6.6 and 6.7).
Unique limitations and uncertainties are specific to each type of analysis, which are
described prior to presentation of results in the subsections below.
6.3 Qualitative Assessment of Climate Impacts
In 2009, under the Endangerment and Cause or Contribute Findings for Greenhouse
Gases Under Section 202(a) of the Clean Air Act ("Endangerment Finding"), the Administrator
considered how climate change threatens the health and welfare of the U.S. population. As part
of that consideration, she also considered risks to minority and low-income individuals and
communities, finding that certain parts of the U.S. population may be especially vulnerable based
on their characteristics or circumstances. These groups include economically and socially
disadvantaged communities; individuals at vulnerable lifestages, such as the elderly, the very
young, and pregnant or nursing women; those already in poor health or with comorbidities; the
disabled; those experiencing homelessness, mental illness, or substance abuse; and/or Indigenous
139 A discussion of potential EJ concerns related to CCS control strategies is available in the outreach and
engagement section of the Preamble for this action, XIV(E)(3).
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or minority populations dependent on one or limited resources for subsistence due to factors
including but not limited to geography, access, and mobility.
Scientific assessment reports produced over the past decade by the U.S. Global Change
Research Program (USGCRP), the IPCC, and the National Academies of Science, Engineering,
and Medicine add more evidence that the impacts of climate change raise potential EJ concerns
(IPCC, 2018; Oppenheimer et al., 2014; Porter et al., 2014; Smith et al., 2014; USGCRP, 2016,
2018).
These reports conclude that poorer or predominantly non-White communities can be
especially vulnerable to climate change impacts because they tend to have limited adaptive
capacities and are more dependent on climate-sensitive resources such as local water and food
supplies or have less access to social and information resources. Some communities of color,
specifically populations defined jointly by ethnic/racial characteristics and geographic location,
may be uniquely vulnerable to climate change health impacts in the U.S. In particular, the 2016
scientific assessment on the Impacts of Climate Change on Human Health found with high
confidence that vulnerabilities are place- and time-specific, lifestages and ages are linked to
immediate and future health impacts, and social determinants of health are linked to greater
extent and severity of climate change-related health impacts (USGCRP, 2016).
In a 2021 report, EPA considered the degree to which four socially vulnerable
populations—defined based on income, educational attainment, race and ethnicity, and age—
may be more exposed to the highest impacts of climate change (U.S. EPA, 2021). The report
found that Blacks and African American populations are approximately 40 percent more likely to
currently live in these areas of the U.S. projected to experience the highest increases in mortality
rates due to changes in temperature. Additionally, Hispanic and Latino individuals in weather-
exposed industries were found to be 43 percent more likely to currently live in areas with the
highest projected labor hour losses due to temperature changes. American Indian and Alaska
Native individuals are projected to be 48 percent more likely to currently live in areas where the
highest percentage of land may be inundated by sea level rise. Overall, the report confirmed
findings of broader climate science assessments that Americans identifying as people of color,
those with low-income, and those without a high school diploma face higher differential risks of
experiencing the most damaging impacts of climate change.
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6.4 Demographic Proximity Analyses of Existing Facilities
Demographic proximity analyses allow one to assess potentially vulnerable populations
residing near affected facilities as a proxy for exposure and the potential for adverse health
impacts that may occur at a local scale due to economic activity at a given location including
noise, odors, traffic, and emissions under these EPA actions and not modeled elsewhere in this
RIA.
Although baseline proximity analyses are presented here, several important caveats
should be noted. It should be noted that facilities may vary widely in terms of the impacts they
already pose to nearby populations. In addition, proximity to affected facilities does not capture
variation in baseline exposure across communities, nor does it indicate that any exposures or
impacts will occur and should not be interpreted as a direct measure of exposure or impact.
These points limit the usefulness of proximity analyses when attempting to answer questions
from EPA's EJ Technical Guidance.
Demographic proximity analyses were performed for all plants with at least one coal-
fired unit greater than 25 MW without retirement or gas conversion plans before 2030 that are
affected by these proposed rulemakings. Due to retirement plans of some plants, the following
subsets of affected facilities were separately evaluated:
• All coal plants (140 facilities) with units potentially subject to the proposed 111 rules:
Comparison of the percentage of various populations (race/ethnicity, age, education,
poverty status, income, and linguistic isolation) living near the facilities to average
national levels.
• Coal plants retiring by January 1, 2032 (3 facilities) with units potentially subject to the
proposed 111 rules: Comparison of the percentage of various populations (race/ethnicity,
age, education, poverty status, income, and linguistic isolation) living near the facilities to
average national levels.140
• Coal plants retiring between January 1, 2032, to January 1, 2040, (19 facilities) with units
potentially subject to the proposed 111 rules: Comparison of the percentage of various
140 These three facilities are Comanche located in Colorado, Four Corners located in New Mexico, and
Independence Steam Electric Station located in Arkansas.
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populations (race/ethnicity, age, education, poverty status, income, and linguistic
isolation) living near the facilities to average national levels.
The current analysis identified all census blocks with centroids within a 10 km and 50 km
radius of the latitude/longitude location of each facility, and then linked each block with census-
based demographic data.141 The total population within a specific radius around each facility is
the sum of the population for every census block within that specified radius, based on each
block's population provided by the 2020 decennial Census.142 Statistics on race, ethnicity, age,
education level, poverty status and linguistic isolation were obtained from the Census' American
Community Survey (ACS) 5-year averages for 2016 to 2020. These data are provided at the
block group level. For the purposes of this analysis, the demographic characteristics of a given
block group - that is, the percentage of people in different races/ethnicities, the percentage
without a high school diploma, the percentage that are below the poverty level, the percentage
that are below two times the poverty level, and the percentage that are linguistically isolated -
are presumed to also describe each census block located within that block group.
In addition to facility-specific demographics, the demographic composition of the total
population within the specified radius (e.g., 10 km or 50 km) for all facilities was also computed
(e.g., all EGUs potentially subject to the 111 rules). In calculating the total populations, to avoid
double-counting, each census block population was only counted once. That is, if a census block
was located within the selected radius (i.e., 10 km or 50 km) for multiple facilities, the
population of that census block was only counted once in the total population. Finally, this
analysis compares the demographics at each specified radius (i.e., 10 km or 50 km) to the
demographic composition of the nationwide population.
Table 6-1 and Table 6-2 show the results of the proximity analysis for the three sets of
affected facilities investigated at the 10 km radius and the 50 km radius, respectively. The
analysis indicates that, on average for all 140 units, the percentage of the population living
within 10 km of these units that is African American, Hispanic/Latino, and Other/Multiracial is
141 The 10 km distance was determined to be the shortest radius around these units that captured a large enough
population to avoid excessive demographic uncertainty.
142 The location of the Census block centroid is used to determine if the entire population of the Census block is
assumed to be within the specified radius. It is unknown how sensitive these results may be to different methods
of population estimation, such as aerial apportionment.
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lower than the national average. The percent of the population that is American Indian within 10
km of the plants (0.8 percent) is above the national average (0.6 percent). This is driven by nine
facilities that have a percent American Indian population living within 10 km ranging from 10.7
percent up to 70.3 percent (median is 14 percent). The percentage of the population living within
50 km of the facilities is below the national average percent for African American, American
Indian, Hispanic/Latino, and Other Multi-racial demographics. In addition, the percentages of the
population within 50 km that are living below poverty, below 2 times the poverty level, over 25
without a high school diploma, and in linguistic isolation are all below their corresponding
national averages.
For the 19 coal plants retiring from January 1, 2032, to January 1, 2040, the percentage of
the population living within 10 km of these units that is African American, American Indian,
Hispanic/Latino, or Other Muti-racial are all below the corresponding national averages. In
addition, the percentages of the population within 10 km that are living below poverty, below 2
times the poverty level, over 25 without a high school diploma, and in linguistic isolation are all
below their corresponding national averages. When we look at the population living within 50
km of these 19 facilities, we see a larger percentage of the population is African American (15
percent), which is above the national average (12 percent). The other demographic percentages at
50 km are below their corresponding national averages.
For the three coal plants retiring by January 1, 2032, the percentage of the population
living within 10 km and 50 km that are American Indian (3.8 percent at 10 km and 10.4 percent
at 50 km) or Hispanic/Latino (46 percent at 10 km and 26 percent at 50 km) are substantially
above their corresponding national averages (0.6 percent and 19 percent, respectively). The
average percent of the population that is American Indian is driven by one facility in New
Mexico with a percent American Indian population of 70 percent within 10 km and 35 percent
within 50 km. Similarly, the average percent of the population that is Hispanic/Latino is driven
by a facility in Colorado with a percent Hispanic/Latino population of 50 percent within 10 km
and 41 percent within 50 km. The percentage of the population that is living below the poverty
level (21 percent at 10 km and 18 percent at 50 km) and below 2 times the poverty level (45
percent at 10 km and 40 percent at 50 km) are substantially above their corresponding national
averages (13 percent and 29 percent, respectively) for both distances. Note, that all three
facilities drive the high poverty percentages.
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Table 6-1 Proximity Demographic Assessment Results Within 10 km of Coal-Fired
Units Greater than 25 MW Affected by these Proposed Rulemakings a'b
Population within 10 km
All Coal Plants
Coal Plants Retiring
Coal Plants Retiring
Nationwide
subject to the
by January 1,2032,
from January 1,2032, to
Demographic
Group
Average for
Comparison
proposed
standard
subject to the
proposed standard
January 1,2040, subject
to the proposed standard
Total Population
329,824,950
3,479,742
102,613
316,119
Number of
140
19
Facilities
J
Race and Ethnicity by Percent
White
60%
72%
45%
84%
African
American
12%
9%
2%
4%
American Indian
0.6%
0.8%
3.8%
0.5%
Hispanic or
Latinob
19%
12%
46%
5%
Other and
Multiracial
9%
6%
3%
6%
Income by Percent
Below Poverty
Level
13%
14%
21%
13%
Below 2x
Poverty Level
29%
32%
45%
29%
Education by Percent
>25 and w/o a
12%
11%
12%
9%
HS Diploma
Linguistically Isolated by Percent
Linguistically
Isolated
5%
2%
2%
1%
a The nationwide population count and all demographic percentages are based on the Census' 2016-2020 American
Community Survey five-year block group averages and include Puerto Rico. Demographic percentages based on
different averages may differ. The total population counts are based on the 2020 Decennial Census block
populations.
b To avoid double counting, the "Hispanic or Latino" category is treated as a distinct demographic category for these
analyses. A person is identified as one of five racial/ethnic categories above: White, African American, American
Indian, Other and Multiracial, or Hispanic/Latino. A person who identifies as Hispanic or Latino is counted as
Hispanic/Latino for this analysis, regardless of what race this person may have also identified as in the Census.
Includes white and nonwhite.
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Table 6-2 Proximity Demographic Assessment Results Within 50 km of Coal-Fired
Units Greater than 25 MW Affected by these Proposed Rulemakings a'b
Population within 50 km
All Coal Plants
Coal Plants Retiring
Coal Plants Retiring from
Nationwide
subject to the
by January 1,2032,
January 1,2032, to
Demographic
Average for
proposed
subject to the
January 1,2040, subject
Group
Comparison
standard
proposed standard
to the proposed standard
Total Population
329,824,950
51,062,363
382,473
10,594,472
Number of
140
19
Facilities
J
Race and Ethnicity by Percent
White
60%
68%
58%
70%
African
American
12%
12%
2%
15%
American
Indian
0.6%
0.5%
10.4%
0.4%
Hispanic or
Latinob
19%
13%
26%
9%
Other and
Multiracial
9%
6%
3%
6%
Income by Percent
Below Poverty
Level
13%
12%
18%
11%
Below 2x
29%
29%
40%
28%
Poverty Level
Education by Percent
>25 and w/o a
12%
10%
12%
10%
HS Diploma
Linguistically Isolated by Percent
Linguistically
Isolated
5%
3%
2%
2%
a The nationwide population count and all demographic percentages are based on the Census' 2016-2020 American
Community Survey five-year block group averages and include Puerto Rico. Demographic percentages based on
different averages may differ. The total population counts are based on the 2020 Decennial Census block
populations.
b To avoid double counting, the "Hispanic or Latino" category is treated as a distinct demographic category for these
analyses. A person is identified as one of five racial/ethnic categories above: White, African American, American
Indian, Other and Multiracial, or Hispanic/Latino. A person who identifies as Hispanic or Latino is counted as
Hispanic/Latino for this analysis, regardless of what race this person may have also identified as in the Census.
Includes white and nonwhite.
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6.5 E J PM2.5 and Ozone Exposure Impacts
This EJ air pollutant exposure143 analysis aims to evaluate the potential for EJ concerns
related to PM2.5 and ozone exposures144 among potentially vulnerable populations. To assess EJ
ozone and PM2.5 exposure impacts, we focus on the first and third of the three EJ questions from
EPA's 2016 EJ Technical Guidance,145 which ask if there are potential EJ concerns associated
with stressors affected by the regulatory actions for population groups of concern in the baseline
and if those potential EJ concerns in the baseline are exacerbated, unchanged, or mitigated under
the regulatory options being considered.146
To address these questions with respect to the PM2.5 and ozone exposures, EPA
developed an analytical approach that considers the purpose and specifics of these proposed
rulemakings, as well as the nature of known and potential exposures and impacts. Specifically, as
1) these proposed rules affects EGUs across the U.S., which typically have tall stacks that result
in emissions from these sources being dispersed over large distances, and 2) both ozone and
PM2.5 can undergo long-range transport, it is appropriate to conduct an EJ assessment of the
contiguous U.S. Given the availability of modeled PM2.5 and ozone air quality surfaces under the
baseline and illustrative scenarios, we conduct an analysis of changes in PM2.5 and ozone
concentrations resulting from the emission changes projected by IPM147 to occur under the
143 The term exposure is used here to describe estimated PM2.5 and ozone concentrations and not individual dosage.
144 Air quality surfaces used to estimate exposures are based on 12 km grids. Additional information on air quality
modeling can be found in the air quality modeling information section.
145 U.S. Environmental Protection Agency (EPA), 2015. Guidance on Considering Environmental Justice During the
Development of Regulatory Actions, https://www.epa.gov/sites/default/files/2015-06/documents/considering-ej-
in-rulemaking-guide-final.pdf
146 EJ question 2, which asks if there are potential EJ concerns (i.e., disproportionate burdens across population
groups) associated with environmental stressors affected by the regulatory action for population groups of
concern for the regulatory options under consideration, was not focused on for several reasons. Importantly, the
total magnitude of differential exposure burdens with respect to ozone and PM2.5 among population groups at the
national scale has been fairly consistent pre- and post-policy implementation across recent rulemakings. As such,
differences in nationally aggregated exposure burden averages between population groups before and after the
rulemaking tend to be very similar. Therefore, as disparities in pre- and post-policy burden results appear
virtually indistinguishable, the difference attributable to the rulemaking can be more easily observed when
viewing the change in exposure impacts, and as we had limited available time and resources, we chose to provide
quantitative results on the pre-policy baseline and policy-specific impacts only, which related to EJ questions 1
and 3. We do however use the results from questions 1 and 3 to gain insight into the answer to EJ question 2 in
the summary (Section 6.8).
147 As discussed in greater detail in Section, IPM is a comprehensive electricity market optimization model that can
evaluate the impacts of regulatory actions affecting the power sector within the context of regional and national
electricity markets. IPM generates least-cost resource dispatch decisions based on user-specified constraints such
as environmental, demand, and other operational constraints. IPM uses a long-term dynamic linear programming
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proposed rule as compared to the baseline scenario, characterizing average and distributional
exposures following implementation of the proposed regulatory options in 2028, 2030, 2035, and
2040. However, several important caveats of this analysis are as follows:
• Although several future years were assessed for health benefits associated with these
proposed rulemakings, there was high year-to-year PM2.5 and ozone concentration change
variability across modeled future years.
• The baseline scenarios for 2028, 2030, 2035 and 2040 represent EGU emissions expected
in 2028, 2030, 2035 and 2040 respectively, but emissions from all other sources are
projected to the year 2026. The 2028, 2030, 2035 and 2040 baselines therefore do not
capture any anticipated changes in ambient ozone and PM2.5 between 2026 and 2028,
2030, 2035 or 2040 that would occur due to emissions changes from sources other than
EGUs.
• Modeling of post-policy air quality concentration changes are based on state-level
emission data paired with facility-level baseline 2026 emissions that were available in the
summer 2021 version of IPM. While the baseline spatial patterns represent 12 km grid
resolution ozone and PM2.5 concentrations associated with the facility level emissions
described above, the post-policy air quality surfaces will capture expected ozone and
PM2.5 changes that result from state-to-state emissions changes but will not capture
heterogenous changes in emissions from multiple facilities within a single state (i.e. all
sources within each state are assumed to increase or decrease in unison for the purpose of
creating air quality surfaces).
• Air quality simulation input information are at a 12 km grid resolution, and population
information is either at the Census tract- or county-level, potentially masking impacts at
geographic scales more highly resolved than the input information.
• The two specific air pollutant metrics evaluated in this assessment, warm season
maximum daily eight-hour ozone average concentrations and average annual PM2.5
concentrations, are focused on longer-term exposures that have been linked to adverse
health effects. This assessment does not evaluate disparities in other potentially health-
relevant metrics, such as shorter-term exposures to ozone and PM2.5.
• PM2.5 EJ impacts were limited to exposures, and do not extend to health effects, given
additional uncertainties associated with estimating health effects stratified by
demographic population and the ability to predict differential PM2.5-attributable EJ health
impacts.
framework that simulates the dispatch of generating capacity to achieve a demand-supply equilibrium on a
seasonal basis and by region. The model computes optimal capacity that combines short-term dispatch decisions
with long-term investment decisions. IPM runs under the assumption that electricity demand must be met and
maintains a consistent expectation of future load. IPM outputs include the air emissions resulting from the
simulated generation mix.
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Population variables considered in this EJ exposure assessment include race, ethnicity,
educational attainment, employment status, health insurance status, linguistic isolation, poverty
status, age, and sex (Table 6-3).148
Table 6-3 Demographic Populations Included in the Ozone and PM2.5 EJ Exposure
Analysis
Demographic
Groups
Ages
Spatial Scale of
Population Data
Race
Asian; American Indian; Black; White
0-99
Census tract
Ethnicity
Hispanic; Non-Hispanic
0-99
Census tract
Educational Attainment
High school degree or more; No high school degree
25-99
Census tract
Employment Status
Employed; Unemployed; Not in the labor force
0-99
County
Health Insurance
Insured; Uninsured
0-64
County
Linguistic Isolation
Speaks English "very well" or better; Speaks English
less than "very well" OR
Speaks English "well" or better; Speaks English less
than "well"
0-99
Census tract
Poverty Status
Above the poverty line; Below the poverty line OR
Above 2x the poverty line; Below 2x the poverty line
0-99
Census tract
Children
0-17
Census tract
Age
Adults
Older Adults
18-64
65-99
Sex
Female; Male
0-99
Census tract
6.5.1 Populations Predicted to Experience PM2.5 and Ozone Air Quality Changes
IPM predicts the proposed rules will lead certain EGUs to decrease emissions, while
others increase emissions, in the four snapshot years analyzed; therefore, the contiguous U.S.
was first grouped into areas where air quality 1) does not change or improves, or 2) worsens as a
result of the proposed rulemakings. Please note, national emissions reduction estimates vary by
year, with 2030 being the snapshot future year in which emission reductions are projected to be
largest (Table 3-5). In the contiguous U.S., it is estimated that at least 75 percent of the U.S.
population is predicted to experience air quality improvements (or a lack of change) for PM2.5
under all policy scenarios analyzed except for the 2028 more stringent regulatory option, in
148 Population projections stratified by race/ethnicity, age, and sex are based on economic forecasting models
developed by Woods and Poole (Woods & Poole, 2015). The Woods and Poole database contains county-level
projections of population by age, sex, and race out to 2050, relative to a baseline using the 2010 Census data.
Population projections for each county are determined simultaneously with every other county in the U.S to
consider patterns of economic growth and migration. County-level estimates of population percentages within
the poverty status and educational attainment groups were derived from 2015-2019 5-year average ACS
estimates. Additional information can be found in Appendix J of the BenMAP-CE User's Manual
(https://www.epa.gov/benmap/benmap-ce-manual-and-appendices).
6-13
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which approximately 54 percent of the U.S. population is predicted to experience a PM2.5 air
quality improvement (Figure 6-1). In contrast, 50-97 percent of the U.S. population is predicted
to experience ozone improvements (or lack of change) due to the proposed rulemakings and the
other 3-50 percent are predicted to experience worsening ozone concentrations. In absolute
terms, this equates to less than 81 million people experiencing worsening PM2.5 concentrations
(or up to 170 million in the 2028 more stringent regulatory option) and up to 196 million people
experiencing worsening ozone concentrations. On average, the average magnitude of areas with
worsening PM2.5 concentration changes due to the rulemakings is much smaller than the
magnitude of improving PM2.5 concentration changes. Excluding the 2028 more stringent
regulatory option, the magnitude of worsening ozone concentration changes is also smaller than
that of improving ozone concentration changes, but to a lesser degree than PM2.5.
Year
Scenario
Pollutant
pm2.s (t^g/m3)
Ozone (ppb)
2028
Proposal
2M^^356M
113M O
^246M
Less Stringent
8M (C^350M
124M O
^234M
More Stringent
170M (3l89M
261MQ
©98M
2030
Proposal
20M • 345M
154M O
211M(^
Less Stringent
23M • 342M
156M O
209M(Q
More Stringent
60M O ^^305M
Ol65M
2OOM0
2035
Proposal
51M Q^^329M
131M Q
0249M
Less Stringent
51M 329M
142 M O
0238M
More Stringent
56M O 324M
107M Q
^273M
2040
Proposal
62M G^^333M
181M Q
0214M
Less Stringent
81M C^314M
196M 0
0199M
More Stringent
37M «^^358M
182 M Q
0213M
•0.05 0.00 0.05 0.10
-0.05 0.00 0.05 0.10
Average Change
Average Change
Figure 6-1 Number of People Residing in the Contiguous U.S. Areas Improving or Not
Changing (Blue) or Worsening (Orange) in 2028, 2030, 2035, and 2040 for PM2.5 and Ozone
and the National Average Magnitude of Pollutant Concentration Changes (jig/m3 and ppb)
for the 3 Regulatory Options
6.5.2 PM2.5 EJ Exposure Analysis
We evaluated the potential for EJ concerns among potentially vulnerable populations
resulting from exposure to PM2.5 under the baseline and proposed regulatory options in this rule.
6-14
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This was done by characterizing the average and distribution of PM2.5 exposures both prior to
and following implementation of the three illustrative scenarios (the proposed regulatory option,
as well as the less and more stringent regulatory options), in 2028, 2030, 2035, and 2040.
As this analysis is based on the same PM2.5 spatial fields as the benefits assessment (see
Section 3 for a discussion of the spatial fields), it is subject to similar types of uncertainty (see
Sections 3.8 and 4.3.8 for discussions of uncertainty). A particularly germane limitation for this
analysis is that the expected concentration changes are quite small, likely making uncertainties
associated with the various input data more relevant.
6.5.2.1 National Aggregated Results
National average baseline PM2.5 concentrations in micrograms per cubic meter (|ig/m3) in
2028, 2030, 2035, and 2040 are shown in the Figure 6-2 heat map. Concentrations represent the
total estimated PM2.5 exposure burden averaged over the 12-month calendar year and are colored
to visualize differences more easily in average concentrations (lighter blue coloring representing
smaller average concentrations and darker blue coloring representing larger average
concentrations). Average national disparities observed in the baseline of these rules are similar to
those described by recent rules (e.g., the Reconsideration of the National Ambient Air Quality
Standards for Particulate Matter149), that is, populations with national average PM2.5
concentrations higher than the reference population ordered from most to least difference are:
those Linguistically isolated, Hispanics, Asians, Blacks, and the less educated (Figure 6-2).
In Figure 6-3, columns labeled "Proposal" "Less Stringent," and "More Stringent"
provide information regarding how all three illustrative scenarios will impact PM2.5
concentrations across various populations, respectively.150 While the national-level PM2.5
concentration reductions were similar for all population groups evaluated in 2028, 2035 and
2040, there were some differences observed in 2030. For example, (Figure 6-2), for all scenarios,
the linguistically isolated, Asian population, and Hispanic population which also have higher
149 https://www.federalregister.gOv/documents/2023/01/27/2023-00269/reconsideration-of-the-national-ambient-air-
quality-standards-for-particulate-matter
150 We report average exposure results to the decimal place where difference between demographic populations
become visible, as we cannot provide a quantitative estimate of the air quality modeling precision uncertainty.
Using this approach allows for a qualitative consideration of uncertainties and the significance of the relative
magnitude of differences.
6-15
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average baseline exposures, are estimated to experience a slightly smaller PM2.5 concentration
reduction than the overall reference population.
The national-level assessment of PM2.5 before and after implementation of these
proposed rulemakings suggests that while EJ exposure disparities are present in the pre-policy
scenario, meaningful EJ exposure concerns are not likely created or exacerbated by the rule for
the population groups evaluated, due to the small difference in magnitudes of PM2.5
concentration reductions across demographic groups.
Year
Population
Qualifier
2028
2030
2035
2040
Reference
Reference (0-99)
7.2
7.1
7.1
7.1
Race
White (0-99)
7.1
7.0
7.0
7.0
American Indian (0-99)
6.7
6.7
6.6
6.6
Asian (0-99)
7.8
7.7
7.6
7.6
Black (0-99)
7.4
7.4
7.3
7.3
Ethnicity
Non-Hispanic (0-99)
7.0
6.9
6.8
6.8
Hispanic (0-99)
8.0
7.9
7.9
7-e|
Educational
More educated (>24: HS or more)
7.1
7.0
7.0
7.0
Attainment
Less educated (>24; no HS)
7.5
7.5
7.4
7.4
Employment
Employed (0-99)
7.3
7.3
7.2
7.2
Status
Unemployed (0-99)
7.2
7.1
7,1
7.1
Not in the labor force (0-99)
7.2
7.1
7.1
7.1
Insurance
Insured (0-64)
7.2
7.2
7.1
7.1
Status
Unisured (0-64)
7.3
7.3
7.2
7.2
Linguistic
English "very well or better" (0-99)
7.1
7.1
7.0
7.0
Isolation
English < "very well" (0-99)
8.0
8.0
7.9
7.9|
English "well or better" (0-99)
7.1
7.1
7.0
7.0
English < "well" (0-99)
8.2
8.1
8.1
8.0|
Poverty
>200%ofthe poverty line (0-99)
7.1
7.1
7.0
7.0
Status
<200%ofthe poverty line (0-99)
7.3
7.3
7.2
7.2
>Povertyline (0-99)
7.2
7.1
7.0
7.0
-------
Year / Scenario
2030 2035
Population
Reference
Race
Ethnicity
Educational
Attainment
Employment
Status
Insurance
Status
Linguistic
Isolation
Poverty
Status
Age
Qualifier
Reference (0-99)
White (0-99)
American Indian (0-99)
Asian (0-99)
Black (0-99)
Non-Hispanic (0-99)
Hispanic (0-99)
More educated (>24: HS or more)
Less educated (>24; no HS)
Employed (0-99)
Unemployed (0-99)
Not in the labor force (0-99)
Insured (0-64)
Unisured (0-64)
English "very well or better" (0-99)
English < "very well" (0-99)
English "well or better" (0-99)
English < "well" (0-99)
>200% of the poverty line (0-99)
<200% of the poverty line (0-99)
>Povertyline (0-99)
-------
level PM2.5 concentration changes varies considerably across states. Depending on the year of
analysis, average population-weighted state-level PM2.5 concentrations are predicted to be
reduced by up to 0.13 |ig/m3 (as seen in Nebraska in 2030). Increases in PM2.5 concentrations for
state-level average populations were rare and largest in 2030 and 2035 under the more stringent
regulatory option in California, and only to a very small magnitude (-0.01 |ig/m3). When
considering differences between demographic populations affected by a particular proposed
policy within a given year, average PM2.5 concentration changes at the state-level only differ
from the reference population by up to 0.02 |ig/m3,152 Therefore, whereas PM2.5 exposure
impacts vary by state, the small magnitude of differential impacts expected from the proposed
rule is not likely to meaningfully exacerbate or mitigate EJ concerns within individual states.
152 Please note that population counts vary greatly by state, and that averaging results of the 48 states shown here
will not reflect national population-weighted exposure estimates.
6-18
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State
<<4uuWQu.i5----^£-'52552555222 2ZzZ2OOoa0:i"^i-p3»5>5S
Year Scenario
2028 Proposal
PMiillig/fll3)
less
Stringent
More
Stringent
Proposal
Less
Stringent
More
Stringent
203S Proposal
Less
Stringent
More
Stnngent
2040 Proposal
Less
Stringent
More
Stringent
Group
Reference
Asian
American Indian
8iack
Hispanic
-------
6.5.2.3 Distributional Results
We also present the cumulative proportion of each population exposed to ascending
levels of PM2.5 concentration changes across the contiguous U.S. averaged at the county level.
Results allow evaluation of what percentage of each subpopulation (e.g., Hispanics) in the
contiguous U.S. experience what change in PM2.5 concentrations compared to what percentage of
the overall reference group (i.e., the total population of contiguous U.S.) experiences similar
concentration changes from EGU emission changes under the three regulatory options in 2028,
2030, 2035, and 2040 (Figure 6-5).
This distributional EJ analysis is also subject to additional uncertainties related to more
highly-resolved input parameters and additional assumptions. For example, this analysis does not
account for potential difference in underlying susceptibility, vulnerability, or risk factors across
populations to PM2.5 exposure. Nor could we include information about differences in other
factors that could affect the likelihood of adverse impacts (e.g., exercise patterns) across groups.
As the baseline scenario is similar to that described by other RIAs (e.g., the Regulatory
Impact Analysis for the Proposed Reconsideration of the National Ambient Air Quality
Standards for Particulate Matter)153, we focus on the PM2.5 changes due to this proposed
rulemaking. The vast majority of each demographic population are predicted to experience PM2.5
concentration changes less than 0.06 |ig/m3 under any regulatory option for all four future years
analyzed. While the greatest impacts, and the greatest differential impacts across population,
occurs in 2030, the distributions of PM2.5 concentration changes across population demographics
are all fairly similar and the small difference in impacts shown in the 2028, 2030, 2035, and 2040
distributional analyses of PM2.5 concentration changes under the various regulatory options
suggests that the proposed rules are not likely to meaningfully exacerbate or mitigate EJ PM2.5
exposure concerns for population groups evaluated.
153 https://www.epa.gov/system/files/documents/2023-01/naaqs-pm_ria_proposed_2022-12.pdf
6-20
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Population
Year Scenario
100%
Q.
O
2028 Proposal -5 50%
#
0%
Race
fm ¦¦
r
Ethnicity
f
Educational Attainment Employment Status
'I ¦ UtHMKM|lMl»Mt I ¦ U-wmoto/M 1049)
Insurance Status
fm
jf ¦
Linguistic Isolation
f
Poverty Status
r
100%
Less i cfw.
Stringent °
0%
r
r
r r
r
r
r
100%
a.
M°re 5 50*
Stringent °
0%
r
r
r f
r
r
r
100%
a.
2030 Proposal -g 50%
#
0%
X
X~~ x~
X"
r
X"
100%
Q.
Less i
Stringent °
0%
x~
X- A"
x-
r
Ya
100%
a.
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Stringent ®
0%
100%
a.
<£
2035 Proposal >g 50%
0%
r
r
f f
E
r
f
100%
Q.
Less i 50%
Stringent 0
# 0%
r
r
f
jr
r
f
100%
Stringent 1 **
0%
r
r
r r
f
r
f
100%
1
2040 Proposal g 50%
*
0%
r
r
r r
f
r
f
100%
T , l 50%
Stringent °
0%
f
t
f f
jr
f
£
100%
T* » f 5°%
Stringent °
0%
r
r
r
f
r
f
-0.04 0.00 0.04 0.08
PM
-0.04 0.00 0.04 0.08
PM
-0.04 0.00 0.04 0.08 -0.04 0.00 0.04 0.08
PM PM
-0.04 0.00 0.04 0.08
PM
-0.04 0.00 0.04 0.08
PM
-0.04 0.00 0.04 0.08
PM
Figure 6-5 Distributions of PM2.5 Concentration (jxg/m3) Changes Across Populations,
Future Years, and Regulatory Options
6.5.3 Ozone EJ Exposure Analysis
To evaluate the potential for EJ concerns among potentially vulnerable populations
resulting from exposure to ozone under the baseline and regulatory options proposed in this rule,
we characterize the distribution of ozone exposures both prior to and following implementation
of the proposed rule, as well as under the more and less stringent regulatory options, in 2028,
2030, 2035, and 2040.
As this analysis is based on the same ozone spatial fields as the benefits assessment (see
Section 3 for a discussion of the spatial fields), it is subject to similar types of uncertainty (see
6-21
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Sections 3.8 and 4.3.8 for discussions of uncertainty). In addition to the small magnitude of
differential ozone concentration changes associated with these proposed rulemakings when
comparing across demographic populations, a particularly germane limitation is that ozone,
being a secondary pollutant, is the byproduct of complex atmospheric chemistry such that direct
linkages cannot be made between specific affected facilities and downwind ozone concentration
changes based on available air quality modeling.
Ozone concentration and exposure metrics can take many forms, although only a small
number are commonly used. The analysis presented here is based on the average April-
September warm season maximum daily eight-hour average ozone concentrations (AS-M03),
consistent with the health impact functions used in the benefits assessment (Section 4). As
developing spatial fields is time and resource intensive, the same spatial fields used for the
benefits analysis were also used for the ozone exposure analysis performed here to assess EJ
impacts.
The construct of the AS-M03 ozone metric used for this analysis should be kept in mind
when attempting to relate the results presented here to the ozone NAAQS and when interpreting
the confidence in the association between exposures and health effects. Specifically, the seasonal
average ozone metric used in this analysis is not constructed in a way that directly relates to
NAAQS design values, which are based on daily maximum eight-hour concentrations.154 Thus,
AS-M03 values reflecting seasonal average concentrations well below the level of the NAAQS
at a particular location do not necessarily indicate that the location does not experience any daily
(eight-hour) exceedances of the ozone NAAQS. Relatedly, EPA is confident that reducing the
highest ambient ozone concentrations will result in substantial improvements in public health,
including reducing the risk of ozone-associated mortality. However, the Agency is less certain
about the public health implications of changes in relatively low ambient ozone concentrations.
Most health studies rely on a metric such as the warm-season average ozone concentration; as a
result, EPA typically utilizes air quality inputs such as the AS-M03 spatial fields in the benefits
assessment, and we judge them also to be the best available air quality inputs for this EJ ozone
exposure assessment.
154 Level of 70 ppb with an annual fourth-highest daily maximum eight-hour concentration, averaged over three
years.
6-22
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6.5.3.1 National Aggregated Results
National average baseline ozone concentrations in ppb in 2028, 2030, 2035, and 2040 are
shown in a heat map (Figure 6-6). Concentrations represent the total estimated daily eight-hour
maximum ozone exposure burden averaged over the 6-month April-September ozone season and
are colored to visualize differences more easily in average concentrations, with lighter green
coloring representing smaller average concentrations and darker green coloring representing
larger average concentrations. Populations with national average ozone concentrations higher
than the reference population ordered from most to least difference were: American Indians,
Hispanics, the Linguistically isolated, Asians, the Less educated, and Children. Average national
disparities observed in the baseline of this rule are fairly consistent across the four future years
and similar to those described by recent rules (e.g., the Regulatory Impact Analysis for Proposed
Federal Implementation Plan Addressing Regional Ozone Transport for the 2015 Ozone National
Ambient Air Quality Standard).155
In Figure 6-7, columns labeled "Proposal" "Less Stringent," and "More Stringent"
provide information regarding how the three illustrative scenarios will impact ozone
concentrations across various populations.156 All national-level ozone concentration changes of
these proposed rulemakings across population groups, years, and regulatory options are predicted
to be relatively small in absolute magnitude (i.e., <0.04 ppb), relative to the magnitude of
disparities in the baseline across populations. When comparing the small changes across
demographic groups, there are some disparate impacts in 2030 for Asian populations, Hispanic
populations, and those linguistically isolated (Figure 6-7). However, in the other years and
regulatory options analyzed, populations are estimated to experience similar ozone concentration
reductions to that of the reference populations.
The national-level assessment of ozone burden concentrations in the baseline and ozone
exposure changes due to the regulatory options suggests that while most policy options and
future years analyzed will not likely mitigate or exacerbate ozone EJ exposure disparities for the
155 https://www.epa.gOv/system/files/documents/2022-03/transport_ria_proposal_fip_2015_ozone_naaqs_2022-
02.pdf
156 We report average exposure results to the decimal place where difference between demographic populations
become visible, as we cannot provide a quantitative estimate of the air quality modeling precision uncertainty.
Using this approach allows for a qualitative consideration of uncertainties and the significance of the relatively
small differences.
6-23
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population groups evaluated, ozone EJ exposure disparities may be exacerbated for some
population groups analyzed in 2030 under all regulatory options. However, the extent to which
disparities may be exacerbated is likely modest, due to the small magnitude of the ozone
concentration changes relative to baseline disparities in ozone concentrations across population
groups.
Population Qualifier
Year
2028 2030 2035 2040
Reference Reference (0-99)
40.8 40.7 40.5 40.4
Race White (0-99)
American Indian (0-99)
Asian (0-99)
Black (0-99)
40.9 40.8 40.6 40.5
42.9 42.9 42.7 42.7
42.0 41.9 41.6 41.4
39.5 39.4 39.2 39.0
Ethnicity Non-Hispanic (0-99)
Hispanic (0-99)
40.3 40.2 39.9 39.8
42.8 42.6 42.4 42.3
Educational More educated (>24: HS or more)
40.6 40.5 40.3 40.3
Attainment Less educated (>24; no HS)
41.2 41.1 41.0 40.9
Employment Employed (0-99)
41.2 41.1 41.0 40.9
Status Unemployed (0-99)
Not in the labor force (0-99)
40.8 40.7 40.5 40.4
40.8 40.7 40.5 40.4
Insurance Insured (0-64)
Status Unisured (0-64)
41.0 40.8 40.7 40.6
40.5 40.4 40.2 40.1
Linguistic English "very well or better" (0-99)
Isolation English < "very well" (0-99)
English "well or better" (0-99)
English < "well" (0-99)
40.7 40.6 40.4 40.3
42.1 42.0 41.8 41.8
40.7 40.6 40.4 40.4
42.2 42.1 41.9 41.8
Poverty >200% of the poverty line (0-99)
Status <200%ofthe poverty line (0-99)
>Povertyline (0-99)
-------
Population Group (age)
Proposal
ro
Less Stringent S
CO
More Stringent
Proposal
ro
Less Stringent S
More Stringent
Proposal
i\)
Less Strinaent S
Ln
More Stringent
Proposal
IN)
Less Stringent §
O
More Stringent
Reference Reference (0-99)
0.01 0.01 -0.01
0.03 0.02
0.01
0.01 0.01 0.01
0.00 0.00 0.00
Race White (0-99)
0.01 0.01 -0.01
0.03 0.03
0.02
0.01 0.01 0.01
0.00 0.00 0.00
American Indian (0-99)
Asian (0-99)
Black (0-99)
0.01 0.01 -0.01
0.03 0.02
0.03
0.01 0.01 0.01
0.01 0.00 0.01
0.01 0.01 -0.02
0.00 0.00
rH
O
O
0.01 0.01 0.01
0.01 0.01 0.01
0.00 0.00 0.00
0.00 0.00 0.00
0.01 0.01 -0.01
0.03 0.02
0.00
Ethnicity Non-Hispanic (0-99)
Hispanic (0-99)
0.01 0.01 -0.01
0.01 0.01 -0.02
0.04 0.03
0.02
0.01 0.01 0.01
0.01 0.01 0.01
0.00 0.00 0.00
0.00 -0.01 0.00
0.00 0.00 0.00
Educational More educated (>24: HS or more)
0.01 0.01 -0.01
0.03 0.03
0.02
0.01 0.01 0.01
0.00 0.00 0.00
Attainment Less educated (>24; no HS)
0.01 0.01 -0.02
0.02 0.01
0.01
0.01 0.00 0.01
0.00 0.00 0.00
Employment Employed (0-99)
Status Not in the labor force (0-99)
Unemployed (0-99)
0.01 0.01 -0.02
0.01 0.01 -0.01
0.01 0.01 -0.01
0.02 0.02
0.03 0.02
0.03 0.02
0.01
0.01
0.01
0.01 0.01 0.01
0.01 0.01 0.01
0.01 0.01 0.01
0.00 0.00 0.00
0.00 0.00 0.00
0.00 0.00 0.00
Insurance Insured (0-64)
0.01 0.01 -0.02
0.03 0.02
0.01
0.01 0.01 0.01
0.00 0.00 0.00
Status Unisured (0-64)
0.01 0.01 -0.01
0.02 0.02
0.01
0.01 0.01 0.01
0.00 0.00 0.00
Linguistic English "very well or better" (0-99)
0.01 0.01 -0.01
0.03 0.03
0.02
o
b
o
b
o
b
H
0.00 0.00 0.00
Isolation English < "very well" (0-99)
English "well or better" (0-99)
0.01 0.01 -0.02
0.01 0.01 -0.01
0.00 -0.01
o
o
0.01 0.01 0.01
0.01 0.01 0.01
0.00 0.00 0.00
0.03 0.03
0.02
0.00 0.00 0.00
English < "well" (0-99)
0.01 0.01 -0.02
-0.01 -0.01 -0.01
0.01 0.01 0.01
0.00 0.00 0.00
Poverty status >200%ofthe poverty line (0-99)
<200% of the poverty line (0-99)
>Povertyllne (0-99)
-------
magnitude than that of predicted ozone decreases. The maximum ozone increases are observed
with the "More Stringent" policy option in 2030 with a maximum state-level population-
weighted average of 0.18 ppb experienced by Asian populations in Delaware (DE) and by
Asians, American Indians, Blacks, Hispanics, the Linguistically isolated, the Less educated, and
Uninsured populations in Maryland (MD). Importantly, Figure 6-8 shows that demographic
groups within most states are predicted to experience very similar exposure impacts as the state
reference populations, with a few potential exceptions (e.g., Pennsylvania [PA] in 2030 and
Virginia [VA] in 2030 and 2035).
When comparing exposure impacts across demographic groups within states, most states
display similar impacts across demographic groups in 2028, 2035, and 2040. However, some
with higher exposures have larger differences in reductions between groups. For example, within
several states, the largest difference in reductions between a population and the reference
population is -0.07 ppb.
Therefore, the state-level assessment of ozone exposure changes due to the regulatory
options suggests that while most policy options and future years analyzed will not likely mitigate
or exacerbate ozone EJ exposure disparities for the population groups evaluated in 2028, 2035,
and 2040, ozone EJ exposure disparities at the state level may be either mitigated or exacerbated
for some population groups analyzed in 2030 under the various regulatory options. However, the
extent to which disparities may be exacerbated is likely modest, due to the small magnitude of
the ozone concentration changes relative to the magnitude of baseline ozone exposure disparities.
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State
Year Scenario
2028 Proposal
Ozone (ppb)
More
Stringent
less
Stringent
More
Stringent
2035 Proposal
Less
Stringent
More
Stringent
2040 Proposal
Less
Stringent
More
Stringent
Group
Reference
Asian
American Indian
Black
Hispanic
-------
6.5.3.3 Distributional Results
We also present cumulative proportion of each population exposed to ascending levels of
ozone concentration changes across the contiguous U.S. Results allow evaluation of what
percentage of each subpopulation (e.g., Hispanics) in the contiguous U.S. experience what
change in ozone concentrations compared to what percentage of the overall reference group (i.e.,
the total population of contiguous U.S.) experiences similar concentration changes from EGU
emission changes under the three illustrative scenarios in 2028, 2030, 2035, and 2040.
This distributional EJ analysis is also subject to additional uncertainties related to more
highly resolved input parameters and additional assumptions. For example, this analysis does not
account for potential difference in underlying susceptibility, vulnerability, or risk factors across
populations expected to experience post-policy ozone exposure changes. Nor could we include
information about differences in other factors that could affect the likelihood of adverse impacts
(e.g., exercise patterns) across groups.
As the baseline scenario is similar to that described by other RIAs (the Regulatory Impact
Analysis for Proposed Federal Implementation Plan Addressing Regional Ozone Transport for
the 2015 Ozone National Ambient Air Quality Standard)157, we focus on the ozone changes due
to these proposed rulemakings. Distributions of 12 km gridded ozone concentration changes
from EGU control strategies of affected facilities under the illustrative scenarios analyzed in
these proposed rulemakings are shown in Figure 6-9. When comparing distributional exposure
impacts across demographic groups, similar impacts are predicted to occur across demographic
groups in 2028, 2035, and 2040. However, certain groups, specifically Asians, Hispanics, and
those linguistically isolated, may experience smaller ozone exposure reductions across the
population distributions in 2030, as compared to the overall reference distribution.
Therefore, the distributional assessment of ozone exposure changes due to the regulatory
options suggests that while most illustrative scenarios and future years analyzed will not likely
mitigate or exacerbate ozone EJ exposure disparities for the population groups evaluated in 2028,
2035, and 2040, distributional ozone EJ exposure disparities may be exacerbated for some
population groups analyzed in 2030 under all illustrative scenarios. However, the extent to which
157 https://www.epa.gOv/system/files/documents/2022-03/transport_ria_proposal_fip_2015_ozone_naaqs_2022-
02.pdf
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disparities may be exacerbated is likely modest, due to the small magnitude of the ozone
concentration changes.
Year Scenario
Race
Ethnicity
Educational Attainment
Population
Employment Status
Insurance Status
Linguistic Isolation
Poverty Status
a 100%
f*
1C, -l-
2028 Proposal
- 50%
0%
J
r.
J
J
r=
Less
Stringent
a 100%
1
•5 50%
*
0%
J
J
J
r
J
f
f
J
r
More
Stringent
% of Pop
111
J
t
J
r
J
f
J
J
J
J
2030 Proposal
% of Pop
3 i i
J
r
I
I
y
Less
Stringent
% of Pop
s 1 i
J
f
/
X
J
f
J
r
J
y
More
Stringent
% of Pop
s 1 1
11
J
J
r
J
r
J
y
2035 Proposal
% of Pop
ill
J
J
J
J
j
j
j
Less
Stringent
% of Pop
s i i
J
J
J
j
j
j
More
Stringent
% of Pop
ill
J
J
J
j
j
j
2040 Proposal
% of Pop
s i 1
J
J
J
j
f
j
Less
Stringent
% of Pop
nil
J
J
J
J
J
J
j
More
Stringent
% of Pop
i 1 1
J
J
J
J
J
j
j
-0.3-0.1 0.1 0.3 0.5
03
-0.3-0.1 0.1 0.3 0.5
03
-0.3-0,1 0.1 0.3 0.5
03
-0.3-0.1 0.1 0.3 0.5
03
-0.3-0.1 0.1 0.3 0.5-0.3-0.1 0.1 0.3 0.5
03 03
-0.3-0.1 0.1 0.3 0.5
03
Figure 6-9 Distributions of Ozone Concentration Changes (ppb) Across Populations,
Future Years, and Regulatory Options
6.6 Qualitative Discussion of EJ PM2.5 Health Impacts
While the potential for EJ concerns related to PM2.5 health outcomes (i.e., premature
mortality) among populations potentially at increased risk of or to PM2.5 exposures have been
evaluated previously (U.S. EPA, 2022a), EJ health impacts of PM2.5 exposures were not
quantitatively evaluated here, due to resource limitations and the lack of substantial differential
EJ impacts of the proposed rulemakings (Section 3.8).
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While quantitative impacts are not analyzed, we can qualitatively speak to the expected
PM2.5-attributable mortality EJ impacts of this proposal, based on prior quantitative results and
the PM2.5 EJ exposure results provided here. For context, the PM ISA and PM ISA Supplement
provided evidence that there are consistent racial and ethnic disparities in PM2.5 exposure across
the U.S., particularly for Black/African Americans, as compared to non-Hispanic White
populations. Additionally, some studies provided evidence of increased PIVh.s-related mortality
and other health effects from long-term exposure to PM2.5 among Black populations. Taken
together, the 2019 PM ISA concluded that the evidence was adequate to conclude that race and
ethnicity modify PM2.5-related risk, and that non-White individuals, particularly Black
individuals, are at increased risk for PM2.5-related health effects, in part due to disparities in
exposure ISA (U.S. EPA, 2019, 2022b).
Qualitatively, as the PM2.5 exposure changes are fairly consistent across demographic
populations, differential impacts are expected to reflect the epidemiologic hazard ratios. This
suggests that PM2.5 improvements would be most beneficial for Black populations, followed by
Hispanic and Asian populations. Conversely, worsening air quality would be disproportionately
harmful to the same groups in the same hierarchy.
6.7 Qualitative Discussion of New Source EJ Impacts
EJ impacts of new sources subject to 111(b) are highly uncertain as the location of new
sources is unknown. Therefore, we do not make predictions regarding potential EJ impacts from
new sources. However, the illustrative scenarios do account for emissions changes at existing
facilities that are expected to result from the 111(b) policy.
6.8 Summary
As with all EJ analyses, data limitations make it quite possible that disparities may exist
that our analysis did not identify. This is especially relevant for potential EJ characteristics,
environmental impacts, and more granular spatial resolutions that were not evaluated. Therefore,
this analysis is only a partial representation of the distributions of potential impacts.
Additionally, EJ concerns for each rulemaking are unique and should be considered on a case-
by-case basis.
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For this proposed rule, we quantitatively evaluate the proximity of affected facilities
populations of potential EJ concern (Section 4) and the potential for disproportionate pre- and
post-policy PM2.5 and ozone exposures and exposure changes across different demographic
groups (Section 5). Each of these analyses depends on mutually exclusive assumptions, was
performed to answer separate questions, and is associated with unique limitations and
uncertainties.
Baseline demographic proximity analyses provide information as to whether there may
be potential EJ concerns associated with environmental stressors. In this case, our proximity
analysis of the full population of potentially affected units greater than 25 MW (140 units)
indicated that the demographic percentages of the population within 10 km and 50 km of the
facilities are relatively similar to the national averages. The proximity analysis of the 19 units
that will retire from January 1, 2032, to January 1, 2040, (a subset of the total 140 units) found
that the percent of the population within 10 km that is African American (15 percent) is higher
than the national average (12 percent). The proximity analysis for the 3 units that will retire by
January 1, 2032, (a subset of the total 140 units) found that for both the 10 km and 50 km
populations: the percent of the population that is American Indian for one facility (4 percent at
10 km and 10 percent at 50 km) is substantially above the national average (0.8 percent), the
percent of the population that is Hispanic/Latino for another facility (46 percent at 10 km and 26
percent at 50 km) is substantially above the national average (19 percent), and finally, all three
facilities were well above the national average for both the percent below the poverty level and
the percent below two times the poverty level.
While the demographic proximity analyses may appear to parallel the baseline analysis of
nationwide ozone and PM2.5 exposures in certain ways, the two should not be directly compared.
The baseline ozone and PM2.5 exposure assessments are in effect an analysis of total burden in
the contiguous U.S., and include various assumptions, such as the implementation of
promulgated regulations. It serves as a starting point for both the estimated ozone and PM2.5
changes due to this proposal as well as a snapshot of air pollution concentrations in several near
future years.
The baseline ozone and PM2.5 exposure analyses respond to question 1 from EPA's EJ
Technical Guidance document more directly than the proximity analyses, as they evaluate a form
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of the environmental stressor primarily affected by the regulatory action (Section 5). Baseline
PM2.5 and ozone exposure analyses show that certain populations, such as Hispanic, Asian, those
linguistically isolated, and the less educated may experience disproportionately higher ozone and
PM2.5 exposures as compared to the national average. Black populations may also experience
disproportionately higher PM2.5 concentrations than the reference group, and American Indian
populations and children may also experience disproportionately higher ozone concentrations
than the reference group. Therefore, there likely are potential EJ concerns associated with
environmental stressors affected by the regulatory action for population groups of concern in the
baseline.
Finally, we evaluate how the post-policy options of this proposed rulemaking are
expected to differentially impact demographic populations, informing questions 2 and 3 from
EPA's EJ Technical Guidance regarding ozone and PM2.5 exposure changes. We infer that
baseline disparities in ozone and PM2.5 concentration burdens are likely to remain after
implementation of any of the regulatory options under consideration due to the small magnitude
of the concentration changes associated with this rulemaking across demographic populations,
relative to baseline burden disparities (EJ question 2). Also, due to the very small differences in
the distributional analyses of post-policy exposure impacts across demographic populations, we
do not find evidence that disparities in populations of potential EJ concerns will be meaningfully
exacerbated or mitigated by the regulatory alternatives under consideration regarding PM2.5
exposures in all future years evaluated and ozone exposures in 2028, 2035, and 2040. However,
in 2030, Asian populations, Hispanic populations, and those linguistically isolated may
experience a slight exacerbation of ozone exposure disparities at the national level (EJ question
3). At the state level, ozone exposure disparities may be either mitigated or exacerbated for
certain demographic groups analyzed in 2030, also to a small degree. Importantly, the action
described in these rules are expected to lower ozone and PM2.5 for most people, including those
areas that struggle to attain or maintain the NAAQS, and thus mitigate some pre-existing health
risks across all populations evaluated.
This EJ air quality analysis concludes that there are disparities across various populations
in the pre-policy baseline scenario (EJ question 1) and infer that these disparities are likely to
persist after promulgation of this proposed rulemaking (EJ question 2). This EJ assessment also
suggests that this action is unlikely to mitigate or exacerbate PM2.5 exposures disparities across
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populations of EJ concern analyzed. Regarding ozone exposures, while most snapshot years for
the illustrative scenarios analyzed will not likely mitigate or exacerbate ozone exposure
disparities for the population groups evaluated, ozone exposure disparities may be exacerbated
for some population groups analyzed in 2030 under all illustrative scenarios. However, the extent
to which disparities may be exacerbated is likely modest, due to the small magnitude of the
ozone concentration changes relative to baseline disparities across populations (EJ question 3).
Importantly, the action described in this proposal is expected to lower PM2.5 and ozone in many
areas, and thus mitigate some pre-existing health risks of air pollution across all populations
evaluated.
6.9 References
IPCC. (2018). Global Warming of 1.5°C. An IPCC Special Report on the impacts of global
warming of 1.5°C above pre-industrial levels and related global greenhouse gas
emission pathways, in the context of strengthening the global response to the threat of
climate change, sustainable development, and efforts to eradicate poverty (V. Masson-
Delmotte, P. Zhai, H.-O. Portner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W.
Moufouma-Okia, C. Pean, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou,
M. I. Gomis, E. Lonnoy, T. Maycock, a. M. Tignor, & T. Waterfield Eds.).
Oppenheimer, M., Campos, M., Warren, R., Birkmann, J., Luber, G., O'Neill, B., & Takahashi,
K. (2014). Emergent risks and key vulnerabilities. In C.B. Field, V.R. Barros, D.J.
Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatteijee, K.L. Ebi, Y.O. Estrada,
R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, &
L.L.White (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A:
Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change (pp. 1039-1099). Cambridge,
United Kingdom and New York, NY: Cambridge University Press.
Porter, J. R., Xie, L., Challinor, A. J., Cochrane, K., Howden, M., Iqbal, M. M., & Lobell, D. B.
(2014). Food security and food production systems. In C.B. Field, V.R. Barros, D.J.
Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatteijee, K.L. Ebi, Y.O. Estrada,
R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, &
L.L.White (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A:
Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change (pp. 485-533). Cambridge,
United Kingdom and New York, NY: Cambridge University Press.
Smith, K. R., Woodward, A., Campbell-Lendrum, D., Chadee, D. D., Honda, Y., Liu, Q., . . .
Sauerborn, R. (2014). Human Health: Impacts, Adaptation, and Co-Benefits. In C.B.
Field, V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatteijee,
K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken,
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P.R. Mastrandrea, & L.L.White (Eds.), Climate Change 2014: Impacts, Adaptation, and
Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 709-
754). Cambridge, United Kingdom and New York, NY: Cambridge University Press.
U.S. EPA. (2019). Integrated Science Assessment (ISA) for Particulate Matter (Final Report).
(EPA/600/R-19/188). Research Triangle Park, NC: U.S. Environmental Protection
Agency, Office of Research and Development, Center for Public Health and
Environmental Assessment, https://www.epa.gov/naaqs/particulate-matter-pm-standards-
integrated-science-assessments-current-review
U.S. EPA. (2021). Climate Change and Social Vulnerability in the United States: A Focus on Six
Impacts. (EPA 43O-R-21-003). Washington DC.
https://www.epa.gov/system/files/documents/2021-09/climate-vulnerability_september-
2021_508.pdf
U.S. EPA. (2022a). Regulatory Impact Analysis for the Proposed Reconsideration of the
National Ambient Air Quality Standards for Particulate Matter. (EPA-452/P-22-001).
Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Health and Environmental Impact Division.
https://www.epa.gOv/system/files/documents/2023-01/naaqs-pm_ria_proposed_2022-
12.pdf
U.S. EPA. (2022b). Supplement to the 2019 Integrated Science Assessment for Particulate
Matter (FinalReport). (EPA/600/R-22/028). Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Research and Development, Center for
Public Health and Environmental Assessment.
https://cfpub.epa.gOv/ncea/i sa/recordisplay.cfm?deid=354490
USGCRP. (2016). The Impacts of Climate Change on Human Health in the United States: A
Scientific Assessment. Washington DC: U.S. Global Change Research Program.
http://dx.doi.org/10.7930/J0R49NQX
USGCRP. (2018). Impacts, Risks, and Adaptation in the United States: Fourth National Climate
Assessment, Volume II. Washington DC: U.S. Global Change Research Program.
http://dx.doi.org/10.7930/NCA4.2018
Woods & Poole. (2015). Complete Demographic Database. Retrieved from
https://www.woodsandpoole.com/
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7 COMPARISON OF BENEFITS AND COSTS
7.1 Introduction
This section presents the estimates of the climate benefits, health benefits, compliance
costs, and net benefits associated with the illustrative scenarios analyzed in this RIA.158 All cost
and benefit analysis begins in 2028, except for monitoring, reporting, and recordkeeping
(MR&R), as some MR&R costs are estimated to begin in 2024. The regulatory impacts are
evaluated for the specific snapshot years of 2028, 2030, 2035, and 2040. We also estimate the
present value (PV) of costs, benefits, and net benefits, calculated for the years 2024 to 2042 from
the perspective of 2024, using both a three percent and seven percent discount rate as directed by
OMB's Circular A-4. All dollars are in 2019 dollars. We also present the equivalent annual value
(EAV), which represents a flow of constant annual values that, had they occurred in each year
from 2024 to 2042, would yield a sum equivalent to the PV. The EAV represents the value of a
typical cost or benefit for each year of the analysis, in contrast to the specific snapshot-year
estimates reported in the costs and benefits sections of this RIA.
There are potential benefits and costs that may result from the proposed rules that have
not been quantified or monetized. Due to current data and modeling limitations, our estimates of
the benefits from reducing CO2 emissions do not include important impacts like ocean
acidification or potential tipping points in natural or managed ecosystems. Unquantified benefits
also include climate benefits from reducing emissions of non-CCh greenhouse gases and benefits
from reducing exposure to SO2, NOx, and hazardous air pollutants (e.g., mercury), as well as
ecosystem effects and visibility impairment. Additionally, there may be health, ecological, and
productivity damages associated with water effluent and intake from coal generation that will be
avoided by these proposed rules.
The compliance costs reported in this RIA are not social costs although in this analysis
we use compliance costs as a proxy for social costs. We do not account for changes in costs and
benefits due to changes in economic welfare in the broader economy arising from shifts in
158 Section 7 pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing coal-
fired EGUs. Please see Section 8 for impact analysis of the proposed standards for existing natural gas-fired
EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
7-1
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production and consumption that may be induced by the proposed requirements. Furthermore,
costs due to interactions with pre-existing market distortions outside the electricity sector are
omitted, as are social costs that may be associated with the net change in power sector subsidies
under the proposal. Additional limitations of the analysis and sources of uncertainty are
described throughout the RIA and summarized in the Executive Summary.
7.2 Methods
EPA calculated the PV of costs, benefits, and net benefits for the years 2024 through
2042, using both a three percent and seven percent discount rate from the perspective of 2024.
All dollars are in 2019 dollars. In order to implement the OMB Circular A-4 requirement for
fulfilling E.O. 12866, we assess one less stringent and one more stringent illustrative scenario
relative to the illustrative proposal scenario.
This calculation of a PV requires an annual stream of values for each year of the 2024 to
2042 timeframe. All cost and benefit analysis begins in 2028, except MR&R costs which are
estimated to begin in 2024. EPA used IPM to estimate cost and emission changes for the
projection years 2028, 2030, 2035, and 2040. The proposed rules have requirements that come
into effect in different years, and the snapshot years approximate the different rule requirements
over the timeframe of analysis in this RIA. For details on how the three illustrative scenarios
reflect the requirements of the rules, see Section 3.2.
In the IPM modeling for this RIA, the 2028 projection year is representative of 2028
alone, the 2030 projection year is representative of 2029 through 2031, the 2035 projection year
is representative of 2032 to 2037, and the 2040 projection year is representative of 2038 to 2042.
Estimates of costs and emission changes in other years are determined from the mapping of
projection years to the calendar years that they represent. Consequently, the cost and emission
estimates from IPM in each projection year are applied to the years which it represents.
Climate benefits estimates are based on these projection year emission estimates and also
account for year-specific interim SC-CO2 values. Health benefits are based on projection year
emission estimates and also account for year-specific variables that influence the size and
distribution of the benefits. These variables include population growth, income growth, and the
baseline rate of death.
7-2
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7.3 Results
We first present net benefit analysis for the three years of detailed analysis, 2028, 2030,
2035, and 2040. Table 7-1 through Table 7-4 present the estimates of the projected compliance
costs, climate benefits, health benefits, and net benefits across the three illustrative scenarios for
the snapshot years 2028, 2030, 2035, and 2040, respectively. The comparison of benefits and
costs in PV and EAV terms for the proposed rules can be found in Table 7-5 for the illustrative
proposal scenario; Table 7-6 presents the results for the less stringent illustrative scenario; and
Table 7-7 presents results for the more stringent illustrative scenario. Estimates in the tables are
presented as rounded values.
As discussed in Section 4 of this RIA, the monetized benefits estimates provide an
incomplete overview of the beneficial impacts of the proposal. In particular, the monetized
climate benefits are incomplete and an underestimate as explained in Section 4.2. In addition,
important health, welfare, and water quality benefits anticipated under these proposed rules are
not quantified or monetized. EPA anticipates that taking non-monetized effects into account
would show the proposals to have greater benefits than the tables in this section reflect.
Simultaneously, the estimates of compliance costs used in the net benefits analysis may provide
an incomplete characterization of the true costs of the rule. The balance of unquantified benefits
and costs is ambiguous but is unlikely to change the result that the benefits of the proposals
exceed the costs by billions of dollars annually.
We also note that the RIA follows EPA's historic practice of using a technology-rich
partial equilibrium model of the electricity and related fuel sectors to estimate the incremental
costs of producing electricity under the requirements of proposed and final major EPA power
sector rules. In Appendix B of this RIA, EPA has also included an economy-wide analysis that
considers additional facets of the economic response to the proposed rules, including the full
resource requirements of the expected compliance pathways, some of which are paid for through
subsidies in the partial equilibrium analysis. The social cost estimates in the economy-wide
analysis and discussed in Appendix B are still far below the projected benefits of the proposed
rules.
7-3
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Table 7-1 Monetized Benefits, Costs, and Net Benefits of the Three Illustrative
Scenarios in 2028 (billion 2019 dollars) a,b
Proposal
Less Stringent
More Stringent
Climate Benefits c
0.60
0.51
0.029
PM2.5 and O3-
related Health
Benefits d
0.68
and
1.6
0.49
and
1.2
-0.051
and -0.42
Total Benefits e
1.3
and
2.2
1.0
and
1.7
-0.022
and -0.39
Compliance Costs
-0.21
-0.19
-0.067
Net Benefits
1.5
and
2.4
1.2
and
1.9
0.045
and -0.32
a We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
0 Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
d Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount
rate of 3 percent.
e Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
Table 7-2 Monetized Benefits, Costs, and Net Benefits of the Three Illustrative
Scenarios in 2030 (billion 2019 dollars) a'b
Proposal
Less Stringent
More Stringent
Climate Benefits c
5.4
5.0
6.5
PM2.5 and O3-
related Health
Benefits d
6.5
and
14
5.9 and
13
6.3 and 14
Total Benefits e
12
and
20
11 and
18
13 and 20
Compliance Costs
4.1
4.1
3.0
Net Benefits
7.8
and
16
6.8 and
14
9.8 and 17
a We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
0 Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
d Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount
rate of 3 percent.
e Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
7-4
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Table 7-3 Monetized Benefits, Costs, and Net Benefits of the Three Illustrative
Scenarios in 2035 (billion 2019 dollars) a,b
Proposal
Less Stringent
More Stringent
Climate Benefits c
2.5
2.4
2.8
PM2.5 and O3-
related Health
Benefits d
2.2
and
4.7
1.9 and
3.9
2.2
and 4.6
Total Benefits e
4.6
and
7.1
4.2 and
6.3
5.0
and 7.4
Compliance Costs
0.28
0.23
0.20
Net Benefits
4.4
and
6.8
4.0 and
6.0
4.8
and 7.2
a We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
0 Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
d Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount
rate of 3 percent.
e Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
Table 7-4 Monetized Benefits, Costs, and Net Benefits of the Three Illustrative
Scenarios in 2040 (billion 2019 dollars) a,b
Proposal
Less Stringent
More Stringent
Climate Benefits c
1.7
1.6
1.6
PM2.5 and 03-related
Health Benefits d
1.8
and
3.6
1.3 and 2.6
1.9
and 3.8
Total Benefits e
3.5
and
5.3
2.9 and 4.2
3.5
and 5.4
Compliance Costs
0.76
0.71
0.51
Net Benefits
2.7
and
4.5
2.2 and 3.5
3.0
and 4.9
a We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
0 Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
d Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount
rate of 3 percent.
e Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
7-5
-------
Table 7-5 Illustrative Proposal Scenario: Present Values and Equivalent Annualized
Values of Projected Monetized Compliance Costs, Benefits, and Net Benefits for 2024 to
2042 (billion 2019 dollars) a b
Climate
Benefits
PM2.5 and O3-
related Health
Benefits
Compliance
Costs
Net
Benefits
3%
3%
7%
3%
7%
2024
-
-
-
0.012
-0.012
-0.012
2025
-
-
-
0.012
-0.012
-0.012
2026
-
-
-
0.013
-0.013
-0.013
2027
-
-
-
0.013
-0.013
-0.013
2028
0.60
1.6
1.4
-0.21
2.4
2.3
2029
5.4
14
12
4.1
15
14
2030
5.4
14
13
4.1
16
14
2031
5.5
14
13
4.1
16
14
2032
2.3
4.3
3.9
0.28
6.4
5.9
2033
2.4
4.4
4.0
0.28
6.5
6.1
2034
2.4
4.5
4.1
0.28
6.7
6.2
2035
2.5
4.7
4.2
0.28
6.8
6.4
2036
2.5
4.8
4.3
0.28
7.0
6.5
2037
2.5
4.9
4.4
0.28
7.1
6.6
2038
1.7
3.4
3.1
0.76
4.3
4.0
2039
1.7
3.5
3.1
0.76
4.4
4.1
2040
1.7
3.6
3.2
0.76
4.5
4.2
2041
1.7
3.6
3.3
0.76
4.6
4.2
2042
1.8
3.7
3.3
0.76
4.7
4.3
Climate
Benefits
PM2.5 and O3-
related Health
Benefits
Compliance
Costs
Net
Benefits
Discount Rate
3%
3%
7%
3% 7%
3%
7%
Present
Value
30
68
44
14 10
85
64
Equivalent
Annualized
Value
2.1
4.8
4.3
0.95 0.98
5.9
5.4
a Annual values from 2024 to 2042 are not discounted. PV and EAV values discounted to 2024. Values have been
rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
0 The health benefits estimates use the larger of the two benefits estimates presented in Table 4-15 through Table
4-19. Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates.
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
7-6
-------
Table 7-6 Illustrative Less Stringent Scenario: Present Values and Equivalent
Annualized Values of Projected Monetized Compliance Costs, Benefits, and Net Benefits
for 2024 to 2042 (billion 2019 dollars) a'b
Climate
Benefits
PM2.5 and O3-
related Health
Benefits
Compliance
Costs
Net
Benefits
3%
3%
7%
3%
7%
2024
-
-
-
0.012
-0.012
-0.012
2025
-
-
-
0.012
-0.012
-0.012
2026
-
-
-
0.013
-0.013
-0.013
2027
-
-
-
0.013
-0.013
-0.013
2028
0.51
1.2
1.0
-0.19
1.9
1.8
2029
5.0
13
11
4.1
13
12
2030
5.0
13
12
4.1
14
12
2031
5.1
13
12
4.1
14
13
2032
2.2
3.6
3.3
0.23
5.6
5.3
2033
2.3
3.7
3.4
0.23
5.8
5.4
2034
2.3
3.8
3.4
0.23
5.9
5.5
2035
2.4
3.9
3.5
0.23
6.0
5.6
2036
2.4
4.0
3.6
0.23
6.2
5.8
2037
2.4
4.1
3.7
0.23
6.3
5.9
2038
1.5
2.5
2.2
0.71
3.3
3.1
2039
1.6
2.5
2.3
0.71
3.4
3.1
2040
1.6
2.6
2.3
0.71
3.5
3.2
2041
1.6
2.6
2.4
0.71
3.5
3.3
2042
1.6
2.7
2.4
0.71
3.6
3.3
Climate
Benefits
PM2.5 and O3-
related Health
Benefits
Compliance
Costs
Net
Benefits
Discount Rate
3%
3%
7%
3% 7%
3%
7%
Present
Value
28
58
38
13 10
73
56
Equivalent
Annualized
Value
2.0
4.1
3.7
0.93 0.96
5.1
4.7
a Annual values from 2024 to 2042 are not discounted. PV and EAV estimates discounted to 2024. Values have been
rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
0 The health benefits estimates use the larger of the two benefits estimates presented in Table 4-15 through Table
4-19. Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates.
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
7-7
-------
Table 7-7 Illustrative More Stringent Scenario: Present Values and Equivalent
Annualized Values of Projected Monetized Compliance Costs, Benefits, and Net Benefits
for 2024 to 2042 (billion 2019 dollars) a'b
Climate
Benefits
PM2.5 and O3-
related Health
Benefits
Compliance
Costs
Net
Benefits
3%
3%
7%
3%
7%
2024
-
-
-
0.012
-0.012
-0.012
2025
-
-
-
0.012
-0.012
-0.012
2026
-
-
-
0.013
-0.013
-0.013
2027
-
-
-
0.013
-0.013
-0.013
2028
0.029
-0.42
-0.37
-0.067
-0.32
-0.28
2029
6.4
13
12
3.0
17
15
2030
6.5
14
12
3.0
17
16
2031
6.6
14
12
3.0
17
16
2032
2.6
4.3
3.8
0.20
6.7
6.3
2033
2.7
4.4
3.9
0.20
6.9
6.4
2034
2.7
4.5
4.0
0.20
7.0
6.6
2035
2.8
4.6
4.1
0.20
7.2
6.7
2036
2.8
4.7
4.2
0.20
7.3
6.9
2037
2.9
4.5
4.1
0.20
7.2
6.8
2038
1.6
3.6
3.3
0.51
4.7
4.4
2039
1.6
3.7
3.3
0.51
4.8
4.4
2040
1.6
3.8
3.4
0.51
4.9
4.5
2041
1.7
3.9
3.5
0.51
5.0
4.6
2042
1.7
3.9
3.5
0.51
5.1
4.7
Climate
Benefits
PM2.5 and O3-
related Health
Benefits
Compliance
Costs
Net
Benefits
Discount Rate
3%
3%
7%
3% 7%
3%
7%
Present
Value
34
65
42
10 7.5
89
68
Equivalent
Annualized
Value
2.4
4.6
4.0
0.70 0.73
6.2
5.7
Annual values from 2024 to 2042 are not discounted. PV and EAV estimates discounted to 2024. Values have been
rounded to two significant figures. Rows may not appear to add correctly due to rounding.
b Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
0 The health benefits estimates use the larger of the two benefits estimates presented in Table 4-15 through Table
4-19. Monetized benefits include those related to public health associated with reductions in PM2 5 and ozone
concentrations. The health benefits are associated with several point estimates.
d Several categories of benefits remain unmonetized and are thus not reflected in the table. Non-monetized benefits
include important climate, health, welfare, and water quality benefits.
7-8
-------
8
IMPACTS OF PROPOSED 111(D) STANDARDS ON EXISTING NATURAL GAS-
FIRED EGUS AND THIRD PHASE OF PROPOSED 111(B) STANDARDS ON NEW
NATURAL GAS-FIRED EGUS
8.1 Introduction
The existing source performance standards modeled using IPM did not include proposed
requirements on existing natural gas-fired combined cycle (NGCC) units as summarized in Table
8-1 below. To estimate the impact of these proposed requirements, EPA performed a
spreadsheet-based analysis using the model output of each of the illustrative scenarios described
earlier in the RIA to produce a range of possible outcomes as outlined in this section of the
RIA.159 This analysis therefore does not include any additional IPM modeling.
Table 8-1 GHG Mitigation Measures for Existing NGCC Units under the Illustrative
Proposal, More Stringent and Less Stringent Scenarios
Affected EGUs
GHG Mitigation Measure
GHG Mitigation Measure
Natural Gas fired Combined
Cycle Units > 300 MW and
operating > 50% capacity factor
in run year 2035 with online
year of 2025 or earlier
Co-fire 30% by volume hydrogen in
run year 2035, and 96% by volume
hydrogen in run year 2040 onwards
CCS with 90 percent capture of CO2,
starting in run year 2035
The new source performance standards modeled using IPM also did not include
additional requirements on new NGCC units —specifically, the proposed requirements for new
base load combustion turbines in the hydrogen co-firing subcategory to comply with a third
phase standard based on co-firing 96 percent low-GHG hydrogen by 2038— as summarized in
Table 8-2. To estimate the impact of these proposed requirements, EPA performed a
spreadsheet-based analysis using the model output of each of the illustrative scenarios to produce
a range of possible outcomes as outlined in this section of the RIA.160 As is the case for the
analysis of existing natural gas-fired combined cycle units, this analysis also does not include
any additional IPM modeling.
159 The spreadsheet analysis for each of the scenarios is included in the docket for this rulemaking.
160 The spreadsheet analysis for each of the scenarios is included in the docket for this rulemaking.
8-1
-------
Table 8-2 GHG Mitigation Measures for New NGCC Units under the Illustrative
Proposal, More Stringent and Less Stringent Scenarios
Affected EGUs
GHG Mitigation Measure
Natural Gas Combined Cycle Units with online year
after 2025 that operate at > 50% capacity factor
Co-fire 96% by volume hydrogen in run year 2040
onwards or install CCS
8.2 Methodology
To estimate the regulatory impacts of the proposed requirements for existing and new
NGCC units described in the previous section, EPA evaluated the impacts from the change in the
existing source standard and new source standard separately. The approach to these analyses is
outlined below.
8.2.1 111(d) Standards on Existing Natural Gas-Fired EGUs
To estimate the impact of the additional existing source standards, EPA relied on the IPM
outputs of the illustrative proposal and less and more stringent illustrative scenarios as the
baseline to estimate the impacts of the additional existing source standards for NGCC units using
the spreadsheet-based approach. Hence this analysis included no additional IPM modeling. Units
that would be subject to these requirements were identified by selecting model plants with
average unit size greater than 300 MW that are projected to operate at greater than 50 percent
capacity factor in the 2035 run year. Of these model plants, those that were projected to operate
at higher capacity factors in 2035, 2040 and 2045 were assumed to install CCS rather than
finding an alternative compliance pathway given plant economics.161 EPA used different capacity
factor cutoffs to construct a range of units assumed to install CCS, with the "low" end reflecting
fewer CCS installations and the "high" end reflecting more CCS installations. Note EPA did not
analyze the impacts of hydrogen co-firing as a compliance measure within this subcategory. All
other model plants within this category were assumed to reduce utilization to 50 percent and,
therefore, were not assumed to install CCS. 80 percent of the reduced dispatch as a result of
161 To construct a range of selected units, EPA assumed model plants > 300 MW average unit size that operated at or
above 80 percent in 2035, 2040 and 2045 formed one end of the range, and model plants > 300 MW average unit
size that operated at or above 85 percent, 70 percent, 65 percent in 2035, 2040 and 2045 formed the other end of
the range.
8-2
-------
reduced utilization and capacity de-rates at units installing CCS were replaced by assuming
increasing generation at existing NGCC units that are not subject to the requirements (i.e., model
plants that operate at less than 50 percent capacity factor), while the remaining 20 percent of the
generation was replaced by incremental non-emitting generation. The amount of capacity
assumed to adopt CCS and the resource mix assumed to fill in dispatch as a result of reduced
utilization were both based on EPA expert judgment based on trends in prior IPM runs.
EPA used projected generation weighted average national CO2 emission rates from each
of the sets of units described above in each run year to estimate the CO2 emission impacts
resulting from these changes. EPA used the generation weighted average national projected fuel
and variable operating costs from model plants that are assumed to reduce dispatch and those
that assumed to increase dispatch to calculate the cost of shifts in generation to lower utilized
existing NGCC model plants. EPA used the average generation weighted national projected costs
for wind and solar additions in each run year to calculate the cost of incremental non-emitting
generation assumed within the analysis. Finally, EPA used cost and performance assumptions
consistent with the IPM post-IRA 2023 reference case to calculate the costs and emissions
reductions associated with CCS installations at existing NGCC units.162
8.2.2 Third Phase of 111(b) Standards on New Natural Gas-Fired EGUs
To estimate the impact of the additional new source standards, EPA relied on the IPM
outputs of the illustrative proposal and less and more stringent illustrative scenarios to determine
the baseline for the spreadsheet-based analysis. Therefore, no incremental IPM modeling was
performed for this analysis. We identified new NGCC model plants that are projected to operate
at greater than 50 percent capacity factor and are projected to co-fire less than 96 percent
hydrogen by volume in run year 2040. Of these model plants, the largest 20 percent of model
plants and the largest 40 percent of model plants were assumed to increase hydrogen co-firing to
form the basis of the low and high ends of the range showed in this section.
EPA did not analyze the impacts of CCS as a compliance measure within this
subcategory. All other model plants within this category were assumed to reduce utilization to 50
162 Available at: https://www.epa.gov/power-sector-modeling/post-ira-2022-reference-case
8-3
-------
percent and therefore did not increase hydrogen co-fire share. 80 percent of the reduced dispatch
as a result of reduced utilization were replaced by assuming increasing generation at existing
NGCC units that are not subject to the requirements (i.e., model plants that operate at less than
50 percent capacity factor), while the remaining 20 percent of the generation was replaced by
incremental non-emitting generation. EPA used projected generation weighted average national
CO2 emission rates from each of the sets of units described above in each run year to track the
CO2 emission impacts resulting from these changes. EPA used the generation weighted average
national projected fuel and variable operating costs from model plants that are assumed to reduce
dispatch and those that assumed to increase dispatch to calculate the cost of shifts in generation
to lower utilized existing NGCC model plants. We used the generation weighted average
national projected costs for wind and solar additions in each run year to calculate the cost of
incremental non-emitting generation assumed within the analysis. Finally, we assumed a $0.5/kg
delivered hydrogen price to calculate the costs of increased hydrogen consumption, consistent
with the hydrogen price assumed in the modeling when the second phase is active. For reference
in the results that follow, these estimates are referred to as "low" and "high" based on the total
amount of associated emissions reductions.
8.3 Estimated Regulatory Impacts
Using the approach outlined above, EPA estimate the impacts on power sector CO2
emissions, costs, generation, and incremental hydrogen demand for the proposed requirements
on existing and new NGCC units. We note the analysis approach used in this section to estimate
emissions impacts of the proposed 111(d) standards on existing natural gas-fired EGUs and the
third phase of proposed 111(b) standards on new natural gas-fired EGUs does not permit the
estimation of changes in emissions of non-CC>2 pollutants.
Because this additional analysis used the IPM outputs from the illustrative scenarios as its
baseline, these results do not capture the potential for interactive effects between the additional
measures and the IPM-modeled measures (e.g., the potential that establishing 111(d)
requirements for existing natural gas-fired EGUs could affect the compliance approaches
undertaken by other EGUs or lead to different shifts in the overall generation mix than those
reflected in the IPM outputs).
8-4
-------
8.3.1 Emissions Reduction Assessment
8.3.1.1 111(d) Standards on Existing Natural Gas-FiredEGUs
Based on the analysis outlined above, EPA estimated the change in CO2 emissions from
the additional measures selected to the outcomes under each of the illustrative scenarios
discussed elsewhere in this RIA (the IPM-modeled aspects of the regulatory approach to existing
fossil-fuel fired steam generating units and new and reconstructed stationary combustion
turbines). These results are summarized in Table 8-3 below, showing results for low and high
ends of a range based on different assumptions in how many model existing plants are assumed
to install CCS.
Table 8-3 Estimated Changes in Power Sector Emissions from Existing Source
Standard under the Three Illustrative Scenarios
Annual CO2
Proposal
Less Stringent
More Stringent
(million
metric tons)
Low
High
Low
High
Low High
2028
0
0
0
0
0 0
2030
0
0
0
0
0 0
2035
-20
-37
-20
-37
-20 -37
2040
-19
-37
-19
-37
-19 -37
8.3.1.2 Third Phase of 111(b) Standards on New Natural Gas-Fired EGUs
Based on the analysis outlined above, EPA estimated the change in emissions from the
measures selected to the outcomes under each of the illustrative scenarios discussed elsewhere in
this RIA. These results are summarized in the Table 8-4 below, showing results for low and high
ends of a range based on different assumptions in how many model new plants are projected to
increase hydrogen co-firing.
8-5
-------
Table 8-4 Estimated Changes in Power Sector Emissions from New Source Standard
under the Three Illustrative Scenarios
Annual CO2
(million
metric tons)
Proposal
Less Stringent
More Stringent
Low
High
Low
High
Low High
2028
0
0
0
0
0 0
2030
0
0
0
0
0 0
2035
0
0
0
0
0 0
2040
-0.22
-2.5
-0.20
-2.5
-2.2 -4.2
8.3.2 Compliance Cost Assessment
8.3.2.1 111(d) Standards on Existing Natural Gas-FiredEGUs
Based on the analysis outlined above, EPA estimated the change in costs from the
measures selected to the outcomes under each of the illustrative scenarios discussed elsewhere in
this RIA. These results are summarized in Table 8-5.
Table 8-5 Estimated Changes in Power Sector Costs from Existing Source Standard
under the Three Illustrative Scenarios (billion 2019 dollars)
Proposal
Less Stringent
More Stringent
Low
High
Low
High
Low High
2028
0
0
0
0
0 0
2030
0
0
0
0
0 0
2035
0.76
1.3
0.76
1.3
0.76 1.3
2040
0.68
1.2
0.68
1.2
0.68 1.2
8.3.2.2 Third Phase of 111(b) Standards on New Natural Gas-Fired EG Us
Based on the analysis outlined above, EPA estimated the change in costs from the
measures selected to the outcomes under each of the illustrative scenarios discussed elsewhere in
this RIA. These results are summarized in Table 8-6.
8-6
-------
Table 8-6 Estimated Changes in Power Sector Costs from New Source Standard under
the Three Illustrative Scenarios (billion 2019 dollars)
Proposal
Less Stringent
More Stringent
Low
High
Low
High
Low
High
2028
0
0
0
0
0
0
2030
0
0
0
0
0
0
2035
0
0
0
0
0
0
2040
0.064
0.21
0.064
0.21
0.24
0.37
EPA did not conduct IPM modeling in order to evaluate the impacts of the requirements on
existing natural gas-fired EGUs and the third phase of the requirements on new natural gas-fired
EGUs, relying instead on a spreadsheet-based analysis as outlined in Section 8 of the RIA. When
relying on IPM projections, EPA estimates retail rate impacts using the methodology outlined in
the Retail Price Model.163 The spreadsheet-based approach described in section 8 does not
provide the necessary inputs to populate the RPM; however, given the trends in total compliance
costs, EPA expects that retail rates are likely to increase at similar levels to those estimated under
the analysis provided in Section 3.6.3 of this RIA. In particular, total compliance costs are
projected to range between 0.8 and 4 billion 2019$ between 2030 and 2040 under the modeled
proposal, and retail rates are projected to rise between 0.1 percent to 2 percent over the 2030 and
2040 period. In comparison, estimated total compliance costs range between 0.7 and 1.4 billion
2019$ between 2030 and 2040 as a result of requirements on existing natural gas-fired EGUs and
the third phase of the requirements on new natural gas-fired EGUs under the spreadsheet-based
approach outlined in Section 8.
8.3.3 Generation Mix and Compliance Outcomes
8.3.3.1 111(d) Standards on Existing Natural Gas-Fired EGUs
Based on the analysis outlined above, EPA estimated the change in generation from the
measures selected to the outcomes under each of the illustrative scenarios discussed elsewhere in
this RIA. These results are summarized in Table 8-7. Because this additional analysis used the
IPM outputs from the illustrative scenarios as its baseline, these results do not capture the
potential for interactive effects between the additional measures and the IPM-modeled measures
163 Available at: https://www.epa.gov/power-sector-modeling/retail-price-model
8-7
-------
(e.g., the potential that establishing 111(d) requirements for existing natural gas-fired EGUs
could affect the compliance approaches undertaken by other EGUs or lead to different shifts in
the overall generation mix than those reflected in the IPM outputs).
Table 8-7 Estimated Changes in Power Sector Generation from Existing Source
Standard under the Three Illustrative Scenarios
Proposal
Low
High
Change in Generation (TWh)
2028
2030
2035
2040
2028
2030
2035
2040
EGUs assumed to Install CCS
0
0
-11.7
-11.7
0
0
-23.4
-23.4
EGUs assumed to reduce dispatch
0
0
-80.3
-67.1
0
0
-54.2
-41.0
Reallocated by each category:
Existing CC
0
0
73.6
63.1
0
0
62.0
51.5
Zero-emitting
0
0
18.4
15.8
0
0
15.5
12.9
Total
0
0
0.0
0.0
0
0
0.0
0.0
Less Stringent
Change in Generation (TWh)
2028
2030
Low
2035
2040
2028
2030
High
2035
2040
EGUs assumed to Install CCS
0
0
-11.7
-11.7
0
0
-23.4
-23.4
EGUs assumed to reduce dispatch
0
0
-81.5
-67.2
0
0
-55.4
-41.1
Reallocated by each category:
Existing CC
0
0
74.6
63.2
0
0
63.0
51.6
Zero-emitting
0
0
18.6
15.8
0
0
15.7
12.9
Total
0
0
0.0
0.0
0
0
0.0
0.0
More Stringent
Change in Generation (TWh)
2028
2030
Low
2035
2040
2028
2030
High
2035
2040
EGUs assumed to Install CCS
0
0
-11.7
-11.7
0
0
-23.4
-23.4
EGUs assumed to reduce dispatch
0
0
-79.7
-67.5
0
0
-53.6
-41.4
Reallocated by each category:
Existing CC
0
0
73.1
63.4
0
0
61.5
51.8
Zero-emitting
0
0
18.3
15.8
0
0
15.4
13.0
Total
0
0
0.0
0.0
0
0
0.0
0.0
Using the methodology outlined above, EPA estimated that 36.8 GW of existing NGCC
capacity had an average modeled unit size of greater than 300 MW and was projected to operate
at greater than 50 percent capacity factor in 2035 under the illustrative scenarios. Of these 36.8
GW, 8.6 GW to 17.3 GW were identified as more likely to install CCS rather than pursue an
alternative compliance pathway based on high levels of utilization across the 2035, 2040 and
-------
2045 run years, and related plant economics. Units installing CCS were assumed to maintain
their capacity factors, but incurred an 18 percent capacity penalty, resulting in reduced dispatch.
The remaining identified existing NGCC capacity was assumed to operate at 50 percent capacity
factor. 80 percent of the reductions in generation were apportioned to existing NGCC units
operating below 50 percent capacity factor and the remaining 20 percent were apportioned to
incremental non-emitting resources. As shown in the table below, the decreases in generation
from affected NGCC units are exactly offset by increases in replacement generation. Since
existing NGCC units are projected to operate similarly across the three illustrative scenarios, the
methodology used to determine potential impacts of the additional existing source requirements
results in similar outcomes across the cases.
Using the methodology outlined above, EPA estimates a minimal impact on the total
amount of accredited capacity as a result of the standards on existing combustion turbines. In
particular, the analysis assumes no incremental retirements at existing NGCC units, and an
installation of 8.6 GW to 17.3 GW of incremental CCS installations by 2035 under the
illustrative scenarios. Since retrofit CCS installations are assumed to incur an 18 percent capacity
penalty, this results in a total reduction in NGCC of 1.5 to 3.1 GW of accredited capacity
nationwide. At the same time, the analysis assumes that an incremental 4.6 to 5.5 GW of zero-
emitting capacity is added or maintained nationwide. To fully offset the reduction of accredited
capacity in NGCC would require that the zero-emitting resources were able to contribute 33
percent of their total capacity to reserve in the low scenario and 56 percent in the high scenario.
To put the capacity totals into context, total US projected peak demand in 2035 is 886 GW,
and there are 58 GW of retirements and 332 GW of capacity additions projected between the
2030 and 2035 model run years under the Proposal modeling.
Using the methodology outlined above, EPA estimates no impact on the total amount of
accredited capacity as a result of the third phase requirement on new combustion turbines.
Using the methodology outlined above, EPA assumes a minimal change (less than one
percent) in natural gas consumption and therefore on delivered natural gas prices.
8-9
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8.3.3.2 Third Phase of 111(b) Standards on New Natural Gas-Fired EGUs
Based on the analysis outlined above, EPA estimated the change in generation from the
measures selected to the outcomes under each of the illustrative scenarios discussed elsewhere in
this RIA. These results are summarized in Table 8-8 below.
Under the modeled illustrative scenarios, IPM projected 25.7 GW of new NGCC additions
under the proposal, 25.3 GW of new NGCC additions under the less stringent scenario and 22.8
GW of new NGCC additions under the more stringent scenario. Of these projected builds, 6.4
GW were projected to co-fire hydrogen under the proposal and less stringent scenario, and 13.6
GW were projected to co-fire hydrogen under the more stringent scenario. Using the
methodology outlined above, EPA estimated that in 2040, 0.4 - 1.5 GW of capacity increased
hydrogen co-fire blends to 96 percent by volume while the remaining capacity reduced dispatch
to below 50 percent under the proposal and less stringent scenarios. EPA did not analyze the
impacts of CCS as a compliance measure within this subcategory. EPA estimated that in 2040
1.6 to 2.7 GW of capacity increased hydrogen co-fire blends to 96 percent by volume and the
remaining capacity reduced dispatch to below 50 percent capacity factor under the more
stringent scenario. 80 percent of the reductions in generation were apportioned to existing NGCC
units operating below 50 percent capacity factor and the remaining 20 percent apportioned to
incremental non-emitting resources. As shown in the table below, the decreases in generation
from affected new NGCC units is exactly offset by increases in replacement generation. Since a
larger amount of new NGCC units are projected to co-fire hydrogen under the more stringent
scenario, reductions are largest under that scenario. Since a similar amount new NGCC units are
projected to co-fire hydrogen under the more and less stringent scenarios, reductions are similar
under those scenarios.
8-10
-------
Table 8-8 Estimated Changes in Power Sector Generation from New Source Standard
under the Three Illustrative Scenarios
Proposal
Low
High
Change in Generation (TWh)
2028 2030 2035 2040
2028 2030 2035 2040
EGUs assumed to increase co-fire share
EGUs assumed to reduce dispatch
Reallocated by each category:
Existing CC
Zero-emitting
0 0 0 0.0
0 0 0 -16.5
0 0 0 0.0
0 0 0 13.2
0 0 0 3.3
0 0 0 0.0
0 0 0 -13.0
0 0 0 0.0
0 0 0 10.4
0 0 0 2.6
Total
O
©
o
o
o
O
©
o
o
o
Less Stringent
Low
High
Change in Generation (TWh)
2028 2030 2035 2040
2028 2030 2035 2040
EGUs assumed to increase co-fire share
EGUs assumed to reduce dispatch
Reallocated by each category:
Existing CC
Zero-emitting
0 0 0 0.0
0 0 0 -16.7
0 0 0 0.0
0 0 0 13.4
0 0 0 3.3
0 0 0 0.0
0 0 0 -13.1
0 0 0 0.0
0 0 0 10.5
0 0 0 2.6
Total
O
©
o
o
o
O
©
o
o
o
More Stringent
Low
High
Change in Generation (TWh)
2028 2030 2035 2040
2028 2030 2035 2040
EGUs assumed to increase co-fire share
EGUs assumed to reduce dispatch
Reallocated by each category:
Existing CC
Zero-emitting
0 0 0 0.0
0 0 0 -29.9
0 0 0 0.0
0 0 0 23.9
0 0 0 6.0
0 0 0 0.0
0 0 0 -27.5
0 0 0 0.0
0 0 0 22.0
0 0 0 5.5
Total
O
©
o
o
o
O
©
o
o
o
Table 8-9 below outlines the incremental hydrogen demand that would be required to fuel
the higher co-firing levels assumed in the analysis of the new source standards. For context, the
analysis of the requirements on existing coal fired EGUs and the two phase NSPS (as outlined in
Section 3) projected Hydrogen consumption varied between 2.5 to 2.9 million metric tons in
2040. As outlined in Section 3, hydrogen is an exogenous input to the model, represented as a
fuel that is available at affected sources at a delivered cost of $l/kg under the baseline, and at a
delivered cost of $0.5/kg in years when the second phase of the proposed NSPS is assumed to be
active. These costs are inclusive of $3/kg subsidies under the IRA. We also note the model does
8-11
-------
not track upstream emissions associated with the production of the hydrogen (or any other
modeled fuels such as coal and natural gas), nor any incremental electricity demand associated
with its production. Similarly, the spreadsheet-based analysis does not estimate any upstream
emissions associated with hydrogen production, nor any incremental electricity demand
associated with its production.
Table 8-9 Estimated Changes in Power Sector Hydrogen Demand from New Source
Standard under the Three Illustrative Scenarios
Hydrogen
Demand
(MMT)
Proposal
Less Stringent
More Stringent
Low
High
Low
High
Low
High
2028
0
0
0
0
0
0
2030
0
0
0
0
0
0
2035
0
0
0
0
0
0
2040
0.00
0.32
0.00
0.33
0.25
0.53
8.4 Climate Benefits Analysis
Using the methods described in Section 4.2, we estimate the social benefits of CO2
reductions expected to occur as a result of the projected CO2 reductions presented in Section
8.3.1 using estimates of the social cost of greenhouse gases (SC-GHG), specifically using the
social cost of carbon (SC-CO2). As mentioned earlier, the analysis approach used in this section
to estimate emissions impacts of the proposed 111(d) standards on existing natural gas-fired
EGUs and the third phase of proposed 111(b) standards on new natural gas-fired EGUs does not
permit the estimation of changes in emissions of non-CC>2 pollutants. Consequently, the benefits
analysis in this section is limited to an assessment of the projected climate benefits arising from
the proposed provisions analyzed in this section.
8.4.1 111(d) Standards on Existing Natural Gas-Fired EGUs
Based on the analysis outlined above this section, EPA estimated the change in CO2
emissions in 2028, 2030, 2035, and 2040 from the measures selected to the outcomes under each
of the illustrative scenarios described elsewhere in this RIA (see Table 8-3). To obtain annual
estimates of CO2 reductions from 2028 to 2042, we mapped the emissions reductions in 2030 as
8-12
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presented in Table 8-3 to calendar years 2029 to 2031, the emissions reductions in 2035 to
calendar years 2032 to 2037, and the emissions reductions in 2040 to calendar years 2038 to
2042. The resulting estimated annual changes in GHG emissions are shown in Table 8-10 below.
Table 8-10 Annual CO2 Emissions Reductions (million metric tons) for the 111(d)
Standards on Existing Natural Gas-Fired EGUs Illustrative Scenarios from 2028 through
2042
Proposal
Scenario
Less Stringent Scenario
More Stringent
Scenario
Low
High
Low
High
Low
High
2028
-
-
-
-
-
-
2029
-
-
-
-
-
-
2030
-
-
-
-
-
-
2031
-
-
-
-
-
-
2032
20
37
20
37
20
37
2033
20
37
20
37
20
37
2034
20
37
20
37
20
37
2035
20
37
20
37
20
37
2036
20
37
20
37
20
37
2037
20
37
20
37
20
37
2038
19
37
19
37
19
37
2039
19
37
19
37
19
37
2040
19
37
19
37
19
37
2041
19
37
19
37
19
37
2042
19
37
19
37
19
37
Total 214 407 215 407 214 407
Table 8-11 through Table 8-13 show the estimated monetary value of the CO2 emissions
reductions estimated to occur over the 2028 to 2042 period for the illustrative scenarios. EPA
estimated the dollar value of the GHG-related effects for each analysis year between 2028 and
2042 by applying the SC-CO2 estimates presented in Table 4-1 to the estimated changes in GHG
emissions in the corresponding year as shown above in Table 8-10. EPA then calculated the
present value (PV) and equivalent annualized value (EAV) of benefits from the perspective of
2024 by discounting each year-specific value to the year 2024 using the same discount rate used
to calculate the SC-CO2. See Table 8-11, Table 8-12, and Table 8-13 for the climate benefit
8-13
-------
estimates for the proposal and less and more stringent illustrative scenarios associated with the
proposed standards on existing natural gas-fired EGUs, respectively.
8-14
-------
Table 8-11 Range of Benefits of Reduced CO2 Emissions from the 111(d) Standards on
Existing Natural Gas-Fired EGUs Illustrative Proposal Scenario, 2028 to 2042 (millions of
2019 dollars)3
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions
Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
-
-
-
-
2029
-
-
-
-
2030
-
-
-
-
2031
-
-
-
-
2032
$400-750
$1,200-2,300
$1,800-3,400
$3,700-7,100
2033
$410-770
$1,300-2,400
$1,800-3,400
$3,800-7,300
2034
$420-790
$1,300-2,400
$1,800-3,500
$3,900-7,400
2035
$430-820
$1,300-2,500
$1,900-3,500
$4,000-7,500
2036
$440-840
$1,300-2,500
$1,900-3,600
$4,000-7,700
2037
$450-860
$1,300-2,600
$1,900-3,600
$4,100-7,800
2038
$460-880
$1,400-2,600
$1,900-3,700
$4,200-7,900
2039
$470-900
$1,400-2,600
$1,900-3,700
$4,200-8,100
2040
$480-920
$1,400-2,700
$2,000-3,800
$4,300-8,200
2041
$490-940
$1,400-2,700
$2,000-3,800
$4,400-8,300
2042
$510-970
$1,400-2,800
$2,000-3,900
$4,400-8,500
PV
EAV
$2,600-5,000
$220-410
$10,000-19,000
$700-1,300
$15,000-29,000
$1000-1,900
$31,000-58,000
$2,100-4,100
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile
at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated
using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon, Methane, and
Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of climate benefits
calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted when discounting
intergenerational impacts.
8-15
-------
Table 8-12 Range of Benefits of Reduced CO2 Emissions from the 111(d) Standards on
Existing Natural Gas-Fired EGUs Illustrative Less Stringent Scenario, 2028 to 2042
(millions of 2019 dollars)3
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions
Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
-
-
-
-
2029
-
-
-
-
2030
-
-
-
-
2031
-
-
-
-
2032
$400-750
$1,200-2,300
$1,800-3,400
$3,800-7,100
2033
$410-770
$1,300-2,400
$1,800-3,400
$3,800-7,300
2034
$420-800
$1,300-2,400
$1,800-3,500
$3,900-7,400
2035
$430-820
$1,300-2,500
$1,900-3,500
$4,000-7,500
2036
$440-840
$1,300-2,500
$1,900-3,600
$4,100-7,700
2037
$450-860
$1,400-2,600
$1,900-3,600
$4,100-7,800
2038
$460-880
$1,400-2,600
$1,900-3,700
$4,200-7,900
2039
$470-900
$1,400-2,600
$1,900-3,700
$4,200-8,100
2040
$480-920
$1,400-2,700
$2,000-3,800
$4,300-8,200
2041
$500-940
$1,400-2,700
$2,000-3,800
$4,400-8,300
2042
$510-970
$1,400-2,800
$2,000-3,900
$4,400-8,500
PV
EAV
$2,600-5,000
$220-410
$10,000-19,000
$700-1,300
$15,000-29,000
$1,000-1,900
$31,000-58,000
$2,100-4,100
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile
at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated
using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon, Methane, and
Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of climate benefits
calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted when discounting
intergenerational impacts.
8-16
-------
Table 8-13 Range of Benefits of Reduced CO2 Emissions from the 111(d) Standards on
Existing Natural Gas-Fired EGUs Illustrative More Stringent Scenario, 2028 to 2042
(millions of 2019 dollars)3
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions
Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
-
-
-
-
2029
-
-
-
-
2030
-
-
-
-
2031
-
-
-
-
2032
$400-750
$1,200-2,300
$1,800-3,400
$3,800-7,100
2033
$410-770
$1,300-2,400
$1,800-3,400
$3,800-7,300
2034
$420-790
$1,300-2,400
$1,800-3,500
$3,900-7,400
2035
$430-820
$1,300-2,500
$1,900-3,500
$4,000-7,500
2036
$440-840
$1,300-2,500
$1,900-3,600
$4,100-7,700
2037
$450-860
$1,300-2,600
$1,900-3,600
$4,100-7,800
2038
$460-880
$1,400-2,600
$1,900-3,700
$4,200-7,900
2039
$470-900
$1,400-2,600
$2,000-3,700
$4,300-8,100
2040
$480-920
$1,400-2,700
$2,000-3,800
$4,300-8,200
2041
$500-940
$1,400-2,700
$2,000-3,800
$4,400-8,300
2042
$510-970
$1,500-2,800
$2,000-3,900
$4,500-8,500
PV
EAV
$2,600-5,000
$220-410
$10,000-19,000
$700-1,300
$15,000-29,000
$1,000-1,900
$31,000-58,000
$2,200-4,100
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile
at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated
using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon, Methane, and
Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of climate benefits
calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted when discounting
intergenerational impacts.
8.4.2 Third Phase of 111(b) Standards on New Natural Gas-Fired EGUs
Based on the analysis outlined above, EPA estimated the change in emissions from the
measures selected to the outcomes under each of the illustrative scenarios for the third phase of
the 111(b) standards on new natural gas-fired EGUs. Using the same model year to calendar year
mapping described in the previous section, the estimated annual change in CO2 emissions are
shown in Table 8-14 below.
8-17
-------
Table 8-14 Annual CO2 Emissions Reductions (million metric tons) for the 111(b)
Standards on New Natural Gas-Fired EGUs Illustrative Scenarios from 2028 through 2042
Proposal Scenario
Less Stringent Scenario
More Stringent
Scenario
Low
High
Low
High
Low
High
2028
-
-
-
-
-
-
2029
-
-
-
-
-
-
2030
-
-
-
-
-
-
2031
-
-
-
-
-
-
2032
-
-
-
-
-
-
2033
-
-
-
-
-
-
2034
-
-
-
-
-
-
2035
-
-
-
-
-
-
2036
-
-
-
-
-
-
2037
-
-
-
-
-
-
2038
0
2
2
4
0
3
2039
0
2
2
4
0
3
2040
0
2
2
4
0
3
2041
0
2
2
4
0
3
2042
0
2
2
4
0
3
Total
1
12
11
21
1
13
Table 8-15 through Table 8-17 show the estimated monetary value of the estimated
changes in CO2 emissions expected to occur over 2028 through 2042 for the illustrative
scenarios. EPA estimated the dollar value of the GHG-related effects for each analysis year
between 2028 and 2042 by applying the SC-GHG estimates presented in Table 4-1 to the
estimated changes in GHG emissions in the corresponding year as shown above in Table 8-14.
EPA then calculated the PV and EAV of benefits from the perspective of 2024 by discounting
each year-specific value to the year 2024 using the same discount rate used to calculate the SC-
GHG.
8-18
-------
Table 8-15 Range of Benefits of Reduced CO2 Emissions from the 111(b) Standards on
New Natural Gas-Fired EGUs Illustrative Proposal Scenario, 2028 to 2042 (millions of 2019
dollars)3
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
-
-
-
-
2029
-
-
-
-
2030
-
-
-
-
2031
-
-
-
-
2032
-
-
-
-
2033
-
-
-
-
2034
-
-
-
-
2035
-
-
-
-
2036
-
-
-
-
2037
-
-
-
-
2038
$5.2-59
$15-170
$22-250
$47-530
2039
$5.3-60
$15-180
$22-250
$47-540
2040
$5.4-62
$16-180
$22-250
$48-550
2041
$5.5-63
$16-180
$22-260
$49-560
2042
$5.7-65
$16-180
$23-260
$50-570
PV
$12-140
$49-560
$74-850
$150-1,700
EAV
$1-12
$3.4-39
$5-57
$10-120
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile
at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated
using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon, Methane, and
Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of climate benefits
calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted when discounting
intergenerational impacts.
8-19
-------
Table 8-16 Range of Benefits of Reduced CO2 Emissions from the 111(b) Standards on
New Natural Gas-Fired EGUs Illustrative Less Stringent Scenario, 2028 to 2042 (millions
of 2019 dollars)3
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
-
-
-
-
2029
-
-
-
-
2030
-
-
-
-
2031
-
-
-
-
2032
-
-
-
-
2033
-
-
-
-
2034
-
-
-
-
2035
-
-
-
-
2036
-
-
-
-
2037
-
-
-
-
2038
$4.7-59
$14-180
$19-250
$42-540
2039
$4.8-61
$14-180
$20-250
$43-550
2040
$4.9-62
$14-180
$20-260
$44-560
2041
$5-64
$14-180
$20-260
$44-570
2042
$5.1-66
$15-190
$20-260
$45-570
PV
$11-140
$44-560
$67-860
$140-1,700
EAV
$0.93-12
$3.1-39
$4.5-57
$9.5-120
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile
at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated
using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon, Methane, and
Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of climate benefits
calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted when discounting
intergenerational impacts.
8-20
-------
Table 8-17 Range of Benefits of Reduced CO2 Emissions from the 111(b) Standards on
New Natural Gas-Fired EGUs Illustrative More Stringent Scenario, 2028 to 2042 (millions
of 2019 dollars)3
SC-CO2 Discount Rate and Statistic (millions 2019 dollars)
Emissions
Year
5% Average
3% Average
2.5% Average
3% 95th Percentile
2028
-
-
-
-
2029
-
-
-
-
2030
-
-
-
-
2031
-
-
-
-
2032
-
-
-
-
2033
-
-
-
-
2034
-
-
-
-
2035
-
-
-
-
2036
-
-
-
-
2037
-
-
-
-
2038
$52-99
$150-290
$220-420
$470-900
2039
$54-100
$160-300
$220-420
$480-920
2040
$55-100
$160-300
$230-430
$490-930
2041
$56-110
$160-310
$230-430
$500-950
2042
$58-110
$160-310
$230-440
$510-960
PV
$130-240
$500-940
$760-1,400
$1,500-2,900
EAV
$10-20
$35-66
$51-96
$110-200
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile
at 3 percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated
using all four estimates. As discussed in the Technical Support Document: Social Cost of Carbon, Methane, and
Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG, 2021), a consideration of climate benefits
calculated using discount rates below 3 percent, including 2 percent and lower, is also warranted when discounting
intergenerational impacts.
8.5 Present Values and Equivalent Annualized Values of Costs and Climate Benefits
This section presents the present value (PV) and equivalent annualized value (EAV)
estimates of compliance costs and climate benefits based on the analysis above. All cost and
benefit analysis in this section begins in 2028. Costs associated with monitoring, reporting, and
recordkeeping (MR&R) are detailed in Section 3.3 and are included in the net benefit analysis in
Section 7.164
164 To limit duplication, MR&R costs are not included in the tables in this section.
8-21
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EPA calculated the PV of costs and climate benefits for the years 2028 through 2042,
using both a three percent and seven percent discount rate from the perspective of 2024. We also
present the EAV, which represents a flow of constant annual values that, had they occurred in
each year from 2024 to 2042, would yield a sum equivalent to the PV. The EAV represents the
value of a typical cost or benefit for each year of the analysis, in contrast to the specific
snapshot-year estimates reported earlier in this section. All dollars are in 2019 dollars. To
implement the OMB Circular A-4 requirement for fulfilling E.O. 12866, we assess one less
stringent and one more stringent illustrative scenario relative to the illustrative proposal scenario.
8.5.1 Compliance Costs
Table 8-18 and Table 8-19 present the estimated costs in PV and EAV terms for the three
illustrative scenarios, discounted at three percent and seven percent, respectively. Estimates in
the tables are presented as rounded values.
Table 8-18 Present Values and Equivalent Annualized Values of Estimated Compliance
Costs of Three Illustrative Scenarios for 2028 to 2042, Calculated using 3 Percent Discount
Rate (billion 2019 dollars)3
111(d) for Existing Gas 111(b) for New Gas
Total Costs
Low High Low
High
Low
High
Proposal Scenario
Present Value
5.5 9.3 0.20
0.65
5.7
10
Equivalent Annualized Value
0.38 0.65 0.014
0.045
0.40
0.70
Less Stringent Scenario
Present Value
5.5 9.3 0.20
0.65
5.7
10
Equivalent Annualized Value
0.38 0.65 0.014
0.046
0.40
0.70
More Stringent Scenario
Present Value
5.5 9.3 0.74
1.1
6.2
10
Equivalent Annualized Value
0.38 0.65 0.052
0.080
0.44
0.73
a PV and EAV values discounted to 2024. Values have been rounded to two significant figures. Rows may not
appear to add correctly due to rounding.
8-22
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Table 8-19 Present Values and Equivalent Annualized Values of Estimated Compliance
Costs of Three Illustrative Scenarios for 2028 to 2042, Calculated using 7 Percent Discount
Rate (billion 2019 dollars)3
111 (d) for Existing 111(b)f01.New G»s
Total Costs
Low
High Low
High
Low
High
Proposal Scenario
Present Value
3.4
5.8 0.11
0.35
3.5
6.2
Equivalent Annualized Value
0.33
0.56 0.011
0.034
0.34
0.60
Less Stringent Scenario
Present Value
3.4
5.8 0.11
0.36
3.5
6.2
Equivalent Annualized Value
0.33
0.56 0.010
0.035
0.34
0.60
More Stringent Scenario
Present Value
3.4
5.8 0.41
0.63
3.8
6.4
Equivalent Annualized Value
0.33
0.56 0.039
0.061
0.37
0.62
a PV and EAV values discounted to 2024. Values have been rounded to two significant figures. Rows may not
appear to add correctly due to rounding.
8.5.2 Climate Benefits
Table 8-20 presents the estimated climate benefits in PV and EAV terms for the three
illustrative scenarios at a three percent discount rate. All estimates in the tables are presented as
rounded values.
8-23
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Table 8-20 Present Values and Equivalent Annualized Values of Estimated Climate
Benefits for the Three Illustrative Scenarios for 2028 to 2042, Calculated using 3 Percent
Discount Rate (billion 2019 dollars)a'b
Climate Benefits Calculated using 3% Discount Rate
111(d) for Existing Gas 111(b) for New Gas
Total Climate
Benefits
Low High Low
High
Low
High
Illustrative Proposal Scenario
Present Value
10 19 0.049
0.56
10
20
Equivalent Annualized Value
0.70 1.3 0.0034
0.039
0.70
1.4
Illustrative Less Stringent Scenario
Present Value
10 19 0.044
0.56
10
20
Equivalent Annualized Value
0.70 1.3 0.0031
0.039
0.71
1.4
Illustrative More Stringent Scenario
Present Value
10 19 0.50
0.94
11
20
Equivalent Annualized Value
0.70 1.3 0.035
0.066
0.74
1.4
a PV and EAV values discounted to 2024. Values have been rounded to two significant figures. Values may not
appear to add correctly due to rounding.
b Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
As seen by comparing Table 8-18 and Table 8-20, the estimated climate benefits
significantly outweigh the estimated compliance costs under all three illustrative scenarios in this
analysis, both for the Low estimates and the High estimates.
8.6 Limitations and Uncertainties
Section 3.7 outlines the uncertainties and limitations of the IPM-based analysis of the
proposed 111(b) standards on new natural gas-fired EGUs and 111(d) standards on existing coal-
fired EGUs, as described in Section 3.2. The analysis of the impacts associated with analysis of
proposed 111(d) standards on existing natural gas-fired EGUs and some elements of the
proposed 111(b) standards on new natural gas-fired EGUs165 presented in Section 8 relies on
these model runs to determine the baseline for the additional spreadsheet-based analysis. As such
all the limitations and uncertainties outlined under Section 3.7 also apply to the estimates
presented in Section 8.
165 Specifically, the requirement for new gas-fired capacity operating at greater than 50 percent annual capacity
factor in run year 2040 to increase hydrogen co-firing to 95 percent by volume or convert to CCS was not
modeled.
8-24
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While the spreadsheet-based analysis was informed by EPA's expert judgement, it was not
based on any incremental IPM runs that would identify the least-cost compliance pathways for
affected sources given the additional standards modeled. As such, the results from this analysis
could differ from the compliance behavior that would be projected under incremental IPM
modeling. Additionally, retail electricity price impacts are not estimated using the retail price
model. Also, please see Section 5.2 for discussion regarding social cost estimation in the context
of this proposed rulemaking.
EPA also is unable to estimate changes in pollutants other than CO2 in the analysis
presented in this section. As a result, we are unable quantify or monetize impacts associated with
PM2.5 or ozone-related concentration changes due to changes in PM2.5, NOx, and SO2 emissions.
Similarly, we are unable to analyze potential environmental justice impact that may be associated
with changes in emissions of these pollutants.
8.7 References
IWG. (2021). Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide
Interim Estimates under Executive Order 13990. Washington DC: U.S. Government,
Interagency Working Group (IWG) on Social Cost of Greenhouse Gases.
https://www.whitehouse.gov/wp-
content/uploads/2021/02/TechnicalSupportDocument_SocialCostofCarbonMethaneNitro
usOxide.pdf? source=email
8-25
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APPENDIX A: AIR QUALITY MODELING
As noted in Section 4, EPA used photochemical modeling to create air quality surfaces166
that were then used in air pollution health benefits calculations of the three illustrative scenarios
of the proposed rules.167 The modeling-based surfaces captured air pollution impacts resulting
from changes in NOx, SO2 and direct PM2.5 emissions from EGUs. This appendix describes the
source apportionment modeling and associated methods used to create air quality surfaces for the
baseline scenario and three illustrative scenarios in four snapshot years: 2028, 2030, 2035 and
2040. EPA created air quality surfaces for the following pollutants and metrics: annual average
PM2.5; April-September average of 8-hr daily maximum (MDA8) ozone (AS-M03).
The ozone source apportionment modeling outputs are the same as those created for the
Regulatory Impact Analysis for the proposed Federal Implementation Plan Addressing Regional
Ozone Transport for the 2015 Ozone National Ambient Air Quality Standard (U.S. EPA, 2022b).
New PM source apportionment modeling outputs were created using the same inputs and
modeling configuration as were used for the available ozone source apportionment modeling.
The basic methodology for determining air quality changes is the same as that used in the RIAs
from multiple previous rules (EPA, 2020; U.S. EPA, 2019, 2020b, 2021b, 2022c). EPA
calculated EGU emissions estimates of NOx and SO2 for baseline and illustrative scenarios in all
four snapshot years using the Integrated Planning Model (IPM) (Section 3 of this RIA). EPA also
used IPM outputs to estimate EGU emissions of PM2.5 based on emission factors described in
U.S. EPA (2021a).168 This appendix provides additional details on the source apportionment
modeling simulations and the associated analysis used to create ozone and PM2.5 air quality
surfaces.
166 The term "air quality surfaces" refers to continuous gridded spatial fields using a 12 km grid resolution.
167 Appendix A pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section 8 for impact analysis of the proposed standards for existing natural gas-fired
EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
168 For details, please see Flat File Generation Methodology and Post Processing Emissions Factors PM CO VOC
NH3 Updated Summer 2021 Reference Case, available at: https://www.epa.gov/power-sector-
modeling/supporting-documentation-2015 -ozone-naaqs-actions
A-l
-------
A.l Air Quality Modeling Simulations
The air quality modeling utilized a 2016-based modeling platform which included
meteorology and base year emissions from 2016 and projected future-year emissions for
2026.169'170 The air quality modeling included photochemical model simulations for a 2016 base
year and 2026 future year to provide hourly concentrations of ozone and PM2.5 component
species nationwide. In addition, source apportionment modeling was performed for 2026 to
quantify the contributions to ozone from NOx emissions and to PM2.5 from NOx, SO2 and
directly emitted PM2.5 emissions from electric generating units (EGUs) on a state-by-state basis.
As described below, the modeling results for 2016 and 2026, in conjunction with EGU emissions
data for the baseline and three illustrative scenarios in 2028, 2030, 2035 and 3040 were used to
construct the air quality surfaces that reflect the influence of emissions changes between the
baseline and the three illustrative scenarios in each year.
The air quality model simulations (i.e., model runs) were performed using the
Comprehensive Air Quality Model with Extensions (CAMx) version 7.10 (Ramboll Environ,
2021).171 The nationwide modeling domain (i.e., the geographic area included in the modeling)
covers all lower 48 states plus adjacent portions of Canada and Mexico using a horizontal grid
resolution of 12 x 12 km is shown in Figure A-l.
169 Information on the emissions inventories used for the modeling described in U.S. EPA (2022e)
170 The air quality modeling performed to support the analyses in this proposed RIA can be found in the Air Quality
Modeling Technical Support Document Federal Implementation Plan Addressing Regional Ozone Transport for
the 2015 Ozone National Ambient Air Quality Standards Proposed Rulemaking (U.S. EPA, 2022b)
171 This CAMx simulation set the Rscale NH3 dry deposition parameter to 0 which resulted in more realistic model
predictions of PM2 5 nitrate concentrations than using a default Rscale parameter of 1
A-2
-------
ML j
j\
v. .1
1 L^Vli
f 1 \
j |
i.
1 J
\
• j V ">
V \ N,
*
cd. m w 244 t s ^
\
f
y
Figure A-l Air Quality Modeling Domain
The contributions to ozone and PM2.5 component species (e.g., sulfate, nitrate,
ammonium, elemental carbon (EC), organic aerosol (OA), and crustal material172) from EGU
emissions in individual states were modeled using the "source apportionment" tool approach. In
general, source apportionment modeling quantifies the air quality concentrations formed from
individual, user-defined groups of emissions sources or "tags". These source tags are tracked
through the transport, dispersion, chemical transformation, and deposition processes within the
model to obtain hourly gridded'73 contributions from the emissions in each individual tag to
hourly gridded modeled concentrations. For this RIA we used the source apportionment
contribution data to provide a means to estimate of the effect of changes in emissions from each
group of emissions sources (i.e., each tag) to changes in ozone and PM2.5 concentrations.
Specifically, we applied outputs from source apportionment modeling for ozone and PM2.5
component species using the 2026 modeled case to obtain the contributions from EGUs
emissions in each state to ozone and PM2.5 component species concentrations in each 12 km
model grid resolution nationwide. Ozone contributions were modeled using the Anthropogenic
Precursor Culpability Assessment (A PC A) tool and PM2.5 contributions were modeled using the
Particulate Matter Source Apportionment Technology (PSAT) tool (Ramboll Environ, 2021).
The ozone source apportionment modeling was performed for the period April through
112 Crustal material refers to elements that are commonly found in the earth's crast such as Aluminum, Calcium,
Iron, Magnesium, Manganese, Potassium, Silicon. Titanium, and the associated oxygen atoms.
173 Hourly contribution information is provided for each grid cell to provide spatial patterns of the contributions from
each tag
A-3
-------
September to provide data for developing spatial fields for the April through September
maximum daily eight hour (MDA8) (i.e., AS-M03) average ozone concentration exposure
metric. The PM2.5 source apportionment modeling was performed for a full year to provide data
for developing annual average PM2.5 spatial fields. Table A-l provides state-level 2026 EGU
emissions that were tracked for each source apportionment tag.
Table A-l 2026 Emissions Allocated to Each Modeled State-EGU Source
Apportionment Tag
State
Ozone Season NOx
Annual NOx emissions
Annual SO2 emissions
Annual PM2.5
Tag
Emissions (tons)
(tons)
(tons)
emissions (tons)
AL
6,205
9,319
1,344
2,557
AR
5,594
9,258
22,306
1,075
AZ
1,341
3,416
2,420
814
CA
6,627
16,286
249
4,810
CO
5,881
12,725
7,311
1,556
CT
1,673
3,740
845
467
DC
37
39
0
53
DE
203
320
126
119
FL
11,590
22,451
8,784
6,555
GA
3,199
5,937
1,177
2,452
IA
8,008
17,946
9,042
1,182
ID
375
705
1
185
IL
8,244
16,777
31,322
3,018
IN
11,052
36,007
34,990
6,281
KS
3,166
4,351
854
709
KY
11,894
25,207
22,940
10,476
LA
10,895
16,949
11,273
3,119
MA
2,115
4,566
839
384
MD
1,484
3,008
273
783
ME
1,233
3,063
1,147
414
MI
11,689
22,378
31,387
3,216
MN
4,192
9,442
7,189
481
MO
10,075
34,935
105,916
3,617
MS
3,631
5,208
30
1,240
MT
3,908
8,760
3,527
1,426
NC
7,175
15,984
6,443
2,720
ND
8,053
19,276
26,188
1,265
NE
8,670
20,274
45,869
1,530
NH
224
483
159
93
NJ
1,969
4,032
915
729
NM
1,266
1,987
0
304
NV
1,577
3,017
0
901
NY
6,248
11,693
1,526
1,649
OH
9,200
27,031
46,780
4,543
OK
2,412
3,426
2
828
OR
1,122
2,145
29
455
A-4
-------
State
Ozone Season NOx
Annual NOx emissions
Annual SO2 emissions
Annual PM2.5
Tag
Emissions (tons)
(tons)
(tons)
emissions (tons)
PA
12,386
23,965
9,685
3,785
RI
233
476
0
68
SC
3,251
7,134
6,292
2,082
SD
478
1,054
889
55
TL*
1,337
2,970
6,953
1,329
TN
790
2,100
1,231
845
TX
16,548
27,164
19,169
5,027
UT
3,571
10,915
11,040
693
VA
3,607
7,270
820
1,805
VT
2
4
0
4
WA
11,78
2,532
158
384
WI
2,097
4,304
821
1,084
WV
7,479
21,450
28,513
2,180
WY
5,026
11,036
8,725
629
* TL represents emissions occurring on tribal lands
Examples of the magnitude and spatial extent of ozone and PM2.5 contributions are
provided in Figure A-2 through Figure A-5 for EGUs in California, Texas, Iowa, and Ohio.
These figures show how the magnitude and the spatial patterns of contributions of EGU
emissions to ozone and PM2.5 component species depend on multiple factors including the
magnitude and location of emissions as well as the atmospheric conditions that influence the
formation and transport of these pollutants. For instance, NOx emissions are a precursor to both
ozone and PM2.5 nitrate. However, ozone and nitrate form under very different types of
atmospheric conditions with ozone formation occurring in locations with ample sunlight and
ambient volatile organic compound (VOC) concentrations while nitrate formation requires colder
and drier conditions and the presence of gas-phase ammonia. California's complex terrain that
tends to trap air and allow pollutant build-up combined with warm sunny summer and cooler dry
winters and sources of both ammonia and VOCs make its atmosphere conducive to formation of
both ozone and nitrate. While the magnitude of EGU NOx emissions in Iowa and California are
similar in the 2026 modeling (Table A-l), the emissions from California lead to larger
contributions to the formation of those pollutants due to the conducive conditions in that state.
Texas and Ohio both had larger NOx emissions than California or Iowa. While maximum ozone
impacts shown for Texas and Ohio EGUs are similar order of magnitude to maximum ozone
impacts from California EGUs, nitrate impacts are much smaller in Ohio and negligible in Texas
due to less conducive atmospheric conditions for nitrate formation in those locations. California
A-5
-------
EGU SO2 emissions in the 2026 modeling are several orders of magnitude smaller than SO2
emissions in Ohio and Texas (Table A-l) leading to much smaller sulfate contributions from
California EGUs than from Ohio and Texas EGUs. PM2.5 organic aerosol EGU contributions in
this modeling come from primary PM2.5 emissions rather than secondary atmospheric formation.
Consequently, the impacts of EGU emissions on this pollutant tend to occur closer to the EGU
sources than impacts of secondary pollutants (ozone, nitrate, and sulfate) which have spatial
patterns showing a broader regional impact. These patterns demonstrate how the model is able
capture important atmospheric processes which impact pollutant formation and transport form
emissions sources.
a) Apr-Sep MDA8 03
If
t—i
Li(
ii W ) J M,
| Vn «
/ .V
1], Ma
c) Annua! PM2 5 Sulfate
Mn= O.OOEtC at 11.11. Ma* = 5.919 at (34111)
JA
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if)
\ ? r-r
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159 23S JIM
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f
b) Annual PM25 Nitrate
ft
/
¥
V /
V
\ L
, -Jf VY\
v
if
Mln - Q.OCE-O a: (1.1). Ms:< - 3114 at (35.113)
d) Annual PM2 5 OA
L__l
f Lwl
fW
I -
i i t ro
' mf
V
y
Mm = aOQE-O ai (1.1). Mb« = 5.542 at 124,12a)
Figure A-2 Maps of California EGU Tag contributions to a) April-September Seasonal
Average MDA8 Ozone (ppb); b) Annual Average PM2.5 Nitrate (jig/in3); c) Annual Average
PM2.5 sulfate (jig/m3); d) Annual Average PM2.5 Organic Aerosol (fig/ni3)
A-6
-------
JOt
/
( /"
V \
a) Apr-Sep MDA8 03
v. —
' Lj
i If I W,
S n )' F#
t—s—X^lXrw
U__ V '' V T i
UTrf
mJr M—
f
-? ~
Mr*^r v
J
Mifi= O.GOEt-Oat (1.1 X Via* = 1.925 at 5306,56}
c) Annual PM2 5 Sulfate
:r
I
f-
(
J__
I
r
| w H' j S
m\
w
7
MHI
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Min - 0.006+0 at (1..U Via*- 0.081 st {20 /,58)
b) Annual PM2 5 Nitrate
1 J
a e us /
#? /
ryin n§i
fW
d3
V \
fsf
Vf
as
J \ \
„ s
1 4 j
13
LL 1 J V£j
f n rP
M-txf
7
" 50
ai
os . 1
\(
\
\\
\
>
Mr. = Q.0Ce+0 at 11.1 J. Mai = 3 5Cf-3,if (?04,i57)
d) Annual PM, s OA
010 W * t-. J
if
i L
?%1 jTCC
ir
-4
#
ft* } JLi
•T y
Mln - ^00t-08-(l.l). Me* - 0.225 at f218,3?)
Figure A-3 Maps of Texas EGU Tag contributions to a) April-September Seasonal
Average MDA8 Ozone (ppb); b) Annual Average PM2.5 Nitrate (fig/1113); c) Annual Average
P1VI2.5 sulfate (p,g/m3); d) Annual Average PM2.5 Organic Aerosol (jig/in3)
A-7
-------
a) Apr-Sep MDA8 03
Min - O.OOE+O at (1,1), Max ¦ 0,608 at (243,150)
c) Annual PM2 5 Sulfate
/
T
/
V \
ff
\L
. nn f Pp
h ml
\ *
\T\ }_
V
Min = O.OOE.O at (1,1), Max = 0.040 at (206,157)
b) Annual PM25 Nitrate
/
—J \
\
If i Jv\
\\lfi
\yy
) L-
yl-
LI), Max
d) Annual PM2 5 OA
Mln ¦ O.OOE+O at (1,1), Max ¦ 0.019 at (206,164)
r
([ytrno#
{\Li rr\iH/m
¦" i Tu
MJ M
&
/
I
r'- *-Jk
Mir.=0.0OE*0 at (1,1), Max = 0,156 at (227,151)
Figure A-4 Maps of Iowa EGU Tag contributions to a) April-Septeinber Seasonal
Average MDA8 Ozone (ppb); b) Annual Average PM2.5 Nitrate (fig/1113); c) Annual Average
P1VI2.5 sulfate (p,g/m3); d) Annual Average PM2.5 Organic Aerosol (jig/in3)
A-8
-------
a) Apr-Sep MDA8 03
ff
t \
( *
ff
b) Annual PM25 Nitrate
r4/\
II U fft, »r &
r~i —Vrjujrf
/ I I \-i L/Vf
*LJ
) <~\
\
\ 1
N
Min - O.OOE+O at (1,1), Mai ¦ 2.269 at (308,134)
c) Annual PM2 5 Sulfate
£
/U_
Ftt
Min » O.OOE+O at (1.1), Max » 0.014 at (310,176)
d) Annual PM25 OM
Cm A
~rWf f
/
I w /
V
V
1
t
V\
V
Min = O.OOE+0 at (1,1), Mai = 0,163 at (308,134)
Mm = O.OOE+O at (1,1), Mai = 0.244 at (316,155)
Figure A-5 Maps of Ohio EGU Tag contributions to a) April-September Seasonal
Average MDA8 Ozone (ppb); b) Annual Average PM2.5 Nitrate (fig/1113); c) Annual Average
P1VI2.5 sulfate (p,g/m3); d) Annual Average PM2.5 Organic Aerosol (jig/in3)
A.2 Applying Modeling Outputs to Create Spatial Fields
In this section we describe the method for creating spatial fields of AS-M03 and annual
average PM2.5 based on the 2016 and 2026 modeling. The foundational data include (1) ozone
and speciated PM2.5 concentrations in each model grid cell from the 2016 and 2026 modeling, (2)
ozone and speciated PM2.5 contributions in 2026 of EGUs emissions from each state in each
model grid cell1?4, (3) 2026 emissions from EGUs that were input to the contribution modeling
(Table A-l), and (4) the EGU emissions from IPM for baseline and the three illustrative
scenarios in each snapshot year. The method to create spatial fields applies scaling factors to
174 Contributions from EGUs were modeled using projected emissions for 2026. The resulting contributions were
used to constract spatial fields in 2028, 2030, 2035 and 2040.
A-9
-------
gridded source apportionment contributions based on emissions changes between 2026
projections and the baseline and the three illustrative scenarios to the 2026 contributions.
Spatial fields of ozone and PM2.5 in 2026 were created based on "fusing" modeled data
with measured concentrations at air quality monitoring locations. To create the spatial fields for
each future emissions scenario, the fused 2026 model fields are used in combination with 2026
state-EGU source apportionment modeling and the EGU emissions for each scenario and
snapshot year. Contributions from each state-EGU contribution "tag" were scaled based on the
ratio of emissions in the year/scenario being evaluated to the emissions in the modeled 2026
scenario. Contributions from tags representing sources other than EGUs are held constant at
2026 levels for each of the scenarios and years. For each scenario and year analyzed, the scaled
contributions from all sources were summed together to create a gridded surface of total modeled
ozone and PM2.5. The process is described in a step-by-step manner below starting with the
methodology for creating AS-M03 spatial fields followed by a description of the steps for
creating annual PM2.5 spatial fields.
Ozone:
1. Create fused spatial fields of 2026 AS-M03 incorporating information from the air quality
modeling and from ambient measured monitoring data. The enhanced Voronoi Neighbor
Average (eVNA) technique (Ding et al., 2016; Gold et al., 1997; U.S. EPA, 2007) was
applied to ozone model predictions in conjunction with measured data to create
modeled/measured fused surfaces that leverage measured concentrations at air quality
monitor locations and model predictions at locations with no monitoring data.
1.1. The AS-M03 eVNA spatial fields are created for the 2016 base year with EPA's
software package, Software for the Modeled Attainment Test - Community Edition
(SMAT-CE)175 (U.S. EPA, 2022d) using 3 years of monitoring data (2015-2017) and the
2016 modeled data.
1.2. The model-predicted spatial fields (i.e., not the eVNA fields) of AS-M03 in 2016 were
paired with the corresponding model-predicted spatial fields in 2026 to calculate the
ratio of AS-M03 between 2016 and 2026 in each model grid cell.
175 SMAT-CE available for download at https://www.epa.gov/scram/photochemical-modeling-tools.
A-10
-------
1.3. To create a gridded 2026 eVNA surfaces, the spatial fields of 2016/2026 ratios created in
step (1.2) were multiplied by the corresponding eVNA spatial fields for 2016 created in
step (1.1) to produce an eVNA AS-M03 spatial field for 2026 using (Eq-1).
( ..... n Modelg2Q26 Eq-1
eVNAg>2„26 - (eVAMs,2„i6) X
• eVNAg 2026 is the eVNA concentration of AS-M03 or PM2.5 component species in grid-
cell, g, in the 2026 future year
• eVNAg 2016 is the eVNA concentration of AS-M03 or PM2.5 component species in grid-
cell, g, in 2016
• Modelg 2026 is the CAMx modeled concentration of AS-M03 or PM2.5 component
species in grid-cell, g, in the 2026 future year
• Modelg 2016 is the CAMx modeled concentration of AS-M03 or PM2.5 component in
grid-cell, g, in 2016
2. Create gridded spatial fields of total EGU AS-M03 contributions for each combination of
scenario and analysis year evaluated.
2.1. Use the EGU ozone season NOx emissions for the 2028 baseline and the corresponding
2026 modeled EGU ozone season emissions (Table A-l) to calculate the ratio of 2028
baseline emissions to 2026 modeled emissions for each EGU state contribution tag (i.e.,
an ozone scaling factor calculated for each state)176. These scaling factors are provided in
Table A-2.
2.2. Calculate adjusted gridded AS-M03 EGU contributions that reflect differences in state-
EGU NOx emissions between 2026 and the 2028 baseline by multiplying the ozone
176 Preliminary testing of this methodology showed unstable results when very small magnitudes of emissions were
tagged especially when being scaled by large factors. To mitigate this issue, scaling factors of 1.00 were applied
to any tags that tracked less than 100 tpy emissions in the original source apportionment modeling. Any
emissions changes in the low emissions state were assigned to a nearby state as denoted in Table A-2 through
Table A-5.
A-l 1
-------
season NOx scaling factors by the corresponding gridded AS-M03 ozone
contributions177 from each state-EGU tag.
2.3. Add together the adjusted AS-M03 contributions for each EGU-state tag to produce
spatial fields of adjusted EGU totals for the 2028 baseline.178
2.4. Repeat steps 2.1 through 2.3 for the three 2028 illustrative scenarios and for the baseline
and illustrative scenarios for each additional snapshot year. All scaling factors for the
baseline scenario and the three illustrative scenarios are provided in Table A-2.
3. Create a gridded spatial field of AS-M03 associated with IPM emissions for the 2028
baseline by combining the EGU AS-M03 contributions from step (2.3) with the
corresponding contributions to AS-M03 from all other sources. Repeat for each of the EGU
contributions created in step (2.4) to create separate gridded spatial fields for the baseline and
three illustrative scenarios for each snapshot year.
Steps 2 and 3 in combination can be represented by equation 2:
• AS-M03g i y is the estimated fused model-obs AS-M03 for grid-cell, "g", scenario, "i"179, and
• eVNAg 2026 is the 2026 eVNA future year AS-M03 concentration for grid-cell "g" calculated
using Eq-1.
• Cg.Tot is the total modeled AS-M03 for grid-cell "g" from all sources in the 2026 source
apportionment modeling
177 The source apportionment modeling provided separate ozone contributions for ozone formed in VOC-limited
chemical regimes (03 V) and ozone formed in NOx-limited chemical regimes (03N). The emissions scaling
factors are multiplied by the corresponding 03N gridded contributions to MDA8 concentrations. Since there are
no predicted changes in VOC emissions in the control scenarios, the 03 V contributions remain unchanged.
178 The contributions from the unaltered 03 V tags are added to the summed adjusted 03N EGU tags.
179 Scenario "i" can represent either the baseline or one of the three illustrative scenarios
180 Snapshot year "y" can represent 2028, 2030, 2035 or 2040
AS-M03g i y — eVNAg 2026
Eq-2
year, y
A-12
-------
• Cg.BC is the 2026 AS-M03 modeled contribution from the modeled boundary inflow;
• Cg.int is the 2026 AS-M03 modeled contribution from international emissions within the
modeling domain;
• Cg,bio is the 2026 AS-M03 modeled contribution from biogenic emissions;
• Cg,fires is the 2026 AS-M03 modeled contribution from fires;
• Cg,usanth.ro is the total 2026 AS-M03 modeled contribution from U.S. anthropogenic sources
other than EGUs;
• CEGUVOC,g,t is the 2026 AS-M03 modeled contribution from EGU emissions of VOCs from state,
"t";
• CEGUNOx,g,t is the 2026 AS-M03 modeled contribution from EGU emissions of NOx from state,
"t"; and
• $NOx,t,i,y is the EGU NOx scaling factor for state, "t", scenario "i", and year, "y".
PM2.5
4. Create fused spatial fields of 2026 annual PM2.5 component species incorporating
information from the air quality modeling and from ambient measured monitoring data. The
eVNA technique was applied to PM2.5 component species model predictions in conjunction
with measured data to create modeled/measured fused surfaces that leverage measured
concentrations at air quality monitor locations and model predictions at locations with no
monitoring data.
4.1. The quarterly average PM2.5 component species eVNA spatial fields are created for the
2016 base year with EPA's SMAT-CE software package using 3 years of monitoring
data (2015-2017) and the 2016 modeled data.
4.2. The model-predicted spatial fields (i.e., not the eVNA fields) of quarterly average PM2.5
component species in 2016 were paired with the corresponding model-predicted spatial
fields in 2026 to calculate the ratio of PM2.5 component species between 2016 and 2026
in each model grid cell.
4.3. To create a gridded 2026 eVNA surfaces, the spatial fields of 2016/2026 ratios created in
step (4.2) were multiplied by the corresponding eVNA spatial fields for 2016 created in
A-13
-------
step (4.1) to produce an eVNA annual average PM2.5 component species spatial field for
2026 using Eq-1.
5. Create gridded spatial fields of total EGU speciated PM2.5 contributions for each combination
of scenario and snapshot year.
5.1. Use the EGU annual total NOx, SO2, and PM2.5 emissions for the 2028 baseline scenario
and the corresponding 2026 modeled EGU NOx, SO2, and PM2.5 emissions from Table
A-l to calculate the ratio of 2028 baseline emissions to 2026 modeled emissions for each
EGU state contribution tag (i.e., annual nitrate, sulfate and directly emitted PM2.5 scaling
factors calculated for each state)181. These scaling factors are provided in Table A-3,
Table A-4 and Table A-5.
5.2. Calculate adjusted gridded annual PM2.5 component species EGU contributions that
reflect differences in state-EGUNOx, SO2, and primary PM2.5 emissions between 2026
and the 2028 baseline by multiplying the annual nitrate, sulfate and directly emitted
PM2.5 scaling factors by the corresponding annual gridded PM2.5 component species
contributions from each state-EGU tag182.
5.3. Add together the adjusted PM2.5 contributions of for each EGU state tag to produce
spatial fields of adjusted EGU totals for each PM2.5 component species.
5.4. Repeat steps 5.1 through 5.3 for the three illustrative scenarios in 2028 and for the
baseline and illustrative scenarios for each additional snapshot year. The scaling factors
for all PM2.5 component species for the baseline and illustrative scenarios are provided in
Table A-3, Table A-4 and Table A-5.
6. Create gridded spatial fields of each PM2.5 component species for the 2028 baseline by
combining the EGU annual PM2.5 component species contributions from step (5.3) with the
181 Preliminary testing of this methodology showed unstable results when very small magnitudes of emissions were
tagged especially when being scaled by large factors. To mitigate this issue, scaling factors of 1.00 were applied
to any tags that had less than 100 tpy emissions in the original source apportionment modeling. Any emissions
changes in the low emissions state were assigned to a nearby state as denoted in Table A-2 through Table A-5.
182 Scaling factors for components that are formed through chemical reactions in the atmosphere were created as
follows: scaling factors for sulfate were based on relative changes in annual SO2 emissions; scaling factors for
nitrate were based on relative changes in annual NOx emissions. Scaling factors for PM2 5 components that are
emitted directly from the source (OA, EC, crustal) were based on the relative changes in annual primary PM2 5
emissions between the 2026 modeled emissions and the baseline and the three illustrative scenarios in each
snapshot year.
A-14
-------
corresponding contributions to annual PM2.5 component species from all other sources.
Repeat for each of the EGU contributions created in step (5.4) to create separate gridded
spatial fields for the baseline and three illustrative scenarios for all other snapshot years.
7. Create gridded spatial fields of total PM2.5 mass by combining the component species
surfaces for sulfate, nitrate, organic aerosol, elemental carbon and crustal material with
ammonium, and particle-bound. Ammonium and particle-bound water concentrations are
calculated for each scenario based on nitrate and sulfate concentrations along with the
ammonium degree of neutralization in the base year modeling (2016) in accordance with
equations from the SMAT-CE modeling software (U.S. EPA, 2022d).
Steps 5 and 6 result in Eq-3 for PM2.5 component species: sulfate, nitrate, organic aerosol,
elemental carbon and crustal material.
• PMs g i y is the estimated fused model-obs PM component species "s" for grid-cell, "g", scenario,
• eVNAsgi2026 is the 2026 eVNA PM concentration for component species "s" in grid-cell "g"
calculated using Eq-1.
• Cs,g,Tot is the total modeled PM component species "s" for grid-cell "g" from all sources in the
2026 source apportionment modeling
• Cs,g,BC is the 2026 PM component species "s" modeled contribution from the modeled boundary
inflow;
• Cs,g,int is the 2026 PM component species "s" modeled contribution from international emissions
within the modeling domain;
• Cs,g,bio is the 2026 PM component species "s" modeled contribution from biogenic emissions;
183 Scenario "i" can represent either baseline or one of the illustrative scenarios.
184 Snapshot year "y" can represent 2028, 2030, 2035, or 2040
PMs,g,i,y = eVNA
s, g, 2026
Eq-3
"i"183, and year, "y"184;
A-15
-------
• Cs,g,fires is the 2026 PM component species "s" modeled contribution from fires;
• Cs,g,usanth.ro is the total 2026 PM component species "s" modeled contribution from U.S.
anthropogenic sources other than EGUs;
• Cegus.qx is the 2026 PM component species "s" modeled contribution from EGU emissions of
NOx, SO2, or primary PM2.5 from state, "t"; and
• Ss,t,i,y is the EGU scaling factor for component species "s", state, "t", scenario "i", and year, "y".
Scaling factors for nitrate are based on annual NOx emissions, scaling factors for sulfate are
based on annual SO2 emissions, scaling factors for primary PM2.5 components are based on
primary PM2.5 emissions
A.3 Scaling Factors Applied to Source Apportionment Tags
Table A-2 Ozone Scaling Factors for EGU Tags in the Baseline and Illustrative
Scenarios
State
Tag
Baseline
Proposal
Less Stringent
More Stringent
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
AL
0.85
0.89
0.58
0.27
0.85
0.82
0.57
0.28
0.85
0.82
0.57
0.28
0.86
0.80
0.56
0.26
AR
0.38
0.27
0.20
0.11
0.33
0.34
0.22
0.12
0.33
0.39
0.21
0.11
0.38
0.43
0.23
0.15
AZ
1.28
2.05
2.80
2.64
1.27
2.21
2.92
2.90
1.27
2.21
2.89
2.88
1.27
2.21
2.80
2.88
CA
0.69
0.37
0.27
0.28
0.70
0.52
0.29
0.27
0.70
0.52
0.29
0.28
0.81
0.41
0.32
0.26
CO
0.71
0.16
0.16
0.09
0.68
0.19
0.17
0.09
0.65
0.18
0.17
0.09
0.72
0.22
0.18
0.09
CT
0.71
0.70
0.66
0.00
0.71
0.71
0.66
0.00
0.71
0.72
0.66
0.00
0.72
0.72
0.66
0.00
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.68
1.68
0.96
0.95
1.68
2.37
0.99
0.95
1.68
2.37
0.99
0.95
1.68
2.38
0.99
0.96
FL
1.09
1.02
0.91
0.83
1.09
1.05
0.86
0.81
1.09
1.04
0.87
0.82
1.06
1.05
0.87
0.81
GA
1.23
1.32
0.70
0.60
1.22
1.20
0.80
0.63
1.22
1.18
0.80
0.64
1.24
1.21
0.80
0.64
IA
1.28
0.96
0.05
0.02
1.27
0.45
0.04
0.03
1.28
0.58
0.04
0.03
1.29
0.42
0.04
0.03
ID
1.06
1.16
0.37
0.48
1.28
1.42
0.45
0.53
1.28
1.42
0.45
0.53
1.18
1.29
0.60
0.66
IL
0.42
0.40
0.27
0.08
0.42
0.53
0.34
0.29
0.42
0.50
0.34
0.29
0.42
0.52
0.34
0.29
IN
0.75
0.55
0.22
0.19
0.90
0.41
0.22
0.19
0.90
0.41
0.22
0.19
0.91
0.42
0.22
0.19
KS
1.02
0.16
0.06
0.05
1.01
0.20
0.06
0.05
1.01
0.19
0.06
0.05
1.01
0.20
0.05
0.05
KY
0.36
0.40
0.20
0.19
0.38
0.41
0.30
0.29
0.38
0.41
0.30
0.30
0.40
0.42
0.27
0.26
LA
0.47
0.46
0.32
0.21
0.47
0.48
0.31
0.22
0.47
0.48
0.31
0.22
0.47
0.49
0.31
0.21
MA
1.20
1.21
1.17
1.23
1.20
1.21
1.16
1.24
1.20
1.21
1.16
1.24
1.22
1.21
1.16
1.25
MD
0.74
0.74
0.70
0.64
0.75
0.87
0.70
0.64
0.74
0.95
0.70
0.64
0.75
1.55
0.70
0.64
ME
1.63
1.14
1.07
1.16
1.63
1.14
1.07
1.16
1.63
1.14
1.07
1.16
1.63
1.14
1.07
1.16
MI
0.73
0.74
0.57
0.35
0.72
0.79
0.61
0.35
0.72
0.76
0.61
0.35
0.73
0.81
0.60
0.36
MN
0.67
0.31
0.14
0.12
0.60
0.37
0.13
0.12
0.61
0.37
0.13
0.12
0.65
0.37
0.13
0.12
MO
0.53
0.25
0.04
0.05
0.51
0.13
0.03
0.02
0.51
0.18
0.05
0.04
0.53
0.13
0.04
0.02
MS
0.73
0.73
0.62
0.29
0.73
0.74
0.72
0.29
0.73
0.74
0.73
0.31
0.73
0.76
0.72
0.31
MT
1.01
0.97
0.93
0.43
1.01
0.27
0.04
0.04
1.01
0.27
0.04
0.04
1.01
0.16
0.04
0.04
NC
0.56
0.36
0.33
0.31
0.53
0.33
0.33
0.32
0.53
0.33
0.33
0.32
0.57
0.36
0.33
0.31
ND
1.46
1.07
0.50
0.50
1.46
0.18
0.07
0.07
1.46
0.19
0.07
0.07
1.46
0.13
0.07
0.07
NE
1.15
0.91
0.13
0.11
1.14
0.11
0.01
0.01
1.14
0.12
0.01
0.01
1.14
0.09
0.01
0.01
NH
1.25
1.30
1.04
1.12
1.25
1.33
1.10
1.14
1.25
1.33
1.10
1.14
1.32
1.32
1.11
1.13
NJ
1.06
1.07
0.96
0.85
1.06
1.20
0.95
0.87
1.07
1.19
0.95
0.86
1.08
1.26
0.97
0.93
A-16
-------
State
Tag
Baseline
Proposal
Less Stringent
More Stringent
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
NM
0.58
0.58
0.46
0.33
0.59
0.74
0.46
0.39
0.59
0.74
0.46
0.39
0.63
0.56
0.46
0.39
NV
0.74
1.12
0.98
0.58
0.75
1.17
0.97
0.46
0.75
1.18
0.97
0.46
0.78
0.79
0.77
0.46
NY
0.89
0.85
0.64
0.52
0.90
0.85
0.64
0.52
0.90
0.85
0.64
0.52
0.90
0.85
0.64
0.52
OH
0.78
0.59
0.32
0.28
0.77
0.45
0.28
0.26
0.77
0.44
0.28
0.26
0.78
0.44
0.27
0.26
OK
0.74
0.67
0.12
0.02
0.73
0.83
0.12
0.02
0.73
0.83
0.16
0.06
0.73
0.88
0.14
0.02
OR
0.33
0.10
0.00
0.00
0.33
0.10
0.00
0.00
0.33
0.10
0.00
0.00
0.35
0.10
0.00
0.00
PA
0.65
0.74
0.57
0.34
0.65
0.88
0.53
0.34
0.65
0.84
0.53
0.34
0.66
0.98
0.54
0.34
RI
1.26
1.26
1.13
1.35
1.26
1.28
1.12
1.35
1.26
1.26
1.12
1.35
1.26
1.26
1.12
1.36
SC
0.98
0.61
0.43
0.34
0.60
0.55
0.41
0.30
0.60
0.55
0.41
0.30
0.84
0.53
0.43
0.31
SD
1.33
1.06
0.08
0.02
1.35
0.48
0.08
0.03
1.35
0.38
0.08
0.03
1.34
0.38
0.08
0.03
TL
1.08
1.03
0.00
0.01
1.08
0.17
0.00
0.01
1.08
0.17
0.00
0.01
1.08
0.13
0.00
0.01
TN
1.99
0.92
0.57
0.55
1.96
0.95
0.58
0.50
1.96
0.99
0.57
0.50
2.11
0.93
0.50
0.47
TX
0.73
0.64
0.44
0.45
0.72
0.66
0.42
0.40
0.72
0.65
0.42
0.40
0.73
0.67
0.42
0.40
UT
1.02
1.10
0.97
0.93
0.54
1.11
0.94
0.92
0.54
1.11
0.94
0.92
0.55
1.10
0.95
0.93
VA
1.22
1.00
0.89
0.67
1.02
1.21
0.81
0.65
1.02
1.21
0.81
0.65
1.23
1.20
0.80
0.70
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.71
0.79
0.49
0.49
0.71
0.94
0.49
0.49
0.71
0.94
0.49
0.49
0.71
0.75
0.49
0.49
WI
1.29
0.96
0.51
0.44
1.29
0.89
0.53
0.45
1.29
1.00
0.53
0.44
1.30
0.94
0.53
0.45
WV
1.03
0.82
0.28
0.01
1.01
0.44
0.29
0.01
1.05
0.50
0.32
0.01
1.06
0.46
0.31
0.00
WY
0.70
0.61
0.62
0.41
0.70
0.63
0.62
0.42
0.70
0.63
0.62
0.42
0.70
0.62
0.62
0.42
*TL = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original source apportionment
modeling. Any emissions changes in that state were assigned to a nearby state. For NOx, the following emissions change assignments
were applied: DC —»MP, VT —»NY
Table A-3 Nitrate Scaling Factors for EGU Tags in the Baseline and Illustrative
Scenarios
State
Tag
Baseline
Proposal
Less Stringent
More Stringent
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
AL
1.08
1.13
0.63
0.31
1.07
1.06
0.62
0.33
1.07
1.06
0.62
0.33
1.08
1.04
0.60
0.31
AR
0.43
0.34
0.17
0.09
0.40
0.36
0.19
0.10
0.40
0.43
0.18
0.09
0.46
0.50
0.21
0.13
AZ
1.36
1.66
1.80
1.64
1.35
1.78
2.21
1.76
1.35
1.78
2.20
1.75
1.40
1.77
2.17
1.75
CA
0.59
0.42
0.30
0.30
0.59
0.48
0.32
0.31
0.59
0.48
0.32
0.32
0.71
0.44
0.35
0.30
CO
0.57
0.16
0.18
0.14
0.56
0.18
0.20
0.14
0.54
0.17
0.20
0.14
0.56
0.21
0.19
0.13
CT
0.68
0.65
0.58
0.00
0.68
0.66
0.58
0.00
0.68
0.66
0.58
0.00
0.68
0.67
0.58
0.00
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.66
1.66
0.94
0.88
1.65
2.27
1.06
0.88
1.66
2.23
1.02
0.88
1.66
2.38
1.11
0.90
FL
1.15
1.04
0.98
0.89
1.15
1.07
0.97
0.88
1.15
1.06
0.97
0.88
1.12
1.07
0.97
0.88
GA
1.30
1.28
0.72
0.57
1.29
1.24
0.78
0.60
1.29
1.23
0.78
0.60
1.31
1.21
0.78
0.60
IA
1.28
0.98
0.04
0.02
1.28
0.41
0.03
0.02
1.28
0.50
0.03
0.02
1.29
0.41
0.03
0.02
ID
0.98
1.07
0.66
0.87
1.24
1.36
0.88
1.03
1.24
1.36
0.88
1.03
1.12
1.22
0.93
1.15
IL
0.41
0.40
0.21
0.07
0.41
0.48
0.33
0.29
0.41
0.47
0.33
0.29
0.41
0.49
0.33
0.29
IN
0.77
0.57
0.15
0.13
0.81
0.37
0.15
0.13
0.82
0.36
0.15
0.13
0.82
0.37
0.15
0.13
KS
1.73
0.20
0.09
0.05
1.73
0.25
0.09
0.04
1.73
0.25
0.09
0.04
1.72
0.24
0.09
0.04
KY
0.47
0.41
0.25
0.22
0.48
0.47
0.35
0.32
0.48
0.47
0.35
0.32
0.51
0.45
0.32
0.29
LA
0.62
0.60
0.35
0.23
0.61
0.62
0.36
0.23
0.61
0.60
0.36
0.23
0.63
0.67
0.36
0.23
MA
1.22
1.22
1.18
1.18
1.22
1.23
1.18
1.19
1.22
1.23
1.18
1.20
1.24
1.23
1.18
1.19
MD
0.84
0.81
0.72
0.65
0.84
0.90
0.72
0.65
0.84
0.95
0.73
0.65
0.84
1.23
0.72
0.66
ME
1.49
1.08
0.93
0.81
1.49
1.08
0.93
0.88
1.49
1.08
0.93
0.88
1.49
1.08
0.93
0.88
MI
0.70
0.73
0.47
0.27
0.69
0.79
0.51
0.28
0.69
0.77
0.51
0.28
0.72
0.82
0.50
0.29
A-17
-------
State
Tag
Baseline
Proposal
Less Stringent
More Stringent
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
MN
0.62
0.27
0.13
0.09
0.57
0.30
0.11
0.09
0.57
0.30
0.11
0.09
0.59
0.30
0.11
0.09
MO
0.83
0.56
0.05
0.02
0.82
0.23
0.02
0.01
0.82
0.27
0.02
0.01
0.83
0.19
0.02
0.01
MS
0.88
0.99
0.66
0.36
0.85
1.03
0.72
0.31
0.85
1.03
0.74
0.34
0.87
1.04
0.72
0.34
MT
1.05
1.01
1.06
0.76
1.05
0.35
0.13
0.05
1.05
0.35
0.14
0.05
1.05
0.30
0.13
0.05
NC
0.75
0.32
0.30
0.29
0.72
0.29
0.31
0.29
0.72
0.30
0.31
0.29
0.75
0.34
0.30
0.27
ND
1.48
1.01
0.52
0.48
1.47
0.34
0.06
0.06
1.47
0.35
0.06
0.06
1.47
0.23
0.06
0.06
NE
1.11
0.88
0.14
0.10
1.11
0.13
0.02
0.01
1.11
0.14
0.02
0.01
1.11
0.11
0.02
0.01
NH
1.11
1.13
1.00
0.98
1.11
1.14
1.02
0.99
1.11
1.14
1.02
0.99
1.16
1.15
1.03
0.99
NJ
1.06
1.08
0.87
0.81
1.06
1.19
0.89
0.83
1.07
1.18
0.89
0.82
1.08
1.26
0.90
0.86
NM
0.56
0.57
0.48
0.28
0.57
0.69
0.52
0.40
0.57
0.69
0.52
0.40
0.66
0.58
0.52
0.40
NV
0.58
0.88
0.76
0.56
0.57
0.92
0.75
0.50
0.57
0.93
0.75
0.50
0.74
0.71
0.64
0.50
NY
0.94
0.92
0.70
0.55
0.94
0.92
0.71
0.55
0.94
0.92
0.71
0.56
0.95
0.92
0.71
0.56
OH
0.83
0.57
0.30
0.21
0.82
0.44
0.20
0.19
0.82
0.43
0.20
0.19
0.82
0.43
0.20
0.19
OK
0.85
0.80
0.18
0.08
0.83
1.07
0.13
0.02
0.83
1.07
0.19
0.08
0.86
1.13
0.15
0.02
OR
0.54
0.24
0.12
0.00
0.52
0.27
0.12
0.00
0.52
0.27
0.12
0.00
0.54
0.26
0.12
0.00
PA
0.65
0.75
0.54
0.38
0.64
0.84
0.51
0.36
0.65
0.82
0.51
0.36
0.66
0.95
0.52
0.36
RI
1.19
1.19
1.07
1.09
1.19
1.20
1.06
1.09
1.19
1.20
1.06
1.09
1.22
1.23
1.06
1.10
SC
1.01
0.63
0.49
0.43
0.75
0.62
0.38
0.29
0.75
0.62
0.38
0.30
0.89
0.59
0.39
0.31
SD
1.28
1.01
0.04
0.01
1.29
0.45
0.04
0.02
1.29
0.45
0.04
0.02
1.29
0.45
0.04
0.02
TL
0.93
0.93
0.00
0.00
0.93
0.29
0.00
0.00
0.93
0.28
0.00
0.00
0.93
0.26
0.00
0.00
TN
1.58
0.69
0.48
0.34
1.58
0.79
0.45
0.32
1.58
0.85
0.45
0.33
1.59
0.65
0.42
0.30
TX
0.97
0.85
0.54
0.49
0.95
0.82
0.47
0.42
0.95
0.82
0.47
0.42
0.96
0.85
0.48
0.43
UT
0.56
0.60
0.56
0.51
0.37
0.55
0.49
0.49
0.37
0.55
0.49
0.49
0.38
0.55
0.50
0.49
VA
1.29
1.08
0.89
0.73
1.04
1.18
0.84
0.69
1.06
1.18
0.85
0.69
1.31
1.23
0.83
0.72
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.72
0.97
0.94
0.92
0.72
0.95
0.91
0.94
0.72
0.95
0.91
0.94
0.79
0.87
0.91
0.95
WI
1.46
1.02
0.45
0.37
1.46
0.79
0.47
0.37
1.46
0.88
0.47
0.37
1.47
0.85
0.47
0.38
WV
1.08
0.70
0.30
0.02
1.07
0.50
0.23
0.00
1.09
0.53
0.25
0.01
1.09
0.51
0.24
0.00
WY
0.68
0.59
0.61
0.42
0.68
0.63
0.63
0.43
0.68
0.62
0.63
0.43
0.68
0.63
0.63
0.43
*TL = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original source apportionment
modeling. Any emissions changes in that state were assigned to a nearby state. For NOx, the following emissions change assignments
were applied: DC —»MP, VT —»NY
Table A-4 Sulfate Scaling Factors for EGU Tags in the Baseline and Illustrative
Scenarios
State
Baseline
Proposal
Less Stringent
More Stringent
Tag
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
AL
1.88
1.79
0.61
0.61
1.98
1.42
0.26
0.26
1.86
1.10
0.47
0.41
2.17
1.33
0.15
0.15
AR
0.06
0.01
0.00
0.00
0.06
0.00
0.00
0.00
0.06
0.03
0.00
0.00
0.07
0.02
0.02
0.02
AZ
1.02
1.86
3.55
0.98
0.91
3.86
3.54
0.97
0.91
3.86
3.54
0.97
0.91
3.86
3.54
0.97
CA
2.42
0.43
0.40
0.40
2.42
0.30
0.25
0.00
2.42
0.30
0.25
0.17
2.42
0.26
0.25
0.00
CO
0.16
0.04
0.00
0.00
0.17
0.04
0.00
0.00
0.17
0.04
0.00
0.00
0.16
0.04
0.00
0.00
CT
0.55
0.55
0.55
0.00
0.55
0.55
0.55
0.00
0.55
0.55
0.55
0.00
0.55
0.55
0.55
0.00
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
0.73
FL
1.50
0.99
0.81
0.81
1.49
0.94
0.42
0.42
1.50
0.96
0.55
0.55
1.44
0.92
0.42
0.42
GA
3.61
2.75
0.00
0.00
3.67
1.36
0.00
0.00
3.67
1.36
0.00
0.00
3.60
1.08
0.00
0.00
IA
1.23
0.95
0.04
0.00
1.23
0.27
0.00
0.00
1.23
0.41
0.00
0.00
1.23
0.26
0.00
0.00
ID
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
A-18
-------
State
Tag
Baseline
Proposal
Less Stringent
More Stringent
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
IL
0.29
0.22
0.09
0.00
0.29
0.22
0.22
0.18
0.29
0.22
0.22
0.18
0.29
0.22
0.22
0.18
IN
1.18
0.64
0.16
0.16
1.15
0.64
0.16
0.16
1.15
0.63
0.16
0.16
1.17
0.64
0.16
0.16
KS
3.03
0.00
0.00
0.00
3.02
0.08
0.00
0.00
3.02
0.06
0.00
0.00
3.02
0.00
0.00
0.00
KY
0.31
0.31
0.17
0.14
0.33
0.33
0.25
0.21
0.33
0.33
0.25
0.21
0.35
0.31
0.22
0.19
LA
0.18
0.03
0.03
0.03
0.11
0.08
0.03
0.03
0.18
0.08
0.03
0.03
0.11
0.08
0.03
0.03
MA
0.98
0.98
0.98
0.97
0.98
0.98
0.98
0.97
0.98
0.98
0.98
0.97
0.98
0.98
0.98
0.97
MD
2.62
1.99
0.99
0.99
2.62
1.89
0.99
0.99
2.62
2.61
0.99
0.99
2.62
1.89
0.99
0.99
ME
1.11
0.88
0.81
0.77
1.11
0.88
0.81
0.78
1.11
0.88
0.81
0.78
1.11
0.88
0.81
0.78
MI
0.24
0.41
0.40
0.01
0.23
0.41
0.40
0.01
0.23
0.41
0.40
0.01
0.24
0.41
0.40
0.01
MN
0.61
0.47
0.13
0.07
0.59
0.20
0.08
0.07
0.59
0.20
0.08
0.07
0.59
0.20
0.08
0.07
MO
0.43
0.31
0.03
0.04
0.43
0.11
0.00
0.00
0.43
0.15
0.01
0.01
0.43
0.09
0.00
0.00
MS
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
MT
1.36
1.15
1.10
0.88
1.36
0.42
0.19
0.11
1.36
0.43
0.19
0.15
1.36
0.42
0.19
0.11
NC
0.65
0.10
0.05
0.05
0.57
0.00
0.00
0.00
0.58
0.00
0.00
0.00
0.65
0.05
0.00
0.00
ND
1.10
0.95
0.71
0.68
1.09
0.65
0.41
0.41
1.09
0.65
0.41
0.41
1.09
0.49
0.41
0.41
NE
1.05
0.97
0.17
0.10
1.05
0.08
0.00
0.00
1.05
0.10
0.00
0.00
1.05
0.08
0.00
0.00
NH
0.52
0.52
0.52
0.48
0.52
0.52
0.52
0.48
0.52
0.52
0.52
0.48
0.52
0.52
0.52
0.48
NJ
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NM
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NV
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NY
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
OH
0.70
0.45
0.07
0.03
0.71
0.28
0.03
0.03
0.72
0.28
0.03
0.03
0.74
0.28
0.03
0.03
OK
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
OR
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
PA
0.78
0.58
0.30
0.24
0.74
0.60
0.15
0.06
0.77
0.60
0.14
0.06
0.78
0.62
0.17
0.06
RI
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
SC
1.44
0.55
0.24
0.19
0.82
0.47
0.00
0.00
0.82
0.47
0.00
0.00
1.20
0.47
0.00
0.00
SD
1.33
1.00
0.00
0.00
1.33
0.34
0.00
0.00
1.33
0.34
0.00
0.00
1.33
0.34
0.00
0.00
TL
0.98
0.98
0.00
0.00
0.98
0.28
0.00
0.00
0.98
0.27
0.00
0.00
0.98
0.27
0.00
0.00
TN
2.33
0.19
0.00
0.00
2.30
0.31
0.00
0.00
2.31
0.35
0.00
0.00
2.46
0.00
0.00
0.00
TX
1.48
0.66
0.72
0.72
1.37
0.44
0.33
0.33
1.39
0.43
0.34
0.34
1.41
0.43
0.34
0.34
UT
0.89
1.03
1.03
0.76
0.63
0.52
0.44
0.44
0.63
0.52
0.44
0.44
0.63
0.52
0.44
0.44
VA
1.13
1.13
0.93
0.91
1.13
0.91
0.80
0.80
1.13
0.91
0.80
0.80
1.13
0.91
0.80
0.80
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
0.34
0.21
0.21
0.16
0.34
0.21
0.21
0.16
0.34
0.21
0.21
0.16
0.34
0.21
0.21
0.16
WI
2.83
1.00
0.00
0.00
2.83
0.46
0.00
0.00
2.83
0.85
0.00
0.00
2.84
0.46
0.00
0.00
WV
1.15
0.58
0.17
0.01
1.14
0.37
0.12
0.00
1.16
0.41
0.13
0.00
1.16
0.38
0.12
0.00
WY
1.30
0.99
1.07
0.66
1.30
1.12
1.13
0.67
1.30
1.10
1.13
0.67
1.30
1.12
1.13
0.67
*TL = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original source apportionment
modeling. Any emissions changes in that state were assigned to a nearby state. For SO2, the following emissions change assignments
were applied: DC -> MP, ID -> MT, MS —»AL, NV —»UT, NM —»AZ, OK —>TX, OR -> WA, RI C'T. VT NY
Table A-5 Primary PM2.5 Scaling Factors for EGU Tags in the Baseline and Illustrative
Scenarios
State
Baseline
Proposal
Less Stringent
More Stringent
Tag
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
AL
1.06
1.08
0.80
0.58
1.06
1.00
0.80
0.60
1.06
1.00
0.80
0.60
1.06
0.99
0.79
0.59
AR
0.85
0.73
0.39
0.28
0.83
0.75
0.47
0.29
0.83
0.84
0.46
0.28
0.89
0.85
0.51
0.35
AZ
1.14
1.59
1.45
1.36
1.12
1.64
1.50
1.42
1.12
1.64
1.50
1.42
1.12
1.64
1.49
1.42
A-19
-------
State
Tag
Baseline
Proposal
Less Stringent
More Stringent
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
2028
2030
2035
2040
CA
0.68
0.54
0.40
0.40
0.66
0.50
0.47
0.47
0.66
0.50
0.46
0.47
0.67
0.63
0.49
0.44
CO
0.63
0.34
0.35
0.26
0.62
0.36
0.37
0.27
0.60
0.35
0.37
0.26
0.63
0.41
0.35
0.27
CT
0.59
0.53
0.39
0.01
0.59
0.56
0.39
0.01
0.59
0.56
0.39
0.01
0.61
0.59
0.39
0.01
DC
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
DE
1.35
1.36
0.96
0.89
1.34
1.65
1.09
0.89
1.35
1.63
1.08
0.89
1.35
1.70
1.11
0.93
FL
0.98
0.93
0.89
0.81
0.98
0.93
0.88
0.81
0.98
0.93
0.89
0.81
0.97
0.93
0.88
0.81
GA
0.86
0.91
0.76
0.64
0.86
0.94
0.79
0.66
0.86
0.93
0.79
0.66
0.86
0.94
0.79
0.66
IA
1.45
1.20
0.18
0.08
1.45
0.67
0.15
0.09
1.45
0.82
0.15
0.09
1.45
0.69
0.15
0.09
ID
0.99
1.15
0.78
1.23
1.40
1.57
1.08
1.54
1.40
1.57
1.07
1.53
1.20
1.38
1.19
1.73
IL
0.41
0.42
0.25
0.14
0.41
0.47
0.29
0.23
0.41
0.46
0.29
0.23
0.42
0.49
0.29
0.23
IN
0.77
0.61
0.32
0.27
0.78
0.46
0.32
0.28
0.78
0.45
0.32
0.28
0.78
0.46
0.32
0.28
KS
1.06
0.12
0.05
0.03
1.05
0.14
0.06
0.03
1.05
0.14
0.06
0.02
1.05
0.14
0.06
0.03
KY
0.14
0.13
0.09
0.08
0.14
0.17
0.14
0.13
0.14
0.17
0.14
0.13
0.15
0.15
0.13
0.11
LA
0.87
0.87
0.68
0.55
0.86
0.91
0.69
0.56
0.87
0.90
0.69
0.56
0.86
0.88
0.67
0.55
MA
0.99
0.99
0.85
0.89
0.99
1.00
0.84
0.90
0.99
1.00
0.84
0.90
1.01
1.02
0.84
0.90
MD
0.67
0.65
0.51
0.39
0.68
0.73
0.56
0.39
0.68
0.78
0.59
0.39
0.68
0.86
0.54
0.39
ME
1.08
1.03
0.98
0.79
1.08
1.03
0.99
0.95
1.08
1.03
0.99
0.95
1.09
1.03
0.99
0.95
MI
0.58
0.65
0.49
0.37
0.57
0.68
0.52
0.37
0.57
0.67
0.52
0.37
0.58
0.68
0.51
0.38
MN
1.02
0.44
0.26
0.21
0.94
0.48
0.25
0.21
0.94
0.48
0.25
0.21
0.98
0.49
0.25
0.22
MO
0.46
0.29
0.07
0.05
0.44
0.20
0.06
0.03
0.44
0.24
0.06
0.04
0.46
0.20
0.06
0.03
MS
1.11
1.14
0.84
0.63
1.10
1.18
0.89
0.61
1.10
1.17
0.91
0.63
1.13
1.19
0.90
0.67
MT
0.97
0.96
0.97
0.72
0.97
0.35
0.18
0.11
0.97
0.35
0.18
0.11
0.97
0.33
0.17
0.11
NC
0.94
0.53
0.53
0.52
0.93
0.54
0.57
0.53
0.93
0.54
0.57
0.53
0.95
0.59
0.56
0.51
ND
2.03
1.51
0.62
0.52
2.02
0.54
0.14
0.13
2.02
0.61
0.14
0.13
2.02
0.42
0.14
0.13
NE
0.39
0.26
0.05
0.04
0.39
0.06
0.02
0.01
0.39
0.06
0.02
0.02
0.39
0.05
0.02
0.01
NH
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
NJ
1.17
1.20
0.92
0.81
1.18
1.37
0.94
0.85
1.19
1.36
0.94
0.85
1.20
1.48
0.96
0.88
NM
0.46
0.45
0.57
0.37
0.46
0.64
0.65
0.44
0.46
0.64
0.65
0.44
0.49
0.52
0.64
0.44
NV
0.66
0.76
0.70
0.74
0.63
0.81
0.69
0.71
0.63
0.81
0.69
0.71
0.62
0.74
0.67
0.73
NY
1.07
1.00
0.68
0.46
1.07
1.01
0.69
0.46
1.07
1.01
0.69
0.46
1.08
1.01
0.69
0.46
OH
0.78
0.65
0.50
0.40
0.77
0.58
0.42
0.38
0.77
0.57
0.42
0.38
0.76
0.55
0.42
0.38
OK
0.70
0.70
0.12
0.05
0.68
0.90
0.11
0.02
0.68
0.90
0.14
0.05
0.70
0.94
0.13
0.02
OR
0.64
0.32
0.17
0.04
0.56
0.33
0.17
0.04
0.56
0.33
0.17
0.04
0.61
0.33
0.17
0.04
PA
0.98
0.97
0.84
0.62
0.94
1.02
0.83
0.65
0.96
1.01
0.83
0.65
0.98
1.08
0.83
0.65
RI
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
SC
0.96
0.74
0.68
0.61
0.77
0.80
0.59
0.49
0.76
0.80
0.59
0.49
0.90
0.76
0.59
0.50
SD
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
TL
1.31
1.31
0.00
0.00
1.31
0.40
0.00
0.00
1.31
0.39
0.00
0.00
1.31
0.36
0.00
0.00
TN
1.17
0.50
0.41
0.32
1.17
0.55
0.41
0.31
1.17
0.56
0.40
0.31
1.18
0.46
0.37
0.28
TX
1.29
1.09
0.74
0.66
1.26
1.05
0.64
0.57
1.26
1.05
0.64
0.57
1.28
1.09
0.66
0.58
UT
1.20
1.26
1.23
1.21
1.15
1.21
1.12
1.14
1.16
1.21
1.12
1.14
1.15
1.20
1.14
1.15
VA
0.95
0.94
0.69
0.53
0.89
0.87
0.64
0.45
0.89
0.87
0.65
0.45
1.00
0.90
0.64
0.48
VT
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
WA
1.39
1.77
1.78
1.65
1.36
1.77
1.80
1.68
1.36
1.77
1.80
1.68
1.36
1.75
1.79
1.74
WI
0.66
0.59
0.43
0.33
0.66
0.62
0.46
0.34
0.66
0.62
0.46
0.34
0.66
0.63
0.46
0.34
WV
1.14
0.81
0.08
0.02
1.15
0.35
0.08
0.02
1.17
0.35
0.09
0.02
1.15
0.35
0.08
0.02
WY
1.24
1.41
1.56
0.93
1.24
1.50
1.59
0.98
1.24
1.49
1.59
0.98
1.24
1.51
1.56
0.96
*TL = tribal lands
**Scaling factors of 1.00 were applied to tags that had less than 100 tpy emissions assigned in the original source apportionment
modeling. Any emissions changes in that state were assigned to a nearby state. For primary PM2 5, the following emissions change
assignments were applied: DC —»MP, NH —»ME, RI —» CT, SD —»ND, VT —» NY
A-20
-------
A.4 Air Quality Surface Results
The spatial fields of baseline AS-M03 and Annual Average PM2.5 in 2028, 2030, 2035,
and 2040 are presented in Figure A-6 through Figure A-13. It is important to recognize that
ozone is a secondary pollutant, meaning that it is formed through chemical reactions of precursor
emissions in the atmosphere. As a result of the time necessary for precursors to mix in the
atmosphere and for these reactions to occur, ozone can either be highest at the location of the
precursor emissions or peak at some distance downwind of those emissions sources. The spatial
gradients of ozone depend on a multitude of factors including the spatial patterns of NOx and
VOC emissions and the meteorological conditions on a particular day. Thus, on any individual
day, high ozone concentrations may be found in narrow plumes downwind of specific point
sources, may appear as urban outflow with large concentrations downwind of urban source
locations or may have a more regional signal. However, in general, because the AS-M03 metric
is based on the average of concentrations over more than 180 days in the spring and summer, the
resulting spatial fields are rather smooth without sharp gradients, compared to what might be
expected when looking at the spatial patterns of MDA8 ozone concentrations on specific high
ozone episode days. PM2.5 is made up of both primary and secondary components. Secondary
PM2.5 species sulfate and nitrate often demonstrate regional signals without large local gradients
while primary PM2.5 components often have heterogenous spatial patterns with larger gradients
near emissions sources. Both secondary and primary PM2.5 contribute to the spatial patterns
shown in Figure A-10 through Figure A-13 as demonstrated by the extensive areas of elevated
concentrations over much of the Eastern U.S. which have large secondary components and
hotspots in urban areas which are impacted by primary PM emissions.
Figure A-6 through Figure A-13 also present the model-predicted air quality changes
between the baseline and the three illustrative scenarios in 2028, 2030, 2035, and 2040 for AS-
M03 and PM2.5. Difference in these figures are calculated as the illustrative scenario minus the
baseline. The spatial patterns shown in the figures are a result of (1) of the spatial distribution of
EGU sources that are predicted to have changes in emissions and (2) of the physical or chemical
processing that the model simulates in the atmosphere. While SO2, NOx, and primary PM2.5
emissions changes all contributed to the PM2.5 changes depicted in Figure A-10 through Figure
A-13, the PM2.5 component species with the largest changes on average was sulfate and
A-21
-------
consequently the SO2 emissions changes have the largest impact on predicted changes in PM2.5
concentrations in most locations through sulfate, ammonium and particle-bound water impacts.
The spatial fields used to create these maps serve as an input to the benefits analysis and the
environmental justice analysis.
2028 Baseline MDA8 O,
75 ppb *
2028 MDA8 03 Policy - Baseline:
Proposal Scenario
m.o
1 65.0 198
60 0
50.0
; f 1 '
4S.0 99
4KB
( 1 1 »f rT
35.0 5B
30.0
25.0
ri ] J
1 ) IV
v~\ \ v
Min: -0.5 ppb V / V \
Max: 0.2 ppb
2028 MDA8 03 Policy - Baseline:
Less Stringent Scenario
Min: -0.5 ppb
Max: 0.2 ppb
0.5 ppb
2028 MDA8 03 Policy - Baseline:
More Stringent Scenario
Min: -0.5 ppb
Max: 0.3 ppb
-0.5 ppb
Figure A-6 Maps of ASM-03 in 2028
Note: Baseline ozone concentrations (ppb) shown in upper left. Change in ozone in the proposal scenario compared
to baseline values (ppb) shown in upper right. Change in ozone in the less stringent scenario compared to baseline
values (ppb) shown in lower left. Change in ozone in the more stringent scenario compared to baseline values shown
in lower right (ppb).
A-22
-------
75 ppb «>
2030 MDA8 O, Policy - Baseline:
Proposal Scenario
Min: -0.9 ppb
Max: 0.4 ppb
/
2030 MDA8 O, Policy - Baseline:
Less Stringent Scenario
Min: -0.9 ppb
Max: 0.4 ppb
¦
0.5 ppb
-0.5 ppb
2030 MDA8 O, Policy -
Baseline:
More Stringent Scenario
%
tf-yh f \ \\f
w
m F^=p -j:-)
T;' {
1 I J
\/-A \
Min: -0.9 ppb
Vj
Max: 0.4 ppb
Figure A-7 Maps of ASM-03 in 2030
Note: Baseline ozone concentrations (ppb) shown in upper left. Change in ozone in the proposal scenario compared
to baseline values (ppb) shown in upper right. Change in ozone in the less stringent scenario compared to baseline
values (ppb) shown in lower left. Change in ozone in the more stringent scenario compared to baseline values shown
in lower right (ppb).
A-23
-------
2035 Baseline MDA8 03
2035 MDA8 03 Policy - Baseline:
Less Stringent Scenario
J
I
J
Min: -0.9 ppb
Max: 0.2 ppb
\\
75 ppb
2035 MDA8 O, Policy - Baseline:
Proposal Scenario
%4
1
Min: -0.9 ppb
Max: 0.2 ppb
V
I
0.5 ppb
2035 MDA8 03 Policy - Baseline:
More Stringent Scenario
¥
-0.5 ppb
Min: -0.9 ppb
Max: 0.2 ppb
\\
0.5 ppb
-0.5 ppb
0.5 ppb
-0.5 ppb
Figure A-8 Maps of ASM-03 in 2035
Note: Baseline ozone concentrations (ppb) shown in upper left. Change in ozone in the proposal scenario compared
to baseline values (ppb) shown in upper right. Change in ozone in the less stringent scenario compared to baseline
values (ppb) shown in lower left. Change in ozone in the more stringent scenario compared to baseline values shown
in lower right (ppb).
A-24
-------
2040 MDA8 03 Policy - Baseline:
More Stringent Scenario
75 ppb
0.5 ppb
-0.5 ppb
I.
¦ o«
I -0.5 ppb
2040 MDA8 O, Policy - Baseline:
Proposal Scenario
| 0.5 ppb
|
¦ -0.5 ppb
- 0.5 ppb
2040 MDA8 O, Policy - Baseline:
Less Stringent Scenario
Figure A-9 Maps of ASM-Q3 in 2040
Note: Baseline ozone concentrations (ppb) shown in upper left. Change in ozone in the proposal scenario compared
to baseline values (ppb) shown in upper right. Change in ozone in the less stringent scenario compared to baseline
values (ppb) shown in lower left. Change in ozone in the more stringent scenario compared to baseline values shown
in lower right (ppb).
A-25
-------
2028 Baseline PM
2028 PM2 s Policy - Baseline:
Less Stringent Scenario
Y /r
j\
r4_
1—
&
V \ v
\ Y i —h
I lUj
i s i n
v LK/I
Min: -0.15 jig/m3
Max: 0.03 |ig/m3
*JT ^ \
15 ng/m3 »'
2028 PM2 5 Policy - Baseline:
Proposal Scenario
i rJ\,
, i
vr
r
Min: -0.15 fig/m3
Max: 0.03 ^.g/m3
-ft
51
1 LLT
U
0.1 |ig/m3
2028 PM2 5 Policy - Baseline:
More Stringent Scenario
~7
f
® -0.1 ng/m3
T
¦c \r
\ 1
Min: -0.09 ng/m'
Max: 0.02 jig/m'
'J
i
"sir*
•0.0?
¦ 0.00
0.1 ng/m3
0.1 ng/m3
-0 02
I -0.1 |ig/m3
Figure A-10 Maps of PM2.5 in 2028
Note: Baseline PM2 5 concentrations (|ig/m3) shown in upper left. Change in PM2 5 in the proposal scenario
compared to baseline values (iig/m!) shown in upper right. Change in PM2.s in the less stringent scenario compared
to baseline values (|ig/m3) shown in lower left. Change in PM2 5 in the more stringent scenario compared to baseline
values shown in lower right ((.ig/m3).
A-26
-------
2030 PM2 5 Policy - Baseline:
Proposal Scenario
10.1 |ig/m3
:
1-0,06
-0.1 p,g/m3
2030 PM2.s Policy - Baseline:
Less Stringent Scenario
0.1 |ig/m3
2030 PM2 5 Policy - Baseline:
More Stringent Scenario
_ 0.1 ug/in1
Figure A-I I Maps of PM2.5 in 2030
Note: Baseline PM2.5 concentrations (|ig/m3) shown in upper left. Change in PM2 5 in the proposal scenario
compared to baseline values (iig/m!) shown in upper right. Change in P\1 • ¦, in the less stringent scenario compared
to baseline values (|ig/m3) shown in lower left. Change in PM2 5 in the more stringent scenario compared to baseline
values shown in lower right (|ig/m3).
-0.1 ng/m3
-0.1 ng/m3
A-27
-------
I 0.1 ng/m3
«
02
06
OB
-0.1 ng/m3
0.1 |ig/m3
-0.1 ng/m3
-0.1 |ig/m3
2035 PM2 S Policy - Baseline:
More Stringent Scenario
15 ng/m3
0.1 ng/m3
2035 PM2 5 Policy - Baseline:
Proposal Scenario
Min: -0.11 jig/m3
Max: 0.33 ng/m3
Min: -0.11 ^ig/m3
Max: 0.34 ^.g/rn3
2035 PM2 5 Policy - Baseline:
Less Stringent Scenario
Figure A-12 Maps of PM2.5 in 2035
Note: Baseline PM2.5 concentrations (ng/m3) shown in upper left. Change in PM2 5 in the proposal scenario
compared to baseline values (ng/m3) shown in upper right. Change in PM2j in the less stringent scenario compared
to baseline values (|ig/m3) shown in lower left. Change in PM2 5 in the more stringent scenario compared to baseline
values shown in lower right (fig/m3).
A-28
-------
2040 Baseline PM
2040 PMj.s Policy - Baseline:
Less Stringent Scenario
Min: -0.09 ng/m3
Max: 0.34 ng/m3
5 |Ug/m3
2040 PM2 5 Policy - Baseline:
Proposal Scenario
1
y
f
Min: -0.11 (ig/m3
Max: 0.25 p.g/m3
v - ^
0.1 ng/m3
2040 PM2 S Policy - Baseline:
More Stringent Scenario
i
"
Min: -0.11 (xg/m3
-0.1 ng/m3
0.1 (Jg/m3
;/
-0.1 ng/m3
0.1 jig/m3
-0.1 (ig/m3
Figure A-13 Maps of PM2.5 in 2040
Note: Baseline PM2.5 concentrations ( |ig/m3) shown in upper left. Change in PM25 in the proposal scenario
compared to baseline values ( ug/in3) shown in upper right. Change in PM2.5 in the less stringent scenario compared
to baseline values (ng/m3) shown in lower left. Change in PMb 5 in the more stringent scenario compared to baseline
values shown in lower right ( ug/in3).
A.5 Uncertainties and Limitations of the Air Quality Methodology
One limitation of the scaling methodology for creating ozone and PM2.5 surfaces
associated with the baseline or illustrative scenarios described above is that the methodology
treats air quality changes from the tagged sources as linear and additive. It therefore does not
account for nonlinear atmospheric chemistry and does not account for interactions between
emissions of different pollutants and between emissions from different tagged sources. The
method applied in this analysis is consistent with how air quality estimations have been made in
several prior regulatory analyses (U.S. EPA, 2012, 2019, 2020a). We note that air quality is
calculated in the same manner for the baseline and for the illustrative scenarios, so any
A-29
-------
uncertainties associated with these assumptions is propagated through results for both the
baseline and the illustrative scenarios in the same manner. In addition, emissions changes
between baseline and illustrative scenarios are relatively small compared to modeled 2026
emissions that form the basis of the source apportionment approach described in this appendix.
Previous studies have shown that air pollutant concentrations generally respond linearly to small
emissions changes of up to 30 percent (Cohan et al., 2005; Cohan and Napelenok, 2011; Dunker
et al., 2002; Koo et al., 2007; Napelenok et al., 2006; Zavala et al., 2009). A second limitation is
that the source apportionment contributions are informed by the spatial and temporal distribution
of the emissions from each source tag as they occur in the 2026 modeled case. Thus, the
contribution modeling results do not allow us to consider the effects of any changes to spatial
distribution of EGU emissions within a state between the 2026 modeled case and the baseline
and illustrative scenarios analyzed in this RIA. Finally, the 2026 CAMx-modeled concentrations
themselves have some uncertainty. While all models have some level of inherent uncertainty in
their formulation and inputs, the base-year 2016 model outputs have been evaluated against
ambient measurements and have been shown to adequately reproduce spatially and temporally
varying concentrations (U.S. EPA, 2022a, 2023).
A.6 References
Cohan, D. S., Hakami, A., Hu, Y., & Russell, A. G. (2005). Nonlinear Response of Ozone to
Emissions: Source Apportionment and Sensitivity Analysis. Environmental Science &
Technology, 39(11), 6739-6748. doi:10.1021/es048664m
Cohan, D. S., & Napelenok, S. L. (2011). Air Quality Response Modeling for Decision Support.
Atmosphere, 2(3), 407-425. Retrieved from https://www.mdpi.eom/2073-4433/2/3/407
Ding, D., Zhu, Y., Jang, C., Lin, C.-J., Wang, S., Fu, J., . . . Qiu, X. (2016). Evaluation of health
benefit using BenMAP-CE with an integrated scheme of model and monitor data during
Guangzhou Asian Games. Journal of Environmental Sciences, 42, 9-18.
doi:https://doi.org/10.1016/j.jes.2015.06.003
Dunker, A. M., Yarwood, G., Ortmann, J. P., & Wilson, G. M. (2002). The Decoupled Direct
Method for Sensitivity Analysis in a Three-Dimensional Air Quality Model
Implementation, Accuracy, and Efficiency. Environmental Science & Technology,
36(13), 2965-2976. doi: 10.1021/esOl 12691
Gold, C. M., Remmele, P. R., & Roos, T. (1997). Voronoi methods in GIS. In M. van Kreveld, J.
Nievergelt, T. Roos, & P. Widmayer (Eds.), Algorithmic Foundations of Geographic
Information Systems (pp. 21-35). Berlin, Heidelberg: Springer Berlin Heidelberg.
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Koo, B., Dunker, A. M., & Yarwood, G. (2007). Implementing the Decoupled Direct Method for
Sensitivity Analysis in a Particulate Matter Air Quality Model. Environmental Science &
Technology, ¥7(8), 2847-2854. doi: 10.102l/es0619962
Napelenok, S. L., Cohan, D. S., Hu, Y., & Russell, A. G. (2006). Decoupled direct 3D sensitivity
analysis for particulate matter (DDM-3D/PM). Atmospheric Environment, 40(32), 6112-
6121. doi:https://doi.org/10.1016/j.atmosenv.2006.05.039
Ramboll Environ. (2021). User's Guide Comprehensive Air Quality Model with Extensions
version 7.10. Retrieved from Novato, CA:
U.S. EPA. (2007). Technical Report on Ozone Exposure, Risk, and Impact Assessments for
Vegetation. (EPA 452/R-07-002). Research Triangle Park, NC: Office of Air Quality
Planning and Standards. https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P 100PVGI.txt
U.S. EPA. (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient
Air Quality Standards for Particulate Matter. (EPA-452/R-12-005). Research Triangle
Park, NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, Health and Environmental Impact Division.
https://www3.epa.gov/ttnecasl/regdata/RIAs/finalria.pdf
U.S. EPA. (2019). Regulatory Impact Analysis for the Repeal of the Clean Power Plan, and the
Emission Guidelines for Greenhouse Gas Emissions from Existing Electric Utility
Generating Units. (EPA-452/R-19-003). Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, Health
and Environmental Impact Division, https://www.epa.gov/sites/production/files/2019-
06/documents/utilities_ria_final_cpp_repeal_and_ace_2019-06.pdf
U.S. EPA. (2020a). Analysis of Potential Costs and Benefits for the National Emission Standards
for Hazardous Air Pollutants: Coal- and Oil-FiredElectric Utility Steam Generating
Units - Subcategory of Certain Existing Electric Utility Steam Generating Units Firing
Eastern Bituminous Coal Refuse for Emissions of Acid Gas Hazardous Air Pollutants.
Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Health and Environmental Impact Division.
https://www.epa.gOv/sites/default/files/2020-04/documents/mats_coal_refuse_cost-
benefit_memo.pdf
U.S. EPA. (2020b). Benefit and Cost Analysis for Revisions to the Effluent Limitations
Guidelines and Standards for the Steam Electric Power Generating Point Source
Category. (EPA-821-R-20-003). Washington DC: U.S. Environmental Protection
Agency, https://www.epa.gov/sites/default/files/2020-
08/documents/steam_electric_elg_2020_final_reconsideration_rule_benefit_and_cost_an
alysis.pdf
U.S. EPA. (2021a). Flat File Generation Methodology: Version: Summer 2021 Reference Case
using EPA Platform v6. U.S. Environmental Protection Agency.
https://www.epa.gov/system/files/documents/2021-09/flat-file-methodology-epa-
platform-v6-summer-2021-reference-case.pdf
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U.S. EPA. (2021b). Regulatory Impact Analysis for the Final Revised Cross-State Air Pollution
Rule (CSAPR) Update for the 2008 Ozone NAAQS. (EPA-452/R-21-002). Research
Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Health and Environmental Impact Division.
https://www.epa.gov/sites/default/files/2021-
03/documents/revised_csapr_update_ria_final.pdf
U.S. EPA. (2022a). Air Quality Model Technical Support Document: 2016 CAMx PM2.5 Model
Evaluation to Support EGUBenefits Assessment. (EPA-452/R-21-002). Research
Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards
U.S. EPA. (2022b). Air Quality Modeling Technical Support Document, Federal Implementation
Plan Addressing Regional Ozone Transport for the 2015 Ozone National Ambient Air
Quality Standards Proposed Rulemaking. (EPA-452/R-21-002). Research Triangle Park,
NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, https://www.epa.gov/system/files/documents/2022-03/aq-modeling-
tsd_proposed-fip.pdf
U.S. EPA. (2022c). Regulatory Impact Analysis for Proposed Federal Implementation Plan
Addressing Regional Ozone Transport for the 2015 Ozone National Ambient Air Quality
Standard. (EPA-452/D-22-001). Research Triangle Park, NC: U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Health and
Environmental Impact Division, https://www.epa.gov/system/files/documents/2022-
03/transport_ria_proposal_fip_2015_ozone_naaqs_2022-02.pdf
U.S. EPA. (2022d). Software for Model Attainment Test - Community Edition (SMAT-CE) User's
Guide Software version 2.1. (EPA-454/B-22-013). Research Triangle Park, NC.
https://www.epa.gov/system/files/documents/2022-
11 /User%27 s%20Manual%20for%20 SMAT-CE%202. l_EPA_Report_l l_30_2022.pdf
U.S. EPA. (2022e). Technical Support Document (TSD): Preparation of Emissions Inventories
for the 2016v2 North American Emissions Modeling Platform. (EPA-454/B-22-001).
Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, https://www.epa.gov/system/files/documents/2022-
02/2016v2_emismod_tsd_february2022 .pdf
U.S. EPA. (2023). Benefit and Cost Analysis for Proposed Supplemental Effluent Limitations
Guidelines and Standards for the Steam Electric Power Generating Point Source
Category. (EPA-821-R-23-003). Washington, D.C.
https://www.epa.gov/system/files/documents/2023-03/steam-electric-benefit-cost-
analysis_proposed_feb-2023 .pdf
Zavala, M., Lei, W., Molina, M. J., & Molina, L. T. (2009). Modeled and observed ozone
sensitivity to mobile-source emissions in Mexico City. Atmos. Chem. Phys., 9(1), 39-55.
doi: 10.5194/acp-9-3 9-2009
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APPENDIX B: ECONOMY-WIDE SOCIAL COSTS AND ECONOMIC IMPACTS
B.l Economy-Wide Modeling
This appendix analyzes the potential economy-wide impacts of the proposed rules using a
computable general equilibrium (CGE) model.185 CGE models are designed to capture
substitution possibilities between production, consumption, and trade; interactions between
economic sectors; and interactions between a policy shock and pre-existing market distortions,
such as taxes that have altered consumption, investment, and labor decisions. As such, CGE
models can provide insights into the effects of regulation that occur outside of the directly
regulated sector because they are able to represent the entire economy in equilibrium in the
baseline and under a regulatory or policy scenario.
In 2015, EPA formed a Science Advisory Board (SAB) panel to explore the use of
general equilibrium approaches, and more specifically CGE models, to prospectively evaluate
the costs, benefits, and economic impacts of environmental regulation. In its final report, the
SAB recommended that the Agency enhance its regulatory analyses using CGE models "to offer
a more comprehensive assessment of the benefits and costs" of regulatory actions by capturing
important interactions between markets and that such efforts will be most informative when there
are both significant cross-price effects and pre-existing distortions in those markets (U.S. EPA
Science Advisory Board, 2017).186 Given the typical level of aggregation in CGE models and
their focus on long run equilibria, the panel observed that CGE modeling results are
complements to, rather than substitutes for, the other types of detailed analysis EPA conducts for
its rulemakings. The report also noted that CGE frameworks offer valuable insights into the
social costs of regulation even when estimates of the benefits of the regulation are not
incorporated into the models, though it highlighted explicit treatment of benefits within a CGE
framework as a long-term research priority. In addition, the panel observed that CGE models
may also offer insights into the ways costs are distributed across regions, sectors, or households.
185 Appendix B pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section 8 for impact analysis of the proposed standards for existing natural gas-fired
EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
186 CGE models provide "a fiscally disciplined, consistent and comprehensive accounting framework. They can
ensure that projected behavior of firms and households in a regulated market is fully consistent with the behavior
of those agents in other markets. Consistent representation of behavior, in turn, leads to connections between
markets, allowing CGE models to pick up effects that spill over from one market to another" (SAB 2017).
B-l
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In response, EPA has invested in building capacity in this class of economy-wide
modeling. A key outcome of this effort is EPA's CGE model of the U.S. economy, called SAGE.
The SAGE model provides an important complement to the analyses typically performed during
regulatory development by evaluating a broader set of economic impacts and offering an
economy-wide estimate of social costs.187 Model version v2.1.0 of SAGE is used in this analysis.
B.2 Overview of the SAGE CGE Model
SAGE is a CGE model that provides a complete, but relatively aggregated, representation
of the entire U.S. economy. CGE models assume that for some discrete period of time an
economy can be characterized by a set of conditions in which supply equals demand in all
markets (referred to as equilibrium). When the imposition of a regulation alters conditions in one
or more markets, the CGE model estimates a new set of relative prices and quantities for all
markets that return the economy to a new equilibrium.188 For example, the model estimates
changes in relative prices and quantities for sector outputs and household consumption of goods,
services, and leisure that allow the economy to return to equilibrium after the regulatory
intervention. In addition, the model estimates a new set of relative prices and demand for factors
of production (e.g., labor, capital, and land) consistent with the new equilibrium, which in turn
determines estimates of household income changes as a result of the regulation (Marten et al.,
2023). In CGE models, the social cost of the regulation is estimated as the change in economic
welfare in the post-regulation simulated equilibrium from the pre-regulation "baseline"
equilibrium.
Unlike engineering cost or partial equilibrium approaches typically used to evaluate the
costs of regulations, CGE models account for how effects in directly regulated sectors interact
with and affect the behavior of other sectors and consumers. Figure B-l uses a simplified circular
flow diagram to depict how input and output markets are generally connected to each other in
187 CGE models may also be able to provide additional information on the benefits of regulatory interventions,
though this is a relatively new but active area of research. Note that until the benefits that accrue to society from
mitigating environmental externalities can be incorporated in a CGE model, the economic welfare measure is
incomplete and needs to be augmented with traditional benefits analysis to develop measures of net benefits.
188 CGE models are generally focused on analyzing medium- or long-run policy effects since they characterize the
new equilibrium (i.e., when supply once again equals demand in all markets). Their ability to capture the
transition path of the economy depends on the degree to which they include characteristics of the economy the
restrict its ability to adjust instantaneously (e.g., rigidities in capital markets).
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CGE models. Following a standard assumption in economics, the model assumes that households
maximize their wellbeing, while firms maximize their profits. Households supply factors of
production to firms in exchange for income (e.g., wages, profits, and interest payments). Firms
use the available factors of production and materials to produce outputs that are then bought and
consumed by households.
Figure B-l Depiction of the Circular Flow of the Economy
The SAGE model includes explicit subnational regional representation within the United
States at the Census Region level. Each region contains representative firms for each of the 23
sectors in the model that vary by the commodity they produce and have region-specific
production technologies. Each region also has five representative households that vary by
income level and have region-specific preferences (see Table B-l). Within the economy,
households and firms are assumed to interact in perfectly competitive markets. In addition to
households and firms, there is a single government in SAGE that represents all state, local and
federal governments within the U.S. The government imposes taxes on capital earnings, labor
earnings, and production and uses that revenue (in addition to deficit spending) to provide
government services, make transfer payments to households, and pay interest on government
debt.
B-3
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Table B-l SAGE Dimensional Details
Time
Sectors
Census
Households
Capital
Periods
Regions
(income)
Vintage
2016-2081
Agriculture, forestry, fishing, and hunting
Northeast
<3 Ok
Extant
(5-year
Crude oil
South
30-50k
New
time steps)
Coal mining
Midwest
50-70k
Metal ore and nonmetallic mineral mining
West
70-150k
Electric power
>150k
Natural gas
Water, sewage, and other utilities
Construction
Food and beverage manufacturing
Wood product manufacturing
Petroleum refineries
Chemical manufacturing
Plastics and rubber products manufacturing
Cement manufacturing
Primary metal manufacturing
Fabricated metal product manufacturing
Electronics and technology manufacturing
Transportation equipment manufacturing
Other manufacturing
Transportation
Truck transportation
Services
Healthcare services
Modeling domestic and international trade presents a unique challenge in that the model's
structure needs to account for the fact that the U.S. can be both an importer and an exporter of
the same good at both the national and regional level. SAGE addresses this issue through use of
the "Armington" approach, which assumes that imported and exported versions of the same good
are not perfect substitutes. In SAGE, this assumption is applied to both international and cross-
regional trade within the United States. In addition, SAGE recognizes that the U.S. is a relatively
large part of the global economy and shifts in its imports and exports have the potential to
influence world prices (i.e., the model assumes the United States is a large, open economy).
SAGE is a forward-looking intertemporal model, which means that households and firms
are assumed to make their decisions taking into account what is expected to occur in future years
and how current decisions will impact those outcomes. In an intertemporal model, care is needed
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to ensure that, in response to a new policy, the economy does not instantaneously jump to a new
equilibrium in a way that is inconsistent with the rate at which the economy can realistically
adjust. SAGE seeks to model a more realistic transition path, in part, by differentiating the
flexibility of physical capital by its age. Under this approach the model distinguishes between
existing capital constructed in response to previous investments and new capital constructed after
the start of the model's simulation. Existing capital is assumed to be relatively inflexible and is
used for its original purpose unless a relatively high cost is incurred to alter its functionality.
New capital is more flexible and easily adjusts to changes in the future. Independent of its
vintage, once capital has been constructed in a specific region it cannot be moved to another
region. While physical capital is not mobile, households can make investments in whatever
region of the country they desire.
The dynamics of the baseline economy in SAGE are informed through the calibration of
key exogenous parameters in the model. Most importantly are population and productivity
growth over time. The model reflects heterogeneity in productivity growth across sectors of the
economy consistent with trends that have been historically observed. In addition, the model
captures improvements in energy efficiency that are expected for firms and households going
forward. Additional baseline characteristics, such as changes to government spending and
deficits and changes to international flows of money and investments, are calibrated to key
government forecasts or informed by historical trends.
The SAGE model relies on many data sources to calibrate its parameters. The foundation
is a state-level dataset produced by IMPLAN that describes the interrelated flows of market
goods and factors of production over the course of a year with a high level of sectoral detail.189
This dataset is augmented by information from other sources, such as the Bureau of Economic
Analysis, Energy Information Administration, Federal Reserve, Internal Revenue Service,
Congressional Budget Office, and the National Bureau of Economic Research. The result is a
static dataset that describes the structure and behavior of the economy in a single year.190 These
data are combined with key behavioral parameters for firms and households that are adopted
189 While the underlying IMPLAN data are proprietary, EPA provides the social accounting matrix based on these
data in the publicly available version of SAGE. The data set for the model may also be built anew by following
the instructions in the model documentation along with a licensed version of IMPLAN (www.IMPLAN.com).
190 SAGE is solved using the General Algebraic Modeling System (GAMS) and PATH solver. The model's build
stream is written in both R and GAMS.
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from the published literature or econometrically estimated specifically for the purposes of
calibrating SAGE. To develop the forward-looking baseline for the model, additional
information on key parameters, such as productivity growth, future government spending, and
energy efficiency improvements are incorporated from sources including the Congressional
Budget Office and Energy Information Administration.
To ensure that SAGE is consistent with economic theory and reflects the latest science,
EPA initiated a separate SAB panel to conduct a technical review of SAGE, completed in August
2020 (U.S. EPA Science Advisory Board, 2020). Peer review of SAGE was in accordance with
requirements laid out for a Highly Influential Science Assessment (HISA) consistent with OMB
guidelines.191 The SAB report commended the agency on its development of SAGE, calling it a
well-designed open-source model. The report included recommendations for refining and
improving the model, including several changes that the SAB advised EPA to incorporate before
using the model in regulatory analysis (denoted as Tier 1 recommendations by the SAB). The
SAB's Tier 1 recommendations, including improving the calibration of government expenditures
and deficits and the foreign trade deficit; allowing for more flexibility in the consumer demand
system; and representing the United States as a large open economy, are incorporated into the
model version used in this analysis (v2.1.0), as are several of the SAB's other medium- and long-
run recommendations. For more details on the SAGE model, complete documentation, source
code and build stream are available on EPA's website.192
B.3 Linking IPM PE Model to SAGE CGE Model
For these rules, EPA has relied on the Integrated Planning Model (IPM), a partial
equilibrium large-scale unit-level linear programming model, to assess the costs of compliance in
the power sector and related energy markets (see Section 3.4 for more details on the use of IPM).
The economy-wide social costs - the sum of all opportunity costs that result from the regulation
in the present and future - may differ from the partial equilibrium estimate of costs depending on
whether there are significant cross-price effects and interactions with other pre-existing market
distortions elsewhere in the economy. The economy-wide measure of social costs may also differ
191 Office of Management and Budget (2004). Issuance ofOMB's 'Final Information Quality Bulletin for Peer
Review.' https://cfpub.epa.gov/si/m05-03.pdf
192 https://www.epa.gov/environmental-economics/cge-modeling-regulatory-analysis
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when demand-side effects are not captured in the partial equilibrium measure or transfer
payments are not netted out of the partial equilibrium measure. The SAB noted that electricity
sector regulations seem a good candidate for economy-wide modeling because of the many
backward and forward linkages that may result in effects in other sectors in the economy (SAB,
2017). For example, changes in the price of electricity can affect its use in the production of
other goods and services. There may also be impacts to upstream industries that supply goods
and services to the electricity sector (e.g., energy commodities), labor markets in response to
changes in factor prices, and household demand due to changes in the end-use price of
electricity.
B. 3.1 Overview of Linking Methodology
To model the economy-wide effects of the proposed rules, we calibrate the SAGE model
inputs that represent the impact of the proposed rules such that sectoral costs in a corresponding
partial equilibrium sub-model of SAGE (called SAGE-PE) align with the partial equilibrium
incremental costs derived from the technology-rich IPM. This approach of aligning partial
equilibrium incremental costs between the two models allows us to avoid confounding the
estimate of economy-wide effects with differences in the models' partial equilibrium
representations of sectors shared by both IPM and SAGE.193 Care is given in translating IPM
outputs for use in SAGE so that the two models adequately capture equivalent partial equilibrium
costs.194
Figure B-2 provides an overview of the approach leveraging the IPM results to introduce
the incremental costs of the proposed rules into the SAGE model. In the first step (characterized
as Step 0), model differences in structure and accounting are reconciled by translating IPM
incremental system costs to a format consistent with the SAGE framework. This includes
193 The SAB (2017) noted that it will "often be necessary and appropriate for EPA to link a GE [general equilibrium]
model having a modest degree of detail to one or more PE models having greater detail. Linked models will
usually involve some degree of inconsistency in the definitions of overlapping variables and parameters, but that
may be acceptable given the increased degree of detail that a linked analysis could provide."
194 There are several valid approaches for linking models (see SAB 2017). In developing a strategy for linking IPM
and SAGE, we adhere to the following criteria: it should be theoretically sensible and produce reasonable results;
it should incorporate identical partial equilibrium responses across both SAGE and IPM without iteratively
linking the models (since IPM is proprietary); it should be practically implementable in the development of a
regulatory analysis; and the outcomes should be available to the public for the purposes of comment and
transparency.
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aligning model years, distributing IPM costs to SAGE model inputs (by fuel, other materials,
labor, and capital), attributing costs to production vintages, and removing transfer payments that
may be important for IPM to capture investment behavior but inappropriate for inputs into SAGE
as they would result in double counting.
The reconciled incremental costs are used to calibrate a representation of the proposed
rules in SAGE-PE, which is a partial equilibrium representation of the electricity sector (and
related primary energy sectors, such as the coal mining and natural gas) as defined from SAGE
that mimics the partial equilibrium behavior of IPM, to the degree that is possible. While SAGE-
PE does not have the technology detail of IPM, it captures aggregate endogenous responses in
electricity and primary energy sector prices, input requirements, trade, and asset values of
existing capital resources. SAGE-PE does not include aspects of the economy represented in the
full SAGE model but that are not captured in IPM. This means that market outcomes in sectors
other than the electricity, coal mining and natural gas sectors, electricity demand, factor prices,
and constraints on factor supply are all treated as exogenous in SAGE-PE.
Step 0
T ranslate
incremental
IPM system
costs to SAGE
accounting
framework.
Model
Inputs
Reconciled
incremental
IPM costs.
Figure B-2 Hybrid Linkage Approach for IPM and SAGE
Because SAGE-PE is a sub-model of SAGE, most of its model equations are described in
Marten et al. (2023). The subset of SAGE equations and variables that comprise SAGE-PE
include conditional profit maximizing production behavior, sub-national and foreign trade, and
market clearing conditions that equate supply and demand in the electricity, coal mining and
natural gas sectors. As in SAGE, SAGE-PE models optimal behavior through a series of
equilibrium conditions formulated as a mixed complementarity problem. Production and trade
B-8
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are characterized through zero profit conditions that require unit costs to be greater than or equal
to unit revenues. Market clearing conditions that equate supply and demand for the electricity,
coal mining and natural gas sectors determine their prices. A second set of market clearing
conditions are used to determine prices in regional trade markets. SAGE-PE maintain an
endogenous rental rate on extant capital to model the changes in the shadow value on existing
capital stock.
A common way to represent an environmental regulation in a CGE model is through a
productivity shock. This can be interpreted as requiring more inputs (e.g., control technologies)
to produce the same amount of output but in compliance with the regulation. In the SAGE and
SAGE-PE models, this is implemented through augmenting the reference productivity indices
denominated by input (materials, fuels, labor, and capital) and is described in detail in the model
documentation (Marten et al., 2023). The productivity shock is differentiated across model year,
regions, sectors, and production vintages. In the baseline, all productivity indices are set to unity
with the exception of those assigned to labor inputs which reflect projections of sector-
differentiated labor productivity.
To align SAGE with IPM, the productivity shock is calibrated so that the incremental
compliance costs are aligned between SAGE-PE and the IPM solution. The incremental SAGE-
PE costs are defined as the difference in production costs between the policy equilibrium and the
baseline. The productivity shock is adjusted to equate SAGE-PE and IPM incremental costs.
Because prices for factors and non-energy inputs are not endogenously determined in SAGE-PE
the incremental input costs for factors and non-energy inputs are driven through quantity demand
changes for labor, new capital, and material inputs. Incremental costs for electricity, coal mining
and natural gas inputs incorporate both changes in prices as well as input demand quantities.
Electricity production in SAGE-PE is exogenous except for adjustments necessary to satisfy
reductions or increases in electricity input demands in the electricity sector and primary energy
sectors in response to the proposed rules. The calibrated productivity shock is then passed to the
full SAGE model to generate social cost, distributional, and indirect impacts of the modeled
policy, where model years 2026 and beyond are endogenously determined. See Schreiber et al.
(2023) for more details on the linking approach.
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B. 3.2 Translating IPM Outputs into SA GE Inputs
IPM produces detailed cost and emissions outputs by model plant (or aggregate
representations of unit-level information of existing generators, or characterizations of new or
retrofit/retire options) and wholesale electricity price impacts by IPM region. This detailed
information is important for quantifying the sectoral compliance behavior attributed to a
regulatory shock. However, to link IPM and SAGE to capture the broader economy-wide
impacts, IPM costs need to be translated to SAGE factors and commodities. Table B-2
summarizes the key dimensions of IPM used to calibrate the inputs for the SAGE model. Key
variables include capital costs, fuel costs, and fixed and variable operations and maintenance
costs. Capital costs are reported both as overnight capital costs and capital flow payments.
Overnight capital costs reflect the total value of the resources used to install a piece of capital
"overnight," or without any financing costs associated with loan repayment. In reality, these
expenditures are not paid immediately but rather spread out over a fixed time period with interest
via capital flow payments. The "cost" of capital in IPM is a combination of a rate of return, tax
payments, and financing charges (embodied in the capital charge rate) and is used to amortize
payments over the lifetime of the capital investment. Costs are further denominated by IPM
region, fuel type, and generator vintage.
Table B-2 IPM Cost Outputs
Time Periods
Cost Categories
IPM Regions
Generator Vintage
2028
Overnight capital costs
67 IPM Regions
Existing
2030-2055
Annualized capital
New
(5-year time steps)
payments
Fuel costs
Fixed operations and
maintenance costs
Variable operations and
maintenance costs
IPM incremental costs are translated into the SAGE framework by: (1) mapping IPM
model years to SAGE model years;195 (2) mapping IPM regions to SAGE regions; (3) splitting
195 IPM year 2028 is mapped to SAGE model year 2026. Subsequent IPM years (2030-2055) are mapped to the
SAGE model year that is one year later (2031-2056). Because SAGE has a longer time horizon than IPM (to
2081), IPM incremental costs in 2055 are expected to continue into the future and are mapped to SAGE model
years 2061-2081.
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delivered fuel costs to separate transportation costs; (4) mapping variable operations and
maintenance costs to specific inputs in SAGE according to the reference cost structure in the
model; (5) attributing fixed operations and maintenance costs to labor; (6) attributing incremental
costs on existing and new generation to production with extant and new capital, respectively; (7)
mapping the input requirements of hydrogen based on engineering assessments by NREL
(2022),196 and (8) removing taxes and transfers from capital payments using the difference
between the capital charge rate and the capital recovery factor to recover the real resource costs.
Aligning the SAGE model with IPM is complicated by the difference in how each model
accounts for capital payments. First, taxes and transfers (e.g., finance payments) need to be
removed from capital costs to recover the real resource requirements for inputs to SAGE.
Second, differences in representation of capital between the two models needs to be reconciled;
SAGE accounts for capital as a cumulatively depreciated asset that represents the aggregate
physical capital stock in the U.S., whereas IPM defines capital more specifically with
heterogeneous terms and costs by technology. The models can be aligned by either targeting
incremental overnight capital costs (e.g., the magnitude and timing of the resource change) or
through targeting capital flow payments. Because the accounting for capital is different between
models, the former approach can lead to significant differences in capital flow payments between
models. Therefore, the second approach is used to align incremental net of tax capital flow
payments when calibrating the productivity shock. Because the representation of capital is
different between the models, differences in induced investment in the capital stock from
targeting consistent capital flow payments can be thought of as a translation of payments (e.g., a
means to translate a fixed term investment into a cumulatively depreciated asset).
Because SAGE does not include an explicit representation of the Inflation Reduction Act
(IRA) in the baseline, the model linkage methodology must be adjusted to account for IRA
investment, production, and fuel subsidies (i.e., ITC/PTC, 45Q and 45V). The SAGE-PE model
is calibrated to match both the real resource requirements for the expected compliance pathway
196 SAGE models an aggregate chemical manufacturing sector with a cost structure likely significantly different than
the costs of producing hydrogen, specifically. Therefore, we use information on production costs from NREL
(2022) to define the additional input requirements to SAGE in response to hydrogen use in the policy case in
IPM. Mapping NREL (2022) to SAGE inputs, we find cost shares for hydrogen are 51 percent for natural gas, 33
percent for capital, 6 percent for labor, 6 percent for electricity, 4 percent for transportation, and 0.02 percent for
water, sewage, and other utilities.
B-ll
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and the impact of the IRA subsidies on the compliance expenditure for the electricity sector. To
accomplish this, the real resource requirements represented by the IRA subsidies are included in
the incremental costs of the proposed rules by adding them to the cost of inputs (i.e., the
incremental costs are net of the subsidy payments).197 To avoid overstating price impacts and
social costs, the net tax rate on electricity sector production is also adjusted within the calibration
of the SAGE-PE model to reflect the IRA subsidies that offset a portion of the compliance
expenditures for the electricity sector. This approach allows the model to explicitly capture the
private costs faced by the electricity sector, the upstream and downstream impacts of the
resource requirements for the subsidized technologies and fuels, and changes to government
budgets associated with the use of subsidies. The SAGE model is closed by assuming the
government budget is balanced through lump sum transfers with households. Aggregate changes
in government budgets can occur in model simulations due to changes in the use of the IRA
subsidies and changes in revenues from other taxes (e.g., output, capital, and labor) as the
economy adjusts in response to the proposed rules. Additional features of the IRA are not
explicitly represented in SAGE at this time.
B.4 Results
This section summarizes the economy-wide impacts of the proposed rules. We report the
SAGE model outcomes from implementing the described framework for linking SAGE with
IPM. Results include aggregate social costs of the proposed rules, changes to gross domestic
product (GDP) and its components, national sectoral output, national sectoral labor demand
changes, and distributional impacts across regions and households.
B. 4.1 Economy-wide Social Costs
Table B-3 presents the economy-wide, general equilibrium social costs from the proposed
rules, calculated as equivalent variation. In this context, equivalent variation is an estimate of the
amount of money that society would be willing to pay to avoid the compliance requirements of
the proposed rules, setting aside health, climate, and other benefits (quantified elsewhere in the
RIA). For comparison, Table B-3 also presents the partial equilibrium private costs estimated to
197ITC/PTC and 45Q subsidies are levied on capital whereas the 45V subsidy is shared amongst hydrogen
producing inputs according to NREL (2022). We assume that changes in ITC/PTC subsidies are zero after 2042.
B-12
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be paid by the electricity sector by IPM inclusive of subsidy payments from the IRA but less
taxes and transfers and mapped to the SAGE model years. For both the partial equilibrium
private costs and the general equilibrium social costs, Table B-3 presents the present value and
annualized costs for the period of 2026 to 2046.
The general equilibrium social costs differ from the partial equilibrium private costs for
several reasons. First, the general equilibrium costs reflect demand responses for electricity and
energy inputs as the economy (inclusive of firms and households) respond to the impacts of the
proposed rules and shift production and consumption behavior. Second, the general equilibrium
costs account for interactions with pre-existing distortions in the economy, mainly taxes and
subsidies. Third, the general equilibrium costs account for effects of reallocation, potential
reductions in aggregate investment, and the resulting effects on economic growth.
The annualized social costs estimated in SAGE are approximately 35 percent larger than
the partial equilibrium private compliance costs (less taxes and transfers). This is consistent with
general expectations based on the empirical literature (Marten et al., 2019). However, we note
that the social cost estimate reflects the combined effect of the proposed rules' requirements and
interactions with IRA subsidies for specific technologies that are expected to see increased use in
response to the proposed rules. We are not able to identify their relative roles at this time.
Finally, we note that, while the partial equilibrium private compliance costs peak in the 2031
SAGE model year, aggregate social costs are spread out more evenly over the model time
horizon as the economy smooths out the impact.
Table B-3 Social Costs (billions of 2019 dollars)
„ ivi ,i | v Partial Equilibrium Private Costs General Equilibrium
0 e ear (Less Taxes and Transfers) Social Costs
2026 -0.27 1.06
2031 3.43 1.18
2036 -0.27 1.27
2041 0.35 1.37
2046 -0.32 L48
Present Value (2026 to 2046) 12.6 17.4
Equivalent Annualized Value 0.9 1.2
Notes: Social costs are calculated as equivalent variation. Present value and annualized cost estimates are based on
linearly interpolating costs between model years and are based on the internal discount rate in SAGE of 4.5 percent.
B-13
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B.4.2 Impacts on GDP
The estimated percent change in real gross domestic product (GDP), or the real value of
the goods and services produced by the U.S. economy, and its components are presented in
Figure B-3. GDP is defined as the sum of the value (price times quantity) of all market goods
and services produced in the economy and is equal to Consumption (C) + Investment (I) +
Government (G) + (Exports (X) - Imports (M)). The proposed rules are estimated to increase
GDP in 2026 by 0.018 percent due to increases in investment, but subsequently result in a
modest decrease in GDP with a peak reduction of 0.024 percent in 2031. GDP is a measure of
economic output and not a measure of social welfare. Thus, the expected social cost of a
regulation will generally not be the same as the expected change in GDP (U.S. EPA, 2015).198
Figure B-3 Percent Change in Real GDP and Components
Figure B-3 also reports changes in the components of GDP from the expenditure side.
The proposed rules are expected to accelerate investments in the electricity sector, leading to a
198 "GE models are strongly grounded in economic theory, which allows social costs to be evaluated using
equivalent variation or other economically-rigorous approaches. Simpler measures, such as changes in gross
domestic product or in household consumption, do not measure welfare accurately and are inappropriate for
evaluating social costs" (U.S. EPA Science Advisory Board, 2017)
B-14
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net increase in aggregate investment in 2026 (0.114 percent) to augment the capital stock for
compliance with the rule. Increased investment reallocates resources away from consumption
and as a result, consumption falls throughout the model time horizon. Aggregate investment is
expected to fall in later model years. The net trade balance is expected to show modest declines
in the initial years as relative prices change domestically due to compliance with the proposed
rules, shifting some purchases towards imports, though the effect is expected to dissipate over
time.
B.4.3 Impacts on Output
SAGE endogenously models production for every sector in the economy, the final
demand for goods by households, and household behavior regarding savings and labor supply.
Therefore, the general equilibrium solution incorporates estimates of how changes in the prices
for electricity, coal mining and natural gas inputs due to the proposed rules affect input demand
in other sectors of the economy and final demand from households, the reallocation of resources
across sectors and time, and changes in household investment and labor choices as relative prices
change (including wages, rental rates on capital, and returns on natural resources).
Figure B-4 presents the percent change in national output for the electricity, coal mining,
and natural gas extraction and distribution sectors in model years 2026, 2031, 2036, and 2041.
These output changes are based on what is expected to occur in the electricity sector as well as
changes elsewhere in the economy. As expected, the largest economy-wide changes,
denominated in percent change, are concentrated in these sectors. These changes reflect the
estimated shifts in generation sources in addition to an economy-wide demand response to
increases in electricity price. As the price of electricity rises, the economy is expected to reduce
demand for electricity through a variety of pathways. Similarly, output changes in the coal
mining and natural gas reflect changes in both the electricity sector and the broader economy
(inclusive of import and export changes). The changes in output from the natural gas sector
reflect both changes in the direct use of natural gas by the electricity sector and changes in its use
in hydrogen production, in addition to other economy-wide changes in demand in response to
price changes.
B-15
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Electric power
Coal mining-
Natural gas ¦
-3% -2% -1% 0% 1% 2% -3% -2% -n
1% 2% -3% -2% -1%
% Change
1% 2% -3% -2% -1%
Figure B-4 Percent Change in Sectoral Output (Electricity, Coal, Natural Gas)
Measured in terms of percent change from the baseline, output changes in other sectors of
the economy are expected to be smaller relative to the electricity, coal mining, and natural gas
sectors. Figure B-5 presents the percent change in output for the remaining sectors of the
economy as reflected in the SAGE model for 2026, 2031, 2036, and 2041 (note the axis scale is
different than in Figure B-4). Modest output reductions are estimated in some relatively more
energy intensive sectors (e.g., chemical manufacturing) and those that support coal use in the
electricity sector (e.g., transportation) whereas output increases in sectors associated with capital
formation in 2026 to support investments needed to comply with proposed rules.
Combining output impacts across all sectors in the economy, Figure B-6 presents the
estimated net economy-wide percent changes in output in 2026, 2031, 2036, and 2041.
Aggregate U.S. production is expected to increase by 0.018 percent in 2026, with declines of
similar magnitude in subsequent years. The model suggests modest increases in production in
2026 in capital forming sectors in anticipation of rule requirements, resulting in an overall
increase in output. In later model years, output reductions in the electricity sector, primary
energy sectors, and energy-intensive sectors slightly outweigh output increases elsewhere in the
economy.
B-16
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Chemical manufacturi
ng
Metal ore and nonmet
alic mineral mining
Petroleum refineries
T ransportation
Crude oil
Wood product manufac
turing
Healthcare services
Food and beverage ma
nufacturing
Water, sewage, and o
ther utilities
Construction
Q> Cement manufacturing
Truck transportation
Agriculture, forestr
y, fishing and hunting
Plastics and rubber
products manufacturing
Other manufacturing
Primary metal manufa
cturing
Fabricated metal pro
duct manufacturing
Electronics and tech
nology manufacturing
Transportation equip
ment manufacturing
4° 4' 4" -& 4*
b" ,Q>" Jb' Qs- <$>-
L
¦S' rP° <>* & ' Jt>' 05" Qj' ,<&" js>" 05" Q>- Q>"
JZ>- <$>• q>- <£, •
% Change
Figure B-5 Percent Change in Sectoral Output (Rest of Economy)
B-17
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8.20%
0.15%
0.10%
0.05%
-------
Healthcare services
Services
Truck transportation
T ransportation
Other manufacturing
Transportation equipment manufacturing
Electronics and technology manufacturing
Fabricated metal product manufacturing
Primary metal manufacturing
Cement manufacturing
w Plastics and rubber products manufacturing
o
Chemical manufacturing
-------
transition dynamics assumed in the model. Slower transition dynamics may widen the gap
between social cost measures with and without accounting for short-term transition dynamics in
the labor market. Hafstead and Williams (2018) develop a two-sector CGE model that
incorporates several wage-setting mechanisms where the adjustment costs from transitioning
between unemployment and employment are realized at much smaller time steps than are typical
in a CGE framework. The authors estimate that the net employment impacts of environmental
policy may be small due to the offsets in the labor demand by unregulated sectors.
Figure B-8 presents the percent change in net labor demand across the economy in 2026,
2031, 2036, and 2041. Shifts in aggregate labor demand are expected to occur as some sectors
require fewer hours worked, some require more hours worked, and wage rates adjust to ensure
there is adequate labor being voluntarily supplied by households to meet firms' demand for
labor. In model year 2026, the model estimates a small aggregate increase in the labor supply to
accommodate additional labor demand across the economy needed to support additional
investments occurring in anticipation of the proposed regulatory requirements. In subsequent
model years expected reductions in output and investment result in small decreases in labor
supply. Figure B-9 presents the estimated percent change in labor demand by electricity, coal,
and natural gas sectors in 2026, 2031, 2036, and 2041. In these sectors, changes in labor demand
are generally reflective of the estimated output changes.
Figure B-10 presents the percent change in sectors other than electricity, natural gas, and
coal for 2026, 2031, 2036, and 2041. The increase in the labor supply in 2026 is driven by
increases in demand for labor in sectors associated with capital formation (e.g., construction,
cement manufacturing) to support new investments.
B-20
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0.20%
3.15%-
0.10%-
0.05%-
OJ
bO
0.00%
-0.05%-
-0.15%
-0.20%
2026
2031
2036
2041
Time
Figure B-8 Percent Change in Economy-wide Labor Demand (All Sectors)
Electric power
Coal mining-
Natural gas-
T 1 i—
T I I 1 !—
1 1 '—I ! 1-
T 1 '—I ! 1—
-3% -2% -1% 0% 1% 2% -3% -2% -1% 0% 1% 2% -3% -2% -1% 0% 1% 2% -3% -2% -1% 0% 1% 2%
% Change
Figure B-9 Percent Change in Labor Demand (Electricity, Coal, Natural Gas)
B-21
-------
Metal ore and nonmet
alic mineral mining
T ransportation
Petroleum refineries
Water, sewage, and o
ther utilities
Healthcare services
Food and beverage ma
nufacturing
Wood product manufac
turing
Cement manufacturing
Construction
j__ Truck transportation
o
4-»
U
cu Primary metal manufa
cturing
Crude oil
Agriculture, forestr
y, fishing and hunting
Fabricated metal pro
duct manufacturing
Other manufacturing
Chemical manufacturi
ng
Plastics and rubber
products manufacturing
Transportation equip
ment manufacturing
Electronics and tech
nology manufacturing
~
]
]
.2% -0.1% 0.0% 0.1%
.2% -0.1% 0.0% 0.1% -0.2% -0.1% 0.0% 0.1% -0.2% -0.1% 0.0% 0.1%
% Change
Figure B-10 Percent Change in Labor Demand (Rest of Economy)
B. 4.6 Household Distributional Impacts
The social costs of regulation are ultimately borne by households through changes in
final goods prices or changes in labor, capital, and resource income. SAGE models
representative households by income quintiles in each of the four Census regions. This allows
the social costs to be separately estimated across the income distribution and for different regions
B-22
-------
of the country, as presented in Figure B-l l.200 In general, the annualized household costs increase
with income and are expected to be highest in the Western Census region and lowest in the
Southern Census Region.
Reg
ions
•
Midwest
•
Northeast
South
•
West
:30k 30-50k 58-70k 70-150k
Income Quintile
>150k
Figure B-ll Distribution of General Equilibrium Social Costs
Estimates in Figure B-l 1 reflect a combined effect of the proposed rules' requirements
and interactions with IRA subsidies that are expected to see increased use in response to the
proposed rules. A regulation may affect the value of government expenditures through relative
prices of goods and services purchased by the government. In addition, it may affect tax
revenues through impacts on the value of the base for ad valorem taxes (e.g., labor and capital
taxes). In these cases, a CGE model must implement a closure rule to ensure that the government
has the funds necessary to support its expenditures. A common assumption in CGE models is to
balance the government's budget through lump sum transfers between households and the
government as a non-distortionary approach to closing the model. This is the approach used in
200 Distributional cost estimates are annualized for the period 2026 to 2046 and divided by the total number of
households of a given income quintile and region using 2016 estimates from the Census' Current Population
Survey.
B-23
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the SAGE model. Given uncertainties in the accounting for the IRA subsidies in this analysis, we
are unable to determine the relative role of this effect in the distributional estimates at this time.
B.5 Limitations to Analysis
The SAGE model and methodology for aligning IPM outputs for use as inputs in SAGE
reflect the best available science for conducting economy-wide modeling of the proposed rules.
However, both the use of SAGE in a regulatory analysis and the framework for linking IPM with
the SAGE model are subject to some uncertainty:
• The costs of complying with existing regulations are largely reflected in the social
accounting matrix, and in projections used to calibrate the SAGE model, but are not
distinguished from non-regulatory related costs (i.e., there is no explicit characterization
of already existing regulations in the constructed baseline). Data underlying the SAGE
baseline ranges from 2016 to 2020, depending on the specific source. As a result, recent
changes in the economy, including new regulations, may not be captured in the source
data used to calibrate the model's baseline. For these reasons, interactions that the
proposed rules may have with compliance activities already underway to meet existing
regulatory requirements may not be explicitly captured in SAGE.
• The methodology used to align IPM and SAGE accounts for partial equilibrium
feedbacks in IPM and represents an improvement over assuming the solution of one
model directly in the other. While a full model linkage, where the models iteratively pass
information back and forth until jointly converging to an equilibrium, may provide a
more complete representation of the economy-wide impacts of the proposed rules, it is
challenging to implement and not feasible at this time.
• To align IPM outputs for use as SAGE inputs, we target the estimated change in capital
flow payments. However, because the representation of capital differs between IPM and
SAGE, the projected stream of capital investments in response to the proposed rules also
likely differs between the two models. See Appendix B.3.2 for a discussion of this
choice.
B-24
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• This analysis attributes all compliance costs for existing generators in IPM to production
with extant capital in the SAGE model. Extant capital in SAGE is assumed to be
relatively inflexible in its ability to accommodate changes in production processes when
compared to new capital. Production with extant and new capital is not equivalent to
differentiating existing and new generation in the IPM modeling framework. For
example, the lifespan of existing generators in IPM can be extended through investments
in ways that are not directly comparable to production with extant capital in the SAGE
model. Given these differences, it is possible that the linked framework may overattribute
incremental costs to less flexible production processes in SAGE.
• Given the level of sectoral aggregation in SAGE, subsidies on specific electricity-sector
technologies are reflected in the SAGE model through a sector-wide adjustment in output
taxes. This sector-wide adjustment is designed to approximate subsidies levied on
specific technologies but may add a degree of uncertainty to the social cost estimate
regarding the degree to which they interact with pre-existing distortions in the economy.
Furthermore, this treatment of subsidies is subject to additional uncertainties related to
the effective magnitude of the subsidy payments. The input composition assumed for the
production of hydrogen, described in Appendix B.3.2, is subject to uncertainty. If the
input composition for hydrogen production differs substantially from what is assumed for
this analysis, it could also affect social cost estimates.
• The purpose of this analysis is to quantify the economy-wide impacts of the proposed
rules. To the extent possible, the analysis models the potential interactions between the
proposed rules and IRA subsidies, but it is beyond the scope of this proposal to evaluate
the social cost of the IRA subsidies in their entirety. Additional effects of the IRA, as
they relate to the proposed rules, beyond the specific subsidies modeled in this RIA could
result in a change in estimated social costs and other economy-wide impacts.
B.6 References
Hafstead, M. A. C., & Williams, R. C. (2018). Unemployment and environmental regulation in
general equilibrium. Journal of Public Economics, 160, 50-65.
doi:https://doi.org/10.1016/j.jpubeco.2018.01.013
B-25
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Marten, A., Schreiber, A., and Wolverton, A. (2023). SAGE Model Documentation (2.1.0).
Washington DC. https://www.epa.gov/environmental-economics/cge-modeling-
regulatory-analysis
Marten, A. L., Garbaccio, R., & Wolverton, A. (2019). Exploring the General Equilibrium Costs
of Sector-Specific Environmental Regulations. Journal of the Association of
Environmental and Resource Economists, 6(6), 1065-1104. doi: 10.1086/705593
NREL. (2022). H2A Hydrogen Production Model: Current Central Hydrogen Production from
Steam Methane Reforming of Natural Gas with CO2 Capture and Sequestration. Version
Aug 2022. https://www.nrel.gov/hydrogen/assets/docs/current-central-steam-methane-
reforming-with-co2-sequestration-version-aug-22.xlsm
OMB. (2004). Issuance of OMB 's 'Final Information Quality Bulletin for Peer Review.
Washington, DC. https://cfpub.epa.gov/si/m05-03.pdf
Rogerson, R. (2015). A Macroeconomic Perspective on Evaluating Environmental Regulations.
Review of Environmental Economics and Policy, 9(2), 219-238. doi:10.1093/reep/rev005
Schreiber, A., Evans, D., Marten, A., Wolverton, A., Davis, W. (2023). Evaluating Economy-
wide Effects of Power Sector Regulations Using the SAGE Model. Working Paper.
Retrieved from
U.S. EPA. (2015). Economy-Wide Modeling: Social Cost and Welfare White Paper.
https://www.epa.gov/system/files/documents/2023-
02/CGE%20social%20cost%20white%20paper%20final.pdf
U.S. EPA Science Advisory Board. (2017). SAB Advice on the Use of Economy-Wide Models in
Evaluating the Social Costs, Benefits, and Economic Impacts of Air Regulations. (EPA-
SAB-17-012). Washington DC
U.S. EPA Science Advisory Board. (2020). Technical Review ofEPA's Computable General
Equilibrium Model, SAGE. (EPA-SAB-20-010). Washington DC
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APPENDIX C: ASSESSMENT OF POTENTIAL COSTS AND EMISSIONS
IMPACTS OF PROPOSED NEW AND EXISTING SOURCE STANDARDS
ANALYZED SEPARATELY
C.l Modeling the Rules Independently
In this appendix, we describe the projected EGU compliance behavior, costs, and
emissions impacts for the proposed Emission Guidelines and proposed NSPS when modeled
independently.201 We also compare the results from each rule modeled individually with the
results presented elsewhere in the RIA that shows the proposed rules combined effects. This
supplementary analysis quantifies the climate benefits of these rules but does not quantify any
additional benefits, for instance health benefits from reductions in other pollutants, because of
time and resource constraints. The GHG mitigation measures modeled under each of these
scenarios are consistent with those applicable to each source category under the proposal, as
outlined in Table C-l and Table C-2.
201 Appendix C pertains to the analysis of the proposed standards for new natural gas-fired EGUs and for existing
coal-fired EGUs. Please see Section 8 for impact analysis of the proposed standards for existing natural gas-fired
EGUs and the third phase of the proposed standards for new natural gas-fired EGUs.
C-l
-------
Table C-l Summary of GHG Mitigation Measures for Existing Sources by Source
Category under the Proposala'b'c'd
Affected EGUs
Subcategory Definition
GHG Mitigation
Measure
Long-term existing coal-fired
steam generating units
Coal-fired steam generating units without
committed retirement prior to 2040
CCS with 90 percent
capture of CO2, starting in
2030
Medium-term existing coal-
fired steam generating units
Coal-fired steam generating units with a
committed retirement by 2040 that are less than
500 MW, and that are not a near-term/low
utilization unit
Natural gas co-firing at 40
percent of the heat input
to the unit, starting in
2030
Near-term existing coal-fired
steam generating units
Coal-fired steam generating units with a
committed retirement prior to 2035 that operate
with annual capacity factors less than 20 percent
in 2030
Routine methods of
operation
Imminent-term existing coal-
fired steam generating units
Coal-fired steam generating units without a
federally enforceable retirement commitment
prior to 2030
Routine methods of
operation
a All years shown in this table reflect IPM run years.
b Coal units that lack existing SCR controls must install these controls in addition to CCS to comply.
0 Coal-fired EGUs that convert entirely to burn natural gas are no longer subject to coal-fired EGU mitigation
measures outlined above.
d The modeling did not include GHG mitigation measure requirements on existing natural gas generation. These
requirements are analyzed separately in Section 8.
C-2
-------
Table C-2 Summary of GHG Mitigation Measures for New Sources by Source Category
under the Proposala,b,c,d
Affected EGUs
Subcategory Definition
1st Component
BSER
2nd Component
BSER
Second Phase
Applicability:
Proposal and Less
Stringent
Scenario
Baseload
Economic NGCC
Additions
NGCC units that commence
construction after 2023 and
operate at an annual capacity
factor of more than 50%
Efficient
generation
30% by volume
hydrogen co-
firing or CCS
Intermediate Load
Economic NGCC
Additions
NGCC units that commence
construction after 2023 and
operate at an annual capacity
factor of less than 50%
Efficient
generation
Efficient
generation
2035
Intermediate load
Economic NGCT
Additions
NGCT units that commence
construction after 2023 and
operate at an annual capacity
factor of more than 20%
Efficient
generation
48% by volume
hydrogen co-
firing
Peaking Economic
NGCT Additions
NGCT units that commence
construction after 2023 and
operate at an annual capacity
factor of less than 20%
Efficient
generation
Efficient
generation
a All years shown in this table reflect IPM run years.
b Delivered hydrogen price is assumed to be $0.5/kg in years in which the second phase of the NSPS is active, and
$ 1/kg in all other years.
0 NGCC unit additions that install CCS are no longer subject to the GHG mitigation measures outlined above.
d The modeling did not include GHG mitigation measure requirements on existing natural gas generation. These
requirements are analyzed separately in Section 8.
C.2 Compliance Cost Assessment
The estimates of incremental costs of supplying electricity under the proposal and under
the proposed Emission Guidelines and proposed NSPS when modeled separately are presented in
Table C-3. Estimates for additional recordkeeping, monitoring, and reporting requirements for
EGUs are also included within the estimates in this table.
C-3
-------
Table C-3 National Power Sector Compliance Cost Estimates for the Illustrative
Scenarios (billions of 2019 dollars)
Proposal
Existing Source
Rule Only
New Source
Rule Only
2024 to 2042 (Annualized)
0.96
1.1
0.17
2024 to 2045 (Annualized)
0.86
1.1
0.18
2028 (Annual)
-0.22
-0.21
0.051
2030 (Annual)
4.1
4.0
0.13
2035 (Annual)
0.27
0.53
-0.21
2040 (Annual)
0.76
1.3
-0.64
2045 (Annual)
-0.048
0.22
-0.30
"2024 to 2042 (Annualized)" reflects total estimated annual compliance costs levelized over the period 2024 through
2042 and discounted using a 3.76 real discount rate.202 This does not include compliance costs beyond 2042. "2024
to 2045 (Annualized)" reflects total estimated annual compliance costs levelized over the period 2024 through 2045
and discounted using a 3.76 real discount rate. This does not include compliance costs beyond 2045. "2028
(Annual)" through "2045 (Annual)" costs reflect annual estimates in each of those run years.203
Existing coal-fired EGUs represent the largest share of affected resources within the
proposal. Hence the existing source rule is responsible for the majority of cost increases
projected under the proposed (combined effect) rule. New sources represent a smaller total share
of the affected sources under this rule, and hence cost increases projected under the proposed
NSPS alone are smaller than under the existing source rule. The projected new source rule costs
are lower than baseline values since the delivered price of hydrogen is assumed to be $0.5/kg
when the second phase of the NSPS is active (starting in 2035), and $l/kg in all other years. At
this lower price assumption, hydrogen would be cost competitive under baseline conditions in
some markets, resulting in lower total projected costs than under the baseline scenario which
does not feature a cost decline.
C.3 Emissions Reduction Assessment
As indicated in Section 3, the CO2 emissions reductions are presented in this RIA from
2028 through 2045 and are based on IPM projections. Table C-4 presents the estimated reduction
202 This table reports compliance costs consistent with expected electricity sector economic conditions. The PV of
costs was calculated using a 3.76 percent real discount rate consistent with the rate used in IPM's objective
function for cost-minimization. The PV of costs was then used to calculate the levelized annual value over a 19-
year period (2024 to 2042) and a 21-year period (2024 to 2045) using the 3.76 percent rate as well. Tables ES-19
and 8-4 report the PV of the annual stream of costs from 2024 to 2042 using 3 percent and 7 percent consistent
with OMB guidance.
203 Cost estimates include financing charges on capital expenditures that would reflect a transfer and would not
typically be considered part of total social costs.
C-4
-------
in power sector CO2 emissions resulting from compliance with the proposed requirements, as
well as the estimated emissions from the proposed Emission Guidelines and proposed NSPS
independently.
The CO2 emission reductions follow an expected pattern: the existing source rule is
responsible for the majority of reductions under the proposal modeling presented in the RIA, and
these reductions occur primarily in the first half of the forecast period. The new source rule is
responsible for a smaller share of reductions, and these reductions occur more towards the latter
half of the forecast period. Cumulative CO2 reductions between 2028-47 under the proposal (713
million metric tons) are greater than under the existing source rule only (711 million metric tons)
and under the proposed NSPS only (an increase of 23 million metric tons). Under the New
Source Rule only, CO2 emissions at new sources declines, but these are offset by increases at
existing sources, particularly through 2030. By 2035 reductions at new sources outweigh
increases in emissions at existing sources. Under the Existing Source Rule only, emissions from
existing sources are lower, and only partially offset by increases in emissions from new sources,
resulting in net emission decreases over the forecast period.
C-5
-------
Table C-4 EGU Annual CO2 Emissions and Emissions Changes (million metric tons)
for the Baseline and the Illustrative Scenarios from 2028 to 2045204
Annual
CO2
Total Emissions
Change from Baseline
(million
metric
tons)
Baseline
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
2028
1,222
1,212
1,209
1,227
-10
-13
4
2030
972
882
871
988
-89
-100
17
2035
608
572
574
606
-37
-34
-3
2040
481
458
457
478
-24
-24
-3
2045
406
387
392
406
-19
-14
0
Cumulative
(2028-47)
12,223
11,510
11,512
12,246
-713
-711
23
Annual
CO2
Total Emissions from Existing Sources only
Change from Baseline
(million
metric
tons)
Baseline
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
2028
1,163
1,144
1,136
1,174
-19
-28
11
2030
911
810
793
934
-101
-118
23
2035
539
518
488
565
-21
-50
26
2040
413
405
379
434
-8
-33
21
2045
334
329
312
355
-6
-22
20
Cumulative
(2028-47)
1,163
1,144
1,136
1,174
-19
-28
11
Annual
CO2
Total Emissions from New Sources only
Change from Baseline
(million
metric
tons)
Baseline
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
2028
59
68
73
52
9
14
-7
2030
61
73
79
54
12
18
-7
2035
70
54
85
41
-16
16
-29
2040
68
52
78
45
-16
9
-24
2045
71
58
79
51
-13
8
-20
Cumulative
(2028-47)
59
68
73
52
9
14
-7
204 This analysis is limited to the geographically contiguous lower 48 states.
C-6
-------
There will also be impacts on non-C02 air emissions associated with EGUs burning fossil
fuels that result from compliance strategies modeled to meet the proposed requirements. These
other emissions include changes in emissions of NOx, SO2, and direct PM2.5 emissions changes,
as well as changes in ozone season NOx emissions. The emissions impacts are presented in
Table C-5.
C-7
-------
Table C-5 EGU Annual Emissions and Emissions Changes for Annual NOx, Ozone
Season (April to September) NOx, SO2, and Direct PM2.5 for the Baseline and Illustrative
Scenarios for 2028 to 2040
Annual
NOx
Total Emissions
Change from Baseline
(Thousand
Tons)
Baseline
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
2028
457
449
447
460
-7
-10
3
2030
368
304
295
371
-64
-73
4
2035
214
193
186
215
-21
-28
1
2040
162
149
145
158
-13
-17
-5
Ozone
Season
NOxa
Total Emissions
Change from Baseline
(Thousand
Tons)
Baseline
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
2028
195
191
190
196
-3
-5
1
2030
163
142
136
164
-22
-27
1
2035
104
97
94
105
-7
-10
0
2040
80
76
74
77
-4
-6
-3
Annual SO2
Total Emissions
Change from Baseline
(Thousand
Tons)
Baseline
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
2028
394
382
378
399
-12
-16
5
2030
282
175
167
286
-107
-115
4
2035
130
89
88
127
-41
-42
-3
2040
89
59
59
83
-30
-30
-6
Direct PM2.5
Total Emissions
Change from Baseline
(Tons)
Baseline
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
2028
75
73
73
75
-1
-1
0
2030
66
60
60
65
-6
-6
0
2035
47
45
44
47
-1
-3
1
2040
38
38
36
39
-1
-2
0
a Ozone season is the May through September period in this analysis.
C-8
-------
C.4 Impacts on Fuel Use and Generation Mix
The proposed NSPS and proposed Emission Guidelines expected to result in significant
GHG emissions reductions. They are also expected to have impacts on the power sector.
Consideration of these potential impacts is an important component of assessing the relative
impact of the illustrative scenarios. In this section we discuss the estimated changes in fuel use,
fuel prices, generation by fuel type, and capacity by fuel type for the 2030, 2035 and 2040 IPM
model run years under the proposal and under the proposed Emission Guidelines and proposed
NSPS independently.
As outlined in Table C-6, under the proposed existing source rule only, coal consumption
falls more than under the proposal, while coal consumption falls least under the proposed new
source rule only. Under the existing source rule only, GHG mitigation measures apply to existing
coal-fired EGUs as outlined in Table C-l. Hence coal capacity reductions are offset by increases
in new source NGCC generation. Under the new source rule-only modeling, the GHG mitigation
measures apply only to new fossil-fuel fired sources, as outlined in Table C-2. Hence generation
and emissions from these sources falls and are compensated for by increases in generation and
emissions from existing sources.
C-9
-------
Table C-6 2028, 2030, 2035 and 2040 Projected U.S. Power Sector Coal Use for the
Baseline and the Illustrative Scenarios
Million Tons
Percent Change from Baseline
Year
Baseline
Proposal
Existing
Source
Rule
Only
New
Source
Rule
Only
Proposal
Existing
Source
Rule
Only
New
Source
Rule
Only
Appalachia
48
48
46
50
-2%
-6%
4%
Interior
51
49
49
51
-4%
-4%
0%
Waste Coal
2028
4
4
4
4
0%
0%
0%
West
148
145
146
149
-2%
-2%
0%
Total
252
246
245
254
-2%
-3%
1%
Appalachia
28
19
17
30
-31%
-41%
5%
Interior
37
31
31
37
-17%
-17%
1%
Waste Coal
2030
4
3
3
4
-32%
-32%
0%
West
107
52
53
106
-51%
-50%
-1%
Total
176
105
103
177
-40%
-41%
1%
Appalachia
11
10
10
14
-8%
-8%
27%
Interior
20
21
20
20
9%
0%
2%
Waste Coal
2035
2
0
0
2
-83%
-85%
-10%
West
48
30
33
43
-37%
-31%
-10%
Total
80
62
63
79
-23%
-22%
-2%
Appalachia
6
7
5
8
34%
-5%
48%
Interior
16
19
19
16
25%
25%
0%
Waste Coal
2040
2
0
0
2
-100%
-100%
-12%
West
39
26
28
34
-33%
-27%
-13%
Total
62
53
53
59
-15%
-14%
-4%
As outlined in Table C-7 gas consumption follows the opposite trend to coal consumption
under the three scenarios shown. Under the existing source rule, gas consumption remains at
similar levels to the proposal (gas generation compensates for declining coal generation), while
under the new source rule, gas generation is moderately lower as a result of GHG mitigation
measures applied to new fossil-fuel fired sources, while similar measures are not applied to
existing coal-fired sources.
C-10
-------
Table C-7 2028, 2030, 2035 and 2040 Projected Power Sector Natural Gas Use for the
Baseline and the Illustrative Scenarios
Trillion Cubic Feet
Percent Change from Baseline
Year
Baseline
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
Proposal
Existing
Source
Rule Only
New
Source
Rule Only
2028
12.5
12.6
12.6
12.5
0%
1%
0%
2030
12.6
13.6
13.7
12.6
8%
8%
0%
2035
9.9
9.9
10.1
9.7
-1%
1%
-2%
2040
8.1
7.9
8.1
7.9
-2%
0%
-3%
As outlined in Table C-8 and Table C-9 coal and gas prices are similar under the Proposal
and Existing Source rules, while changes are smaller under the Proposed NSPS.
Table C-8 2028, 2030, 2035 and 2040 Projected Minemouth and Power Sector Delivered
Coal Price (2019 dollars) for the Baseline and the Illustrative Scenarios
$/MMBtu
Percent Change from Baseline
Existing New
_ „ . Source Source
Baseline Proposal _ , _ ,
Rule Rule
Only Only
Existing New
„ , Source Source
pro,>osal Rul(, Rul(,
Only Only
Minemouth 1.16 1.16 1.15 1.16
2028
Delivered 1.59 1.58 1.58 1.60
0% 0% 0%
-1% -1% 0%
Minemouth 1.17 1.27 1.26 1.17
2030
Delivered 1.47 1.47 1.46 1.48
8% 7% 0%
0% 0% 1%
Minemouth 1.34 1.41 1.40 1.35
2035
Delivered 1.38 1.40 1.40 1.41
5% 4% 1%
2% 1% 2%
Minemouth 1.42 1.49 1.48 1.44
2040
Delivered 1.42 1.45 1.45 1.46
5% 4% 1%
2% 2% 3%
C-ll
-------
Table C-9 2028, 2030, 2035 and 2040 Projected Henry Hub and Power Sector Delivered
Natural Gas Price (2016 dollars) for the Baseline and the Illustrative Scenarios
$/MMBtu
Percent Change from Baseline
Existing New
„ „ . Source Source
Baseline Proposal _ , _ ,
Rule Rule
Only Only
Existing New
„ , Source Source
Rule Rule
Only Only
HemyHub 3.0 3.0 3.0 3.0
2028
Delivered 3.0 3.0 3.0 3.0
0% 0% 0%
0% 0% 0%
Hemy Hub 2.4 2.6 2.6 2.4
2030
Delivered 2.5 2.8 2.8 2.5
10% 10% 0%
9% 9% 0%
HemyHub 1.9 1.8 1.9 1.8
2035
Delivered 2.1 2.0 2.1 2.0
-2% 0% -2%
-2% 1% -3%
HemyHub 2.0 2.0 2.1 2.0
2040
Delivered 2.2 2.1 2.2 2.1
-2% 1% -3%
-3% 1% -3%
As outlined in Table C-10 the generation mix remains generally similar under the proposal
and existing source rules, but the non-imposition of GHG mitigation measures on new fossil-
fired sources under the existing source rule only scenario results in some increase in generation
from new NGCC capacity relative to the proposal. Under the new source only scenario, the
overall generation mix is similar to the baseline, with the exception of higher coal dispatch
driven by the GHG mitigation measures on new fossil-fired sources reducing the total dispatch of
new NGCC units.
C-12
-------
Table C-10 2028, 2030, 2035 and 2040 Projected U.S. Generation by Fuel Type for the
Baseline and the Illustrative Scenarios
Generation (TWh)
Year
Baseline
Proposal
Existing
Source
Rule
Only
New
Source
Rule
Only
Proposal
Existing
Source
Rule
Only
New
Source
Rule
Only
Coal
484
472
468
489
-2%
-3%
1%
Natural Gas
1,773
1,783
1,789
1,766
1%
1%
0%
Nuclear
765
765
765
765
0%
0%
0%
Hydro
Non-Hydro RE
2028
294
964
294
966
295
966
293
964
0%
0%
0%
0%
0%
0%
Oil/Gas Steam
30
30
29
31
0%
-2%
3%
Other
30
30
30
30
0%
0%
0%
Grand Total
4,341
4,341
4,342
4,339
0%
0%
0%
Coal
309
170
166
315
-45%
-46%
2%
Natural Gas
1,771
1,879
1,889
1,765
6%
7%
0%
Nuclear
734
734
734
734
0%
0%
0%
Hydro
Non-Hydro RE
2030
303
1,269
303
1,278
303
1,276
302
1,266
0%
1%
0%
1%
0%
0%
Oil/Gas Steam
33
50
45
34
52%
38%
6%
Other
29
29
29
29
0%
0%
0%
Grand Total
4,447
4,442
4,443
4,446
0%
0%
0%
Coal
120
87
86
122
-28%
-28%
2%
Natural Gas
1,402
1,419
1,429
1,390
1%
2%
-1%
Nuclear
660
660
660
661
0%
0%
0%
Hydro
Non-Hydro RE
2035
329
2,180
328
2,186
328
2,188
328
2,179
0%
0%
0%
0%
0%
0%
Oil/Gas Steam
16
18
15
21
13%
-9%
27%
Other
29
29
29
29
0%
0%
0%
Grand Total
4,736
4,728
4,736
4,729
0%
0%
0%
Coal
79
65
64
78
-17%
-18%
0%
Natural Gas
1,164
1,173
1,169
1,163
1%
0%
0%
Nuclear
616
616
616
616
0%
0%
0%
Hydro
Non-Hydro RE
2040
346
2,826
346
2,818
346
2,839
345
2,814
0%
0%
0%
0%
0%
0%
Oil/Gas Steam
3
3
3
3
-3%
-23%
0%
Other
28
28
27
28
0%
0%
0%
Grand Total
5,061
5,050
5,063
5,048
0%
0%
0%
Percent Change from Baseline
As outlined in Table C-l 1 the capacity mix follows similar trends to those seen under the
generation mix table. The capacity mix under the proposal and existing source rule scenarios are
similar, while the capacity mix under the baseline and new source rule only scenarios are similar.
C-13
-------
The new source rule only is projected to result in less new NGCC and more existing coal
capacity relative to the baseline, while the existing source rule only is projected to result in less
coal capacity and more new NGCC capacity relative to the projected proposal results.
C-14
-------
Table C-ll 2028, 2030, 2035 and 2040 Projected U.S. Capacity by Fuel Type for the
Baseline and the Illustrative Scenarios
Capacity (GW)
Year
Baseline
Proposal
Existing
Source
Rule
Only
New
Source
Rule
Only
Proposal
Existing
Source
Rule
Only
New
Source
Rule
Only
Coal
100
99
100
101
-2%
-1%
0%
Natural Gas
463
467
468
461
1%
1%
0%
Nuclear
96
96
96
96
0%
0%
0%
Hydro
2028
102
102
102
102
0%
0%
0%
Non-Hydro RE
315
316
315
315
0%
0%
0%
Oil/Gas Steam
63
63
63
63
0%
0%
0%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,146
1,149
1,151
1,144
0%
0%
0%
Coal
69
59
56
70
-15%
-18%
1%
Natural Gas
461
465
467
459
1%
1%
0%
Nuclear
92
92
92
92
0%
0%
0%
Hydro
Non-Hydro RE
2030
104
403
104
405
104
404
104
403
0%
0%
0%
0%
0%
0%
Oil/Gas Steam
60
69
69
62
15%
14%
2%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,196
1,200
1,199
1,196
0%
0%
0%
Coal
44
13
13
46
-70%
-70%
4%
Natural Gas
470
494
490
472
5%
4%
1%
Nuclear
84
84
84
84
0%
0%
0%
Hydro
2035
108
108
108
108
0%
0%
0%
Non-Hydro RE
668
670
669
669
0%
0%
0%
Oil/Gas Steam
59
67
67
59
13%
14%
-1%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,439
1,443
1,438
1,444
0%
0%
0%
Coal
35
10
9
36
-73%
-73%
2%
Natural Gas
513
533
530
515
4%
3%
0%
Nuclear
79
79
79
79
0%
0%
0%
Hydro
2040
110
110
110
110
0%
0%
0%
Non-Hydro RE
868
867
872
866
0%
0%
0%
Oil/Gas Steam
59
67
67
58
14%
14%
-1%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,672
1,672
1,675
1,672
0%
0%
0%
Percent Change from Baseline
C-15
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-23-006
Environmental Protection Health and Environmental Impacts Division May 2023
Agency Research Triangle Park, NC
------- |