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Regulatory Impact Analysis for the Final
Federal Good Neighbor Plan Addressing
Regional Ozone Transport for the 2015 Ozone
National Ambient Air Quality Standard
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EPA-452/R-23-001
March 2023
Regulatory Impact Analysis for Final Federal Good Neighbor Plan Addressing Regional Ozone
Transport for the 2015 Ozone National Ambient Air Quality Standard
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 Quality Planning and
Standards, U.S. Environmental Protection Agency. Questions related to this document should be
addressed to U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, C439-02, Research Triangle Park, North Carolina 27711 (email:
oaqpseconomics@epa.gov). Please submit comments on this document to the following docket:
EPA-HQ-OAR-2021 -0668.
ACKNOWLEDGEMENTS
In addition to EPA staff from the Office of Air Quality Planning and Standards, personnel from
the Office of Atmospheric Programs and the Office of Policy's National Center for
Environmental Economics contributed data and analysis to this document.
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TABLE OF CONTENTS
TABLE OF CONTENTS v
LIST OF TABLES ix
LIST OF FIGURES xv
EXECUTIVE SUMMARY 18
Overview 18
ES.l Identifying Needed Emissions Reductions and Regulatory Requirements 19
ES.2 Baseline and Analysis Years 23
ES.3 Air Quality Modeling 23
ES.4 Control Strategies and Emissions Reductions 24
ES.4.1 EGUs 27
ES.4.2 Non-EGUs 29
ES.5 Costs 32
ES.6 Benefits 33
ES.6.1 Health Benefits Estimates 33
ES.6.2 Climate Benefits 35
ES.6.3 Total Monetized Human Health and Climate Benefits 35
ES.6.4 Additional Unquantified Benefits 38
ES.7 Environmental Justice Impacts 39
ES.8 Results of Benefit-Cost Analysis 41
CHAPTER 1: INTRODUCTION AND BACKGROUND 46
Overview 46
1.1 Background 47
1.1.1 Role of Executive Orders in the Regulatory Impact Analysis 49
1.1.2 Alternatives Analyzed 49
1.1.3 The Need for Regulation 54
1.2 Overview and Design of the RIA 54
1.2.1 Methodology for Identifying Needed Reductions 54
1.2.2 States Covered by the Rule 58
1.2.3 Regulated Entities 59
1.2.4 Baseline and Analysis Years 60
1.2.5 Emissions Controls, Emissions, and Cost Analysis Approach 62
1.2.6 Benefits Analysis Approach 63
1.3 Organization of the Regulatory Impact Analysis 64
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CHAPTER 2: INDUSTRY SECTOR PROFILES 65
Overview 65
2.1 Background 65
2.2 Power Sector Overview 65
2.2.1 Generation 66
2.2.2 Transmission 74
2.2.3 Distribution 75
2.3 Sales, Expenses, and Prices 75
2.3.1 Electricity Prices 76
2.3.2 Prices of Fossil Fuels Used for Generating Electricity 78
2.3.3 Changes in Electricity Intensity of the U.S. Economy from 2015 to 2021 79
2.4 Industrial Sectors Overview 81
2.4.1 Cement and Cement Product Manufacturing 82
2.4.2 Iron and Steel Mills and Ferroalloy Manufacturing 85
2.4.3 Glass and Glass Product Manufacturing 86
2.4.4 Pipeline Transportation of Natural Gas 87
2.4.5 Industrial Boilers 88
2.4.6 Municipal Waste Combustors 90
2.5 References 90
CHAPTER 3: AIR QUALITY IMPACTS 92
Overview 92
3.1 Air Quality Modeling Platform 93
3.2 Applying Modeling Outputs to Create Spatial Fields 95
3.2.1 Spatial Distribution of Ozone Impacts 96
3.2.2 Spatial Distribution of PM2.5 Impacts 109
3.3 Uncertainties and Limitations 113
3.4 References 115
APPENDIX 3 A: IMPACTS ON OZONE DESIGN VALUES OF THE FINAL RULE IN 2026
118
3A. 1 Projected Impacts on Ozone Design Values 118
CHAPTER 4: COST, EMISSIONS, AND ENERGY IMPACTS 123
Overview 123
4.1 Regulatory Control Alternatives 124
4.1.1 EGU Regulatory Control Alternatives Analyzed 128
4.1.2 Non-EGU Regulatory Control Alternatives Analyzed 131
4.2 Power Sector Modeling Framework 132
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4.3 The EPA's Power Sector Modeling of the Baseline run and Three Regulatory Control
Alternatives 135
4.3.1 The EPA's IPM Baseline run v.6.20 136
4.3.2 Methodology for Evaluating the Regulatory Control Alternatives 137
4.3.3 Methodology for Estimating Compliance Costs 142
4.4 Estimating Emissions Units, Emissions Reductions, and Costs for Non-EGUs 144
4.5 Estimated Impacts of the Regulatory Control Alternatives 147
4.5.1 Emissions Reduction Assessment for EGUs 147
4.5.2 Compliance Cost Assessment for EGUs 155
4.5.3 Impacts on Fuel Use, Prices and Generation Mix 158
4.5.4 Emissions Reductions and Compliance Cost Assessment for Non-EGUs for 2026 . 169
4.5.5 Total Emissions Reductions and Compliance Costs for EGUs and Non-EGUs 172
4.6 Social Costs 174
4.7 Limitations 177
4.8 References 182
APPENDIX 4A: INFLATION REDUCTION ACT EGU SENSITIVITY RUN RESULTS... 184
4A. 1 Modeling the IRA in IPM 184
4A.1.1 Compliance Cost Assessment for EGUs 185
4A. 1.2 Emissions Reduction Assessment for EGUs 187
4A. 1.3 Impacts on Fuel Use and Generation Mix 192
CHAPTER 5: BENEFITS 197
Overview 197
5.1 Estimated Human Health Benefits 198
5.1.1 Health Impact Assessment for Ozone and PM2.5 200
5.1.2 Selecting Air Pollution Health Endpoints to Quantify 201
5.1.3 Characterizing Uncertainty in the Estimated Benefits 210
5.1.4 Estimated Number and Economic Value of Health Benefits 213
5.2 Climate Benefits from Reducing CO2 220
5.3 Total Human Health and Climate Benefits 234
5.4 Additional Unquantified Benefits 236
5.4.1 NO: Health Benefits 239
5.4.2 S02 Health Benefits 239
5.4.3 Ozone Welfare Benefits 240
5.4.4 NO2 and SO2 Welfare Benefits 240
5.4.5 Visibility Impairment Benefits 241
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5.4.6 Water Quality and Availability Benefits 242
5.4.7 Hazardous Air Pollutant Impacts 247
5.5 References 247
CHAPTER 6: ECONOMIC IMPACTS 258
Overview 258
6.1 Small Entity Analysis 258
6.1.1 EGU Small Entity Analysis and Results 258
6.1.2 Non-EGU Small Entity Impacts and Results 265
6.1.3 Conclusion 267
6.2 Labor Impacts 268
6.2.1 EGU Labor Impacts 269
6.2.2 Overview of Methodology 270
6.2.3 Overview of Power Sector Employment 271
6.2.4 Projected Sectoral Employment Changes due to the Final Rule 272
6.2.5 Non-EGU Labor Impacts 273
6.2.6 Conclusions 275
6.3 References 276
CHAPTER 7: ENVIRONMENTAL JUSTICE IMPACTS 277
7.1 Introduction 277
7.2 Analyzing EJ Impacts in This Final Rule 279
7.3 Demographic Proximity Analyses 280
7.3.1 EGU Proximity Assessments 281
7.3.2 Non-EGU Proximity Analysis 283
7.4 EJ Ozone and PM2.5 Exposure Impacts 285
7.4.1 Ozone Exposure Analysis 287
7.4.2 PM2.5 Exposure Analysis 300
7.5 Qualitative Assessment of CO2 307
7.6 Summary 309
7.7 References 312
CHAPTER 8: COMPARISON OF BENEFITS AND COSTS 314
Overview 314
8.1 Results 315
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LIST OF TABLES
Table ES-1. Summary of Non-EGU Industries, Emissions Unit Types, Form of Final Emissions
Limits, and Final Emissions Limits 22
Table ES-2. Regulatory Control Alternatives for EGUs and Non-EGUs 25
Table ES-3. EGU Ozone Season NOx Emissions Changes and Annual Emissions Changes for
NOx, SO2, PM2.5, and CO2 for the Regulatory Control Alternatives from 2023 - 2042 27
Table ES-4. Ozone Season NOx Emissions and Emissions Reductions (tons) for the Final Rule
and the Less and More Stringent Alternatives for Non-EGUs in 2026 30
Table ES-5. Non-EGU Industries, Emissions Unit Types, Assumed Control Technologies that
Meet Final Emissions Limits, Estimated Number of Control Installations 31
Table ES-6. Total National Compliance Cost Estimates (millions of 2016$) for the Final Rule
and the Less and More Stringent Alternatives 32
Table ES-7. Estimated Discounted Monetized Value of Avoided Ozone-Related Premature
Mortality and Illness for the Final Rule and the Less and More Stringent Alternatives in 2023
(95% Confidence Interval; millions of 2016$) 34
Table ES-8. Estimated Discounted Monetized Value of Avoided Ozone and PM2.5-Attributable
Premature Mortality and Illness for the Final Rule and the Less and More Stringent Alternatives
in 2026 (95% Confidence Interval; millions of 2016$) 34
Table ES-9. Combined Monetized Health and Climate Benefits for the Final Rule and Less and
More Stringent Alternatives for 2023 (millions of 2016$) 36
Table ES-10. Combined Monetized Health and Climate Benefits for the Final Rule and Less and
More Stringent Alternatives for 2026 (millions of 2016$) 37
Table ES-11. Combined Monetized Health and Climate Benefits for the Final Rule and Less and
More Stringent Alternatives for 2030 (millions of 2016$) 38
Table ES-12. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2023 for the U.S. (millions of 2016$) 42
Table ES-13. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2026 for the U.S. (millions of 2016$) 42
Table ES-14. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2030 for the U.S. (millions of 2016$) 43
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Table ES-15. Summary of Present Values and Equivalent Annualized Values for the 2023-2042
Timeframe for Estimated Monetized Compliance Costs, Benefits, and Net Benefits for the Final
Rule (millions of 2016$, discounted to 2023) 45
Table 1-1. Regulatory Control Alternatives for EGUs and Non-EGUs 51
Table 2-1. Total Net Summer Electricity Generating Capacity by Energy Source, 2014 and 2021
67
Table 2-2. Net Generation in 2015 and 2021 (Trillion kWh = TWh) 69
Table 2-3. Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Average Heat
Rate in 2020 72
Table 2-4. Total U.S. Electric Power Industry Retail Sales, 2015 and 2021 (billion kWh) 76
Table 3-1. Impact on EGU Ozone Season NOx Emissions of each Regulatory Control
Alternative in 2023 and in 2026 (1,000 tons) 98
Table 3-2. Impact on Non-EGU Ozone Season NOx Emissions of each Regulatory Control
Alternative in 2026 (1,000 tons) 99
Table 3-3. Impact on EGU Annual NOx, SO2, and PM2.5 Emissions of each Regulatory Control
Alternative for EGUs in 2026 (1,000 tons) 110
Table 3A-1. Ozone Impacts at Projected Nonattainment and Maintenance-Only Receptors (ppb)
for the Final Rule Modeled Control Case in 2026 119
Table 3 A-2. Ozone Impacts at Violating-Monitor Maintenance-Only Receptors (ppb) for the
Final Rule Modeled Control Case in 2026 120
Table 3A-3. Impact on EGU and Non-EGU Ozone Season NOx Emissions by State in the 2026
Modeled Control Case (1,000 tons) 121
Table 4-1. Summary of Non-EGU Industries, Emissions Unit Types, Form of Final Emissions
Limits, and Final Emissions Limits 126
Table 4-2. Regulatory Control Alternatives for EGUs and Non-EGUs 127
Table 4-3. Illustrative NOx Ozone Season Emission Budgets (Tons) Evaluated by IPM Run Year
130
Table 4-4. Ozone Season NOx Emissions and Emissions Reductions for the Final Rule and the
Less and More Stringent Alternatives for Non-EGUs 132
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Table 4-5. Summary of Methodology for Calculating Compliance Costs Estimated Outside of
IPM for the Transport FIP for the 2015 Ozone NAAQS, 2023 (2016$) 143
Table 4-6. EGU Ozone Season NOx Emissions and Emissions Changes for the Baseline run and
the Regulatory Control Alternatives from 2023 - 2045 148
Table 4-7. EGU Annual Emissions and Emissions Changes for NOx, SO2, PM2.5, and CO2 for
the Regulatory Control Alternatives for 2023-2045 151
Table 4-8. National Power Sector Compliance Cost Estimates (millions of 2016$) for the
Regulatory Control Alternatives 155
Table 4-9. 2023, 2025 and 2030 Projected U.S. Power Sector Coal Use for the Baseline and the
Regulatory Control Alternatives 159
Table 4-10. 2023, 2025 and 2030 Projected U.S. Power Sector Natural Gas Use for the Baseline
and the Regulatory Control Alternatives 160
Table 4-11. 2023, 2025 and 2030 Projected Minemouth and Power Sector Delivered Coal Price
(2016$) for the Baseline and the Regulatory Control Alternatives 160
Table 4-12. 2023, 2025 and 2030 Projected Henry Hub and Power Sector Delivered Natural Gas
Price (2016$) for the Baseline and the Regulatory Control Alternatives 161
Table 4-13. 2023, 2025 and 20230 Projected U.S. Generation by Fuel Type for the Baseline and
the Regulatory Control Alternatives 161
Table 4-14. 2023, 2025 and 2030 Projected U.S. Capacity by Fuel Type for the Baseline run and
the Regulatory Control Alternatives 164
Table 4-15. Average Retail Electricity Price by Region for the Baseline and the Regulatory
Control Alternatives, 2023 166
Table 4-16. Average Retail Electricity Price by Region for the Baseline and the Regulatory
Control Alternatives, 2025 167
Table 4-17. Average Retail Electricity Price by Region for the Baseline and the Regulatory
Control Alternatives, 2030 168
Table 4-18. Non-EGU Industries, Emissions Unit Types, Assumed Control Technologies that
Meet Final Emissions Limits, Estimated Number of Control Installations 170
Table 4-19. Non-EGU Industries, Emissions Unit Types, Assumed Control Technologies,
Estimated Total Annual Costs (2016$), Estimated Ozone Season NOx Emissions Reductions in
2026 171
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Table 4-20. Summary of Non-EGU Industries, Emissions Unit Types, Assumed Control
Technologies, Estimated Average Cost/Ton (2016$) 172
Table 4-21. Estimated Emissions Reductions for 2026-2042 (ozone season tons) and Estimated
Annual Total Costs for the Less and More Stringent Alternatives 172
Table 4-22. Total Estimated NOx Emissions Reductions (ozone season, thousand tons) and
Compliance Costs (million 2016$), 2023-2042 173
Table 4-23. Total National Compliance Cost Estimates (millions of 2016$) for the Final Rule
and the Less and More Stringent Alternatives 174
Table 4A-1. IRA Provisions Modeled in IPM 185
Table 4A-2. National Power Sector Compliance Cost Estimates (millions of 2016$) for the Final
Rule With and Without the IRA 186
Table 4A-3. EGU Ozone Season NOx Emissions and Emissions Changes (thousand tons) for the
Baseline run and Final Rule with and without IRA from 2023 - 2045 187
Table 4A-4. EGU Annual Emissions and Emissions Changes for Annual NOx, SO2, PM2.5, and
CO2 for the Baseline run and Final Rule with and without IRA from 2023 - 2045 188
Table 4A-5. 2023, 2025 and 2030 Projected U.S. Power Sector Coal Use for the Baseline and
the Final Rule with and without IRA 192
Table 4A-6. 2023, 2025 and 2030 Projected U.S. Power Sector Natural Gas Use for the Baseline
and the Final Rule with and without IRA 193
Table 4A-7. 2023, 2025 and 2030 Projected Minemouth and Power Sector Delivered Coal Price
(2016$) for the Baseline and the Final Rule with and without IRA 194
Table 4A-8. 2023, 2025 and 2030 Projected Henry Hub and Power Sector Delivered Natural
Gas Price (2016$) for the Baseline and the Final Rule with and without IRA 194
Table 4A-9. 2023, 2025 and 20230 Projected U.S. Generation by Fuel Type for the Baseline and
the Final Rule with and without IRA 195
Table 4A-10. 2023, 2025 and 2030 Projected U.S. Capacity by Fuel Type for the Baseline and
the Final Rule with and without IRA 196
Table 5-1. Health Effects of Ambient Ozone and PM2.5 204
Table 5-2. Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses
for the Final Rule and More and Less Stringent Alternatives for 2023 (95% Confidence Interval)
214
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Table 5-3. Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses
for the Final Rule and More and Less Stringent Alternatives for 2026 (95% Confidence Interval)
215
Table 5-4. Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses for
the Final Rule and More and Less Stringent Alternatives for 2026 (95% Confidence Interval) 217
Table 5-5. Estimated Discounted Economic Value of Avoided Ozone-Related Premature
Mortality and Illness for the Final Rule and the Less and More Stringent Alternatives in 2023
(95% Confidence Interval; millions of 2016$) 218
Table 5-6. Estimated Discounted Economic Value of Avoided Ozone and PM2.5-Attributable
Premature Mortality and Illness for the Final Rule and the Less and More Stringent Alternatives
in 2026 (95% Confidence Interval; millions of 2016$) 218
Table 5-7. Stream of Human Health Benefits from 2023 through 2042: Monetized Benefits
Quantified as Sum of Long-Term Ozone Mortality for EGUs and Non-EGUs and Long-Term
PM2.5 Mortality for EGUs (Discounted at 3%; millions of 2016$) 219
Table 5-8. Stream of Human Health Benefits from 2023 through 2042: Monetized Benefits
Quantified as Sum of Short-Term Ozone Mortality for EGUs and Non-EGUs and Long-Term
PM2.5 Mortality for EGUs (Discounted at 7%; millions of 2016$) 220
Table 5-9. Interim Social Cost of Carbon Values, 2020-2050 (2016$/Metric Tonne CO2) 229
Table 5-10. Estimated Climate Benefits from Changes in C02 Emissions 2023 - 2040 (Millions
of 2016$) 233
Table 5-11. Combined Health Benefits and Climate Benefits for the Final Rule and More and
Less Stringent Alternatives for 2023 (millions of 2016$) 234
Table 5-12. Combined Health Benefits and Climate Benefits for the Final Rule and More and
Less Stringent Alternatives for 2026 (millions of 2016$) 235
Table 5-13. Combined Health Benefits and Climate Benefits for the Final Rule and More and
Less Stringent Alternatives for 2030 (millions of 2016$) 235
Table 5-14. Unquantified Health and Welfare Benefits Categories 236
Table 6-1. SBA Size Standards by NAICS Code 260
Table 6-2. Projected Impact of the Transport FIP for the 2015 Ozone NAAQS on Small Entities
in 2026 264
Table 6-3. Non-EGU SBA Size Standards by NAICS Code
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Table 6-4. Summary of Sales Test Ratios for 2026 for Firms Affected by Proposed Rule 266
Table 6-5. Summary of Small Parent Company Small Business Size Standards 267
Table 6-6. Changes in Labor Utilization: Construction-Related (Number of Job-Years of
Employment in a Single Year) 272
Table 6-7. Changes in Labor Utilization: Recurring Non-Construction (Number of Job-Years of
Employment in a Single Year) 273
Table 6-8. Relevant Industry Employment (2020) 274
Table 6-9. Employment per $1 million Output 275
Table 7-1. Population Demographics for EGU Facilities 283
Table 7-2. Population Demographics for Non-EGU Facilities 284
Table 7-3. Demographic Populations Included in the Ozone and PM2.5 EJ Exposure Analyses 287
Table 8-1. EGU Ozone Season NOx Emissions Changes and Annual Emissions Changes for
NOx, SO2, PM2.5, and CO2 for the Regulatory Control Alternatives from 2023 - 2042 316
Table 8-2. Non-EGU Ozone Season NOx Emissions and Emissions Reductions for the Final
Rule and the Less and More Stringent Alternatives 318
Table 8-3. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2023 for the U.S. (millions of 2016$) 320
Table 8-4. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2026 for the U.S. (millions of 2016$) 320
Table 8-5. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2030 for the U.S. (millions of 2016$) 321
Table 8-6. Undiscounted Streams Health Benefits, Climate Benefits, Costs, and Net Benefits for
2023 - 2042 (millions of 2016$) 322
Table 8-7. Summary of Present Values and Equivalent Annualized Values for the 2023-2042
Timeframe for Estimated Monetized Compliance Costs, Benefits, and Net Benefits for the Final
Rule (millions of 2016$, discounted to 2023) 323
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LIST OF FIGURES
Figure 2-1. Regional Differences in Generating Capacity (GW), 2021 68
Figure 2-2. National Coal-fired Capacity (GW) by Age of EGU, 2021 70
Figure 2-3. Average Annual Capacity Factor by Energy Source 71
Figure 2-4. Cumulative Distribution in 2019 of Coal and Natural Gas Electricity Capacity and
Generation, by Age 73
Figure 2-5. Fossil Fuel-Fired Electricity Generating Facilities, by Size 74
Figure 2-6. Real National Average Electricity Prices (including taxes) for Three Major End-Use
Categories 77
Figure 2-7. Relative Increases in Nominal National Average Electricity Prices for Major End-
Use Categories (including taxes), With Inflation Indices 78
Figure 2-8. Relative Real Prices of Fossil Fuels for Electricity Generation; Change in National
Average Real Price per MMBtu Delivered to EGU 79
Figure 2-9. Relative Growth of Electricity Generation, Population and Real GDP Since 2014.. 80
Figure 2-10. Relative Change of Real GDP, Population and Electricity Generation Intensity
Since 2014 81
Figure 2-11. Geographical Distribution of Non-EGU Ozone Season NOx Reductions and
Summary of Reductions by Industry and by State 82
Figure 3-1 Air Quality Modeling Domain 94
Figure 3-2. 2023 Baseline AS-M03 Concentrations (ppb) 101
Figure 3-3. 2026 Baseline AS-M03 Concentration (ppb) 102
Figure 3-4. Reduction in AS-M03 (ppb): 2023 Less Stringent EGU-only Alternative vs the 2023
Baseline (scale: +0.5 ppb) 103
Figure 3-5. Reduction in AS-M03 (ppb): 2023 Final Rule EGU-only Alternative vs the 2023
Baseline (scale: +0.5 ppb) 103
Figure 3-6. Reduction in AS-M03 (ppb): 2023 More Stringent EGU-only Alternative vs the
2023 Baseline (scale: + 0.5 ppb) 104
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Figure 3-7. Reduction in AS-M03 (ppb): 2026 Less Stringent EGU-only Alternative vs the 2026
Baseline (scale: + 1.0 ppb) 104
Figure 3-8. Reduction in AS-M03 (ppb): 2026 Final Rule EGU-only Alternative vs the 2026
Baseline (scale: + 1.0 ppb) 105
Figure 3-9. Reduction in AS-M03 (ppb): 2026 More Stringent EGU-only Alternative vs the
2026 Baseline (scale: + 1.0 ppb) 105
Figure 3-10. Reduction in AS-M03 (ppb): 2026 Less Stringent non-EGU-only Alternative vs the
2026 Baseline (scale: + 1.0 ppb) 106
Figure 3-11. Reduction in AS-M03 (ppb): 2026 Final Rule non-EGU-only Alternative vs the
2026 Baseline (scale: + 1.0 ppb) 106
Figure 3-12. Reduction in AS-M03 (ppb): 2026 More Stringent non-EGU-only Alternative vs
the 2026 Baseline (scale: + 1.0 ppb) 107
Figure 3-13. Reduction in AS-M03 (ppb): 2026 Less Stringent EGU+non-EGU Alterative vs the
2026 Baseline (scale: + 1.0 ppb) 107
Figure 3-14. Reduction in AS-M03 (ppb): 2026 Final Rule EGU+non-EGU Alternative vs the
2026 Baseline (scale: + 1.0 ppb) 108
Figure 3-15. Reduction in AS-M03 (ppb): 2026 More Stringent EGU+non-EGU Alternative vs
the 2026 Baseline (scale: + 2.0 ppb) 108
Figure 3-16. 2026 Baseline Annual Average PM2.5 Concentrations (|ig/m3) Ill
Figure 3-17. Reduction in annual average PM2.5 (|ig/m3): 2026 Less Stringent EGU-only
Alternative vs the 2026 Baseline (scale: + 0.2 |ig/m3) 112
Figure 3-18. Reduction in Annual Average PM2.5 (|ig/m3): 2026 Final Rule EGU-only
Alternative vs the 2026 Baseline (scale: + 0.2 |ig/m3) 112
Figure 3-19. Reduction in Annual Average PM2.5 (|ig/m3): 2026 More Stringent EGU-only
Alternative vs the 2026 Baseline (scale: + 0.2 |ig/m3) 113
Figure 4-1. Electricity Market Module Regions 169
Figure 5-1 Stylized Relationship between the PM2.5 Concentrations Considered in Epidemiology
Studies and our Confidence in the Estimated PM-related Premature Deaths 212
Figure 5-2. Frequency Distribution of SC-CO2 Estimates for 2030 230
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Figure 7-1. Heat Map of the National Average AS-M03 Ozone Concentrations in the Baseline
and Reductions in Concentrations Due to this Rulemaking Across Demographic Groups in 2023
(ppb) 293
Figure 7-2. Heat Map of the National Average AS-M03 Ozone Concentrations in the Baseline
and Reductions in Concentrations Due to this Rulemaking Across Demographic Groups in 2026
(ppb) 293
Figure 7-3. Heat Map of State Average AS-M03 Ozone Concentration Reductions (Green) and
Increases (Red) by Demographic Group for EGUs in 2023 (ppb) 295
Figure 7-4. Heat Map of State Average AS-M03 Ozone Concentration Reductions by
Demographic Group for EGUs and Non-EGUs in 2026 (ppb) 296
Figure 7-5. Distributions of Ozone Concentration Changes Across Populations and Regulatory
Alternatives in 2023 299
Figure 7-6. Distributions of Ozone Concentration Changes Across Populations, Affected
Facilities, and Regulatory Alternatives in 2026 300
Figure 7-7. Heat Map of the National Average PM2.5 Concentrations in the Baseline and
Reductions in Concentrations Due to this Rulemaking Across Demographic Groups in 2026
(Lig/ms) 302
Figure 7-8. Heat Map of State Average PM2.5 Concentration Reductions by Demographic Group
for EGUs and Non-EGUs in 2026 (|ig/m3) 304
Figure 7-9. Distributions of PM2.5 Concentration Changes Across Populations, Affected
Facilities, and Regulatory Alternatives in 2026 306
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EXECUTIVE SUMMARY
Overview
This document presents the regulatory impact analysis (RIA) for the final rule, the
Federal Good Neighbor Plan Addressing Regional Ozone Transport for the 2015 Ozone National
Ambient Air Quality Standards (Transport FIP for the 2015 ozone NAAQS). This RIA provides
the EPA's analysis of a variety of potential impacts (i.e., consequences) of the final rule and is
used to inform the EPA and the public about these potential impacts. In the rule, the EPA
promulgates implementation mechanisms to achieve enforceable emissions reductions required
to eliminate ozone precursor emissions that significantly contribute to nonattainment or interfere
with maintenance of the 2015 ozone NAAQS in other states.1 The initial phase of emissions
reductions will begin in the 2023 ozone season with further emissions reductions being required
in later years.
The EPA is promulgating new or revised FIPs for 23 states. For 22 states the FIPs include
new NOx ozone season emission budgets for EGU sources, with implementation of these
emission budgets beginning in the 2023 ozone season.2 The EPA is expanding the Cross-State
Air Pollution Rule (CSAPR) NOx Ozone Season Group 3 Trading Program beginning in the
2023 ozone season. Specifically, the FIPs require electric generating units (EGUs) within the
borders of the 22 states to participate in a revised version of the CSAPR NOx Ozone Season
Group 3 Trading Program created by the Revised CSAPR Update. Affected EGUs within the
borders of 12 states currently participating in the Group 3 Trading Program under FIPs or SIPs
remain in the program, with revised provisions beginning in the 2023 ozone season. The FIPs
also require affected EGUs within the borders of seven states currently covered by the CSAPR
NOx Ozone Season Group 2 Trading Program (the "Group 2 trading program") under existing
FIPs or existing SIPs to transition from the Group 2 program to the revised Group 3 trading
program beginning with the 2023 control period. Lastly, the EPA is issuing new FIPs for three
1 The 2015 ozone NAAQS is an 8-hour standard that was set at 70 parts per billion (ppb). See 80 FR 65291
(December 28, 2015).
2 In 2023, the 22 states with EGU reduction requirements include AL, AR, IL, IN, KY, LA, MD, MI, MN, MS, MO,
NV, NJ, NY, OH, OK, PA, TX, UT, VA, WV, and WI. There are no EGU reductions being required from
California, which if included would make 23 states.
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states not currently covered by any CSAPRNOx ozone season trading program (Minnesota,
Nevada, and Utah).
For non-electric generating units (non-EGUs), the FIPs that EPA is promulgating for 20
states include new NOx emissions limitations, with initial compliance dates for these emissions
limitations beginning in 2026.3
Consistent with OMB Circular A-4 and EPA's Guidelines for Preparing Economic
Analyses (2010), this RIA presents the benefits and costs of the final rule from 2023 through
2042. For the proposal RIA and this final RIA, we selected a 20-year analytical period because it
is generally representative of and covers the lifetime of the capital equipment anticipated to be
installed in response to the rule. Costs, benefits, and other impacts from compliance strategies
are likely to occur beyond 2042. The estimated health benefits are expected to arise from reduced
ozone and PM2.5 concentrations, and the estimated climate benefits are from reduced greenhouse
gas (GHG) emissions. The estimated costs for EGUs are the costs of installing and operating
controls and the increased costs of producing electricity to comply with the revised version of the
Group 3 trading program. The estimated costs for non-EGUs are the costs of installing and
operating controls to meet the ozone season NOx emissions limitations. The estimated costs that
the EPA reports for non-EGUs do not include monitoring, recordkeeping, reporting, or testing
costs, which the EPA summarizes in Section X.B.2 of the final rule preamble and discusses in
Chapter 4, Section 4.4 below. Unquantified benefits and costs are described qualitatively. The
RIA also provides estimates of other impacts of the final rule including its effect on retail
electricity prices, fuel production for electricity generation, EGU-related employment, and
environmental justice (EJ) impacts.
ES.l Identifying Needed Emissions Reductions and Regulatory Requirements
To reduce interstate emission transport under the authority provided in CAA section
110(a)(2)(D)(i)(I), the final rule further limits ozone season NOx emissions from EGUs and non-
EGUs using the same framework used by the EPA in developing the CSAPR. The Interstate
Transport Framework provides a 4-step process to address the requirements of the good neighbor
provision for ground-level ozone and fine particulate matter (PM2.5) NAAQS: (1) identifying
3 In 2026, the 20 states with non-EGU reduction requirements include AR, CA, IL, IN, KY, LA, MD, MI, MS, MO,
NV, NJ, NY, OH, OK, PA, TX, UT, VA, and WV.
19
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downwind receptors that are expected to have problems attaining or maintaining the NAAQS;
(2) determining which upwind states contribute to these identified problems in amounts
sufficient to "link" them to the downwind air quality problems (i.e., here, an amount of
contribution equal to or greater than 1 percent of the NAAQS); (3) for states linked to downwind
air quality problems, identifying upwind emissions that significantly contribute to downwind
nonattainment or interfere with downwind maintenance of the NAAQS; and (4) for states that are
found to have emissions that significantly contribute to nonattainment or interfere with
maintenance of the NAAQS downwind, implementing the necessary emissions reductions
through enforceable measures. In this action, the EPA applies this 4-step Interstate Transport
Framework for the Transport FIP for the 2015 ozone NAAQS.
For EGUs, in identifying levels of uniform control stringency the EPA assessed the same
NOx emissions controls that the Agency analyzed in the CSAPR Update and the Revised
CSAPR Update, all of which are considered to be widely available for EGUs: (1) fully operating
existing SCR, including both optimizing NOx removal by existing operational SCRs and turning
on and optimizing existing idled SCRs; (2) installing state-of-the-art NOx combustion controls;
(3) fully operating existing SNCRs, including both optimizing NOx removal by existing
operational SNCRs and turning on and optimizing existing idled SNCRs; (4) installing new
SNCRs; (5) installing new SCRs; and (6) generation shifting (i.e., emission reductions
anticipated to occur from generation shifting from higher to lower emitting units). The selected
levels of uniform control stringency were represented by $1,800 per ton of NOx (2016$) in 2023
and $11,000 per ton of NOx (2016$) in 2026.4
Based on this uniform control stringency analysis, the rule establishes NOx emissions
budgets requiring fossil fuel-fired EGUs in 22 states to participate in an allowance-based ozone
season (May 1 through September 30) trading program beginning in 2023. The EGUs covered by
the FIPs and subject to the budget are fossil-fired EGUs with >25-megawatt (MW) capacity. Any
new fossil fuel-fired EGU serving a generator with a nameplate capacity exceeding 25 MW
capacity that meets the applicability criteria and is deployed in any of the states covered by this
4 The EGU NOx Mitigation Strategies Final Rule TSD, in the docket (Docket ID No. EPA-HQ-OAR-2021-0688),
describes how these costs per ton were chosen for the EGU stringency in this rule. Generation shifting is not
included as a control strategy when establishing the budgets in the final rule. However, generation shifting is a
control strategy that the EPA expects will be used for compliance. For additional discussion, please see Chapter 4.
20
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rule's EGU ozone-season NOx program would be subject to the same requirements as other
covered EGUs. For details on the derivation of emissions budgets, please see Section V.C. of the
final rule preamble.
In this rule, we introduce additional features to the allowance-based trading program
approach for EGUs, including dynamic adjustments of the emissions budgets over time and a
backstop daily emission rate for most coal-fired units, along with an adjustment to the total size
of the allowance bank, which is 21 percent of the sum of the state emissions budgets for the
current control period until 2030 (at which point it declines to 10.5%), that were not included in
previous CSAPRNOx ozone season trading programs. These enhancements will help maintain
control stringency over time and improve emissions performance at individual units, offering an
extra measure of assurance that existing pollution controls will be operated during the ozone
season.
In this final action, the EPA is retaining the industries and many of the emissions unit
types included in the proposal. At proposal, the EPA developed an analytical framework and
applicability criteria to determine which industries and emissions unit types required NOx
limitations in the non-electric generating unit "sector" (non-EGUs).5'6 The rule includes ozone
season NOx emissions limitations for non-EGUs with an initial compliance date of 2026 for 20
states. A summary of the non-EGU industries, emissions unit types, form of final emissions
limits, and final emissions limits is presented below in Table ES-1. A more detailed summary of
the emissions limits can be found in Section I.B. of the preamble. For a discussion of changes to
emissions limits between the proposed FIP and the final rule, see Chapters 1 and 4 of this RIA,
and Section V.C of the preamble to the final rule and the Final Non-EGU Sectors TSD.
5 A February 28, 2022 memorandum, titled Screening Assessment of Potential Emissions Reductions, Air Quality
Impacts, and Costs from Non-EGU Emissions Units for 2026, documents the analytical framework used to identify
industries and emissions unit types included in the proposed FIP. The memorandum is available in the docket here:
https://www.regulations.gov/document/EPA-HQ-OAR-2021-0668-0150.
6 To further evaluate the industries and emissions unit types identified and to establish the proposed emissions
limits, the EPA reviewed Reasonably Available Control Technology (RACT) rules, New Source Performance
Standards (NSPS) rules, National Emissions Standards for Hazardous Air Pollutants (NESHAP) rules, existing
technical studies, rules in approved state implementation plan (SIP) submittals, consent decrees, and permit limits.
That evaluation is detailed in the Non-EGU Sectors Technical Support Document (TSD) prepared for the proposed
FIP. The TSD is available in the docket here: https://www.regulations.gov/document/EPA-HQ-OAR-2021-0668-
0145.
21
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Table ES-1. Summary of Non-EGU Industries, Emissions Unit Types, Form of Final
Emissions Limits, and Final Emissions Limits
Industry
Emissions
Form of Final
Final Emissions Limits
Unit Type
Emissions Limits
Pipeline Transportation of
Reciprocating
Grams per horsepower
Four Stroke Rich Burn: 1.0 g/hp-hr
Natural Gas
Internal
per hours (g/hp-hr)
Four Stroke Lean Burn: 1.5 g/hp-hr
Combustion
Two Stroke Lean Burn: 3.0 g/hp-hr
Engines
Cement and Concrete Product
Kilns
Pounds per ton (lbs/ton)
Long Wet: 4.0 lb/ton
Manufacturing
of clinker
Long Dry: 3.0 lb/ton
Pre heater: 3.8 lb/ton
Precalciner: 2.3 lb/ton
Preheater/Precalciner: 2.8 lb/ton
Iron and Steel Mills and
Reheat
lbs/mmBtua
Test and set limit based on
Ferroalloy Manufacturing
Furnaces
installation of Low-NOx Burners
Glass and Glass Product
Furnaces
lbs/ton glass produced
Container Glass Furnace: 4.0 lb/ton
Manufacturing
Pressed/Blown Glass Furnace: 4.0
lb/ton
Fiberglass Furnace: 4.0 lb/ton
Flat Glass Furnace: 9.2 lb/ton
Iron and Steel Mills and
Boilers
lbs/mmBtua
Coal: 0.20 lb/mmBtu
Ferroalloy Manufacturing
Residual Oil: 0.20 lb/mmBtu
Metal Ore Mining
Distillate Oil: 0.12 lb/mmBtu
Basic Chemical Manufacturing
Natural Gas: 0.08 lb/mmBtu
Petroleum and Coal Products
Manufacturing
Pulp, Paper, and Paperboard
Mills
Solid Waste Combustors and
Combustors
ppmvd on a 24-hour
110 ppmvd on a 24-hour averaging
Incinerators
or
averaging period and
period
Incinerators
ppmvd on a 30-day
105 ppmvd on a 30-day averaging
averaging period
period
aHeat input limit.
For the final rule, using the list of emissions units estimated to be captured by the
applicability criteria, the assumed control technologies that would meet the emissions limits, and
information on control efficiencies and default cost/ton values from the control measures
database (CMDB),7 the EPA estimated NOx emissions reductions and costs for the year 2026.
For additional details about the steps taken to estimate emissions units, emissions reductions, and
costs, see the memorandum titled Summary of Final Rule Applicability Criteria and Emissions
Limits for Non-EGU Emissions Units, Assumed Control Technologies for Meeting the Final
7 More information about the Control Strategy Tool (Co ST) and the control measures database (CMDB) can be
found at the following link: https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-
analysis-modelstools-air-pollution.
22
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Emissions Limits, and Estimated Emissions Units, Emissions Reductions, and Costs available in
the docket.8
ES.2 Baseline and Analysis Years
The final rule sets forth the requirements to eliminate states' significant contribution to
downwind nonattainment or interference with maintenance of the 2015 ozone NAAQS. To
develop and evaluate control strategies for addressing these obligations, it is important to first
establish a baseline projection of air quality in the analysis years of 2023 and 2026, taking into
account currently on-the-books Federal regulations, enforcement actions, state regulations,
population, expected electricity demand growth, and where possible, economic growth.
Establishing this baseline for the analysis then allows us to estimate the incremental costs and
benefits of the additional emissions reductions that will be achieved by this rule.
The analysis in this RIA focuses on benefits, costs and certain impacts from 2023 through
2042. We focus on 2023 because it is by the 2023 ozone season, corresponding with the 2024
Moderate area attainment date, that significant contribution from upwind states' must be
eliminated to the extent possible. In addition, impacts for 2026 are important because this ozone
season corresponds with the 2027 Serious area attainment date, and it is by this ozone season that
that additional requirements for NOx emissions reductions for EGUs and non-EGUs begin to
apply for states whose upwind linkage to downwind receptors persists. Costs, benefits, and other
impacts from compliance strategies are likely to persist beyond 2026, and the RIA provides costs
and benefits through 2042.
ES.3 Air Quality Modeling
The air quality modeling for the Transport FIP for the 2015 ozone NAAQS used a 2016-
based modeling platform that included meteorology and base year emissions from 2016 and
projected emissions for 2023 and 2026. The air quality modeling to support the analyses in this
final RIA included photochemical model simulations for the 2016 base year and 2026 future
8 The estimates prepared using the 2019 inventory and information from the CMDB identify proxies for emissions
units, as well as emissions reductions, and costs associated with the assumed control technologies that would meet
the final emissions limits. Emissions units subject to the final rule emissions limits may be different than those
estimated in this assessment. Further, the estimated emissions reductions from and costs to meet the final rule
emissions limits may be different than those estimated in this assessment. The costs do not include monitoring,
recordkeeping, reporting, or testing costs.
23
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year. The model simulations included source apportionment modeling for the 2026 baseline to
quantify the contributions to ozone from EGU and from non-EGU NOx emissions and the
contributions to PM2.5 from EGU emissions of NOx, SO2, and directly emitted primary PM2.5.9
Source apportionment modeling for ozone and PM2.5 was performed to provide contributions on
a state-by-state basis. All of the air quality model simulations were performed using the
Comprehensive Air Quality Model with Extensions (CAMx) version 7.10. The CAMx
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.
The modeling results for 2016 and 2026, in conjunction with emissions data for the 2023
baseline, 2026 baseline, the final rule, and more and less stringent alternatives (regulatory control
alternatives) in 2023 and 2026, were used to construct the air quality spatial fields that reflect the
influence of emissions changes between the baseline and each regulatory control alternative.
These spatial fields provide the air quality inputs to calculate health benefits for the Transport
FIP for the 2015 ozone NAAQS and to inform the environment justice impact analysis in
Chapter 7. The spatial fields were constructed based on a method that uses ozone and PM2.5
contributions from emissions in individual states and state-level emissions reductions for each of
the regulatory control alternatives coupled with baseline spatial fields of ozone and PM2.5
concentrations. This method, as described in Chapter 3, was used most recently in the RIA for
this proposal. In addition to the modeling to create spatial fields, we also performed air quality
modeling to assess the parts per billion (ppb) impacts on projected ozone design values at
monitoring sites nationwide in 2026 attributable to the EGU and non-EGU ozone season NOx
emissions reductions projections from this final rule.
ES.4 Control Strategies and Emissions Reductions
The RIA analyzes emissions budgets for EGUs and ozone season emissions limits for
non-EGUs, as well as a more and a less stringent alternative to the final rule. The more and less
stringent alternatives differ from the Transport FIP for the 2015 ozone NAAQS in that they set
9 The ozone source apportionment modeling used for the proposed rule analyses is also used for this final rule
analysis. In this regard, the contribution modeling is based on 2026 base case emissions that were developed for the
proposed rule. At proposal, benefits associated with reductions in PM2 5 concentrations were derived based on
Benefit per Ton estimates for EGUs. For this final rule, we performed source apportionment modeling for PM2 5
using the same 2026 emissions inventory that was used as input to the ozone source apportionment modeling.
24
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different EGU NOx ozone season emission budgets and different dates for compliance with unit-
specific emission limits for the affected EGUs and estimate different control technologies for
some emissions units for the affected non-EGUs. Table ES-2. below presents the less stringent
alternatives, final rule requirements, and more stringent alternatives for EGUs and non-EGUs.
While the EGUs are required to comply with emissions budgets in 2023, tightening in 2026 for
some states, along with a backstop emission rate for coal units, Table ES-2 also describes
exogenously imposed compliance assumptions (i.e., control strategies) in the power sector
modeling for purposes of the analysis (e.g., installation of state-of-the-art combustion controls
and fully operating SNCRs and SCRs). Other control strategies are endogenous to the EGU
analysis, such as changes in the dispatch order of generators and installation of post-combustion
controls.
For non-EGUs, to establish the emissions limits, the EPA reviewed Reasonably Available
Control Technology (RACT) rules, New Source Performance Standards (NSPS) rules, National
Emissions Standards for Hazardous Air Pollutants (NESHAP) rules, existing technical studies,
rules in approved state implementation plan (SIP) submittals, consent decrees, and permit limits.
We assumed control technologies would be adopted for compliance with the limitations in this
analysis. For the purposes of summarizing the results of the benefits and costs of these
alternatives, the less stringent alternative for EGUs is presented with the less stringent alternative
for non-EGUs. However, the cost, emissions, and energy impacts for the EGU and non-EGU
alternatives are evaluated separately.
Table ES-2. Regulatory Control Alternatives for EGUs and Non-EGUs
Regulatory Control NOx Controls Implemented for EGUs within IPM1 b
Alternative _
1) 2023 onwards: Fully operate existing selective catalytic reduction (SCRs)
during ozone season
2) 2023 onwards: Fully operate existing selective non-catalytic reduction
Less Stringent Alternative (SNCRs) during ozone season
3) In 2023 install state-of-the-art combustion controls0
4) In 2030 model run year, impose backstop emission rate on coal units greater
than 100 MW within the 19-state region that lack SCR controls.d
(All Controls above and)
Final Rule ^ 'n 2025 model run year, impose Engineering Analysis derived emissions
budgets that assume installation of SCR controls on coal units greater than
100 MW within the 19-state region that lack SCR controls.
More Stringent Alternative
(Controls 1-5 above and)
6) In 2025 model run year, impose backstop emission rate on coal units greater
than 100 MW within the 19-state region that lack SCR controls, forcing units
to retrofit or retire.
25
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Regulatory Control NOx Emissions Limits for Non-EGUs - Emissions Unit Types, Industries,
Alternative and Controls Assumed for Compliance
1)
Reciprocating internal combustion engines in Pipeline Transportation of
Natural Gas - Adjust Air-to-Fuel Ratio
2)
Kilns in Cement and Cement Product Manufacturing - install SNCR
3)
Reheat furnaces in Iron and Steel Mills and Ferroalloy Manufacturing - install
Low NOx burners (LNB)
Less Stringent Alternative 4)
Furnaces in Glass and Glass Product Manufacturing - install LNB
5)
Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SNCR
6)
Combustors or Incinerators in Solid Waste Combustors and Incinerators -
install Advanced NSCR (ANSCR) or LN™ and SNCRe
(Controls 2, 3, 4, and 6 above, plus changes in assumed controls noted below)
7) Reciprocating internal combustion engines in Pipeline Transportation of
Natural Gas - depending on engine type, install Layered Combustion, non-
Final Rule selective catalytic reduction (NSCR), or SCR
8) Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SCR (coal- or
oil-fired) or LNB and FGR (natural gas-fired only)
(Controls 3,6,7 above, plus changes in assumed controls noted below)
9) Kilns in Cement and Cement Product Manufacturing - install SCR
10) Furnaces in Glass and Glass Product Manufacturing - install SCR
More Stringent Alternative 11) Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SCR (natural
gas-fired only)
a 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. For this analysis, IPM maps the calendar year 2023 to run year 2023,
calendar years 2024-2026 to run year 2025 and calendar years 2027-2029 to run year 2028. For model details, please
see Chapter 2 of the IPM documentation.
b NOx mass budgets are imposed in all run years in IPM (2023-2050) consistent with the measures highlighted in
this table.
0 The final rule implementation allows for the reduction associated with state-of-the-art combustion controls to occur
by 2024. It is captured in 2023 in this analysis to fully assess the impact of the mitigation measures occurring prior
to 2026.
d For the 19 states with EGU obligations that are linked in 2026 the EPA is determining that the selected EGU
control stringency also includes emissions reductions commensurate with the retrofit of SCR at coal steam-fired
units of 100 MW or greater capacity (excepting circulating fluidized bed units (CFB)), new SNCR on coal-fired
units of less than 100 MW capacity and on CFBs of any capacity size, and SCR on oil/gas units greater than 100
MW that have historically emitted at least 150 tons of NOx per ozone season. The EPA evaluated the EGU sources
within the state of California and found there were no covered coal steam sources greater than 100 MW that would
have emissions reduction potential according to EPA's assumed EGU SCR retrofit mitigation technologies. The 19
states are: Arkansas, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, Nevada,
New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah, Virginia, and West Virginia.
e Covanta has developed a proprietary low NOx combustion system (LN™) that involves staging of combustion air.
The system is a trademarked system and Covanta has received a patent for the technology.
For 2023, total ozone season NOx emissions reductions of 10,000 tons are from EGUs;
for 2026 total ozone season NOx emissions reductions of 70,000 tons are from EGUs and non-
EGUs, and for 2030 total ozone season NOx emissions reductions of 79,000 tons are from EGUs
and non-EGUs.
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ES.4.1 EGUs
For the NOx controls for EGUs identified in Table ES-2, under the final rule and the less
stringent and more stringent alternatives, 232 EGUs not already doing so in 2019 are assumed to
fully operate existing SCRs. Under the final rule and the less stringent and more stringent
alternatives, 39 units are assumed to fully operate existing SNCRs. Under the final rule and the
less stringent and more stringent alternatives, 9 units are assumed to install state-of-the-art
combustion controls. The book-life of the new combustion controls is assumed to be 15 years.
By 2030 the final rule is projected to result in an additional 14 GW of coal retirements
nationwide relative to the baseline, constituting a reduction of 13 percent of national coal
capacity, partially reflecting some earlier retirements under the rule relative to the baseline.
Additionally, the rule is projected to incentivize an incremental 8 GW of SCR retrofit at coal
plants. The rule is also projected to result in an incremental 3 GW of renewable capacity
additions in 2025, consisting primarily of solar capacity builds. These builds reflect early action
or builds that would otherwise have occurred later in the forecast period.
Table ES-3. shows the ozone season NOx emissions reductions expected from the final
rule as well as the more and less stringent alternatives analyzed from 2023 through 2027, and for
2030, 2035, and 2042. In addition, Table ES-3 also shows the annual NOx, SO2, PM2.5, and CO2
emissions reductions expected from the final rule as well as the more and less stringent
alternatives analyzed from 2023 through 2027, and for 2030, 2035, and 2042.10 Under the more
stringent alternative, the modeling projects a higher ratio of SCR retrofits to retirements,
resulting in higher emissions projected under this alternative in later years.
Table ES-3. EGU Ozone Season NOx Emissions Changes and Annual Emissions Changes
for NOx, SO2, PM2.5, and CO2 for the Regulatory Control Alternatives from 2023 - 204211
Final Rule
Less Stringent
Alternative
More Stringent
Alternative
2023
NOx (ozone season)
10,000
10,000
10,000
NOx (annual)
15,000
15,000
15,000
SO2 (annual)
1,000
3,000
1,000
CO2 (annual, thousand metric)
-
-
-
10 EGU results reflect IPM outputs for model run years (2023, 2025, 2028, 2030, 2035, 2040, and 2045). All other
years are linearly interpolated.
11 This analysis is limited to the geographically contiguous lower 48 states.
27
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Final Rule
Less Stringent
Alternative
More Stringent
Alternative
PM2.5 (annual) ...
2024
NOx (ozone season)
21,000
10,000
33,000
NOx (annual)
25,000
15,000
57,000
SO2 (annual)
19,000
5,000
59,000
CO2 (annual, thousand metric)
10,000
4,000
20,000
PM2.5 (annual)
1,000
-
1,000
2025
NOx (ozone season)
32,000
10,000
56,000
NOx (annual)
35,000
15,000
99,000
SO2 (annual)
38,000
7,000
118,000
CO2 (annual, thousand metric)
21,000
8,000
40,000
PM2.5 (annual)
2,000
1,000
2,000
2026
NOx (ozone season)
25,000
8,000
49,000
NOx (annual)
29,000
12,000
88,000
SO2 (annual)
29,000
5,000
104,000
CO2 (annual, thousand metric)
16,000
6,000
34,000
PM2.5 (annual)
1,000
-
2,000
2027
NOx (ozone season)
19,000
6,000
43,000
NOx (annual)
22,000
9,000
78,000
SO2 (annual)
21,000
4,000
91,000
CO2 (annual, thousand metric)
10,000
3,000
28,000
PM2.5 (annual)
1,000
-
2,000
2030
NOx (ozone season)
34,000
33,000
31,000
NOx (annual)
62,000
59,000
50,000
SO2 (annual)
93,000
98,000
51,000
CO2 (annual, thousand metric)
26,000
23,000
8,000
PM2.5 (annual)
1,000
1,000
-
2035
NOx (ozone season)
29,000
30,000
27,000
NOx (annual)
46,000
46,000
41,000
SO2 (annual)
21,000
19,000
15,000
CO2 (annual, thousand metric)
16,000
15,000
8,000
PM2.5 (annual)
1,000
1,000
-
2042
NOx (ozone season)
22,000
22,000
22,000
NOx (annual)
23,000
22,000
21,000
SO2 (annual)
15,000
15,000
7,000
CO2 (annual, thousand metric)
9,000
8,000
4,000
PM2.5 (annual)
-
-
-
Emissions changes for NOx, SO2, and PM2 5 are in tons.
The Public Law 117-169, 136 Stat. 1818 (August 16, 2022), commonly known as the
Inflation Reduction Act of 2022 (IRA) includes significant additional new generation incentives
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targeting more efficient and lower-emitting sources of generation that is likely to meaningfully
affect the U.S. generation mix in the future and increase the pace of new lower-emitting
generation replacing some of older higher-emitting generating capacity. We include an appendix
to Chapter 4 to describe the EGU compliance behavior, costs, and emissions reductions that
include adjustments made to the IPM baseline to account for the potential effects of the IRA of
2022 on the power sector costs, emission reductions, and other impacts from this final rule.
ES.4.2 Non-EGUs
Table ES-4 below provides a summary of the 2019 ozone season emissions for non-
EGUs for the 20 states subject to the rule in 2026, along with the estimated ozone season
reductions for the final rule and the less and more stringent alternatives for 2026.12 The EPA did
not estimate emissions reductions of SO2, PM2.5, CO2 and other pollutants that may be associated
with controls on non-EGU emissions units; based on the estimated emissions reductions of NOx
and typical relationships between NOx and these other pollutants, there are likely to be
reductions of those additional pollutants. For the final rule, the EPA prepared an assessment
summarized in the memorandum titled Summary of Final Rule Applicability Criteria and
Emissions Limits for Non-EGU Emissions Units, Assumed Control Technologies for Meeting the
Final Emissions Limits, and Estimated Emissions Units, Emissions Reductions, and Costs, and
the memorandum includes estimated emissions reductions by state for the rule. Table ES-5
below shows the industries, emissions unit types, assumed control technology that meets the
final emissions limits and the estimated number of emissions units expected to install each
control (Table ES-1 above summarizes the industries, emissions unit types, and assumed controls
for the final rule). For additional results for 2026 - including estimated emissions reductions and
costs by state and estimated emissions reductions and costs by state and industry - see the above
cited memo. The analysis in the RIA assumes that the estimated reductions in 2026 for non-
EGUs will be the same in later years.
12 EPA determined that the 2019 inventory was appropriate because it provided a more accurate prediction of
potential near-term emissions reductions. The analysis assumes that the 2019 emissions from the emissions units
will be the same in 2026 and later years.
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Table ES-4. Ozone Season NOx Emissions and Emissions Reductions (tons) for the Final
Rule and the Less and More Stringent Alternatives for Non-EGUs in 2026
State
2019 Ozone
Season
Emissions3
Final Rule -
Ozone Season
NOx Reductions
Less Stringent -
Ozone Season
NOx Reductions
More Stringent -
Ozone Season
NOx Reductions
AR
8,790
1,546
457
1,690
CA
16,562
1,600
1,432
4,346
IL
15,821
2,311
751
2,991
IN
16,673
1,976
1,352
3,428
KY
10,134
2,665
583
3,120
LA
40,954
7,142
1,869
7,687
MD
2,818
157
147
1,145
MI
20,576
2,985
760
5,087
MO
11,237
2,065
579
4,716
MS
9,763
2,499
507
2,650
NJ
2,078
242
242
258
NV
2,544
0
0
0
NY
5,363
958
726
1,447
OH
18,000
3,105
1,031
4,006
OK
26,786
4,388
1,376
5,276
PA
14,919
2,184
1,656
4,550
TX
61,099
4,691
1,880
9,963
UT
4,232
252
52
615
VA
7,757
2,200
978
2,652
WV
6,318
1,649
408
2,100
Totals
302,425
44,616
16,786
67,728
a The 2019 ozone season emissions are calculated as 5/12 of the annual emissions from the following two emissions
inventory files: nonegu_SmokeFlatFile_2019NEI_POINT_20210721_controlupdate_13sep2021_v0 and
oilgas_SmokeFlatFile_2019NEIPOINT 2021072 lcontrolupdatel 3 sep202 lvO.
30
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Table ES-5. Non-EGU Industries, Emissions Unit Types, Assumed Control Technologies that Meet Final Emissions Limits,
Estimated Number of Control Installations
Estimated
Number of
Units Per
Assumed Control Technologies that
Assumed
Industry/Industries
Emissions Unit Type
Meet Final Emissions Limits
Control
Reciprocating Internal
NSCR or Layered Combustion
Pipeline Transportation of Natural Gas
Combustion Engines
(Reciprocating)
323
Layered Combustion (2-cycle Lean Burn)
394
SCR (4-cycle Lean Burn)
158
NSCR (4-cycle Rich Burn)
30
Cement and Concrete Product
Manufacturing
Kiln
SNCR
16
Iron and Steel Mills and Ferroalloy
Manufacturing
Reheat Furnaces
LNB
19
Glass and Glass Product Manufacturing
Furnaces
LNB
61
Iron and Steel Mills and Ferroalloy
Manufacturing
Boilers
LNB + FGR (Gas, No Coal or Oil)
151
Metal Ore Mining
SCR (Any Coal, Any Oil)
15
Basic Chemical Manufacturing
Petroleum and Coal Products
Manufacturing
Pulp, Paper, and Paperboard Mills
Solid Waste Combustors and Incinerators3
Combustors or Incinerators
ANSCR
57
LN™ and SNCR
4
Total
1,228
a Twelve MWCs have existing controls, and we estimated these units will use more reagent in those controls to meet the final emissions limits.
31
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ES.5 Costs
Table ES-6 below summarizes the present value (PV) and equivalent annualized value
(EAV) of the total national compliance cost estimates for EGUs and non-EGUs for the final rule
and the less and more stringent alternatives. The compliance cost estimate for EGUs is the
incremental electricity generation system cost associated with complying with the emission
budgets and backstop emission rate. Chapter 4, Section 4.3 describes the modeling and
methodology used to estimate EGU costs and Section 4.5 presents results, including impacts on
fuel use, prices, and generation mix. The compliance cost estimate for non-EGUs is the
engineering cost of installing pollution controls. Chapter 4, Section 4.4 describes the
methodology used to estimate non-EGU costs and Section 4.5 presents results, including average
cost-per-ton estimates across industries and assumed technologies. These compliance cost
estimates are used as a proxy for the social cost of the rule. We present the PV of the costs over
the twenty-year period 2023 to 2042. We also present the EAV, which represents a flow of
constant annual values that, had they occurred in each year from 2023 to 2042, would yield a
sum equivalent to the PV. The EAV represents the value of a typical cost for each year of the
analysis.
Table ES-6. Total National Compliance Cost Estimates (millions of 2016$) for the Final
Rule and the Less and More Stringent Alternatives
Final Rule
Less Stringent
Alternative
More Stringent Alternative
3 Percent
7 Percent
3 Percent
7 Percent
3 Percent
7 Percent
Present Value
EGU 2023-2042
$6,800
$3,900
$6,800
$3,900
$9,500
$6,500
Present Value
Non-EGU 2026-2042
$6,700
$4,300
$1,700
$1,100
$15,000
$9,500
Present Value
Total 2023-2042
$13,000
$8,200
$8,400
$5,000
$24,000
$16,000
EGU
Equivalent
Annualized Value
$460
$370
$450
$370
$640
$620
Non-EGU
Equivalent
Annualized Value
$450
$400
$110
$100
$1,000
$900
Total
Equivalent
Annualized Value
$910
$770
$570
$470
$1,600
$1,500
Note: Values have been rounded to two significant figures
32
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ES.6 Benefits
ES. 6.1 Health Benefits Estimates
The final rule is expected to reduce ozone season and annual NOx emissions. In the
presence of sunlight, NOx and volatile organic compounds (VOCs) can undergo a chemical
reaction in the atmosphere to form ozone. Reducing NOx emissions generally reduces human
exposure to ozone and the incidence of ozone-related health effects, though the degree to which
ozone is reduced will depend in part on local concentration levels of VOCs. In addition to NOx,
the rule is also expected to reduce emissions of direct PM2.5 and SO2 throughout the year from
EGUs. 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.
In this RIA for the Transport FIP for the 2015 ozone NAAQS, the EPA quantifies
benefits of changes in ozone and PM2.5 concentrations. The health effects and effect estimates,
and how they were selected, are described in the technical support document for the 2022 PM
NAAQS Reconsideration Proposal RIA titled Estimating PM2.5- and Ozone-Attributable Health
Benefits. The approach for updating the endpoints and to identify suitable epidemiologic studies,
baseline incidence rates, population demographics, and valuation estimates is summarized in
Chapter 5.
Table ES-7 and Table ES-8 report the estimated economic value of avoided premature
deaths and illness in 2023 and 2026 relative to the baseline along with the 95% confidence
interval. The number of reduced estimated deaths and illnesses from the final rule and more and
less stringent alternatives is calculated from the sum of individual reduced mortality and illness
risk across the population. In each of these tables, for each discount rate and regulatory control
alternative, multiple benefits estimates are presented reflecting alternative ozone and PM2.5
mortality risk estimates.
33
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Table ES-7. Estimated Discounted Monetized Value of Avoided Ozone-Related Premature
Mortality and Illness for the Final Rule and the Less and More Stringent Alternatives in
2023
(95% Confidence Interval; millions of 2016$)a'b
Disc.
Rate
Pollutant
Final Rule
More Stringent Alternative
Less Stringent Alternative
3%
Ozone
Benefits
$100 $820
($27 to and ($91 to
$220)° $2,100)d
$110 $840
($28 to and ($94 to
$230)° $2,200)d
$100 $810
($27 to and ($91 to
$220)° $2,100)d
7%
Ozone
Benefits
$93 $730
($17 to and ($75 to
210)° $l,900)d
$96 $750
($18 to and ($77 to
$210)° $2,000)d
$93 $730
($17 to and ($75 to
$210)° $l,900)d
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should
not be summed.
b We estimated ozone benefits for changes in NOx for the ozone season for EGUs in 2023. This table does not
include benefits from reductions for non-EGUs because reductions from these sources are not expected prior to 2026
when the final standards would apply to these sources.
0 Using the pooled short-term ozone exposure mortality risk estimate.
d Using the long-term ozone exposure mortality risk estimate.
Table ES-8. Estimated Discounted Monetized Value of Avoided Ozone and PM2.5-
Attributable Premature Mortality and Illness for the Final Rule and the Less and More
Stringent Alternatives in 2026 (95% Confic
ence Interval; millions of 2016$)a'b
Disc
Rate
Pollutant
Final Rule
More Stringent Alternative
Less Stringent Alternative
3%
Ozone
Benefits
$1,100 $9,400
($280 to and ($1,000 to
$2,400)° $25,000)d
$1,900 $15,000
(470 to and ($1,700 to
$4,000)° $40,000)d
$420 $3,400
($110 to and ($380 to
$900)° $8,900)d
PM
Benefits
$2,000 $4,400
($220 to and ($430 to
$5,300) $12,000)
$6,400 $14,000
($690 to and ($1,300 to
$17,000) $37,000)
$530 $1,100
($57 to and ($110 to
$1,400) $3,100)
Ozone
plus PM
Benefits
$3,200 $14,000
($500 to and ($1,500 to
$7,700)° $36,000)d
$8,300 $29,000
($1,200 to and ($3,000 to
$21,000)° $77,000)d
$950 $4,600
($160 to and ($490 to
$2,300)° $12,000)d
7%
Ozone
Benefits
$1,000 $8,400
($180 to and ($850 to
$2,300)c $22,000)d
$1,700 $14,000
($300 to and ($1,400 to
$3,800)° $36,000)d
$380 $3,100
($68 to and ($310 to
$850)° $8,100)d
PM
Benefits
$1,800 $3,900
($190 to and ($380 to
$4,700) $11,000)
$5,800 $12,000
($600 to and ($1,200 to
$15,000) $33,000)
470 $1,000
($50 to and ($100 to
$1,200) $2,800)
Ozone
plus PM
Benefits
$2,800 $12,000
($370 to and ($1,200 to
$7,000)° $33,000)d
$7,500 $26,000
($910 to and ($2,600 to
$19,000)° $69,000)d
$850 $4,100
($120 to and ($410 to
$2,100)° $ll,000)d
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 We estimated changes in NOx for the ozone season and annual changes in PM2 5 and PM2 5 precursors in 2026.
0 Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Di et al. (2017)
long-term PM2 5 exposure mortality risk estimate.
d Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Di et al. (2017) long-term PM2 5
exposure mortality risk estimate.
34
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ES.6.2 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 this final rule. Climate benefits from reducing emissions of CChcan be monetized
using estimates of the social cost of carbon (SC-CO2). See Chapter 5, Section 5.2 for more
discussion of the approach to monetization of the climate benefits associated with this rule.
ES. 6.3 Total Monetized Human Health and Climate Benefits
Tables ES-9 through ES-11 below present the total monetized health and climate benefits
for the final rule and the less and more stringent alternatives for 2023, 2026, and 2030.
35
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Table ES-9. Combined Monetized Health and Climate Benefits for the Final Rule and Less
and More Stringent Alternatives for 2023 (millions of 2016$)
Health and Climate Benefits
SC-CO2 Discount (Discount Rate Applied to Health
Rate and Statistic Benefits)
3% 7%
Final Rule
Climate Benefits
Only3
5% (average)
$100 and $820
$94 and $730
$1
3% (average)
$100 and $820
$98 and $740
$5
2.5% (average)
$110 and $820
$100 and $740
$7
3% (95th percentile)
$110 and $830
$110 and $750
$14
Less Stringent Alternative
5% (average)
$100 and $810
$94 and $730
$1
3% (average)
$100 and $820
$97 and $730
$4
2.5% (average)
$110 and $820
$99 and $730
$6
3% (95th percentile)
$110 and $830
$100 and $740
$12
More Stringent Alternative
5% (average)
$110 and $840
$97 and $750
$1
3% (average)
$110 and $840
$100 and $760
$5
2.5% (average)
$120 and $850
$100 and $760
$7
3% (95th percentile)
$120 and $850
$110 and $770
$14
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the social cost of carbon (SC-CO2) (model average at 2.5 percent, 3 percent, and 5 percent discount
rates; 95th percentile at 3 percent discount rate).
36
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Table ES-10. Combined Monetized Health and Climate Benefits for the Final Rule and
Less and More Stringent Alternatives for 2026 (millions of 2016$)
SC-CO2 Discount
Rate and Statistic
Health and Climate Benefits
(Discount Rate Applied to Health Benefits)
Climate
Benefits
Only3
3%
7%
Final Rule
5% (average)
$3,500 and $14,000
$3,100 and $13,000
$340
3% (average)
$4,300 and $15,000
$3,900 and $13,000
$1,100
2.5% (average)
$4,800 and $15,000
$4,400 and $14,000
$1,600
3% (95th percentile)
$6,600 and $17,000
$6,200 and $16,000
$3,400
Less Stringent Alternative
5% (average)
$1,100 and $4,700
$980 and $4,200
$130
3% (average)
$1,400 and $5,000
$1,300 and $4,500
$420
2.5% (average)
$1,600 and $5,200
$1,500 and $4,700
$620
3% (95th percentile)
$2,200 and $5,800
$2,100 and $5,400
$1,300
More Stringent Alternative
5% (average)
$8,900 and $30,000
$13,000 and $27,000
$640
3% (average)
$10,000 and $31,000
$14,000 and $28,000
$2,100
2.5% (average)
$11,000 and $32,000
$15,000 and $29,000
$3,100
3% (95th percentile)
$15,000 and $35,000
$18,000 and $32,000
$6,400
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the social cost of carbon (SC-CO2) (model average at 2.5 percent, 3 percent, and 5 percent discount
rates; 95th percentile at 3 percent discount rate).
37
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Table ES-11. Combined Monetized Health and Climate Benefits for the Final Rule and
Less and More Stringent Alternatives for 2030 (millions of 2016$)
SC-CO2 Discount
Rate and Statistic
Health and Climate Benefits
(Discount Rate Applied to Health Benefits)
Climate
Benefits
Only3
3%
7%
Final Rule
5% (average)
$3,900 and $15,000
$3,500 and $14,000
$470
3% (average)
$4,900 and $16,000
$4,500 and $15,000
$1,500
2.5% (average)
$5,600 and $17,000
$5,200 and $15,000
$2,200
3% (95th percentile)
$8,000 and $19,000
$7,600 and $18,000
$4,600
Less Stringent Alternative
5% (average)
$1,400 and $5,300
$1,300 and $4,800
$420
3% (average)
$2,300 and $6,200
$2,300 and $5,700
$1,300
2.5% (average)
$3,000 and $6,800
$2,900 and $6,300
$2,000
3% (95th percentile)
$5,100 and $8,900
$5,000 and $8,400
$4,100
More Stringent Alternative
5% (average)
$9,200 and $31,000
$8,300 and $28,000
$150
3% (average)
$9,500 and $31,000
$8,600 and $28,000
$480
2.5% (average)
$9,700 and $32,000
$8,800 and $28,000
$700
3% (95th percentile)
$10,000 and $32,000
$9,500 and $29,000
$1,400
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the social cost of carbon (SC-CO2) (model average at 2.5 percent, 3 percent, and 5 percent discount
rates; 95th percentile at 3 percent discount rate).
ES. 6.4 Additional Unquantified Benefits
Data, time, and resource limitations prevented the EPA from quantifying the estimated
health impacts or monetizing estimated benefits associated with direct exposure to NO2 and SO2
(independent of the role NO2 and SO2 play as precursors to ozone and PM2.5), as well as
ecosystem effects, and visibility impairment due to the absence of air quality modeling data for
these pollutants in this analysis. 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 ozone, PM2.5, NO2 or SO2. For a qualitative description of these and water quality
benefits, please see Chapter 5, section 5.4.
38
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ES.7 Environmental Justice Impacts
Environmental justice (EJ) concerns for each rulemaking are unique and should be
considered on a case-by-case basis, and the EPA's EJ Technical Guidance13 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?
3. For the regulatory option(s) under consideration, are potential EJ concerns created
or mitigated compared to the baseline?"
To address these questions, the 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. 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 this rule but not modeled here (Chapter 7, Section 7.3) and 2) the distribution of
ozone and PM2.5 concentrations in the baseline and changes due to the final rulemaking 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
(Chapter 7, Section 7.4). 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 such as, local NO2
emitted from sources affected by the regulatory action for certain population groups of concern
(Chapter 7, Section 7.3). The baseline demographic proximity analyses suggest that larger
percentages of Hispanics, African Americans, people below the poverty level, people with less
13 U.S. Environmental Protection Agency (EPA), 2015. Guidance on Considering Environmental Justice During the
Development of Regulatory Actions.
39
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educational attainment, and people linguistically isolated are living within 5 km and 10 km of an
affected EGU, compared to national averages. It also finds larger percentages of African
Americans, people below the poverty level, and with less educational attainment living within 5
km and 10 km of an affected non-EGU facility. Relating these results to question 1, we conclude
that there may be potential EJ concerns associated with directly emitted pollutants that are
affected by the regulatory action (e.g., NO2) for certain population groups of concern in the
baseline. However, as proximity to affected facilities does not capture variation in baseline
exposure across communities, nor does it indicate that any exposures or impacts will occur, these
results should not be interpreted as a direct measure of exposure or impact.
Because the pollution impacts that are the focus of this rule are often substantially
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 ozone and PM2.5 exposure analyses respond to question 1 from the
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
(Chapter 7, Section 7.4). Baseline ozone and PM2.5 exposure analyses show that certain
populations, such as Hispanics, Asians, those linguistically isolated, those less educated, and
children may experience disproportionately higher ozone and PM2.5 exposures as compared to
the national average. American Indians 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 post-policy regulatory alternatives of this final rulemaking are
expected to differentially impact demographic populations, informing questions 2 and 3 from the
EPA's EJ Technical Guidance with regard to ozone and PM2.5 exposure changes. We infer that
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 subgroups, relative to baseline disparities (question 2). Also, due to the very small
differences observed in the distributional analyses of post-policy ozone and PM2.5 exposure
impacts, we do not find evidence that potential EJ concerns related to ozone or PM2.5 exposures
40
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will be meaningfully exacerbated or mitigated in the regulatory alternatives under consideration,
compared to the baseline (question 3). Importantly, the action described in this rule is expected
to lower ozone and PM2.5 in many areas, including in ozone nonattainment areas, and thus
mitigate some pre-existing health risks across all populations evaluated.
ES.8 Results of Benefit-Cost Analysis
Below we present the annual costs and benefits estimates for 2023, 2026, and 2030,
respectively. This analysis uses annual compliance costs reported above as a proxy for social
costs. The estimated annual compliance costs to implement the rule, as described in this RIA, are
approximately $57 million in 2023 and $570 million in 2026 (2016$).
The estimated monetized health benefits from reduced ozone and PM2.5 concentrations
from implementation of the rule are approximately $100 and $820 million in 2023 (2016$, based
on a real discount rate of 3 percent). The estimated monetized climate benefits from reduced
GHG emissions are approximately $5 million in 2023 (2016$, based on a real discount rate of 3
percent). For 2026, the estimated monetized health benefits from implementation of the rule are
approximately $3,200 and $14,000 million (2016$, based on a real discount rate of 3 percent).
The estimated monetized climate benefits from reduced GHG emissions are approximately
$1,100 million in 2026 (2016$, based on a real discount rate of 3 percent).
The EPA calculates the monetized net benefits of the rule by subtracting the estimated
monetized compliance costs from the estimated monetized health and climate benefits in 2023,
2026, and 2030. The benefits include those to public health associated with reductions in ozone
and PM2.5 concentrations, as well as those to climate associated with reductions in GHG
emissions. The annual monetized net benefits of the rule in 2023 (in 2016$) are approximately
$48 and $760 million using a 3 percent real discount rate. The annual monetized net benefits of
the rule in 2026 are approximately $3,700 and $14,000 million using a 3 percent real discount
rate. The annual monetized net benefits of the rule in 2030 are approximately $3,600 and
$15,000 million using a 3 percent real discount rate. Table ES-12 presents a summary of the
monetized health and climate benefits, costs, and net benefits of the rule and the less and more
stringent alternatives for 2023. Table ES-13. presents a summary of these impacts for the rule
and the less and more stringent alternatives for 2026. Table ES-14 presents a summary of these
impacts for the rule and the less and more stringent alternatives for 2030. These results present
41
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an incomplete overview of the effects of the rule because important categories of benefits-
including benefits from reducing other types of air pollutants, and water pollution - were not
monetized and are therefore not reflected in the cost-benefit tables. We anticipate that taking
non-monetized effects into account would show the rule to be more net beneficial than these
tables reflect.
Table ES-12. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and
More Stringent Alternatives for 2023 for the U.S. (millions of 2016$)a'b
, _ . Less Stringent More Stringent
Final Rule ... ,? ... ,.s
Alternative Alternative
Health Benefits0 $100 and $820 $100 and $810 $110 and $840
Climate Benefits $5 $4 $5
Total Benefits $100 and $820 $100 and $820 $110 and $840
Costs'1 $57 $56 $49
Net Benefits $48 and $760 $48 and $760 $66 and $800
a We focus results to provide a snapshot of costs and benefits in 2023, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Rows may not appear to add correctly due to rounding.
0 The benefits are associated with two point estimates from two different epidemiologic studies. For the purposes of
presenting the values in this table the health and climate benefits are discounted at 3%.
d The costs presented in this table are 2023 annual estimates for each alternative analyzed. For EGUs, an NPV of
costs was calculated using a 3.76% real discount rate consistent with the rate used in IPM's objective function for
cost-minimization. For further information on the discount rate use, please see Chapter 4, Table 4-8.
Table ES-13. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and
More Stringent Alternatives for 2026 for the U.S. (millions of 2016$)a'b
, _ . Less Stringent More Stringent
Final Rule ... ,? ... ,.s
Alternative Alternative
Health Benefits0 $3,200 and $14,000 $950 and $4,600 $8,300 and $29,000
Climate Benefits $1,100 $420 $2,100
Total Benefits $4,300 and $15,000 $1,400 and $5,000 $10,000 and $31,000
Costs'1 $570 $110 $2,100
Net Benefits $3,700 and $14,000 $1,300 and $4,900 $8,300 and $29,000
a We focus results to provide a snapshot of costs and benefits in 2026, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Rows may not appear to add correctly due to rounding.
0 The benefits are associated with two point estimates from two different epidemiologic studies. For the purposes of
presenting the values in this table the health and climate benefits are discounted at 3%.
d The costs presented in this table are 2026 annual estimates for each alternative analyzed. For EGUs, an NPV of
costs was calculated using a 3.76% real discount rate consistent with the rate used in IPM's objective function for
cost-minimization. For further information on the discount rate use, please see Chapter 4, Table 4-8.
42
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Table ES-14. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and
More Stringent Alternatives for 2030 for the U.S. (millions of 2016$)a'b
Final Rule
Less Stringent
Alternative
More Stringent
Alternative
Health Benefits0
$3,400 and $15,000
$1,000 and $4,900
$9,000 and $31,000
Climate Benefits
$1,500
$1,300
$500
Total Benefits
$4,900 and $16,000
$2,300 and $6,200
$9,500 and $31,000
Costs'1
$1,300
$920
$2,100
Net Benefits
$3,600 and $15,000
$1,400 and $5,300
$7,400 and $29,000
a We focus results to provide a snapshot of costs and benefits in 2030, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Rows may not appear to add correctly due to rounding.
0 The benefits are associated with two point estimates from two different epidemiologic studies. For the purposes of
presenting the values in this table the health and climate benefits are discounted at 3%.
d The costs presented in this table are 2030 annual estimates for each alternative analyzed. For EGUs, an NPV of
costs was calculated using a 3.76% real discount rate consistent with the rate used in IPM's objective function for
cost-minimization. For further information on the discount rate use, please see Chapter 4, Table 4-8.
As part of fulfilling analytical guidance with respect to E.O. 12866, the EPA presents
estimates of the present value (PV) of the monetized benefits and costs over the twenty-year
period 2023 to 2042. To calculate the present value of the social net-benefits of the final rule,
annual benefits and costs are discounted to 2023 at 3 percent and 7 discount rates as directed by
OMB's Circular A-4. The EPA also presents the equivalent annualized value (EAV), which
represents a flow of constant annual values that, had they occurred in each year from 2023 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 mentioned earlier
in the RIA. Note that EGU costs reported in this RIA for years not explicitly modeled are
mapped to modeled years. For this analysis, IPM maps the calendar year 2023 to run year 2023,
calendar years 2024-2026 to run year 2025 and calendar years 2027-2029 to run year 2028. Non-
EGU costs are assumed to be constant throughout the time horizon.
The health benefits analysis quantifies changes in ozone concentrations in 2023 and
changes in ozone and PM2.5 in 2026 for each of the three regulatory control alternatives (i.e.,
final rule, less stringent alternative, and more stringent alternative). Analyses were also run for
each year between 2023 and 2042, using the air quality model surfaces, but accounting for the
change in population size in each year, income growth, and baseline mortality incidence rates at
five-year increments. However, because of uncertainties associated with baseline air quality
projections beyond 2026, annual health benefits beyond 2026 are based on 2026 air quality
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changes. The 2023 ozone concentration changes were assumed through 2025 and the 2026 ozone
and PM2.5 concentration changes were assumed until 2042. Finally, climate benefits are mapped
using the same model year mapping from IPM applied for the EGU cost analysis. GHG
emissions reductions are multiplied by year specific social cost of carbon values.
For the twenty-year period of 2023 to 2042, the PV of the net benefits, in 2016$ and
discounted to 2023, is $200,000 million when using a 3 percent discount rate and $140,000
million when using a 7 percent discount rate. The EAV is $13,000 million per year when using a
3 percent discount rate and $12,000 million when using a 7 percent discount rate. The
comparison of benefits and costs in PV and EAV terms for the final rule can be found in Table
ES-15. Estimates in the table are presented as rounded values.
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Table ES-15. Summary of Present Values and Equivalent Annualized Values for the 2023-
2042 Timeframe for Estimated Monetized Compliance Costs, Benefits, and Net Benefits for
the Final Rule (millions of 2016$, discounted to 2023)
Health Benefits
Climate
Benefits
Cost
Net Benefits
3%
7%
3%
3%
7%
3%
7%
2023
$820
$730
$5
$57
$57
$770
$680
2024
$810
$700
$1,000
($5)
($5)
$1,300
$1,200
2025
$8,600
$7,100
$1,000
($5)
($4)
$9,600
$8,100
2026
$13,000
$10,000
$1,000
$520
$460
$13,000
$10,000
2027
$13,000
$9,700
$230
$530
$450
$13,000
$9,700
2028
$12,000
$8,900
$230
$510
$420
$12,000
$8,700
2029
$12,000
$8,500
$230
$500
$400
$12,000
$8,800
2030
$12,000
$8,200
$1,200
$1,000
$800
$12,000
$8,600
2031
$12,000
$7,800
$1,200
$1,000
$740
$12,000
$8,200
2032
$12,000
$7,500
$740
$1,100
$760
$12,000
$7,700
2033
$11,000
$7,000
$730
$1,000
$710
$11,000
$7,200
2034
$11,000
$6,700
$720
$1,000
$660
$11,000
$6,900
2035
$11,000
$6,400
$710
$970
$620
$11,000
$6,500
2036
$11,000
$6,100
$700
$950
$580
$11,000
$6,300
2037
$11,000
$5,800
$690
$920
$540
$11,000
$6,000
2038
$11,000
$5,400
$860
$890
$500
$11,000
$5,700
2039
$10,000
$5,100
$850
$870
$470
$9,900
$5,400
2040
$10,000
$4,900
$830
$840
$440
$10,000
$5,300
2041
$10,000
$4,600
$820
$820
$410
$9,900
$4,900
2042
$10,000
$4,400
$810
$790
$380
$9,800
$4,600
PV
$200,000
$130,000
$15,000
$14,000
$9,400
$200,000
$140,000
2023-2042
EAV
2023-2042
$13,000
$12,000
$970
$910
$770
$13,000
$12,000
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CHAPTER 1: INTRODUCTION AND BACKGROUND
Overview
In this final rule, the Federal Good Neighbor Plan Addressing Regional Ozone Transport
for the 2015 Ozone National Ambient Air Quality Standards (Transport FIP for the 2015 ozone
NAAQS), the EPA sets implementation mechanisms to achieve enforceable emissions reductions
required to eliminate significant contribution to nonattainment or interference with maintenance
of the 2015 ozone NAAQS in other states. The initial phase of emissions reductions will begin in
the 2023 ozone season with further emissions reductions being required in later years.14
The EPA is promulgating new or revised FIPs for 23 states. For 22 states the FIPs include
new NOx ozone season emission budgets for EGU sources, with implementation of these
emission budgets beginning in the 2023 ozone season.15 The EPA is expanding the CSAPRNOx
Ozone Season Group 3 Trading Program beginning in the 2023 ozone season. Specifically, the
FIPs require electric generating units (EGUs) within the borders of the 22 states to participate in
a revised version of the CSAPR NOx Ozone Season Group 3 Trading Program created by the
Revised CSAPR Update. Affected EGUs within the borders of twelve states currently
participating in the Group 3 Trading Program under FIPs or SIPs remain in the program, with
revised provisions beginning in the 2023 ozone season. The FIPs also require affected EGUs
within the borders of seven states currently covered by the CSAPR NOx Ozone Season Group 2
Trading Program (the "Group 2 trading program") under existing FIPs or existing SIPs to
transition from the Group 2 program to the revised Group 3 trading program beginning with the
2023 control period. Lastly, the EPA is issuing new FIPs for three states not currently covered by
any CSAPRNOx ozone season trading program (Minnesota, Nevada, and Utah).
14 The 2015 ozone NAAQS is an 8-hour standard that was set at 70 parts per billion (ppb). See 80 FR 65291
(December 28, 2015).
15 In 2023, the 22 states with EGU reduction requirements include AL, AR, IL, IN, KY, LA, MD, MI, MN, MS,
MO, NV, NJ, NY, OH, OK, PA, TX, UT, VA, WV, and WI. There are no EGU reductions being required from
California, which if included would make 23 states.
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The FIPs that EPA is promulgating for 20 states include new NOx emissions limitations
for non-electric generating unit (non-EGU) sources, with initial compliance dates for these
emissions limitations beginning in 2026.16
Consistent with OMB Circular A-4 and the EPA's Guidelines for Preparing Economic
Analyses (2010), this regulatory impact analysis (RIA) presents the benefits and costs of the final
rule from 2023 through 2042. The estimated monetized benefits are those health benefits
expected to arise from reduced ozone and PM2.5 concentrations and the benefits from reductions
in greenhouse gases. The estimated monetized costs for EGUs are the costs of installing and
operating controls and other increased costs of producing electricity to comply with the revised
version of the Group 3 trading program. The estimated monetized costs for non-EGUs are the
costs of installing and operating controls to meet the ozone season NOx emissions limitations.17
The estimated costs for non-EGUs do not include monitoring, recordkeeping, reporting, or
testing costs. Unquantified benefits and costs are described qualitatively. The RIA also provides
(i) estimates of other impacts of the rule including its effect on retail electricity prices and fuel
production, (ii) an assessment of how expected compliance with the rule will affect
concentrations at nonattainment and maintenance receptors, and (iii) an assessment of potential
environmental justice concerns. This chapter contains background information relevant to the
rule and an outline of the chapters of this RIA.
1.1 Background
Clean Air Act (CAA or the Act) section 110(a)(2)(D)(i)(I), which is also known as the
"good neighbor provision," requires states to prohibit emissions that will contribute significantly
to nonattainment or interfere with maintenance in any other state with respect to any primary or
secondary NAAQS. The statute vests states with the primary responsibility to address interstate
emission transport through the development of good neighbor State Implementation Plans (SIPs),
which are one component of larger SIP submittals typically required three years after the EPA
16 In 2026, the 20 states with non-EGU reduction requirements include AR, CA, IL, IN, KY, LA, MD, MI, MS, MO,
NV, NJ, NY, OH, OK, PA, TX, UT, VA, and WV.
17 For non-EGUs, we prepared a memorandum for the final rule that summarizes the (i) industries affected, (ii)
applicability criteria, (iii) final emissions limits, (iv) estimated emissions units, and (v) estimated emissions
reductions and costs (the memorandum, titled Summary of Final Rule Applicability Criteria and Emissions Limits
for Non-EGU Emissions Units, Assumed Control Technologies for Meeting the Final Emissions Limits, and
Estimated Emissions Units, Emissions Reductions, and Costs, is available in the docket here:
https ://www. regulations. gov/document/EP A-HQ-0 AR-2021 -0668.
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promulgates a new or revised NAAQS. These larger SIPs are often referred to as "infrastructure"
SIPs or iSIPs. See CAA section 110(a)(1) and (2).
The EPA originally published the Cross-State Air Pollution Rule (CSAPR) on August 8,
2011, to address interstate transport of ozone pollution under the 1997 ozone National Ambient
Air Quality Standards (NAAQS).18 On October 26, 2016, the EPA published the CSAPR
Update, which finalized Federal Implementation Plans (FIPs) for 22 states that the EPA found
failed to submit a complete good neighbor State Implementation Plan (SIP) (15 states)19 or for
which the EPA issued a final rule disapproving their good neighbor SIP (7 states).20 The FIPs
promulgated for these states included new electric generating unit (EGU) oxides of nitrogen
(NOx) ozone season emission budgets to reduce interstate transport for the 2008 ozone
NAAQS.21 These emissions budgets took effect in 2017 in order to assist downwind states with
attainment of the 2008 ozone NAAQS by the 2018 Moderate area attainment date. The EPA
acknowledged at the time that the FIPs promulgated for 21 of the 22 states only partially
addressed good neighbor obligations under the 2008 ozone NAAQS.22
On March 31, 2021, the EPA promulgated the Revised CSAPR Update (RCU) in
response to the United States Court of Appeals for the District of Columbia Circuit's (D.C.
Circuit) September 13, 2019, remand of the CSAPR Update. The D.C. Circuit found that the
CSAPR Update was unlawful to the extent it allowed those states to continue their significant
contributions to downwind ozone problems beyond the statutory dates by which downwind states
must demonstrate their attainment of the air quality standards. The RCU resolved 21 states'
outstanding interstate ozone transport obligations with respect to the 2008 ozone NAAQS and
established a new Group 3 ozone season emissions trading program for EGUs for twelve states.
18 CSAPR also addressed interstate transport of fine particulate matter (PM2 5) under the 1997 and 2006 PM2 5
NAAQS.
19 Alabama, Arkansas, Illinois, Iowa, Kansas, Maryland, Michigan, Mississippi, Missouri, New Jersey, Oklahoma,
Pennsylvania, Tennessee, Virginia, and West Virginia.
20 Indiana, Kentucky, Louisiana, New York, Ohio, Texas, and Wisconsin.
21 The 2008 ozone NAAQS is an 8-hour standard that was set at 75 parts per billion (ppb). See 73 FR 16436 (March
27, 2008).
22 In the CSAPR Update, the EPA found that the finalized Tennessee emission budget fully addressed Tennessee's
good neighbor obligation with respect to the 2008 ozone NAAQS. As such, the number of states included was
reduced from 22 to 21 states.
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As described in the preamble of this rule, to reduce interstate emission transport under the
authority provided in CAA section 110(a)(2)(D)(i)(I) for the more protective 2015 ozone
NAAQS, this rule further limits ozone season (May 1 through September 30) NOx emissions
from EGUs in 22 states beginning in 2023 and non-EGUs in 20 states beginning in 2026 using
the Interstate Transport Framework. The Interstate Transport Framework, the framework
developed by the EPA in the original CSAPR, provides a 4-step process to address the
requirements of the good neighbor provision for ground-level ozone and fine particulate matter
(PM2.5) NAAQS: (1) identifying downwind receptors that are expected to have problems
attaining or maintaining the NAAQS; (2) determining which upwind states contribute to these
identified problems in amounts sufficient to "link" them to the downwind air quality problems
(i.e., here, a 1 percent contribution threshold); (3) for states linked to downwind air quality
problems, identifying upwind emissions that significantly contribute to downwind nonattainment
or interfere with downwind maintenance of the NAAQS; and (4) for states that are found to have
emissions that significantly contribute to nonattainment or interfere with maintenance of the
NAAQS downwind, implementing the necessary emissions reductions through enforceable
measures.
1.1.1 Role of Executive Orders in the Regulatory Impact Analysis
Several statutes and executive orders apply to federal rulemakings. In accordance with
Executive Orders 12866 and 13563 and the guidelines of OMB Circular A-4, the RIA analyzes
the benefits and costs associated with emissions reductions for compliance with the rule. OMB
Circular A-4 recommends analysis of one potential regulatory control alternative more stringent
than the final rule and one less stringent than the final rule. This RIA evaluates the benefits,
costs, and certain impacts of a more and a less stringent alternative to the selected alternative in
this rule.
1.1.2 Alternatives Analyzed
In response to OMB Circular A-4, this RIA analyzes the Transport FIP for the 2015 ozone
NAAQS emission budgets for EGUs and ozone season emissions limits for non-EGUs, as well
as a more and a less stringent alternative to the final rule. For EGUs, the Transport FIP for the
2015 ozone NAAQS requires EGUs in the 22 states to participate in the CSAPR NOx Ozone
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Season Group 3 Trading Program created by the Revised CSAPR Update. For non-EGUs, the
Transport FIP for the 2015 ozone NAAQS requires units subject to the rule to meet ozone season
emissions limits.
The less stringent alternative differs from the Transport FIP for the 2015 ozone NAAQS in
that it sets different EGU NOx ozone season emission budgets. The more stringent alternative
differs from the Transport FIP for the 2015 ozone NAAQS in that it features different dates for
compliance with unit-specific emission rates for the affected EGUs. The more and less stringent
alternatives also estimate different control technologies for some emissions units for the affected
non-EGUs under the assumption that they would be subject to different emission rates. Table 1-1
below presents the less stringent alternatives, final rule requirements, and more stringent
alternatives for EGUs and non-EGUs.
For EGUs, one of the primary ways the final Transport FIP for the 2015 ozone NAAQS
differs from the proposal is the compliance date for the backstop emission rate. At proposal, both
the proposed rule and more stringent alternative imposed the backstop emission rate in 2026. The
EPA continues to view the backstop emission rate as an important element of the rule to ensure
the elimination of significant contribution as determined at Step 3 of the Interstate Transport
Framework for all large coal units, and the rule therefore imposes this rate beginning in 2024 for
units that already have SCR installed. However, in the final rule, to facilitate power sector
transition planning and in response to concerns from commenters, the EPA is deferring the
imposition of the backstop emissions rate for units that do not have SCR until the second ozone
season following installation of the control or 2030 at the latest. The modeling of the final rule
includes the backstop emission rate in the 2030 model run year and the more stringent alternative
includes the backstop emission rate in the 2025 model run year (corresponding to 2026).
For the non-EGU industries, in the final rule we made some minor changes to the non-
EGU emissions units covered, the applicability criteria, as well as provided for facility-wide
emissions averaging for engines and for a low-use exemption to eliminate the need to install
controls on low-use boilers; the changes make directly comparing the alternatives analyzed
between proposal and this final rule challenging. Please see Section 1.2.1 below for a more
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detailed discussion of the changes made and Table 1-1 below for a summary of the alternatives
analyzed in the final rule.
Table 1-1. Regulatory Control Alternatives for EGUs and Non-EGUs
Regulatory Control
Alternative
NOx Controls Implemented for EGUs within IPMa'b
1) 2023 onwards: Fully operate existing selective catalytic reduction (SCRs)
during ozone season
2) 2023 onwards: Fully operate existing selective non-catalytic reduction
(SNCRs) during ozone season
3) In 2023 install state-of-the-art combustion controls0
4) In 2030 model run year, impose backstop emission rate on coal units greater
than 100 MW within the 19-state region that lack SCR controls.d
Less Stringent Alternative
Final Rule
More Stringent Alternative
Regulatory Control
Alternative
(All Controls above and)
5) In 2025 model run year, impose Engineering Analysis derived emissions
budgets that assume installation of SCR controls on coal units greater than
100 MW within the 19-state region that lack SCR controls.
(Controls 1-5 above and)
6) In 2025 model run year, impose backstop emission rate on coal units greater
than 100 MW within the 19-state region that lack SCR controls, forcing units
to retrofit or retire.
NOx Emissions Limits for Non-EGUs - Emissions Unit Types, Industries,
and Controls Assumed for Compliance
1) Reciprocating internal combustion engines in Pipeline Transportation of
Natural Gas - Adjust Air-to-Fuel Ratio
2) Kilns in Cement and Cement Product Manufacturing - install SNCR
3) Reheat furnaces in Iron and Steel Mills and Ferroalloy Manufacturing - install
Low NOx burners (LNB)
4) Furnaces in Glass and Glass Product Manufacturing - install LNB
5) Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SNCR
6) Combustors or Incinerators in Solid Waste Combustors and Incinerators -
install Advanced NSCR (ANSCR) or LN™ and SNCRe
Less Stringent Alternative
Final Rule
(Controls 2, 3, 4, and 6 above, plus changes in assumed controls noted below)
7) Reciprocating internal combustion engines in Pipeline Transportation of
Natural Gas - depending on engine type, install Layered Combustion, non-
selective catalytic reduction (NSCR), or SCR
8) Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SCR (coal- or
oil-fired) or LNB and FGR (natural gas-fired only)
(Controls 3,6,7 above, plus changes in assumed controls noted below)
9) Kilns in Cement and Cement Product Manufacturing - install SCR
10) Furnaces in Glass and Glass Product Manufacturing - install SCR
11) Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SCR (natural
gas-fired only)
a 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. For this analysis, IPM maps the calendar year 2023 to run year 2023,
calendar years 2024-2026 to run year 2025 and calendar years 2027-2029 to run year 2028. For model details, please
see Chapter 2 of the IPM documentation
More Stringent Alternative
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b NOx mass budgets are imposed in all ran years in IPM (2023-2050) consistent with the measures highlighted in
this table.
0 The final rule implementation allows for the reduction associated with state-of-the-art combustion controls to occur
by 2024. It is captured in 2023 in this analysis to fully assess the impact of the mitigation measures occuring prior to
2026.
d For the 19 states with EGU obligations that are linked in 2026 the EPA is determining that the selected EGU
control stringency also includes emissions reductions commensurate with the retrofit of SCR at coal steam-fired
units of 100 MW or greater capacity (excepting circulating fluidized bed units (CFB)), new SNCR on coal-fired
units of less than 100 MW capacity and on CFBs of any capacity size, and SCR on oil/gas units greater than 100
MW that have historically emitted at least 150 tons of NOx per ozone season. The EPA evaluated the EGU sources
within the state of California and found there were no covered coal steam sources greater than 100 MW that would
have emissions reduction potential according to EPA's assumed EGU SCR retrofit mitigation technologies. The 19
states are: Arkansas, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, Nevada,
New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah, Virginia, and West Virginia.
e Covanta has developed a proprietary low NOx combustion system (LN™) that involves staging of combustion air.
The system is a trademarked system and Covanta has received a patent for the technology.
The illustrative emission budgets in this RIA represent EGU NOx ozone season emission
budgets for each state beginning in 2023.23 All three scenarios use emission budgets that were
developed using the selected level of uniform control stringency represented by $1,800 per ton of
NOx (2016$) in 2023 and $11,000 per ton of NOx (2016$) in 2026. The final rule and less-
stringent alternative scenarios defer the backstop emission rate for existing coal EGUs lacking
SCR controls in the 2030 run year,24 while the more stringent alternative imposes the backstop
emission rate on these units in the 2025 run year (reflective of imposition in the 2026 calendar
year). The backstop emission rate is imposed by these years (2025 or 2030 depending on
scenario) on all coal units within the 19-state region25 that are greater than 100 MW and lack
SCR controls (excepting circulating fluidized bed (CFB) units). Across all three scenarios,
optimization of existing controls and installation of state-of-the-art combustion controls (which
23 The budget setting process is described in section VLB. of the preamble and in detail in the Ozone Transport
Policy Analysis Final Rule Technical Support Document (TSD).
24 IPM uses model years to represent the full planning horizon being modeled. By mapping multiple calendar years
to a ran year, the model size is kept manageable. For this analysis, IPM maps the calendar year 2023 to ran year
2023, calendar years 2024-2026 to ran year 2025 and calendar years 2027-2029 to ran year 2028. For model details,
please see Chapter 2 of the IPM documentation, available at:
https://www.epa.gov/system/files/documents/2021 -09/epa-platform-v6-summer-2021 -reference-case-09-11-21-
v6.pdf
25 For the 19 states with EGU obligations that are linked in 2026 the EPA is determining that the selected EGU
control stringency also includes emissions reductions commensurate with the retrofit of SCR at coal steam-fired
units of 100 MW or greater capacity (excepting circulating fluidized bed units (CFB)), new SNCR on coal-fired
units of less than 100 MW capacity and on CFBs of any capacity size, and SCR on oil/gas units greater than 100
MW that have historically emitted at least 150 tons of NOx per ozone season. The EPA evaluated the EGU sources
within the state of California and found there were no covered coal steam sources greater than 100 MW that would
have emissions reduction potential according to EPA's assumed EGU SCR retrofit mitigation technologies. The 19
states are: Arkansas, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, Nevada,
New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah, Virginia, and West Virginia.
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reflect emission rate limits) is assumed in the 2023 run year (although the rule would not require
state of the art combustion control installation until 2024).
The state emission budgets in this RIA are illustrative for several reasons. First, they
reflect an estimate of the future budget based on the EPA's preset budget methodology
throughout the analytic time frame of the analysis. However, as described in the preamble, the
implemented state budget may be either the preset budget or the dynamic budget starting in
2026. Second, the budgets are illustrative as the utilized 2023 preset budgets reflect full
implementation of existing control optimization and upgrade to state-of-the-art combustion
control potential. However, the final rule state emission budgets and implementation allows the
limited number of reductions related to state-of-the-art combustion controls to be realized up
through 2024. Finally, the illustrative budgets reflected in this RIA reflect budgets derived using
the EPA's data and engineering analysis up through October 2022. The preset budgets reflected
in the final rule are slightly different in some cases due to new data or comment incorporation
that occurred between October of 2022 and January 2023. The Agency conducted additional
sensitivity analysis using IPM demonstrating that the substituting in the final preset state
emission budgets instead of the illustrative ones modeled made no significant difference in the
cost implications described in the body of the RIA.
For non-EGUs, the less stringent alternative assumes less stringent control technologies
for the reciprocating internal combustion engines in Pipeline Transportation of Natural Gas and
boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore Mining, Basic Chemical
Manufacturing, Petroleum and Coal Products Manufacturing, and Pulp, Paper, and Paperboard
Mills relative to the final rule. The more stringent alternative assumes more stringent control
technologies for the kilns in Cement and Concrete Products Manufacturing, the furnaces in Glass
and Glass Products Manufacturing, and the natural gas fired boilers in Iron and Steel Mills and
Ferroalloy Manufacturing, Metal Ore Mining, Basic Chemical Manufacturing, Petroleum and
Coal Products Manufacturing, and Pulp, Paper, and Paperboard Mills relative to the final rule.
See Section V.C. of the preamble for details on the emissions limits in the final rule.
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1.1.3 The Needfor 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 imposed on another party as a result of one's actions. For example, the smoke from a
factory may adversely affect the health of local residents and adversely affect the property in
nearby neighborhoods. Pollution emitted in one state may be transported across state lines and
affect air quality in a neighboring state.
From an economics perspective, achieving emissions reductions (i.e., by establishing the
EGU NOx ozone-season emissions budgets in this rule) through a market-based mechanism is a
straightforward and cost-effective remedy to address an externality in which firms emit
pollutants, resulting in health and environmental problems without compensation for those
incurring the problems. Capping emissions through allowance allocations incentivizes those who
emit the pollutants to reduce their emissions, which lessens the impact on those who suffer the
health and environmental problems from higher levels of pollution. In addition, emissions rates
for non-EGU sources work toward addressing this market failure by requiring affected facilities
to reduce NOx emissions.
1.2 Overview and Design of the RIA
1.2.1 Methodology for Identifying Needed Reductions
To apply the first and second steps of the CSAPR 4-step Interstate Transport Framework
to interstate transport for the 2015 ozone NAAQS, the EPA performed air quality modeling to
project ozone concentrations at air quality monitoring sites in 2023 and 2026. The EPA
evaluated projected ozone concentrations for the 2023 analytic year at individual monitoring
sites and considered current ozone monitoring data at these sites to identify receptors that are
anticipated to have problems attaining or maintaining the 2015 ozone NAAQS. This analysis was
then repeated using projected ozone concentrations for 2026. In these analyses, downwind air
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quality problems are defined by receptors that are projected to be unable to attain (i.e.,
nonattainment receptor) or maintain (i.e., maintenance receptor) the 2015 ozone NAAQS.26
To apply the second step of the Interstate Transport Framework, the EPA used air quality
modeling to quantify the contributions from upwind states to ozone concentrations in 2023 and
2026 at downwind receptors. Once quantified, the EPA then evaluated these contributions
relative to a screening threshold of 1 percent of the NAAQS. States with contributions that equal
or exceed 1 percent of the NAAQS are identified as warranting further analysis for significant
contribution to nonattainment or interference with maintenance.27 States with contributions
below 1 percent of the NAAQS are considered to not significantly contribute to nonattainment or
interfere with maintenance of the NAAQS in downwind states.
To apply the third step of the Interstate Transport Framework, the EPA applied a multi-
factor test to evaluate cost, available emissions reductions, and downwind air quality impacts to
determine the appropriate level ofNOx control stringency that addresses the impacts of interstate
transport on downwind nonattainment or maintenance receptors. The EPA used this multi-factor
assessment to gauge the extent to which emissions reductions are needed, and to ensure any
required reductions do not result in over-control.
For EGUs, in identifying levels of uniform control stringency the EPA assessed the same
NOx emissions controls that the Agency analyzed in the CSAPR Update and the Revised
CSAPR Update, all of which are considered to be widely available for EGUs: (1) fully operating
existing SCR, including both optimizing NOx removal by existing operational SCRs and turning
on and optimizing existing idled SCRs; (2) installing state-of-the-art NOx combustion controls;
(3) fully operating existing SNCRs, including both optimizing NOx removal by existing
operational SNCRs and turning on and optimizing existing idled SNCRs; (4) installing new
SNCRs; (5) installing new SCRs; and (6) generation shifting (i.e., emission reductions
anticipated to occur from generation shifting from higher to lower emitting units). The selected
26 See Section IV.D of the preamble for a full discussion of the final rule's approach to receptor identification,
including the consideration of "violating monitor" maintenance-only receptors.
27 The EPA assessed the magnitude of the maximum projected design value for 2023 at each receptor in relation to
the 2015 ozone NAAQS. Where the value exceeds the NAAQS, the EPA determined that receptor to be a
maintenance receptor for purposes of defining interference with maintenance. That is, monitoring sites with a
maximum design value that exceeds the NAAQS are projected to have a maintenance problem in 2023.
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levels of uniform control stringency were represented by $1,800 per ton of NOx (2016$) in 2023
and $11,000 per ton of NOx (2016$) in 2026.28
For non-EGUs, the EPA developed an analytical framework to determine which
industries and emission unit types to include in a proposed Transport FIP for the 2015 ozone
NAAQS transport obligations. A February 28, 2022 memorandum, titled Screening Assessment
of Potential Emissions Reductions, Air Quality Impacts, and Costs from Non-EGU Emissions
Units for 2026, documents the analytical framework used to identify industries and emissions
unit types included in the proposed FIP.29 To further evaluate the industries and emissions unit
types identified and to establish the proposed emissions limits, the EPA reviewed Reasonably
Available Control Technology (RACT) rules, New Source Performance Standards (NSPS) rules,
National Emissions Standards for Hazardous Air Pollutants (NESHAP) rules, existing technical
studies, rules in approved state implementation plan (SIP) submittals, consent decrees, and
permit limits. That evaluation is detailed in the Non-EGU Sectors Technical Support Document
(TSD) prepared for the proposed FIP.30 The EPA is retaining the industries and many of the
emissions unit types included in the proposal in this final action. For a discussion of changes to
emissions limits between the proposed FIP and the final rule, see Section V.C of the preamble to
the final rule and the Final Non-EGU Sectors TSD.
Below is a summary of the adjustments and additions to the emissions limits for non-
EGUs the EPA made between the proposed FIP and this final rule.
• For Pipeline Transportation of Natural Gas, the EPA is finalizing the same emissions
limits as proposed; however, the EPA is adjusting the applicability criteria to exclude
emergency engines. Further, to allow for the industry to install controls on the
engines with the largest potential for emissions reductions at cost-effective
thresholds, the final regulations allow for the use of facility-wide emissions averaging
for engines in the industry.
28 EGU NOx Mitigation Strategies Final Rule TSD, in the docket for this rule (Docket ID No. EPA-HQ-OAR-2021-
0688).
29 The memorandum is available in the docket here: https://www.regulations.gov/document/EPA-HQ-OAR-2021-
0668-0150.
30 The TSD is available in the docket here: https://www.regulations.gov/document/EPA-HQ-OAR-2021-0668-0145.
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• For Cement and Concrete Product Manufacturing, in the final rule the EPA has
removed the daily source cap limit, which could have resulted in an artificially
restrictive NOx emissions limit for affected cement kilns due to lower operating
periods resulting from to the COVID-19 pandemic.
• For Iron and Steel and Ferroalloy Manufacturing, the EPA is only finalizing a test-
and-set requirement for reheat furnaces premised on the installation of low-NOx
burners. By not finalizing the other proposed emissions limits that were likely to
require the installation of SCR, the EPA has addressed the various concerns regarding
the feasibility and cost-effectiveness of installation of the other proposed controls at
other unit types at these facilities.
• For Glass and Glass Product Manufacturing, the EPA is finalizing alternative
standards that apply during startup, shutdown, and idling conditions.
• For boilers in Iron and Steel and Ferroalloy Manufacturing, Metal Ore Mining, Basic
Chemical Manufacturing, Petroleum and Coal Products Manufacturing, and Pulp,
Paper, and Paperboard Mills, the EPA is finalizing a low-use exemption to eliminate
the need to install controls on low-use boilers that would have resulted in relatively
small reductions.
• For municipal waste combustors in Solid Waste Combustors and Incinerators, the
EPA is finalizing emissions limits, summarized in Table ES-1.
For the final rule, to determine NOx emissions reduction potential for the industries and
emissions unit types with the exception of Solid Waste Combustors and Incinerators, we used a
2019 inventory prepared from the emissions inventory system (EIS) to estimate a list of
emissions units captured by the applicability criteria for the final rule. For Solid Waste
Combustors and Incinerators, the EPA estimated the list for MWCs using the 2019 inventory, as
well as the NEEDS-v6-summer-2021 -reference-case workbook.31 Based on the review of RACT,
NSPS, NESHAP rules, as well as SIPs, consent decrees, and permits, we also assumed certain
control technologies could meet the final emissions limits.32 Rather than run the Control Strategy
Tool to estimate emissions reductions and costs, we programmed the assessment using R to
31 Available here: https://www.epa.gov/power-sector-modeling/national-electric-energy-data-system-needs-v6.
32 The Technical Support Document (TSD) for the Final Rule, Non-EGU Sectors TSD is available in the docket.
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estimate N0X emission reductions and their costs.33 Specifically, using the list of emissions units
estimated to be captured by the final rule applicability criteria, the assumed control technologies
that would meet the emissions limits, and information on control efficiencies and default cost/ton
values from the control measures database (CMDB),34 the EPA estimated NOx emissions
reductions and costs for the year 2026. We estimated emissions reductions using the actual
emissions from the 2019 emissions inventory. For additional details about the steps taken to
estimate emissions units, emissions reductions, and costs, see the memorandum titled "Summary
of Final Rule Applicability Criteria and Emissions Limits for Non-EGU Emissions Units,
Assumed Control Technologies for Meeting the Final Emissions Limits, and Estimated Emissions
Units, Emissions Reductions, and Costs'' available in the docket.
1.2.2 States Covered by the Rule
For EGUs, the Transport FIP for the 2015 ozone NAAQS requires EGUs in 22 states to
participate in the CSAPR NOx Ozone Season Group 3 Trading Program created by the Revised
CSAPR Update.35
• The following twelve states currently participating in the Group 3 Trading Program
would remain in the program, with revised provisions beginning in the 2023 ozone
season, under this rule: Illinois, Indiana, Kentucky, Louisiana, Maryland,
Michigan, New Jersey, New York, Ohio, Pennsylvania, Virginia, and West
Virginia.
• Affected EGUs in seven states currently covered by the CSAPR NOx Ozone
Season Group 2 Trading Program - Alabama, Arkansas, Mississippi, Missouri,
Oklahoma, Texas, and Wisconsin - would transition from the Group 2 program to
the revised Group 3 trading program beginning with the 2023 control period.
33 R is a free software environment for statistical computing and graphics. Additional information is available here:
https://www.r-project.org/. The R code that processed the data to estimate the emissions reductions and costs is
available upon request.
34 More information about the Control Strategy Tool (CoST) and the control measures database (CMDB) can be
found at the following link: https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-
analysis-modelstools-air-pollution.
35 As explained in Section V.C.I of the preamble, the EPA finds that EGU sources within the State of California are
sufficiently controlled such that no further emissions reductions are needed from them to eliminate significant
contribution to downwind states.
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• Affected EGUs in three states not currently covered by any CSAPR trading
program for seasonal NOx emissions - Minnesota, Nevada, and Utah - would enter
the Group 3 trading program in the 2023 control period following the effective date
of this final rule.
In addition, the EPA is revising other aspects of the Group 3 trading program to provide
improved environmental outcomes and increase compliance, as described in Section VI of the
preamble. Revisions include dynamic adjustments of the emissions budgets over time and a
backstop daily emission rate for most coal-fired units, along with an adjustment to the total size
of the allowance bank. The final rule does not revise the budget stringency and geography of the
existing CSAPR NOx Ozone Season Group 1 trading program.
Aside from the seven states moving from the Group 2 trading program to the Group 3
trading program under the rule, this action otherwise leaves unchanged the budget stringency of
the existing CSAPR NOx Ozone Season Group 2 trading program.
For non-EGUs, the rule includes NOx emissions limitations with an initial compliance date
of May 1, 2026, applicable to certain non-EGU stationary sources in 20 states: Arkansas,
California, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri,
Nevada, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah, Virginia, and
West Virginia.
1.2.3 Regulated Entities
The rule affects EGUs in 22 states that have a nameplate capacity of greater than 25
megawatts (MWe), which generally fall in 22 states within the utility sector (electric, natural gas,
other systems) classified as code 221112 by the North American Industry Classification System
(NAICS). In addition, the rule affects certain non-EGUs in 20 states in the following industries,
as defined by 4- or 6-digit NAICS: Pipeline Transportation of Natural Gas, 4862; Cement and
Concrete Product Manufacturing, 3273; Iron and Steel Mills and Ferroalloy Manufacturing,
3311; Glass and Glass Product Manufacturing, 3272; Metal Ore Mining, 2122; Basic Chemical
Manufacturing, 3251; Petroleum and Coal Products Manufacturing, 3241; Pulp, Paper, and
Paperboard Mills, 3221; Solid Waste Combustors and Incinerators, 562213. For additional
discussion of the non-EGUs affected, see Section V.C. of the preamble.
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1.2.4 Baseline and Analysis Years
As described in the preamble, the EPA aligns implementation of this rule with relevant
attainment dates for the 2015 ozone NAAQS. The rule requires emissions reductions to be
achieved as expeditiously as practicable and, to the extent possible, by the next applicable
nonattainment dates for downwind areas for the 2015 ozone NAAQS. Thus, initial emissions
reductions from EGUs will be required beginning in the 2023 ozone season and prior to the
August 3, 2024, attainment date for areas classified as Moderate nonattainment for the 2015
ozone NAAQS. The remaining emissions reduction obligations will be phased in as soon as
possible thereafter. Substantial additional reductions from potential new post-combustion control
installations at EGUs as well as from installation of new pollution controls at non-EGUs will
phase in beginning in the 2026 ozone season, associated with the August 3, 2027, attainment date
for areas classified as Serious nonattainment for the 2015 ozone NAAQS. The final rule will
allow individual facilities limited additional time to fully implement the required emissions
reductions. For EGUs, the emissions trading program budget stringency associated with retrofit
of post-combustion controls will be phased in over two ozone seasons (2026-2027). For
industrial sources, the final rule provides a process for individual facilities to seek a one-year
extension, with the possibility of up to two additional years, based on a specific showing of
necessity. More information regarding the timing elements of the rule can be found in Section
VI.A of the preamble.
To develop and evaluate control strategies for addressing these obligations, it is important
to first establish a baseline projection of air quality in the analysis years of 2023 and 2026, taking
into account currently on-the-books Federal regulations, enforcement actions, state regulations,
population, and where possible, economic growth.36 Establishing this baseline for the analysis
then allows us to estimate the incremental costs and benefits of the additional emissions
reductions that will be achieved by the rule. Federal rules included in the baseline are: the
Revised Cross-State Air Pollution Rule (CSAPR) Update, the Standards of Performance for
Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources,
Reciprocating Internal Combustion Engine (RICE) New Source Performance Standards (NSPS),
36 The technical support document (TSD) for the 2016v2 emissions modeling platform titled Preparation of
Emissions Inventories for the 2016v2 North American Emissions Modeling Platform is included in the docket for
this rule. The TSD includes additional discussion on mobile source rules included in the baseline.
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Natural gas turbines NSPS, Greenhouse Gas Emissions Standards and Fuel Efficiency Standards
for Medium- and Heavy-Duty Engines and Vehicles - Phase 2, and 2017 and Later Model Year
Light-Duty Vehicle GHG Emissions and Corporate Average Fuel Economy Standards.
The analysis in this RIA focuses on benefits, costs and certain impacts from 2023 through
2042. We focus on 2023 because it is by the 2023 ozone season, corresponding with the 2024
attainment date for areas classified as Moderate nonattainment, that significant contribution from
upwind states' must be eliminated to the extent possible. In addition, impacts for 2026 are
important because this ozone season corresponds with the 2027 Serious area attainment date and
it is by this ozone season that additional requirements for NOx emissions reductions for EGUs
and non-EGUs begin to apply for states whose upwind linkage to downwind receptors persists.
The EPA's analysis for the third step of the Interstate Transport Framework reflects emissions
reductions for 2023 from EGUs based on a control stringency at a representative cost threshold
of $1,800 per ton. Those reductions are commensurate with optimization of existing SCRs and
SNCRs and installation of state-of-the-art combustion controls. For 2026, the selected control
stringency (at a representative cost per ton threshold for EGUs of $11,000 and an overall
estimated average cost per ton for non-EGUs of $5,339/ton (2106$), with average cost by
industry ranging from $939/ton to $14,595/ton) includes additional EGU controls and estimated
non-EGU emissions reductions. See Section V.D of the preamble for additional discussion.
Additional benefits and costs are expected to occur after 2026 as EGUs subject to this rule
continue to comply with the tighter allowance budget, which is below their baseline emissions,
and non-EGUs remain subject to ozone season emissions limits.
The Public Law 117-169, 136 Stat. 1818 (August 16, 2022), commonly known as the
Inflation Reduction Act of 2022 (IRA) includes significant additional new generation incentives
targeting more efficient and lower-emitting sources of generation that is likely to meaningfully
affect the US generation mix in the future and increase the pace of new lower-emitting
generation replacing some of older higher-emitting generating capacity.
In addition, we include an appendix to Chapter 4 to describe the EGU compliance
behavior, costs, and emissions reductions that include adjustments made to the IPM baseline to
account for the potential effects of the IRA of 2022 on the power sector costs, emission
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reductions, and other impacts from this final rule. This supplementary analysis quantifies the
incremental impacts of the Transport FIP for the 2015 ozone NAAQS under this alternative
baseline characterization and compares impacts to the main analyses in Chapter 4. As described
in Chapter 4, the power sector analyses that inform air quality modeling in subsequent chapters
in this RIA do not include the IRA due to time limitations. However, in the interests of
completeness the appendix seeks to quantify the impacts of the IRA on the analyses of power
sector impacts of the final rule.
1.2.5 Emissions Controls, Emissions, and Cost Analysis Approach
The EPA estimated the effects of the EGU control strategies in the final rule, including
their projected compliance costs, using the Integrated Planning Model (IPM), as well as certain
costs that are estimated outside the model but use IPM inputs for their estimation. These cost
estimates reflect costs incurred by the power sector and include (but are not limited to) the costs
of purchasing, installing, and operating NOx control technology, changes in fuel costs, and
changes in the generation mix. A description of the methodologies used to estimate the costs and
economic impacts to the power sector is contained in Chapter 4 of this RIA. This analysis also
provides estimates of NOx emissions changes during the May through September ozone season
and year-round, as well as annual emissions changes in PM2.5, SO2, and carbon dioxide (CO2)
due to changes in power sector operation.
As described in Section 1.2.1 for non-EGUs, to determine NOx emissions reduction
potential for the industries and emissions unit types, except for Solid Waste Combustors and
Incinerators, we used a 2019 inventory prepared from the emissions inventory system (EIS) to
estimate a list of emissions units captured by the applicability criteria for the final rule and
programmed the assessment's estimated emission reductions and costs using R.37 For Solid
Waste Combustors and Incinerators, the EPA estimated the list for MWCs using the 2019
inventory, as well as the NEEDS-v6-summer-2021 -reference-case workbook. The EPA did not
run the Control Strategy Tool (CoST) to estimate emissions reductions.
37 R is a free software environment for statistical computing and graphics. Additional information is available here:
https://www.r-pr0ject.0rg/.The R code that processed the data to estimate the emissions reductions and costs is
available upon request.
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Using the list of emissions units estimated to be captured by the applicability criteria, the
assumed control technologies that would meet the emissions limits, and information on control
efficiencies and default cost/ton values from the control measures database (CMDB)38'39, the
EPA estimated NOx emissions reductions and costs for the year 2026. We estimated emissions
reductions using the actual emissions from the 2019 emissions inventory. The EPA did not
estimate emissions reductions of SO2, PM2.5, CO2 and other pollutants that may be associated
with controls on non-EGU emissions units. In the assessment, we matched emissions units by
Source Classification Code (SCC) from the inventory to the applicable control technologies in
the CMDB. We modified SCC codes as necessary to match control technologies to inventory
records. For additional details about the steps taken to estimate emissions units, emissions
reductions, and costs, see the memorandum titled Summary of Final Rule Applicability Criteria
and Emissions Limits for Non-EGU Emissions Units, Assumed Control Technologies for Meeting
the Final emissions Limits, and Estimated Emissions Units, Emissions Reductions, and Costs
available in the docket.
1.2.6 Benefits Analysis Approach
Implementing the Transport FIP for the 2015 ozone NAAQS is expected to reduce
emissions of PM2.5, NOx and SO2 throughout the year. Because NOx and SO2 are also precursors
to 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. In addition, we estimate the climate benefits of CO2 emissions reductions expected from
this final rule using the SC-CO2 estimates.
38 More information about the Control Strategy Tool (CoST) and the control measures database (CMDB) can be
found at the following link: https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-
analysis-modelstools-air-pollution.
39 The estimates using the 2019 inventory and information from the CMDB identify proxies for emissions units, as
well as emissions reductions, and costs associated with the assumed control technologies that would meet the final
emissions limits. Emissions units subject to the final rule emissions limits may be different than those estimated in
this assessment; the estimated emissions reductions from and costs to meet the final rule emissions limits may be
different than those estimated in this assessment. The costs do not include monitoring, recordkeeping, reporting, or
testing costs.
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Organization of the Regulatory Impact Analysis
This RIA is organized into the following remaining chapters:
Chapter 2: Industry Sector Profiles. This chapter describes the electric power sector in
detail, as well as provides an overview of the other non-EGU industries.
Chapter 3: Air Quality Impacts. The data, tools, and methodology used for the air quality
modeling are described in this chapter, as well as the post-processing techniques used to
produce air quality metric values for input into the analysis of benefits and costs.
Chapter 4: Cost, Emissions, and Energy Impacts. The chapter summarizes the data
sources and methodology used to estimate the costs and other impacts incurred by the
power sector. The chapter summarizes the non-EGU assessment used to estimate
emissions reductions and costs for the non-EGU industries.
Chapter 5: Benefits. The chapter presents the health-related benefits of the ozone and PM
related air quality improvements and the climate benefits of CO2 emissions reductions.
Chapter 6: Economic Impacts. The chapter includes a discussion of small entity,
economic, and labor impacts.
Chapter 7: Environmental Justice Impacts. This chapter includes an assessment of
downwind ozone impacts across communities with potential environmental justice
concerns.
Chapter 8: Comparison of Benefits and Costs. The chapter compares estimates of the
total benefits with total costs and summarizes the net benefits of the three regulatory
control alternatives analyzed.
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CHAPTER 2: INDUSTRY SECTOR PROFILES
Overview
This chapter discusses important aspects of the regulated industries that relate to the final
rule with respect to the interstate transport of emissions of nitrogen oxides (NOx) that contribute
significantly to nonattainment or interfere with maintenance of the 2015 ozone NAAQS in
downwind states. This chapter describes types of existing power-sector sources affected by the
regulation and provides background on the power sector and electricity generating units (EGUs).
In addition, this chapter also briefly describes the relevant non-EGU industries included in the
regulation.
2.1 Background
In the past decade there have been significant 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 normal
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 by renewable and
unconventional methods. Many of these trends will continue to contribute to the evolution of the
power sector. 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 as base load energy in addition to supplying electricity during peak load. Additionally rapid
growth in the penetration of renewables has led to their now constituting a significant share of
generation. This chapter presents data on the evolution of the power sector from 2014 through
2021. Projections of future power sector behavior and the impact of this proposed rule are
discussed in more detail in Chapter 4 of this RIA.
2.2 Power Sector Overview
The production and delivery of electricity to customers consists of three distinct
segments: generation, transmission, and distribution.
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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
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. 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. Units are also unavailable
during routine and unanticipated outages for maintenance. These factors result in the mix of
generating capacity types available (e.g., the share of capacity of each type of EGU) being
substantially different than the mix of the share of total 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 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
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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 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 over 2015-2021.
In 2021 the power sector comprised a total capacity40 of 1,179 GW, an increase of 105
GW (or 10 percent) from the capacity in 2015 (1,074 GW). The largest change over this period
was the decline of 70 GW of coal capacity, reflecting the retirement/rerating of over a third of
the coal fleet. This reduction in coal capacity was offset by an increase in natural gas capacity of
52 GW, and an increase in solar (48 GW) and wind (60 GW) capacity over the same period.
Additionally, significant amounts of distributed solar (23 GW) were also added.
Table 2-1. Total Net Summer Electricity Generating Capacity by Energy Source, 2014
and 2021
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
Note: This table presents generation capacity. Actual net generation is presented in Table 2-2.
Source: EIA. Electric Power Annual 2022, Tables 4.2
40 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
chapter (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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The information in Table 2-1 presents information about the generating capacity in the
entire U.S. The Federal Implementation Plan (FIP) Addressing Regional Ozone Transport for the
2015 Ozone National Ambient Air Quality Standards (Transport FIP for the 2015 ozone
NAAQS), however, directly affects EGUs in 22 eastern states. The share of generating capacity
from each major type of generation differs between the FIP for the 2015 NAAQS Ozone Region
and the rest of the U.S. (non-region). Figure 2-1 shows the mix of generating capacity for each
region. In 2021, the overall capacity in the Transport FIP for the 2015 Ozone NAAQS Region is
56 percent of the national total, reflecting the larger total population in the region. The mix of
capacity is noticeably different in the two regions. In the Transport FIP for the 2015 Ozone
NAAQS Region in 2020, coal makes up a significantly larger share of total capacity (23 percent)
than it does in the rest of the country (16 percent). The share of natural gas in the Transport FIP
for the 2015 Ozone NAAQS Region is 50 percent as compared to 41 percent in the rest of the
country. The difference in the share of coal's capacity is primarily balanced by relatively more
hydro, wind, and solar capacity in the rest of country compared to the Transport FIP for the 2015
Ozone NAAQS Region.
700
600
¦ Wind & Solar
500
¦ Nuclear
400
¦ Hydro
¦ Other
300
200
¦ Gas
100
¦ Coal
0
In-Region
Non-Region
Figure 2-1. Regional Differences in Generating Capacity (GW), 2021
Source: NEEDSv6.21
In 2021, electric generating sources produced a net 4,157 TWh to meet national
electricity demand, which was around 2% higher than 2015. 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
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share from fossil fuels in 2021 (60%) was 11% less than the share in 2010 (69%). Moreover, the
share of fossil generation supplied by coal fell from 65% in 2010 to 36% by 2021, while the
share of fossil generation supplied by natural gas rose from 35% to 64% 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.
Table 2-2. Net Generation in 2015 and 2021 (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
1,333
33%
1,579
38%
18%
246
Nuclear
797
19%
778
19%
-2%
-19
Hydro
244
6%
246
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: EIA. Electric Power Annual 2022, Tables 3.2
The average age of coal-fired power plants that have retired between 2015 and 2021 is
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 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.41 As shown in Figure 2-2 below, 65% of the coal fleet in 2021 had an average age
of over 40 years.
41 EIA, Today in Energy (April 17, 2017) available at lutps://\vww.cia.gov/todavincncrgy/detail.php'.'id=30812
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Figure 2-2. National Coal-fired Capacity (GW) by Age of EGU, 2021
Source: NEEDS v6
Coal-fired and nuclear generating units have historically supplied "base load" electricity,
the portion of electricity loads that are continually present and typically operate throughout all
hours of the year. Although much of the coal fleet has historically operated as base load, there
can be notable differences across various facilities (see Table 2-3). For example, 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. Gas-fired generation is better able to vary
output and is the 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. Moreover, as shown in Figure 2-3,
average annual coal capacity factors have declined from 67% to 49% over the 2010-2021 period,
indicating that a larger share of units are operating in non-baseload fashion. Over the same
period, natural gas capacity factors have risen from an annual average of 28% to 37%.
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70%
< 20%
10%
0%
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
— — —Coal — — —Natural Gas
Figure 2-3. Average Annual Capacity Factor by Energy Source
Source: EIA. Electric Power Annual 2022, Tables 3.2 and 4.2
Table 2-3 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.
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Table 2-3. Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Average
Heat Rate in 2020
Avg. Net
Total Net
Unit Size
Summer
Summer
Avg. Heat
Grouping
% of All
Capacity
Capacity
% Total
Rate
(MW) No. Units
Units
Avg. Age
(MW)
(MW)
Capacity
(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 2-4 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 2-4 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 2-4. Cumulative Distribution in 2019 of Coal and Natural Gas Electricity Capacity
and Generation, by Age
Source: eGRID 2020 (January 2022 release from EPA eGRlD 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 2-5.
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Figure 2-5. 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
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,42 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
regional operator;43 in others, individual utilities44 coordinate the operations of their generation,
42 These three network interconnections are the Western Interconnection, comprising the western parts of both the
US and Canada (approximately the area to the west of the Rocky Mountains), the Eastern Interconnection,
comprising the eastern parts of both the US 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/PublisliingImages/NERC%20Interconnections.pdf.
43 For example, PMJ Interconnection. LLC, Western Area Power Administration (which comprises 4 sub-regions).
44 For example, Los Angeles Department of Power and Water. Florida Power and Light.
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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. As discussed below, 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.
2.3 Sales, Expenses, and Prices
These 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 produced45 (see Table 2-4). Some of these uses are highly
variable, such as heating and air conditioning in residential and commercial buildings, while
45 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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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 2015 and 2021.
Table 2-4. Total U.S. Electric Power Industry Retail Sales, 2015 and 2021 (billion kWh)
2015
2021
Sales/Direct
Use (Billion Share of Total
kWh) End Use
Sales/Direct
Use (Billion Share of Total
kWh) End Use
Residential
Sales Commercial
Industrial
Transportation
1,404 36%
1,361 35%
987 25%
8 0%
1,470 37%
1,328 34%
1,001 25%
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, 2021
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.
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 both of the necessary extensive distribution
network reaching to virtually every part of the country and every building, and also the fact that
generating stations are increasingly located relatively far from population centers (which
increases 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 historically have been less
variable. Overall industrial customer prices are usually considerably closer to the wholesale
marginal cost of generating electricity than residential and commercial prices.
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On a state-by-state basis, all retail electricity prices vary considerably. In 2021, the
national average retail electricity price (all sectors) was 11.18 cents/KWh, with a range from 7.5
cents (Louisiana) to 27 cents (Hawaii).46
Average national retail electricity prices decreased between 2010 and 2021 by 8 percent
in real terms (2019$), and 5% between 2015-21.47 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. The real year prices for 2010 through 2021 are shown in Figure 2-6.
14
QJ 6
o
s—
o_
>- 4
u
u z
_OJ
LU
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 2021, Table 2.4.
Most of these electricity price decreases occurred between 2014 and 2015, when nominal
residential electricity prices followed inflation trends, while nominal commercial and industrial
electricity prices declined. The years 2016 and 2017 saw an increase in nominal commercial and
industrial electricity prices, while 2018 and 2019 saw flattening of this growth. Industrial
electricity prices declined in 2019 and 2020 due to the effects of the pandemic. Prices rose in
2021 as a result of higher input fuel prices and increasing demand. The increase in nominal
46 EIA State Electricity Profiles with Data for 2021 (http://www.eia.gov/electricity/state/)
47 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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electricity prices for the major end use categories, as well as increases in the GDP price index for
comparison, are shown in Figure 2-7.
25%
4
/
20% /'
-5%
Residential Commercial Industrial — — —GDP Price
Figure 2-7. Relative Increases in Nominal National Average Electricity Prices for Major
End-Use Categories (including taxes), With Inflation Indices
Source: EIA. Electric Power Annual 2021, Table 2.4.
2.3.2 Prices of Fossil Fuels Usedfor Generating Electricity
Another important factor in the changes in electricity prices are the changes in delivered
fuel prices48 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$) 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 2-8 shows the relative changes
in real price of all 3 fossil fuels between 2010 and 2021.
48 Fuel prices in this section are all presented in terms of price per MMBtu to make the prices comparable.
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40%
20%
o
H
O
-80%
Coal Petroleum Natural Gas
Figure 2-8. 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 2015 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 2-9 shows the growth of electricity generation, population
and real GDP during this period.
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25%
Real GDP Generation Population
Figure 2-9. Relative Growth of Electricity Generation, Population and Real GDP Since
2014
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 2-10.
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25%
20%
-15% • ~~ -s.
-20%
-25%
Real GDP / Capita Generation / Capita Generation / Real GDP
Figure 2-10. Relative Change of Real GDP, Population and Electricity Generation Intensity
Since 2014
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.
2.4 Industrial Sectors Overview
The final rule establishes various ozone season NOx emission limits beginning in 2026,
including emissions limits for reciprocating internal combustion engines in Pipeline
Transportation of Natural Gas; for kilns in Cement and Cement Product Manufacturing; for
reheat furnaces in Iron and Steel Mills and Ferroalloy Manufacturing; for furnaces in Glass and
Glass Product Manufacturing; for boilers in Iron and Steel Mills and Ferroalloy Manufacturing,
Metal Ore Mining, Basic Chemical Manufacturing, Petroleum and Coal Products Manufacturing,
and Pulp, Paper, and Paperboard Mills; and combustors or incinerators in Solid Waste
Combustors and Incinerators.49 Figure 2-11 shows the locations50 of the estimated non-EGU
emissions reductions by industry. For additional discussion of the emissions limits, see Section
I.B. of the preamble. The following sections provide overviews of these industries. For
additional information on these non-EGU industries please see the Final Non-EGU Sectors TSD
in the docket.
49 Boilers with design capacity of 100 lmnBtu/lir or greater.
511 Facility location information is based on the 2019 inventory, which is discussed in Chapter 4, Section 4.5.4.
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Non-EGU Ozone Season NOx Reductions
O 500-1000 tons # Cement and Concrete Product Manufacturing
O 100-500 tons O Glass and Glass Product Manufacturing
° Under 100 tons O Iron and Steel Mills and Ferroalloy Manufacturing
O Pipeline Transportation of Natural Gas
# Applicable Boilers from Affected Industries
# Municipal Waste Combustors
Concrete Glass Transportation
and
Product Product B M_ _ of Natural
Gas
Figure 2-11. Geographical Distribution of Non-EGU Ozone Season NOx Reductions and
Summary of Reductions by Industry and by State
2.4.1 Cement and Cement Product Manufacturing
Hydraulic cement (primarily portland cement) is a key component of an important
construction material: concrete. Concrete is used in a wide variety of applications (e.g.,
residential and commercial buildings, public works projects), and cement demand is influenced
by national and regional trends in these sectors.
Portland cement is a fine powder, gray or white in color, that consists of a mixture of
hydraulic cement materials comprising primarily calcium silicates, aluminates and alumino-
ferrites. More than 30 raw materials are known to be used in the manufacture of portland cement,
and these materials can be divided into four distinct categories: calcareous, siliceous,
argillaceous, and ferriferous (containing iron). These materials are chemically combined through
pyroprocessing (heat) and subjected to subsequent mechanical processing operations to form
gray and white portland cement. Gray portland cement is used for structural applications and is
the more common type of cement produced. White portland cement has lower iron and
manganese contents than gray portland cement and is used primarily for decorative purposes.
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There are two processes for manufacturing cement: the wet process and the dry process.
In the wet process, water is added to the raw materials during the blending process and before
feeding the mixture into the rotary kiln. In contrast, the dry process feeds the blended material
directly into the rotary kiln in a dry state. Newer dry process plants also use preheater and
precalciner technologies that partially heat and calcine the blended raw materials before they
enter the rotary kiln. These technologies can increase the overall energy efficiency of the cement
plant and reduce production costs. The fuel efficiency differences between the wet and dry
processes have led to a substantial decline in clinker capacity provided by the wet process over
the last 3 decades. (Van Oss and Padovani, 2002). The number of wet process plants fell from 32
in 2000 to 7 in 2017 (DOI, USGS, 2020).
Cement kilns are used by the cement industry in the production of cement. Portland
cement, used in almost all construction applications, is the industry's primary product.
Essentially all of the NOx emissions associated with cement manufacturing are generated in the
kilns because of high process temperatures. To manufacture cement, raw materials such as
limestone, cement rock, sand, iron ore, clay and shale are crushed, blended, and fed into a kiln.
These materials are then heated in the kiln to temperatures above 2900°F to induce a chemical
reaction (called "fusion") that produces cement "clinker," a round, marble-sized, glass-hard
material. The clinker is then cooled, mixed with gypsum and ground to produce cement. Clinker
is also defined as the product of a portland cement kiln from which finished cement is
manufactured by milling and grinding.
Nearly all cement clinker is produced in large rotary kiln systems. The rotary kiln is a
refractory brick lined cylindrical steel shell equipped with an electrical drive to rotate it at 1-3
revolutions per minute, through which hot combustion gases flow counter-currently to the feed
materials. The kiln can be fired with coal, oil, natural gas, waste (e.g., solvents) or a combination
of these fuels. There are various types of kilns in use, including long wet kilns, long dry kilns,
kilns with a preheater and kilns with a precalciner. The long wet and dry kilns and most
preheater kilns have only one fuel combustion zone, whereas the newer precalciner kilns and
preheater kilns with a riser duct have two fuel combustion zones.
In a wet kiln, the ground raw materials are suspended in water to form a slurry and
introduced into the inlet feed. This kiln type employs no preheating of the dry feed. In a long dry
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kiln, the raw materials are dried to a powder and introduced into the inlet feed in a dry form, but
this kiln type employs no preheating of the dry feed. Currently more cement plants use the dry
process because of its lower energy requirement. In a precalciner kiln, the feed to the kiln system
is preheated in cyclone chambers; the kiln uses a second burner to calcine material in a separate
vessel attached to the preheater before the final fusion in a kiln that forms clinker.
Because the typical operating temperatures of these kilns differ, the NOx formation
mechanisms also differ among these kiln types. In a primary combustion zone at the hot end of a
kiln, the high temperatures lead to predominantly thermal NOx formation. In the secondary
combustion zone, however, lower gas-phase temperatures suppress thermal NOx formation. The
temperatures at which these kilns operate influence what NOx control technologies can be
applied. For instance, SNCR can operate effectively at typical cement kiln temperatures (above
1500°F), while SCR typically operates effectively at lower temperatures (550-800°F). Energy
efficiency is also important in reducing NOx emissions; for example, a high thermal efficiency
equates to less heat and fuel being consumed and, therefore, less NOx is produced.
Portland cement is produced using a combination of variable inputs such as raw
materials, labor, electricity, and fuel. U.S. Census data for the cement industry (North American
Industry Classification System [NAICS] 32731: cement manufacturing) provides an initial
overview of aggregated industry expenditures on these inputs (Department of Commerce [DOC],
Bureau of the Census, 2021). In 2019, the total value of shipments was $9 billion, and the
industry spent approximately $1.5 billion on materials, parts, and packaging, or 16.6% of the
value of shipments. Total compensation for all employees (includes payroll and fringe benefits)
amounted to $1.4 billion (15.6%) and included 15,590 employees.
A review and description of market characteristics (i.e., degree of concentration, entry
barriers, and product differentiation) can enhance our understanding of how U.S. cement markets
operate. These characteristics provide indicators of a firm's ability to influence market prices by
varying the quantity of cement it sells. For example, in markets with large numbers of sellers and
identical products, firms are unlikely to be able to influence market prices via their production
decisions (i.e., they are "price takers"). However, in markets with few firms, significant barriers
to entry (e.g., licenses, legal restrictions, or high fixed costs), or products that are similar but can
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be differentiated, the firm may have some degree of market power (i.e., set or significantly
influence market prices).
Cement sales are often concentrated locally among a small number of firms for two
reasons: high transportation costs and production economies of scale. Transportation costs
significantly influence where cement is ultimately sold; high transportation costs relative to unit
value provide incentives to produce and sell cement locally in regional markets (USITC, 2006).
To support this claim, the empirical literature has typically pointed to Census of Transportation
data showing over 80% of cement shipments were made within a 200-mile radius (Jans and
Rosenbaum, 1997) and reported evidence of high transportation costs per dollar of product value
from case studies (Ryan, 2006). The cement industry is also very capital intensive, and entry
requires substantial investments. In addition, large plants are typically more economical because
they can produce cement at lower unit costs; this reduces entry incentives for small sized cement
plants and firms. EPA has recognized these aspects of the cement industry and its market
structure in its economic impact analyses of rules on this industry in previous reports, such as the
RIA prepared in 2010 for the portland cement NESHAP and NSPS (EPA, 2010).
2.4.2 Iron and Steel Mills and Ferroalloy Manufacturing
Iron is produced from iron ore, and steel is produced by progressively removing
impurities from iron ore or ferrous scrap. The first step is iron making. Primary inputs to the iron
making process are iron ore or other sources of iron, coke or coal, and flux. Pig iron is the
primary output of iron making and the primary input to the next step in the process, steel making.
Metal scrap and flux are also used in steel making. The steel making process produces molten
steel that is shaped into solid forms at forming mills. Finishing mills then shape, harden, and
treat the semi-finished steel to yield its final marketable condition.
Steel often undergoes additional, referred to as secondary, metallurgical processes after it
is removed from the steel making furnace. Secondary steel making takes place in vessels, smaller
furnaces, or the ladle. These sites do not have to be as strong as the primary refining furnaces
because they are not required to contain the powerful primary processes. Secondary steel making
can have many purposes, such as removal of oxygen, sulfur, hydrogen, and other gases by
exposing the steel to a low-pressure environment; removal of carbon monoxide through the use
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of deoxidizers such as aluminum, titanium, and silicon; and changing of the composition of
unremovable substances such as oxides to further improve mechanical properties.
In 2019, the United States produced 87.8 million metric tons of steel (USGS, 2019). Steel
is primarily used as a major input to consumer products such as automobiles and appliances.
Therefore, the demand for steel is a derived demand that depends on a diverse base of consumer
products. In addition, the Infrastructure Investment and Jobs Act, signed into law in 2021, will
likely increase demand in both the iron and steel industry as well as the concrete and cement
industry. The historic investment in roads, bridges, airports, and other physical infrastructure
around the country will require large inputs from these industries.
U.S. Census data for the iron and steel industry (North American Industry Classification
System [NAICS] 331110: Iron and steel mills and ferroalloy manufacturing) provides an initial
overview of aggregated industry expenditures on these inputs (Census Bureau, 2021). In 2019,
the total value of shipments was $93.7 billion, and the industry spent approximately $56.4 billion
on materials, parts, and packaging, or 60% of the value of shipments. Total compensation for all
employees (includes payroll and fringe benefits) amounted to $10.1 billion (10.8%) and included
85,707 employees.
2.4.3 Glass and Glass Product Manufacturing
Commercially produced glass can be classified as soda-lime, lead, fused silica,
borosilicate, or 96 percent silica. Soda-lime glass consists of sand, limestone, soda ash, and cullet
(broken glass). The manufacturing of such glass occurs in four phases: (1) preparation of raw
material, (2) melting in the furnace, (3) forming and (4) finishing. The products of the glass
manufacturing industry are flat glass, container glass, and pressed and blown glass. The
procedures for manufacturing glass are the same for all products except forming and finishing.
Container glass and pressed and blown glass use pressing, blowing, or pressing and blowing to
form the desired product. Flat glass, which is the remainder, is formed by float, drawing, or
rolling processes.
As the sand, limestone, and soda ash raw materials are received, they are crushed and
stored in separate elevated bins. These materials are then transferred through a gravity feed
system to a weigher and mixer, where the material is mixed with cullet to ensure homogeneous
melting. The mixture is conveyed to a batch storage bin where it is held until dropped into the
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feeder to the glass melting furnace. All equipment used in handling and preparing the raw
material is housed separately from the furnace and is usually referred to as a batch plant.
The glass melting furnaces contribute to most of the total emissions from the glass plant.
Essentially all the NOx emissions associated with glass manufacturing are generated in the
melting furnaces due to the high process temperatures. These materials are then heated in the
furnace to temperatures around 3000°F to induce fusion that produces molten glass. After molten
glass is produced, it then goes to be shaped by pressing, blowing, pressing and blowing, drawing,
rolling, or floating to produce the desired product. The end products undergo finishing
(decorating or coating) and annealing (removing unwanted stress area in the glass) as required.
During the inspection process, any damaged or undesirable glass is transferred back to the batch
plant to be used as cullet.
Glass manufacturing furnaces can vary between the various categories of glass produced
(container, flat, or pressed/blown). This is because the different types of glass vary in
composition and quality specifications. Therefore, each type of glass produced requires different
energy inputs to fuse the raw materials. As a result, the emissions from similar furnaces
producing different types of glass can vary significantly. Furnaces can also be fired with gaseous
or liquid fuels.
U.S. Census data for the glass manufacturing industry (North American Industry
Classification System [NAICS] 32721) provides an initial overview of aggregated industry
expenditures on these inputs (Census Bureau, 2021). In 2019, the total value of shipments was
$27.6 billion, and the industry spent approximately $10.9 billion on materials, parts, and
packaging, or 40% of the value of shipments. Total compensation for all employees (includes
payroll and fringe benefits) amounted to $5.3 billion and included 91,988 employees.
2.4.4 Pipeline Transportation of Natural Gas
This industry comprises establishments primarily engaged in the pipeline transportation
of natural gas from processing plants to local distribution systems. This industry includes the
storage of natural gas because the storage is usually done by the pipeline establishment and
because a pipeline is inherently a network in which all the nodes are interdependent.
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U.S. Census data for the pipeline transportation of natural gas industry (North American
Industry Classification System [NAICS] 486210) provides an initial overview of aggregated
industry expenditures on these inputs (Census Bureau, 2021). In 2019, the total value of
shipments was $27.6 billion, annual payroll totaled $3.3 billion, and the industry included 27,294
employees.
2.4.5 Industrial Boilers
This rulemaking includes NOx emission limits on boilers from an additional five
industries. One of those industries is Iron and Steel Mills and Ferroalloy Manufacturing, which
was discussed above; the remaining four industries are discussed briefly below.
This first industry is Metal Ore Mining. Taconite, the principal iron ore mined in the
United States, has a low (20 percent to 30 percent) iron (Fe) content and is found in hard, fine-
grained, banded iron formations. The main taconite iron ore deposits are located near Lake
Superior in Minnesota (Mesabi Iron Range) and Michigan (Marquette Iron Range). The taconite
mining operations in Michigan and Minnesota accounted for virtually all domestic iron ore
production (Kirk, 1999).
The next industry is the pulp, paper, and paperboard mills industry. Manufacturing of
paper and paper products is a complex process that is carried out in two distinct phases: the
pulping of wood and the manufacture of paper. Pulping is the conversion of fibrous wood into a
"pulp" material suitable for use in paper, paperboard, and building materials. Pulping and
papermaking may be integrated at the same production facility, or facilities may produce either
pulp or paper alone. In addition to facilities that produce pulp and/or paper, there are numerous
establishments that do not manufacture paper, but convert paper into secondary products.
Steam boilers are pivotal in the paper industry for the process of drying the paper, energy
requirement, and the cooking of wood chips in the digester. The steam is used for cooking wood
chips, dryer cans, and to produce power for the plant. Power can be produced through the
combustion of bark, black liquor, and fuel oil to reduce the cost with large electric demand and
increase reliability versus outside power sources. Firms engaged in pulp and paper
manufacturing under the North American Industry Classification System (NAICS) code 3221. In
2019, the pulp and paper industry shipped products valued at over $76 billion and included
92,283 employees (U.S. Census Bureau, 2021). This industry has declined in the United States
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with a 22% decrease in the number of establishments and a 42% decrease in the number of
employees from 2000 to 2019.
The next industry is the petroleum and coal products manufacturing industry. The
impacted boilers in this industry come from petroleum refineries. Petroleum pumped directly out
of the ground, or crude oil, is a complex mixture of hydrocarbons (chemical compounds that
consist solely of hydrogen and carbon) and various impurities, such as salt. To manufacture the
variety of petroleum products recognized in everyday life, this complex mixture must be refined
and processed over several stages. Boilers are used for several functions in a petroleum refining
facility. The steam generated from the boiler can be used to power turbines and pumps or for
heating of facilities and processes. Large refineries use lots of steam to heat crude oil during the
distillation process.
The process of refining crude oil into useful petroleum products can be separated into two
phases and a number of supporting operations. In the first phase, crude oil is desalted and then
separated into its various hydrocarbon components (known as "fractions"). These fractions
include gasoline, kerosene, naphtha, and other products. In the second phase, the distilled
fractions are converted into petroleum products (such as gasoline and kerosene) using three
different types of downstream processes: combining, breaking, and reshaping (EPA, 1995).
The petroleum refining industry is comprised of establishments primarily engaged in
refining crude petroleum into finished petroleum products. Examples of these products include
gasoline, jet fuel, kerosene, asphalt, lubricants, and solvents. Firms engaged in petroleum
refining are categorized under the North American Industry Classification System (NAICS) code
324110. In 2019, the petroleum refining industry shipped products valued at over $547 billion
and included 63,659 employees (U.S. Census Bureau, 2021).
The fourth industry is basic chemical manufacturing, which includes establishments
primarily engaged in manufacturing chemicals using basic processes, such as thermal cracking
and distillation. Chemicals manufactured in this industry group are usually separate chemical
elements or separate chemically-defined compounds.
The chemicals industry is one of the most complex and diverse industries in the U.S., and simple
characterizations are impossible. While the EIA Manufacturing Energy Consumption Survey
(MECS) identifies 10 significant steam-consuming product categories within the chemical
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industry, it identifies only nine for the food, paper, refining and primary metals industries,
combined. The major steam consuming processes in the chemical industry include stripping,
fractionalization, power generation, mechanical drive, quenching and dilution.
U.S. Census data for the basic chemical manufacturing industry (North American Industry
Classification System [NAICS] 3251) provides an initial overview of aggregated industry
expenditures. In 2019, the value of shipments for the industry was $206 billion and included
143,000 employees (U.S. Census Bureau, 2021).
2.4.6 Municipal Waste Combustors
Municipal solid waste (MSW) combustion is the process of reducing the volume of MSW
through incineration (combustion). Because combustion reduces waste volume by as much as 90
percent, this method of waste management has the potential to significantly reduce the need for
landfills. Combustion has two principal functions—MSW volume reduction and energy
generation—and produces residual products of ash and emissions to the ambient air. The inputs
are capital services (e.g., combustor unit, land, building, air pollution control devices), operating
services (e.g., labor services, maintenance services, fuel for startup, utility services), and MSW.
Municipal waste combustors (MWCs) can be classified according to three principal
types: mass burn (MB), modular (MOD), and refuse-derived fuel (RDF) combustors. Variations
exist within these categories, and some designs incorporate features of more than one type.
Regardless of the technology, each MWC plant site or facility has at least one, and potentially
more than one, individual combustor unit. Typically, an MWC plant has two or three units on
site.
The U.S. Economic Census (U.S. Bureau of the Census) classifies affected MWCs in a
category called solid waste combustors and incinerators (NAICS 562213). Between 2012 and
2017 the industry declined from 109 establishments and $2.5 billion in sales to 61 establishments
and $1.3 billion in sales (U.S. Census Bureau, 2021). In 2020 the industry consisted of 60
establishments, an annual payroll of $191 million, and 1,803 employees (U.S. Census Bureau,
2021).
2.5 References
American Iron and Steel Institute (AISI). 1989. Steelmaking Flowlines. Washington, DC: AISI
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Hogan, William T., and Frank T. Koelble. 1996. "Steel's Coke Deficit: 5.6 Million Tons and
Growing." New Steel 12(12):50-59.
Jans, I. and D. I. Rosenbaum. 1997. "Multimarket Contact and Pricing: Evidence from the U.S.
Cement Industry." International Journal of Industrial Organization 15:391-412.
Kirk, W.S. 1999. "Iron Ore." U.S. Geological Survey, Mineral Commodity Summaries.
Available at: http://minerals.usgs.gov/minerals/pubs/commodity/iron_ore/340399.pdf.
Ryan, S. 2006. "The Cost of Environmental Regulation in a Concentrated Industry."
U.S. Census Bureau. 2021. "Annual Survey of Manufactures: Summary Statistics for Industry
Groups and Industries in the U.S.: 2019 and 2018" Available at:
https://data.census.gov/cedsci/
U.S. Department of the Interior, U.S. Geological Survey. 2020. 2017 Minerals Yearbook,
Cement. Washington, DC: U.S. Department of the Interior. Tables 11 and 15. Available at:
http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/.
U.S. Environmental Protection Agency. 1995. Economic Impact Analysis for Petroleum
Refineries NESHAP. EPA-452/R-95-003, Final Report. Washington DC: Government
Printing Office.
U.S. Environmental Protection Agency. 2010. Regulatory Impact Analysis: Amendments to
the NESHAP and NSPS for the Portland Cement Manufacturing Industry. Final Report.
Available at Regulatory Impact Analysis: Amendments to the National Emission Standards
for Hazardous Air Pollutants and New Source Performance Standards (NSPS) for the
Portland Cement Manufacturing Industry - Final Report (epa.gov).
U.S. International Trade Commission (USITC). 2006. Gray Portland Cement and Cement
Clinker from Japan Investigation No. 73 l-TA-461 (Second Review). Publication 3856.
USGS. 2019. "2019 Minerals Yearbook: Iron and Steel" Available at:
https://www.usgs.gov/centers/national-minerals-information-center/iron-and-steel-
statistics-and-information
Van Oss, H.G., and A.C. Padovani. 2002. "Cement Manufacture and the Environment Part I:
Chemistry and Technology." Journal of Industrial Ecology 6(1):89-105.
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CHAPTER 3: AIR QUALITY IMPACTS
Overview
This chapter presents the impacts on ozone concentrations in 2023 and ozone and PM2.5 in
2026 from emissions reductions associated with the three regulatory control alternatives (i.e.,
final rule, less stringent alternative, and more stringent alternative) analyzed in this RIA.51
Specifically, for 2023 we analyzed the impacts of ozone season (i.e., May through September)
NOx emissions reductions from EGUs on April through September average Maximum Daily
Average 8-hour ozone concentrations (AS-M03) for each of the three control alternatives. For
202652 we analyzed the impacts on AS-M03 from ozone season NOx emissions reductions from
EGUs and from non-EGU separately and combined for each of the three alternatives. In addition,
for 2026 we also analyzed the impacts on annual average PM2.5 concentrations from the changes
in EGU emissions of NOx, SO2, and directly emitted PM2.5 outside of the ozone season that are
expected to result from certain EGU NOx controls that are expected to operate year-round and
generation shifting in response to the implementation of EGU controls in the three regulatory
control alternatives (see Chapter 4).53
In this chapter we first describe the methods for developing spatial fields of air quality
concentrations54 for the baseline and regulatory control alternatives in 2023 and 2026. These
spatial fields provide the air quality data that are used in the environment justice (EJ) analysis
and the analysis of health benefits from reduced concentrations of ozone and PM2.5 that are
expected to result from this final rule. In brief, the spatial fields are constructed based on a
method that utilizes 2026 baseline ozone and PM2.5 contributions from emissions in individual
states, state-level emissions for the baseline and each of the regulatory control alternatives, along
51 The 2023 and 2026 baseline and regulatory controls alternatives are described in Chapter 4.
52 The baseline EGU emissions and emissions reductions from the three EGU regulatory control alternatives that
were used to create spatial fields for 2026 align with the 2025 EGU baseline and control alternatives emissions
described in Chapter 4.
53 The approach for creating spatial fields of annual average PM2 5 concentrations is not capable of handling
emissions reductions that vary by season. In this regard, our impact analysis for annual average PM2.5 does not
include NOx emissions reductions during the ozone season. Excluding ozone season NOx reductions is not expected
to bias the annual impacts because NOx emissions primarily affect concentrations of PM nitrate, which is a
secondary pollutant that is formed during the cooler months of the year with near zero concentrations measured
during the summer. Similarly, we do not include the impacts of non-EGU NOx reductions on annual average PM2.5
because the non-EGU emissions limits are only required to operate during the ozone season.
54 Spatial fields are comprised of gridded pollutant concentration and contribution data at 12 km resolution covering
the portion of the U.S. within the air quality modeling domain.
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with baseline spatial fields of ozone and PM2.5 concentrations. The basic methodology for
determining air quality changes for this final rule are the same as those used in the proposal RIA
and in RIAs for multiple previous rules (U.S. EPA, 2019; U.S. EPA, 2020a; U.S. EPA, 2020b;
U.S. EPA, 2021).
In Section 3.1 we describe the air quality modeling platform; in Section 3.2 we describe the
method for processing air quality modeling outputs to create spatial fields; in Section 3.3 we
describe how this method was applied for the analyses in this RIA; in Section 3.4 we present
maps showing the impacts on AS-M03 and annual PM2.5 for each of the regulatory control
alternatives compared to the corresponding baseline; and in Section 3.5 we identify uncertainties
and limitations in the application of the method for generating spatial fields of pollutant
concentrations.
In Appendix 3A, we provide the estimated impacts on projected 2026 ozone design values
that are expected to result from the emissions reductions from the combined EGU and non-EGU
final rule case. The impacts on design values are based on air quality modeling of the 2026 final
rule baseline and the 2026 final rule.
3.1 Air Quality Modeling Platform
The EPA used photochemical air quality modeling as part of the process to create spatial
fields that reflect the influence of emissions changes between the baseline and each of the
regulatory control alternatives in each year, as applicable, for this final rule RIA. The model
simulations (i.e., model runs) were performed using the Comprehensive Air Quality Model with
Extensions (CAMx) version 7.1055 (Ramboll Environ, 2021). 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 12x 12 km, as shown in
Figure 3-1. Model predictions were evaluated by comparing predictions of base year 2016 ozone
and PM2.5 concentrations to ambient measurements (U.S. EPA, 2022a; 2022b). Ozone and PM2.5
model evaluations showed model performance that was comparable to other contemporaneous
model applications and, therefore, deemed adequate for the purpose of creating spatial fields for
the purposes of this RIA.
55 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.
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• j V ">
V \ N,
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Figure 3-1 Air Quality Modeling Domain
As noted above, the process for creating spatial fields utilized ozone and PM2.5
concentrations as well as the contributions from EGU and non-EGU emissions in individual
states. The contributions to assess the impacts on AS-M03 for the final rule are the same as
those used for the proposed rule. That is, for this final rule analysis we used the 2026 ozone
concentrations and corresponding EGU and non-EGU contribution predictions from the 2016
version 2 (i.e., 2016v2) emissions platform that was developed and used for proposal.56 In the
proposal RIA, we relied on benefit per ton estimates to compute the benefits expected from
reductions in annual average PM2.5 concentrations. For this final rule we conducted PM2.5 state-
by-state source apportionment air quality modeling to quantify contributions to annual PM2.5
from EGU emissions of NOx, SO2, and directly emitted PM2.5 in 2026. The data from this
modeling were used to develop spatial fields of annual average PM2.5 for the 2026 baseline and
each of the three EGU regulatory control alternatives in that year. In order to provide consistency
between the analyses for ozone and the analyses for PM2.5, the source apportionment modeling
for PM2.5 was performed using the same inputs and model configuration as we used for the ozone
source apportionment modeling performed for the proposed rule analysis.
56 The 2016v2 emissions platform includes emissions data for 2016, 2023, 2026, and 2032. For the final rule, the
EPA developed a version 3 (v3) emissions inventory, which reflects updates based largely on comments on the
proposal. As described in the text, for this final rule RIA. we use the v2 modeling in a relative sense coupled with
the v3 emissions to create spatial fields for the final rule 2023 and 2026 baseline scenarios and the regulatory control
alternatives.
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The contributions to ozone and PM2.5 component species (e.g., sulfate, nitrate,
ammonium, elemental carbon (EC), organic aerosol (OA), and crustal material57) were modeled
using the source apportionment tools in CAMx. Ozone contributions were modeled using the
Anthropogenic Precursor Culpability Assessment (APCA) tool and PM2.5 contributions were
modeled using the Particulate Matter Source Apportionment Technology (PSAT) tool (Ramboll,
2021). In general, source apportionment modeling quantifies the air quality concentrations
formed from individual, user-defined groups of emissions sources or "tags."58 These source tags
are tracked through the transport, dispersion, chemical transformation, and deposition processes
within the model to obtain hourly gridded59 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 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 the 2026 baseline state-by-state EGU and non-EGU source
apportionment modeling to obtain the contributions from EGU and non-EGU emissions in each
state to concentrations and the contributions in each 12x12 km model grid cell nationwide. The
ozone source apportionment modeling was performed for the period April through September to
provide data for developing spatial fields for the April through September AS-M03 ozone
exposure metric. The PM2.5 source apportionment modeling was performed for a full year to
provide data for developing spatial fields of annual average PM2.5.
3.2 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 air quality modeling for 2016v2 and 2026v2. The foundational data
include (1) ozone and speciated PM2.5 concentrations in each model grid cell from the 2016 and
2026 v2 modeling, (2) ozone contributions in 2026v2 from EGU and non-EGU ozone season
emissions in each state and speciated PM2.5 contributions in 2026v2 from annual EGU emissions
in each state in each model grid cell, (3) 2026v2 emissions from EGUs and non-EGUs that were
57 Crustal material refers to elements that are commonly found in the earth's crust such as Aluminum, Calcium, Iron,
Magnesium, Manganese, Potassium, Silicon, Titanium and the associated oxygen atoms.
58 Each state was treated as a separate source tag. Note that point source (EGU and non-EGU) sources on tribal lands
were assigned to a national "tribal land" tag.
59 Hourly contribution information is provided for each grid cell to provide spatial patterns of the contributions from
each tag.
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inputs to the contribution modeling, and (4) the EGU and non-EGU v3 emissions from the final
rule 2023 and 2026 baseline scenarios and each of the three regulatory control alternatives in
2023 and 2026.
The method to create spatial fields applies scaling factors to gridded source
apportionment contributions based on emissions changes between the 2026v2 baseline and the
2023v3 and 2026v3 baseline and regulatory control alternatives. This method is described in
detail below.
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 these fused model fields are used in combination with 2026 state-
EGU and non-EGU source apportionment modeling and the EGU and non-EGU emissions for
each regulatory control alternative and analytic year, as applicable. Contributions from each
contribution "tag" were scaled based on the ratio of emissions in the year/alternative being
evaluated to the emissions in the modeled 2026 scenario. Contributions from tags representing
sources other than EGUs and non-EGUs are held constant at 2026 levels for each of the
alternatives and year. For each alternative 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. For ozone, the process for creating spatial
fields of AS-M03 concentrations is explained using an EGU control case as an illustrative
example. This process was performed to create AS-M03 spatial fields for the 2023 and 2026
baselines and for the EGU and non-EGU regulatory control alternatives analyzed for this final
rule RIA. For annual PM2.5, we describe the steps for creating spatial fields for the 2026 baseline
and EGU regulatory control alternatives.
3.2.1 Spatial Distribution of Ozone Impacts
When interpreting the spatial fields of AS-M03 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
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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 maximum daily 8-hour average (MDA8) ozone
concentrations on specific high ozone episode days.
The impacts of the regulatory control alternatives for EGUs in 2023 and 2026 on ozone
season EGU NOx emissions for all states are provided in Table 3-1.60 The impacts of the
regulatory control alternatives for non-EGUs in 2026 on ozone season non-EGUNOx emissions
by state are provided in Table 3-2. Note that negative values in Tables 3-1 and 3-2 denote a
reduction in emissions and positive values denote an increase in emissions.61 The spatial fields of
baseline AS-M03 in 2023 and 2026 are presented in Figure 3-2 and Figure , respectively. The
distribution of AS-M03 baseline concentrations in 2023 and 2026 are similar, but the
concentrations are somewhat lower in 2026, as is expected due to emissions reductions resulting
from continued implementation of existing "on-the-books" rules and regulations. The figures
show that, from a regional perspective, the highest AS-M03 concentrations are in the inter-
mountain and southwest portions of the western U.S. where contributions from background
sources are dominant outside of urban areas, and in southern and central California where there
are high emissions of ozone precursor pollutants. Within the eastern U.S. the highest
concentrations are seen in the Ohio Valley and portions of the Midwest, as well as along the
Northeast Corridor and near urban areas such as Atlanta and Houston.
60 Emission reductions at sources on tribal lands are included in the tribal lands categories all of the emissions tables
in this chapter.
61 The imposition of the final rule results in changes in regional electricity flows, resulting in changes in net imports.
As a result, some states (even those not subject to the rule) may see changes in emissions as a result of generation
shifting.
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Table 3-1. Impact on EGU Ozone Season NOx Emissions of each Regulatory Control
Alternative in 2023 and in 2026 (1,000 tons)
2023
EGU Ozone Season
VOx Emissions
2026 EGU Ozone Season
NOx Emissions
State
Final -
Baseline
Less
Stringent
- Baseline
More
Stringent
- Baseline
Final -
Baseline
Less
Stringent -
Baseline
More
Stringent -
Baseline
Alabama
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
Arizona
0.0
0.0
0.0
-0.3
0.0
0.5
Arkansas
-0.3
-0.3
-0.3
-5.7
-0.4
-7.0
California
0.1
0.1
0.1
0.0
0.0
0.0
Colorado
0.0
0.0
0.0
0.1
0.0
0.0
Connecticut
0.0
0.0
0.0
0.0
0.0
0.0
Delaware
0.0
0.0
0.0
0.0
0.0
0.0
District of
Columbia
0.0
0.0
0.0
0.0
0.0
0.0
Florida
0.0
0.0
0.0
0.0
0.0
0.0
Georgia
0.0
0.0
0.1
0.0
0.0
0.6
Idaho
0.0
0.0
0.0
0.1
0.0
0.0
Illinois
-0.1
-0.1
-0.1
0.3
0.0
0.9
Indiana
-0.1
-0.1
-0.1
-1.1
0.1
-2.0
Iowa
0.0
0.0
0.0
0.3
-0.1
-0.1
Kansas
0.0
0.0
0.0
0.4
0.0
1.0
Kentucky
-0.8
-0.8
-1.1
-2.3
-0.6
-6.0
Louisiana
-0.3
-0.3
-0.3
-4.0
-1.7
-4.0
Maine
0.0
0.0
0.0
0.0
0.0
0.0
Maryland
0.0
0.0
0.0
0.0
0.0
0.0
Massachusetts
0.0
0.0
0.0
0.0
0.0
0.0
Michigan
0.0
0.0
0.0
-2.1
0.1
-3.4
Minnesota
-1.0
-1.0
-1.0
-1.2
-1.2
-1.2
Mississippi
-1.0
-1.0
-1.0
-0.1
-0.2
0.0
Missouri
-1.8
-1.8
-1.8
-4.8
-1.8
-6.3
Montana
0.0
0.0
0.0
0.0
0.0
0.0
Nebraska
0.0
0.0
0.0
0.1
0.0
0.0
Nevada
-0.5
-0.5
-0.5
0.0
0.0
0.0
New
Hampshire
0.0
0.0
0.0
0.0
0.0
0.0
New Jersey
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
New Mexico
0.0
0.0
0.0
0.0
0.0
0.0
New York
-0.2
-0.2
-0.2
-0.2
-0.2
-0.1
North Carolina
0.0
0.0
-0.1
0.4
0.0
0.3
North Dakota
0.0
0.0
0.0
0.1
0.1
0.1
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2023
EGU Ozone Season
VOx Emissions
2026 EGU Ozone Season
NOx Emissions
State
Final -
Baseline
Less
Stringent
- Baseline
More
Stringent
- Baseline
Final -
Baseline
Less
Stringent -
Baseline
More
Stringent -
Baseline
Ohio
-0.2
-0.2
-0.2
-1.5
-1.5
-1.5
Oklahoma
-1.4
-1.4
-1.4
-2.2
-1.3
-4.4
Oregon
0.0
0.0
0.0
0.0
0.0
0.0
Pennsylvania
0.0
-0.1
0.0
0.1
-0.1
0.0
Rhode Island
0.0
0.0
0.0
0.0
0.0
0.0
South Carolina
0.0
0.0
0.0
0.2
0.1
0.0
South Dakota
0.0
0.0
0.0
0.0
0.0
0.0
Tennessee
0.0
0.0
0.0
0.0
-0.1
0.6
Texas
-1.2
-1.2
-1.2
-1.1
-1.3
-14.3
Utah
-1.5
-1.5
-1.5
-4.8
-0.1
-5.9
Vermont
0.0
0.0
0.0
0.0
0.0
0.0
Virginia
0.0
0.0
0.0
0.2
0.0
-0.2
Washington
0.0
0.0
0.0
0.0
0.0
0.0
West Virginia
1.2
1.2
1.3
-1.7
1.0
-2.9
Wisconsin
-0.4
-0.4
-0.4
0.1
0.0
0.0
Wyoming
0.0
0.0
0.0
0.5
-0.5
0.8
Tribal Lands
0.0
0.0
0.0
-1.3
0.0
-1.3
Nationwide
-9.9
-9.8
-10.0
-31.8
-9.9
-56.0
Table 3-2. Impact on Non-EGU Ozone Season NOx Emissions of each Regulatory Control
Alternative in 2026 (1,000 tons)
State
2026 No
]>
i-EGU Ozone Season
Ox Emissions
Policy -
Baseline
Less
Stringent
- Baseline
More
Stringent
- Baseline
Alabama
0.0
0.0
0.0
Arizona
0.0
0.0
0.0
Arkansas
-1.6
-0.5
-1.7
California
-1.6
-1.5
-4.5
Colorado
0.0
0.0
0.0
Connecticut
0.0
0.0
0.0
Delaware
0.0
0.0
0.0
District of Columbia
0.0
0.0
0.0
Florida
0.0
0.0
0.0
99
-------
State
2026 No
]>
i-EGU Ozone Season
Ox Emissions
Policy -
Baseline
Less
Stringent
- Baseline
More
Stringent
- Baseline
Georgia
0.0
0.0
0.0
Idaho
0.0
0.0
0.0
Illinois
-2.4
-0.8
-3.1
Indiana
-2.0
-1.4
-3.5
Iowa
0.0
0.0
0.0
Kansas
0.0
0.0
0.0
Kentucky
-3.0
-0.7
-3.5
Louisiana
-8.5
-2.2
-9.2
Maine
0.0
0.0
0.0
Maryland
-0.1
-0.1
-1.1
Massachusetts
0.0
0.0
0.0
Michigan
-3.2
-0.8
-5.4
Minnesota
0.0
0.0
0.0
Mississippi
-2.9
-0.6
-3.1
Missouri
-2.1
-0.6
-4.8
Montana
0.0
0.0
0.0
Nebraska
0.0
0.0
0.0
Nevada
0.0
0.0
0.0
New Hampshire
0.0
0.0
0.0
New Jersey
-0.2
-0.2
-0.3
New Mexico
0.0
0.0
0.0
New York
-1.0
-0.7
-1.5
North Carolina
0.0
0.0
0.0
North Dakota
0.0
0.0
0.0
Ohio
-3.4
-1.1
-4.3
Oklahoma
-7.7
-2.4
-9.3
Oregon
0.0
0.0
0.0
Pennsylvania
-2.3
-1.7
-4.7
Rhode Island
0.0
0.0
0.0
South Carolina
0.0
0.0
0.0
South Dakota
0.0
0.0
0.0
Tennessee
0.0
0.0
0.0
Texas
-6.6
-2.7
-14.1
Utah
-0.4
-0.1
-1.0
Vermont
0.0
0.0
0.0
Virginia
-1.8
-0.8
-2.2
100
-------
State
2026 No
.IS
ii-EGU Ozone Season
Ox Emissions
Policy -
Baseline
Less
Stringent
- Baseline
More
Stringent
- Baseline
Washington
0.0
0.0
0.0
West Virginia
-2.0
-0.5
-2.5
Wisconsin
0.0
0.0
0.0
Wyoming
0.0
0.0
0.0
Tribal Lands
0.0
0.0
0.0
Nationwide
-52.9
-19.4
-79.7
2023 Final Rule Baseline
Figure 3-2. 2023 Baseline AS-M03 Concentrations (ppb)
70.0
65.0
60.0
55.0
50.0
45.0
40.0
35.0
30.0
25.0
20.0
101
-------
2026 Final Rule Baseline
70.0
65.0
60.0
55.0
50.0
45.0
40.0
35.0
30.0
25.0
20.0
Figure 3-3. 2026 Baseline AS-M03 Concentration (ppb)
The estimated impacts on AS-M03 between the baseline and each of the regulatory
control alternatives for 2023 and 2026 are presented in Figure 3-4 through Figure 3-15. The ppb
differences shown in Figures 3-4 through 3-15 are calculated as the regulatory control alternative
minus the baseline (i.e., negative values indicate reductions in pollutant concentrations). Note
that the scale for the impacts of the more stringent alternative in 2026, as shown in Figure 3-15,
is larger than the scale used to display the impacts for the less stringent alternative and final aile
alternatives in Figures 3-13 and 3-14, respectively.
The spatial patterns of the impacts of emissions reductions are a result of (1) the location
of EGU and non-EGU sources with reduced ozone season NOx emissions between the baseline
and the corresponding regulatory control alternatives and (2) the physical or chemical processing
that the model simulates in the atmosphere. In this respect, ozone reductions are greatest in
proximity to the affected sources with regional impacts in areas further downwind from these
sources. Increases in ozone concentrations in parts of West Virginia seen in the 2023 regulatory
control alternatives reflect the increase in ozone season EGU NOx emissions in this state, as
indicated in Table 3-1.
102
-------
0.50
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
-0.30
-0.40
-0.50
Figure 3-4. Reduction In AS-M03 (ppb): 2023 Less Stringent EGU-only Alternative
vs the 2023 Baseline (scale: + 0.5 ppb)
2023 Final Rule EGU Policy Scenario - Baseline
2023 Final Rule EGU Less Stringent Scenario - Baseline
247
159 239 318
Min = -0.525 at (211,104), Max = 0.272 at (310,131)
159 239 318
Min = -0.526 at (211,104), Max = 0.270 at (310,131)
Figure 3-5. Reduction in AS-MQ3 (ppb): 2023 Final Rule EGU-only Alternative vs
the 2023 Baseline (scale: + 0.5 ppb)
103
-------
Figure 3-6. Reduction in AS-M03 (ppb): 2023 More Stringent EGU-only Alternative vs the
2023 Baseline (scale: + 0.5 ppb)
2023 Final Rule EGU More Stringent Scenario - Baseline
247
2026 Final Rule EGU Less Stringent Scenario - Baseline
80 159 239 318 397
Min = -0.514 at (211,104), Max = 0.174 at (310,131)
1.00
0.80
0.60
0.40
0.20
0.00
-0.20
-0.40
-0.60
-0.80
-1.00
159 239 318
Min = -0.526 at (211,104), Max = 0.282 at (310,131)
Figure 3-7. Reduction in AS-M03 (ppb): 2026 Less Stringent EGU-only Alternative vs the
2026 Baseline (scale: + 1.0 ppb)
104
-------
Figure 3-8. Reduction in AS-M03 (ppb): 2026 Final Rule EGU-only Alternative vs the 2026
Baseline (scale: + 1.0 ppb)
2026 Final Rule EGU More Stringent Scenario - Baseline
2026 Final Rule EGU Policy Scenario - Baseline
247
159 239 318
Min = -2.483 at (238,87), Max = 0.052 at (146,178)
159 239
Min = -3.162 at (238,87), Max = 0.270 at (78,86)
Figure 3-9. Reduction in AS-M03 (ppb): 2026 More Stringent EGU-only Alternative
vs the 2026 Baseline (scale: + 1.0 ppb)
105
-------
2026 Final Rule NonEGU Less Stringent Scenario - Baseline
I
1.00
0.80
0.60
0.40
0.20
0.00
-0.20
-0.40
-0.60
-0.80
-1.00
Figure 3-10. Reduction in AS-M03 (ppb): 2026 Less Stringent non-EGU-only Alternative
vs the 2026 Baseline (scale: + 1.0 ppb)
2026 Final Rule NonEGU Policy Scenario - Baseline
159 239
Min = -1.340 at (251,60), Max = 0.00E+0 at (1,1)
Figure 3-11. Reduction in AS-M03 (ppb): 2026 Final Rule non-EGU-only Alternative vs
the 2026 Baseline (scale: + 1.0 ppb)
106
-------
2026 Final Rule NonEGU More Stringent Scenario - Baseline
SO 159 233 318 337
Min = -1.479 at (251,60), Max = 0.00E+0 at (1,1)
Figure 3-12. Reduction in AS-M03 (ppb): 2026 More Stringent non-EGU-only Alternative
vs the 2026 Baseline (scale: + 1.0 ppb)
2026 Final Rule EGU + NonEGU Less Stringent Scenario - Baseline
247
159 239 318
Min = -0.743 at (211,103), Max = 0.016 at (310,131)
Figure 3-13. Reduction in AS-M03 (ppb): 2026 Less Stringent EGU+non-EGU Alterative
vs the 2026 Baseline (scale: + 1.0 ppb)
107
-------
Figure 3-14. Reduction in AS-M03 (ppb): 2026 Final Rule EGU+non-EGU
Alternative vs the 2026 Baseline (scale: + 1.0 ppb)
0.00
0.80
149
0.40
-0.40
] -1.20
8-1.60
2026 Final Rule EGU + NoriEGU Policy Scenario - Baseline
159 239 318
Min = -2.939 at (238,87), Max = 0.031 at (146,178)
2026 Final Rule EGU + NonEGU More Stringent Scenario - Baseline
198
159 239 318
= -3.757 at (238,87), Max = 0.198 at (78,86)
397
Figure 3-15. Reduction in AS-MOS (ppb): 2026 More Stringent EGU+non-EGU
Alternative vs the 2026 Baseline (scale: + 2.0 ppb)
108
-------
3.2.2 Spatial Distribution of PM2.5 Impacts
In contrast to ozone, PM2.5 is comprised of both primary and secondary components.
Secondary PM2.5 species sulfate and nitrate often exhibit relatively smooth regional patterns
without large local gradients while primary PM2.5 components often have heterogenous spatial
patterns with largest gradients near emissions sources. The spatial field of 2026 baseline annual
PM2.5 is provided in Figure 3-16. Both secondary and primary PM2.5 contribute to the spatial
pattern of 2026 baseline annual PM2.5 as illustrated by the extensive areas of elevated
concentrations over much of the East that are comprised of secondary PM2.5 component species.
In addition, relatively high concentrations are mainly evident in urban areas and in close
proximity to major point sources. These "hot spots" generally reflect the impact of primary PM
emissions. Locally high concentrations are also evident in parts of the Northwest as a result of
wood stove emissions during the cooler months of the year (Hadley, 2021). High PM2.5
concentrations are also evident in California's Central Valley mainly comprised of particulate
nitrate and sulfate (Hasheminassab, 2014).
The impacts of the regulatory control alternatives for EGUs in 2026 on annual EGU
NOx, SO2, and PM2.5 emissions by state are provided in Table 3-3. Note that negative values in
Table 3-3 denote a reduction in emissions and positive values denote an increase in emissions. In
Figures 3-17 through 3-19 we present the changes in annual average PM2.5 concentrations
between the 2026 baseline and the three EGU regulatory control alternatives. The spatial patterns
of changes in annual average PM2.5 are a result of (1) of the spatial distribution of EGU sources
that are predicted to have changes in emissions in the control alternatives compared to the
baseline and (2) of the physical or chemical processing that the model simulates in the
atmosphere. The emissions data in Table 3-3 show that the reductions in SO2 emissions expected
to result from the final rule and more stringent alternative are much larger than emissions
reductions of NOx or PM2.5. Geographically, the SO2 emissions reductions are most notable in
Arkansas and Louisiana. In addition, there are relatively large reductions in SO2 emissions in
Kentucky, Michigan, and Texas. The spatial pattern of reductions in annual average PM2.5
concentrations, as shown in Figures 3-17 through 3-19, are consistent with the location of SO2
emissions reductions. The largest reductions in PM2.5 are found in and downwind of the states
with the largest reductions in emissions.
109
-------
Table 3-3. Impact on EGU Annual NOx, SO2, and PM2.5 Emissions of each Regulatory
Control Alternative for EGUs in 2026 (1,000 tons)3
Final
iule - Baseline
Less Stringent -
Baseline
More Stringent
- Baseline
State
NOx
S02
pm25
NOx
S02
pm25
NOx
S02
pm25
Alabama
-0.2
-0.2
0.0
-0.1
-0.1
0.0
-1.1
-1.3
-0.1
Arizona
-0.5
-0.8
0.0
0.1
0.1
0.0
0.8
1.4
0.0
Arkansas
-0.6
-15.8
-0.3
-0.6
-0.1
0.0
-6.8
-19.7
-0.2
California
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.0
-0.1
Colorado
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
Connecticut
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Delaware
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
District of
Columbia
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Florida
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Georgia
0.0
0.1
0.0
-0.1
0.0
0.0
1.4
1.4
0.1
Idaho
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Illinois
0.1
0.1
0.1
0.0
0.0
0.0
1.1
2.4
0.1
Indiana
-0.8
-1.9
-0.1
-1.1
-2.8
-0.2
1.0
1.3
0.2
Iowa
-0.1
0.1
0.0
-0.1
-0.1
0.0
0.1
0.0
0.0
Kansas
-0.1
0.1
0.0
0.0
0.0
0.0
1.6
0.6
0.3
Kentucky
0.0
5.7
0.0
-0.3
8.5
0.0
-11.5
-22.7
-0.3
Louisiana
-2.7
-15.3
-0.4
-2.6
-9.5
-0.3
-3.0
-15.7
-0.4
Maine
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Maryland
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Massachusetts
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Michigan
0.0
-3.0
-0.2
0.1
0.0
0.0
-8.1
-19.4
-0.8
Minnesota
-1.9
-0.3
0.0
-1.9
-0.2
0.0
-1.7
-0.2
0.0
Mississippi
-0.1
-0.1
0.0
0.0
-0.1
0.0
0.2
0.3
0.1
Missouri
0.1
-2.6
-0.2
0.1
0.0
0.0
-7.2
-1.7
-0.4
Montana
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Nebraska
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Nevada
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
New Hampshire
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
New Jersey
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
New Mexico
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
New York
0.0
0.0
0.0
-0.1
0.0
0.0
0.1
0.0
0.0
North Carolina
0.0
0.3
0.0
0.0
0.0
0.0
0.3
-1.4
0.0
North Dakota
0.6
0.9
0.0
0.4
0.6
0.0
1.0
1.3
0.1
Ohio
-2.1
-2.5
-0.3
-2.1
-2.2
-0.2
-2.1
-2.3
-0.2
Oklahoma
-2.1
2.0
0.0
-2.3
3.4
0.0
-4.8
2.3
0.0
Oregon
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
110
-------
State
Final
?ule - Baseline
Less Stringent -
Baseline
More Stringent - Baseline
NOx
S02
pm2.5
NOx
S02
PM2.5
NOx
S02
pm2.5
Pennsylvania
0.4
0.2
0.2
-0.1
-0.2
0.0
1.5
1.5
0.5
Rhode Island
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
South Carolina
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
-0.2
0.0
South Dakota
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tennessee
-0.1
0.0
0.0
-0.1
-0.1
0.0
2.2
2.9
0.6
Texas
0.1
-1.2
0.0
-0.1
-2.0
0.0
-17.3
-45.2
-0.6
Utah
0.0
-3.0
-0.1
0.0
-0.7
0.0
-12.9
0.8
0.0
Vermont
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Virginia
0.0
0.0
0.1
0.0
0.0
0.1
0.1
0.0
-0.1
Washington
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.0
West Virginia
3.0
-1.8
-0.2
3.0
0.0
0.0
-7.4
-5.9
-0.8
Wisconsin
0.0
0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
Wyoming
0.9
1.6
0.0
-1.1
-1.3
0.0
1.6
2.6
0.0
Tribal Data
0.0
-0.4
-0.2
0.0
0.0
0.0
-3.0
-0.9
-0.5
Nationwide
-6.2
-37.7
-1.5
-8.9
-6.8
-0.7
-73.0
-118.1
-2.3
a The imposition of the final rule results in changes in regional electricity flows, resulting in changes in net imports.
As a result, some states (even those not subject to the rule) may see changes in emissions as a result of generation
shifting.
1 30 159 239 318 397
Figure 3-16. 2026 Baseline Annual Average PM2.5 Concentrations (fig/in3)
111
-------
2026 Final Rule Annual PM2.5 Less Stringent - Baseline
198
149
0.20
0.16
0.12
0.08
0.04
0.00
-0.04
-0.08
-0.12
-0.16
Figure 3-17. Reduction in annual average P1VI2.5 (jig/in3): 2026 Less Stringent EGU-only
Alternative vs the 2026 Baseline (scale: + 0.2 jig/m3)
2026 Final Rule Annual PM2.5 Policy Scenario - Baseline
-0.20
239
318
397
Figure 3-18. Reduction in Annual Average PM2.5 (fig/m3): 2026 Final Rule EGU-only
Alternative vs the 2026 Baseline (scale: + 0.2 jig/m3)
112
-------
2026 Final Rule Annual PM2.5 More Stringent Scenario - Baseline
1 80 159 239 318 397
Figure 3-19. Reduction in Annual Average PM2.5 (fig/m3): 2026 More Stringent EGU-only
Alternative vs the 2026 Baseline (scale: + 0.2 jug/1113)
3.3 Uncertainties and Limitations
One limitation of the scaling methodology for creating ozone and PM2.5 surfaces
associated with the baseline and regulatory control alternatives described above is that it 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. This is consistent with
how air quality estimations have been treated in past regulatory analyses (U.S. EPA 2012; 2019;
2020b). We note that air quality is calculated in the same manner for the baseline and the
regulatory control alternatives, so any uncertainty associated with these assumptions is carried
through both sets of scenarios in the same manner and is thus not expected to impact the air
quality differences between scenarios. In addition, emissions changes between baseline and the
regulatory control alternatives are relatively small compared to modeled 2026 emissions that
form the basis of the source apportionment approach described in Section 3.1. Previous studies
have shown that air pollutant concentrations generally respond linearly to small emissions
changes of up to 30 percent (Dunker et al., 2002; Cohan et al., 2005; Napelenok et al., 2006; Koo
113
-------
et al., 2007; Zavala et al., 2009; Cohan and Napelenok, 2011) and that linear scaling from source
apportionment can do a reasonable job of representing impacts of 100 percent of emissions from
individual sources (Baker and Kelly 2014). Therefore, while simplistic, it is reasonable to expect
that the emissions concentration differences between the baseline and regulatory control
alternatives can be adequately represented using this methodology and any uncertainty should be
weighed against the speed in which this method may be used to account for spatial differences in
the effect of EGU emissions on ozone concentrations.
A second limitation is that the source apportionment contributions represent 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 represent any changes
to "within tag" spatial distributions. As a result, the method does not account for any changes of
spatial patterns that would result from changes in the relative magnitude of sources within a
source tag in the scenarios investigated here.
In addition, the 2023 and 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 ozone
concentrations (U.S. EPA, 2022a; U.S. EPA, 2022b).
The regulatory control alternatives lead to decreased concentrations of ozone, the extent
to which varies by location, relative to the baseline. However, the analysis does not account for
how interaction with NAAQS compliance would affect the benefits and costs of the regulatory
control alternatives, which introduces uncertainty in the benefits and costs of the alternatives. To
the extent the Transport FIP for the 2015 ozone NAAQS will decrease NOx and consequentially
ozone concentrations, these changes may affect compliance with existing NAAQS standards and
subsequently affect the actual benefits and costs of the rule. In areas not projected to attain the
2015 ozone NAAQS without further emissions reductions from the baseline, states may be able
to avoid applying some emissions control measures to reduce emissions from local sources as a
result of this rule. If compliance behavior with the 2015 ozone NAAQS were accounted for in
the baseline in this RIA there may be additional social benefits from reduced compliance costs,
114
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while the level and spatial pattern of changes in ozone concentrations, and their associated health
and ecological benefits, would differ. The directional effect on the benefits, costs, and net-
benefits of this source of uncertainty is ambiguous.
Similarly, the regulatory control alternatives may project decreases in ozone
concentrations in areas attaining the NAAQS in the baseline. In practice, these potential changes
in concentrations may influence NAAQS compliance plans in these areas, which in turn would
further influence concentrations and the cost of complying with the NAAQS. However, such
behavior will be mitigated by NAAQS requirements such as Prevention of Significant
Deterioration (PSD) requirements. This RIA does not account for how interaction with NAAQS
compliance would affect the benefits and costs of the regulatory control alternatives.
3.4 References
Cohan, D.S., Hakami, A., Hu, Y.T., Russell, A.G. (2005). Nonlinear response of ozone to
emissions: Source apportionment and sensitivity analysis. Environ. Sci. Technol., 39,
6739-6748.
Cohan, D.S., Napelenok, S.L. (2011). Air Quality Response Modeling for Decision Support.
Atmosphere, 2, 407-425.
Ding, D., Zhu, Y., Jang, C., Lin, C., Wang, S., Fu, J., Gao, J., Deng, S., Xie, J., Qui, X. (2015).
Evaluation of heath benefit using BenMAP-CE with an integrated scheme of model and
monitor data during Guangzhou Asian Games. Journal of Environmental Science, 29,
178-188.
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): 2965-76.
Available at: https://doi.org/10.1021/es0112691.
Gold ,C., Remmele, P.R., Roos, T. (1997) In: Algorithmic Foundation of Geographic
Information Systems. In: Lecture Notes in Computer Science, Vol. 1340 (van Kereveld
M, Nievergelt J, Roos T, Widmayer P, eds) Berlin, Germany: Springer-Verlag. Voronoi
methods in GIS. pp. 21-35.
Hadley, O., Cutler, A., R. Schumaker, R. Bond. (2021). Wildfires and wood stoves: Woodsmoke
toxicity and chemical characterization study in the northwestern United States.
Atmospheric Environment, 254, 118347. Available at:
https://doi.Org/10.1016/j.atmosenv.2021.118347.
115
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Hasheminassab, S., Daher, N., Saffari, A., Wang, D., Ostro, D., Sioutas, C. (2014). Spatial and
temporal variability of sources of ambient fine particulate matter (PM2.5) in California.
Atmospheric Chemistry and Physics. 14, 12085-12097. Available at: http://www.atmos-
chem-phys.net/14/12085/2014/
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, 41 (8): 2847-54. Available at: https://doi.org/10.1021/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):
6112-21. Available at: 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. Ramboll Environ International Corporation, Novato, CA.
U.S. EPA (2007). Technical Report on Ozone Exposure, Risk, and Impact Assessments for
Vegetation. Prepared by Abt Associates Inc. for U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Health and Environmental Impacts
Division. EPA 452/R-07-002. Research Triangle Park, NC.
U.S. EPA (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient
Air Quality Standards for Particulate Matter. U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Health and Environmental Impacts
Division. EPA-452/R-12-005. Research Triangle Park, NC.
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. U.S. Environmental Protection Agency, Office of Air Quality Planning
and Standards, Health and Environmental Impacts Division. EPA-452/R-19-003.
Research Triangle Park, NC.
U.S. EPA (2020a). Benefit and Cost Analysis for Revisions to the Effluent Limitations
Guidelines and Standards for the Steam Electric Power Generating Point Source
Category. U.S. Environmental Protection Agency, Office of Water, Engineering and
Analysis Division. EPA-821-R-20-003. Washington D.C.
U.S. EPA (2020b). Analysis of Potential Costs and Benefits for the "National Emission
Standards for Hazardous Air Pollutants: Coal- and Oil-Fired Electric Utility Steam
Generating Units - Subcategory of Certain Existing Electric Utility Steam Generating
Units Firing Easter. Memo to Docket for rulemaking: "National Emission Standards for
Hazardous Air Pollutants: Coal- and Oil-Fired Electric 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" (EPA-
HQ-OAR-2018-0794), April 8, 2020.
U.S. EPA (2021). Regulatory Impact Analysis for the Final Revised Cross-State Air Pollution
Rule (CSAPR) Update for the 2008 Ozone NAAQS. U.S. Environmental Protection
116
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Agency, Office of Air Quality Planning and Standards Health and Environmental Impacts
Division, EPA-452/R-21-002. Research Triangle Park, NC.
U.S. EPA (2022a). 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. Environmental Protection Agency, Office
of Air Quality Planning and Standards. Available at:
https://www.epa.gov/system/files/documents/2022-03/aq-modeling-tsd_proposed-fip.pdf
U.S. EPA (2022b). Air Quality Model Technical Support Document: 2016 CAMx PM2.5 Model
Evaluation to Support EGU Benefits Assessment. U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards.
U.S. EPA (2022c). Software for Model Attainment Test - Community Edition (SMAT-CE)
User's Guide Software version 2.1. U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards. EPA-454/B-22-013. Research Triangle Park, NC.
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, 39-55.
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APPENDIX 3A: IMPACTS ON OZONE DESIGN VALUES OF THE FINAL RULE IN
2026
In this appendix we provide the estimated impacts on projected 2026 ozone design values
that are expected to result from the combined EGU and non-EGU final rule analyzed in this RIA.
As described in Chapter 1, the regulatory control alternatives include the final rule along with
alternatives that reflect less stringent and more stringent controls on EGUs and non-EGUs.
Because of timing constraints, we were only able to perform full-scale photochemical air quality
modeling to quantify the ozone impacts for the 2026 final rule.
3A.1 Projected Impacts on Ozone Design Values
The "ppb" impacts in 2026 from the final rule control case are provided in Table 3A-1 for
those monitoring sites that are identified as nonattainment or maintenance-only receptors in 2026
and/or in 2023, based on air quality modeling and monitored data. Table 3 A-2 provides the same
information for the additional violating monitor-based maintenance-only receptors in 2023.62
For the final rule control case, the largest reductions in ozone design values at the
receptors in Tables 3A-1 and 3A-2 are predicted to occur in the Houston-Galveston-Brazoria,
Texas area. In this area the reductions from the final rule case range from 0.7 to 0.9 ppb. At most
of the receptors in both the Dallas/Ft Worth and the New York/Coastal Connecticut areas the
reductions in ozone range from 0.4 to 0.5 ppb. At receptors in Indiana, Michigan, and Wisconsin
near the shoreline of Lake Michigan, ozone is projected to decline by 0.3 to 0.4 ppb, but by as
much as 0.5 ppb at the receptor in Muskegon, MI. Lesser reductions of 0.1 ppb are predicted in
the urban and near-urban receptors in Chicago. In the West, ozone reductions just under 0.2 ppb
are predicted at receptors in Denver with slightly greater reductions, just above 0.2 ppb, at
receptors in Salt Lake City. At receptors in Phoenix, California, El Paso/Las Cruces, and
southeast New Mexico the reductions in ozone are predicted to be less than 0.1 ppb. The
geographical variations of the impacts on design values are generally consistent with the spatial
fields in Figure 3-14, which shows the impact on AS-M03 of the final rule case EGU+non-EGU
NOx reductions in 2026. Table 3A-3 provides the impacts on EGU+non-EGU ozone season NOx
62 The approaches for identifying modeling-based and violating monitor-based receptors are described in the
preamble for this final rule.
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emissions that result from the emissions controls modeled in the final rule case. Note that
negative values in Table 3A-3 denote a reduction in emissions whereas positive values denote an
increase in emissions. The impacts on emissions are rank ordered by the amount of emissions
reduction (i.e., negative values are at the top). That is, in Table 3 A-3 the states with the largest
NOx emissions reductions in the final rule case are at the top of the list. Examining the emissions
data in Table 3 A-3 together with the ppb impacts in Table 3 A-l and 3 A-2 indicate that the
largest reductions in receptor design values are projected to occur near and downwind of the
states with the largest reductions in ozone season EGU+non-EGU NOx emissions.
Table 3A-1. Ozone Impacts at Projected Nonattainment and Maintenance-Only Receptors
(ppb) for the Final Rule Modeled Control Case in 2026
Site ID
State
County
Final Rule Case
40278011
Arizona
Yuma
-0.06
60650016
California
Riverside
-0.06
60651016
California
Riverside
-0.08
80350004
Colorado
Douglas
-0.17
80590006
Colorado
Jefferson
-0.14
80590011
Colorado
Jefferson
-0.11
80690011
Colorado
Larimer
-0.24
90010017
Connecticut
Fairfield
-0.38
90013007
Connecticut
Fairfield
-0.45
90019003
Connecticut
Fairfield
-0.46
90099002
Connecticut
New Haven
-0.43
170310001
Illinois
Cook
-0.08
170314201
Illinois
Cook
-0.09
170317002
Illinois
Cook
-0.11
350130021
New Mexico
Dona Ana
-0.02
350130022
New Mexico
Dona Ana
-0.03
350151005
New Mexico
Eddy
-0.02
350250008
New Mexico
Lea
-0.02
480391004
Texas
Brazoria
-0.82
481210034
Texas
Denton
-0.42
481410037
Texas
El Paso
-0.03
481671034
Texas
Galveston
-0.92
482010024
Texas
Harris
-0.68
482010055
Texas
Harris
-0.75
482011034
Texas
Harris
-0.72
482011035
Texas
Harris
-0.70
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Site ID
State
County
Final Rule Case
490110004
Utah
Davis
-0.22
490353006
Utah
Salt Lake
-0.22
490353013
Utah
Salt Lake
-0.15
550590019
Wisconsin
Kenosha
-0.21
551010020
Wisconsin
Racine
-0.22
551170006
Wisconsin
Sheboygan
-0.30
Table 3A-2. Ozone Impacts at Violating-Monitor Maintenance-Only Receptors (ppb) for
the Final Rule Modeled Control Case in 2026
Site ID
State
County
Final Rule Case
40070010
Arizona
Gila
-0.07
40130019
Arizona
Maricopa
-0.04
40131003
Arizona
Maricopa
-0.05
40131004
Arizona
Maricopa
-0.05
40131010
Arizona
Maricopa
-0.05
40132001
Arizona
Maricopa
-0.04
40132005
Arizona
Maricopa
-0.06
40133002
Arizona
Maricopa
-0.04
40134004
Arizona
Maricopa
-0.05
40134005
Arizona
Maricopa
-0.04
40134008
Arizona
Maricopa
-0.05
40134010
Arizona
Maricopa
-0.06
40137020
Arizona
Maricopa
-0.04
40137021
Arizona
Maricopa
-0.06
40137022
Arizona
Maricopa
-0.05
40137024
Arizona
Maricopa
-0.04
40139702
Arizona
Maricopa
-0.05
40139704
Arizona
Maricopa
-0.06
40139997
Arizona
Maricopa
-0.04
40218001
Arizona
Pinal
-0.03
80013001
Colorado
Adams
-0.13
80050002
Colorado
Arapahoe
-0.18
80310002
Colorado
Denver
-0.13
80310026
Colorado
Denver
-0.13
90079007
Connecticut
Middlesex
-0.49
90110124
Connecticut
New London
-0.41
170310032
Illinois
Cook
-0.10
170311601
Illinois
Cook
-0.10
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Site ID
State
County
Final Rule Case
181270024
Indiana
Porter
-0.23
260050003
Michigan
Allegan
-0.39
261210039
Michigan
Muskegon
-0.50
320030043
Nevada
Clark
-0.15
350011012
New Mexico
Bernalillo
-0.04
350130008
New Mexico
Dona Ana
-0.02
361030002
New York
Suffolk
-0.39
390850003
Ohio
Lake
-0.70
480290052
Texas
Bexar
-0.28
480850005
Texas
Collin
-0.48
481130075
Texas
Dallas
-0.45
481211032
Texas
Denton
-0.41
482010051
Texas
Harris
-0.69
482010416
Texas
Harris
-0.73
484390075
Texas
Tarrant
-0.30
484391002
Texas
Tarrant
-0.38
484392003
Texas
Tarrant
-0.38
484393009
Texas
Tarrant
-0.32
490571003
Utah
Weber
-0.27
550590025
Wisconsin
Kenosha
-0.22
550890008
Wisconsin
Ozaukee
-0.24
Table 3A-3. Impact on EGU and Non-EGU Ozone Season NOx Emissions by State in the
2026 Modeled Control Case (1,000 tons)
State
Final - Baseline
Louisiana
-12.6
Oklahoma
-9.9
Texas
-7.7
Arkansas
-7.3
Missouri
-6.9
Michigan
-5.3
Kentucky
-5.3
Utah
-5.2
Ohio
-4.9
West Virginia
-3.7
Indiana
-3.1
Mississippi
-3.0
Pennsylvania
-2.1
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State
Final - Baseline
Illinois
-2.1
California
-1.7
Virginia
-1.6
Tribal
-1.3
Minnesota
-1.2
New York
-1.2
New Jersey
-0.3
Arizona
-0.3
Alabama
-0.2
Maryland
-0.1
Nevada
0.0
Rhode Island
0.0
Florida
0.0
Maine
0.0
Oregon
0.0
Vermont
0.0
District of Columbia
0.0
Washington
0.0
Montana
0.0
Delaware
0.0
Massachusetts
0.0
New Hampshire
0.0
New Mexico
0.0
Connecticut
0.0
Tennessee
0.0
South Dakota
0.0
Georgia
0.0
Nebraska
0.1
Idaho
0.1
Colorado
0.1
North Dakota
0.1
Wisconsin
0.1
South Carolina
0.2
Iowa
0.3
North Carolina
0.4
Kansas
0.4
Wyoming
0.5
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CHAPTER 4: COST, EMISSIONS, AND ENERGY IMPACTS
Overview
This chapter reports the compliance costs, emissions, and energy analyses performed for
the Federal Good Neighbor Plan Addressing Regional Ozone Transport for the 2015 Ozone
National Ambient Air Quality Standards (Transport FIP for the 2015 ozone NAAQS). The EPA
used the Integrated Planning Model (IPM)63 to conduct the electric generating units (EGU)
analysis discussed in this chapter and information from the Control Measures Database
(CMDB)64 and the 2019 emissions inventory to conduct analysis for non-electric generating
units (non-EGUs) for 2026. As explained in detail below, this chapter presents analysis for three
regulatory control alternatives that differ in the level of EGU nitrogen oxides (NOx) ozone
season emissions budgets in the 22 states subject to this action beginning in 2023. These
regulatory control alternatives impose different budget levels for EGUs. The different budget
levels are calculated assuming the application of different NOx mitigation technologies. The
analysis for EGUs in the chapter does not include effects from certain provisions of the Inflation
Reduction Act (IRA) of 2022 in the baseline. The effects of accounting for the IRA on the power
sector costs, emission reductions and other impacts of this final rule are provided in a sensitivity
analysis presented in Appendix 4A. The chapter also presents three regulatory control
alternatives for non-EGUs that differ in the control technologies assumed to be adopted for
compliance.
The chapter is organized as follows: following a summary of the regulatory control
alternatives analyzed and a summary of the 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 a few years. We then present a summary of the
results of the non-EGU assessment for 2026. Section 4.6 of this chapter describes the
relationship between the compliance cost estimates and social costs.
63 Information on IPM can be found at the following link: https://www.epa.gov/airmarkets/power-sector-modeling.
64 More information about the Control Strategy Tool (CoST) and the control measures database (CMDB) can be
found at the following link: https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-
analysis-modelstools-air-pollution.
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4.1 Regulatory Control Alternatives
This rule establishes NOx emissions budgets requiring fossil fuel-fired electric generating
units (EGUs) in 22 states to participate in an allowance-based ozone season (May 1 through
September 30) trading program beginning in 2023. The EGUs covered by the FIPs and subject to
the budget are fossil-fired EGUs with >25-megawatt (MW) capacity. For details on the
derivation of these budgets, please see Section V.C. of the preamble.
The FIP requirements establish ozone season NOx emissions budgets for EGUs in 22
states (Alabama, Arkansas, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan,
Minnesota, Mississippi, Missouri, Nevada, New Jersey, New York, Ohio, Oklahoma,
Pennsylvania, Texas, Utah, Virginia, West Virginia, and Wisconsin) and require EGUs in these
states to participate in a revised version of the Cross-State Air Pollution Rule (CSAPR) NOx
Ozone Season Group 3 Trading Program that was previously established in the Revised CSAPR
Update.65 The EPA is amending existing FIPs for 12 states currently participating in the CSAPR
NOx Ozone Season Group 3 Trading Program (Illinois, Indiana, Kentucky, Louisiana, Maryland,
Michigan, New Jersey, New York, Ohio, Pennsylvania, Virginia, and West Virginia) to replace
their existing emissions budgets established in the Revised CSAPR Update (with respect to the
2008 ozone NAAQS) with new emissions budgets. For seven states currently covered by the
CSAPR NOx Ozone Season Group 2 Trading Program under SIPs or FIPs, the EPA is issuing
new FIPs for two states (Alabama and Missouri) and amending existing FIPs for five states
(Arkansas, Mississippi, Oklahoma, Texas, and Wisconsin) to transition EGU sources in these
states from the Group 2 program to the revised Group 3 trading program, beginning with the
2023 ozone season. The EPA is issuing new FIPs for three states not currently covered by any
CSAPR NOx ozone season trading program: Minnesota, Nevada, and Utah.
In this rule, we introduce additional features to the allowance-based trading program
approach for EGUs, including dynamic adjustments of the emissions budgets over time and a
backstop daily emission rate for most coal-fired units, along with an adjustment to the total size
of the allowance bank, which is 21 percent of the sum of the state emissions budgets for the
65 As explained in Section V.C. 1 of the preamble, the EPA is making a finding that EGU sources within the State of
California are sufficiently controlled such that no further emissions reductions are needed from them to eliminate
significant contribution to downwind states.
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current control period until 2030 (at which point it declines to 10.5%), that were not included in
previous CSAPRNOx ozone season trading programs. These enhancements will help maintain
control stringency over time and improve emissions performance at individual units, offering an
extra measure of assurance that existing pollution controls will be operated during the ozone
season. This analysis incorporates the daily emission rate requirement for units with existing
controls by forcing operation of these controls in the ozone season for affected sources starting in
the 2023 run year (although the rule would not impose some of these limits until 2024).
The additional EGU emissions reductions66 beginning in 2026 are based on the feasibility
of control installation for EGUs in 19 states (19-state region) that remain linked to downwind
nonattainment and maintenance receptors in 2026.67 Starting in 2030, consistent with the
structure of the final rule, this analysis imposes the backstop emission rate for certain larger coal-
fired units that do not already have SCR installed, which forces these units identified as having
SCR retrofit potential to either install new SCR retrofits, find other means of compliance, or
retire.68 The analysis does not explicitly capture the dynamic budget adjustments over time in the
modeling, but the forced operation of controls during the ozone season over the forecast period
(even in the absence of binding mass limits) approximates this feature of the program design.69
For details of the controls modeled for each of the regulatory control alternatives please see
66 The model was not explicitly constrained to limit the bank to 21% of the sum of state budgets in the first period
and 10.5% thereafter. However, the model solve was reviewed to ensure that any allowances withdrawn from the
bank did not violate this threshold. If this condition had been violated (which did not occur for these runs), the
model would have been re-run with an additional limit incorporated.
67 For EGUs, the 19 states linked in 2026 include Arkansas, Illinois, Indiana, Kentucky, Louisiana, Maryland,
Michigan, Mississippi, Missouri, Nevada, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah,
Virginia, and West Virginia. The EPA evaluated the EGU sources within the state of California and found there
were no covered coal steam sources greater than 100 MW that would have emissions reduction potential according
to the EPA's assumed EGU SCR retrofit mitigation technologies.
68 The rule assumes SCR retrofit potential starting in 2026, and this is reflected in the 2026/27 state emission
budgets. The daily backstop emission rate does not apply for large coal units that do not already have SCR controls
until the second ozone season after they install the control or by 2030 at the latest. The EPA's IPM model run years
are 2025, 2028 and 2030. The SCR compliance behavior is generally expected to occur no later than 2030.
Therefore, the EPA models this daily backstop emission rate in 2030 (when choosing between model run year 2025
and 2028) while imposing 2026 and 2027 SCR-retrofit-related emission reductions reflected in those control
periods' emission budgets in the model run-year 2025 to model compliance cost in the first years by which the
technology may be put into place for some units. (In this case, we are treating 2025 as sufficiently reflective of
conditions in 2026 to be usable for this RIA analysis.)
69 In years in which the dynamic budgets are implemented, the budgets would be calculated based on historical heat
input data and assuming optimization of existing controls as well as installation of the controls required by the rule.
While the modeling does not include lower budgets in response to modeled declines in heat input, forcing existing
controls to operate in an environment of fluctuating future heat input approximates the underlying behavior and
captures the associated costs.
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Table 4-2 below.
This rule also includes NOx emissions limitations with an initial compliance date of 2026
applicable to certain non-EGU stationary sources in 20 states: Arkansas, California, Illinois,
Indiana, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, Nevada, New Jersey,
New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah, Virginia, and West Virginia. Table 4-1
presents the industries, emissions unit types, form of emissions limit, and NOx emissions
limitations for the final rule. For the less and more stringent alternatives, specific emission limits
are not identified, and certain control technologies are assumed for compliance with emissions
limits that would be more or less stringent than the final rule.
Table 4-1. Summary of Non-EGU Industries, Emissions Unit Types, Form of Final
Emissions Limits, and Final Emissions Limits
Industry
Emissions
Unit Type
Form of Final
Emissions Limits
Final Emissions Limits
Pipeline Transportation of
Natural Gas
Reciprocating
Internal
Combustion
Engines
Grams per horsepower
per hours (g/hp-hr)
Four Stroke Rich Burn: 1.0 g/hp-
hr
Four Stroke Lean Burn: 1.5 g/hp-
hr
Two Stroke Lean Burn: 3.0 g/hp-
hr
Cement and Concrete Product
Manufacturing
Kilns
Pounds per ton (lbs/ton)
of clinker
Long Wet: 4.0 lb/ton
Long Dry: 3.0 lb/ton
Pre heater: 3.8 lb/ton
Precalciner: 2.3 lb/ton
Preheater/Precalciner: 2.8 lb/ton
Iron and Steel Mills and Reheat
Ferroalloy Manufacturing Furnaces
lbs NOx per/ton of steel
and lbs/mmBtua
Test and set limit based on
installation of Low-NOx Burners
Glass and Glass Product
Manufacturing
Furnaces
lbs/ton glass produced
Container Glass Furnace: 4.0
lb/ton
Pressed/Blown Glass Furnace:
4.0 lb/ton
Fiberglass Furnace: 4.0 lb/ton
Flat Glass Furnace: 9.2 lb/ton
Iron and Steel Mills and
Ferroalloy Manufacturing
Metal Ore Mining
Basic Chemical Manufacturing
Petroleum and Coal Products
Manufacturing
Pulp, Paper, and Paperboard
Mills
Boilers
lbs/mmBtua
Coal: 0.20 lb/mmBtu
Residual Oil: 0.20 lb/mmBtu
Distillate Oil: 0.12 lb/mmBtu
Natural Gas: 0.08 lb/mmBtu
Solid Waste Combustors and
Incinerators
Combustors
or
Incinerators
ppmvd on a 24-hour
averaging period and
ppmvd on a 30-day
averaging period
110 ppmvd on a 24-hour
averaging period
105 ppmvd on a 30-day
averaging period
"Heat input limit.
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This regulatory impact analysis (RIA) evaluates the benefits, costs and certain impacts of
compliance with three regulatory control alternatives: the Transport FIP for the 2015 ozone
NAAQS, a less-stringent alternative, and a more-stringent alternative. Table 4-2 below presents
the less stringent alternatives, final rule requirements, and more stringent alternatives for EGUs
and non-EGUs. For the purposes of summarizing the results of the benefits and costs of these
alternatives, the less stringent alternative for EGUs is presented with the less stringent alternative
for non-EGUs. However, the cost, emissions, and energy impacts for the EGU and non-EGU
alternatives are evaluated separately.
Table 4-2. Regulatory Control Alternatives for EGUs and Non-EGUs
Regulatory Control NOx Controls Implemented for EGUs within IPM1 b
Alternative
Less Stringent Alternative
1)
2)
3)
4)
2023 onwards: Fully operate existing selective catalytic reduction (SCRs)
during ozone season
2023 onwards: Fully operate existing selective non-catalytic reduction
(SNCRs) during ozone season
In 2023 install state-of-the-art combustion controls0
In 2030 model run year, impose backstop emission rate on coal units greater
than 100 MW within the 19-state region that lack SCR controls.d
(All Controls above and)
5) In 2025 model run year, impose Engineering Analysis derived emissions
budgets that assume installation of SCR controls on coal units greater than
100 MW within the 19-state region that lack SCR controls.
Final Rule
(Controls 1-5 above and)
6) In 2025 model run year, impose backstop emission rate on coal units greater
than 100 MW within the 19-state region that lack SCR controls, forcing units
to retrofit or retire.
More Stringent Alternative
Regulatory Control
Alternative
NOx Emissions Limits for Non-EGUs - Emissions Unit Types, Industries,
and Controls Assumed for Compliance
Less Stringent Alternative
1)
2)
3)
4)
5)
6)
Reciprocating internal combustion engines in Pipeline Transportation of
Natural Gas - Adjust Air-to-Fuel Ratio
Kilns in Cement and Cement Product Manufacturing - install SNCR
Reheat furnaces in Iron and Steel Mills and Ferroalloy Manufacturing -
install Low NOx burners (LNB)
Furnaces in Glass and Glass Product Manufacturing - install LNB
Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SNCR
Combustors or Incinerators in Solid Waste Combustors and Incinerators -
install Advanced NSCR (ANSCR) or LN™ and SNCRe
(Controls 2, 3, 4, and 6 above, plus changes in assumed controls noted below)
7) Reciprocating internal combustion engines in Pipeline Transportation of
Natural Gas - depending on engine type, install Layered Combustion, non-
selective catalytic reduction (NSCR), or SCR
8) Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SCR (coal- or
oil-fired) or LNB and FGR (natural gas-fired only)
Final Rule
More Stringent Alternative
(Controls 3,6,7 above, plus changes in assumed controls noted below)
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9) Kilns in Cement and Cement Product Manufacturing - install SCR
10) Furnaces in Glass and Glass Product Manufacturing - install SCR
11) Boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products
Manufacturing, and Pulp, Paper, and Paperboard Mills - install SCR (natural
gas-fired only)
a 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. For this analysis, IPM maps the calendar year 2023 to run year 2023,
calendar years 2024-2026 to run year 2025 and calendar years 2027-2029 to run year 2028. For model details, please
see Chapter 2 of the IPM documentation.
b NOx mass budgets are imposed in all run years in IPM (2023-2050) consistent with the measures highlighted in
this table.
0 The final rule implementation allows for the reduction associated with state-of-the-art combustion controls to occur
by 2024. It is captured in 2023 in this analysis to fully assess the impact of the mitigation measures occuring prior to
2026.
d For the 19 states with EGU obligations that are linked in 2026 the EPA is determining that the selected EGU
control stringency also includes emissions reductions commensurate with the retrofit of SCR at coal steam-fired
units of 100 MW or greater capacity (excepting circulating fluidized bed units (CFB)), new SNCR on coal-fired
units of less than 100 MW capacity and on CFBs of any capacity size, and SCR on oil/gas units greater than 100
MW that have historically emitted at least 150 tons of NOx per ozone season. The EPA evaluated the EGU sources
within the state of California and found there were no covered coal steam sources greater than 100 MW that would
have emissions reduction potential according to the EPA's assumed EGU SCR retrofit mitigation technologies. The
19 states are: Arkansas, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, Nevada,
New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah, Virginia, and West Virginia.
e Covanta has developed a proprietary low NOx combustion system (LN™) that involves staging of combustion air.
The system is a trademarked system and Covanta has received a patent for the technology.
4.1.1 EGU Regulatory Control Alternatives Analyzed
The illustrative emission budgets in this RIA represent EGU NOx ozone season emission
budgets for each state in 2023 and in 2026.70 This RIA analyzes the Transport FIP for the 2015
ozone NAAQS emission budgets, as well as a more and a less stringent alternative to the
Transport FIP for the 2015 ozone NAAQS. The more and less stringent alternatives differ from
the final rule in that they set different NOx ozone season emission budgets for the affected EGUs
and different dates for compliance with the backstop emission rate. All three scenarios use
emission budgets that were developed using uniform control stringency represented by $900 per
ton of NOx (2016$) in 2023 (i.e., optimizing existing controls and installation of state-of-the-art
combustion controls). The final rule and more stringent alternative use emission budgets that
were developed using a uniform control stringency represented by $11,000 per ton of NOx
(2016$) in 2025 (i.e., installation of SCR and SNCR post-combustion controls), while the less
70 Mapping each year in the analysis time period to a representative model run year enables IPM to perform multiple
year analyses while keeping the model size manageable. IPM considers the costs in all years in the planning horizon
while reporting results only for model run years. Run year 2023 is mapped to calendar year 2023, while run year
2025 is mapped to 2024-26, run year 2028 is mapped to 2027-29, run year 2030 is mapped to 2030-31, run year
2035 is mapped to 2032-37, run year 2040 is mapped to 2038-42, while run year 2045 is mapped to 2043-47.
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stringent alternative uses emissions budgets that were developed using a uniform control
stringency represented by $11,000 per ton of NOx (2016$) in 2030. The final rule and less-
stringent alternative defer the backstop emission rate to the 2030 run year, while the more
stringent alternative imposes the backstop emission rate in the 2025 run year (reflective of
imposition in the 2026 calendar year). The backstop emission rate is imposed by the relevant run
year (2025 or 2030 depending on alternative) on all coal units within the 19-state region that are
greater than 100 MW and lack SCR controls (excepting circulating fluidized bed (CFB) units).
The state emission budgets in this RIA are illustrative for several reasons. First, they
reflect an estimate of the future budget based on the EPA's preset budget methodology.
However, as described in the preamble, the implemented state budget may be either the preset
budget or the dynamic budget starting in 2026. As noted above, other parameters are used to
capture the dynamic budget impacts in this modeling, as the future heat input needed to derive
that budget number is not yet known. Second, the budgets are illustrative as the utilized 2023
preset budgets reflect full implementation of existing control optimization and upgrade to state-
of-the-art combustion control potential. However, the final rule state emission budgets and
implementation allows the limited number of reductions related to state-of-the-art combustion
control to be realized up through 2024. Finally, the illustrative budgets in this RIA were derived
using draft results from the EPA's data and engineering analysis up through October 2022. The
preset budgets reflected in the final rule are slightly different in some cases due to new data or
comment incorporation that occurred between October of 2022 and January 2023. The Agency
conducted additional sensitivity analysis using IPM demonstrating that the substituting in the
final preset state emission budgets instead of the illustrative ones modeled made no significant
difference in the cost implications described in the body of the RIA. The analysis is provided in
the docket for this rulemaking.
The three illustrative regulatory control alternatives presented in this RIA provide a
reasonable approximation of the impacts of the rule, as well as an evaluation of the relative
impacts of two regulatory alternatives. Table 4-3. reports the illustrative EGU NOx ozone season
emission budgets that are evaluated in this RIA for the 2023 - 2030 IPM run years. As described
above, starting in 2023, IPM is constrained to disallow emissions from affected EGUs in the 22
states to exceed the sum of emissions budgets but for the ability to use banked allowances from
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previous years for compliance. For individual states, IPM is constrained to disallow emissions
from exceeding 121% of the state emission budget (the assurance levels). In the IPM modeling
of these RIA alternatives, no further reductions in budgets occur after 2030, and budgets remain
in place for future years.71 These budgets are imposed in addition to the control measures
outlined in Table 4-2.
Table 4-3. Illustrative NOx Ozone Season Emission Budgets (Tons) Evaluated by IPM Run
Year
Region
Final Rule and More Stringent
Alternative
Less Stringent Alternative
2023
2025
2028
2030
2023
2025
2028
2030
Alabama
6,595
6,236
6,236
4,610
6,595
6,236
6,236
4,610
Arkansas
8,927
4,031
4,031
3,582
8,927
8,700
8,700
3,582
Illinois
7,474
5,363
4,555
4,050
7,474
6,415
4,985
4,050
Indiana
12,440
8,633
8,633
6,307
12,440
9,658
9,658
6,307
Kentucky
13,204
7,862
7,862
7,679
13,204
12,515
12,515
7,679
Louisiana
9,311
3,864
2,969
2,969
9,311
9,089
6,684
2,969
Maryland
1,206
592
592
592
1,206
592
592
592
Michigan
10,275
5,997
5,997
5,691
10,275
8,626
8,626
5,691
Minnesota
5,504
2,905
2,905
1,663
5,504
2,905
2,905
1,663
Mississippi
5,024
1,859
1,527
1,527
5,024
4,763
2,817
1,527
Missouri
12,598
7,329
7,329
6,770
12,598
11,063
11,063
6,770
Nevada
2,391
1,051
1,051
818
2,391
1,051
1,051
818
New Jersey
768
768
768
768
768
768
768
768
New York
3,858
3,333
3,333
3,333
3,858
3,858
3,858
3,333
Ohio
9,134
7,953
6,934
6,399
9,134
7,953
6,934
6,399
Oklahoma
10,271
3,842
3,842
3,842
10,271
9,044
9,044
3,842
Pennsylvania
8,918
7,146
7,146
4,816
8,918
8,691
8,691
4,816
Texas
40,294
22,964
22,407
21,631
40,294
36,173
34,678
21,631
Utah
15,755
2,604
2,604
2,604
15,755
9,934
9,934
2,604
Virginia
3,065
2,373
2,373
1,951
3,065
2,756
2,756
1,951
West Virginia
13,306
9,678
9,678
9,678
13,306
11,958
11,958
9,678
Wisconsin
6,295
3,407
3,407
3,407
6,295
3,407
3,407
3,407
Aggregated State
Emission Budgets
206,616
119,789
116,178
104,685
206,616
176,153
167,860
104,685
71 In 2030 onwards, dynamic budgets may cause the budgets to decrease. While the EPA does not model this
feature, the assumption of continued optimization of existing controls approximates compliance behavior and
associated costs that would result from dynamic budgets.
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Note that EGUs have flexibility in determining how they will comply with the allowance trading
program. As discussed below, the way that they comply may differ from the methods forecast in
the modeling for this RIA. See Section 4.3 for further discussion of the modeling approach used
in the analysis presented below.
4.1.2 Non-EGU Regulatory Control Alternatives Analyzed
As discussed in Section I.B. of the preamble and Sections 4.4 and 4.5 below, we used the
list of emissions units estimated to be captured by the applicability criteria, the assumed control
technologies that would meet the emissions limits, and information on control efficiencies and
default cost per ton values from the control measures database (CMDB), to estimate NOx
emissions reductions and costs for the year 2026. We estimated emissions reductions using the
actual emissions from the 2019 emissions inventory. The EPA did not estimate emissions
reductions of SO2, PM2.5, CO2 and other pollutants that may be associated with controls on non-
EGU emissions units. For details about the non-EGU assessment and the steps taken to estimate
emissions units, emissions reductions, and costs, see the memorandum titled Summary of Final
Rule Applicability Criteria and Emissions Limits for Non-EGU Emissions Units, Assumed
Control Technologies for Meeting the Final Emissions Limits, and Estimated Emissions Units,
Emissions Reductions, and Costs available in the docket.72
The rule imposes emissions limits on each of the emission unit types identified in Table
4-1. The less stringent alternative assumes less stringent control technologies for the
reciprocating internal combustion engines in Pipeline Transportation of Natural Gas and boilers
in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore Mining, Basic Chemical
Manufacturing, Petroleum and Coal Products Manufacturing, and Pulp, Paper, and Paperboard
Mills relative to the final rule. The more stringent alternative assumes more stringent control
technologies for the kilns in Cement and Concrete Products Manufacturing, the furnaces in Glass
and Glass Products Manufacturing, and the natural gas-fired boilers in Iron and Steel Mills and
Ferroalloy Manufacturing, Metal Ore Mining, Basic Chemical Manufacturing, Petroleum and
Coal Products Manufacturing, and Pulp, Paper, and Paperboard Mills relative to the final rule.
Table 4-4 below provides a summary of the 2019 ozone season emissions for non-EGUs for the
72 https://www.regulations.gov/document/EPA-HQ-OAR-2021-0668
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20 states subject to the FIP in 2026, along with the estimated ozone season reductions for the
final rule and the less and more stringent alternatives.
Table 4-4. Ozone Season NOx Emissions and Emissions Reductions for the Final Rule and
the Less and More Stringent Alternatives for Non-EGUs
State
2019 Ozone
Season
Emissions3
Final Rule:
Ozone Season
NOx Reductions
Less Stringent:
Ozone Season
NOx Reductions
More Stringent:
Ozone Season NOx
Reductionsb
AR
8,790
1,546
457
1,690
CA
16,562
1,600
1,432
4,346
IL
15,821
2,311
751
2,991
IN
16,673
1,976
1,352
3,428
KY
10,134
2,665
583
3,120
LA
40,954
7,142
1,869
7,687
MD
2,818
157
147
1,145
MI
20,576
2,985
760
5,087
MO
11,237
2,065
579
4,716
MS
9,763
2,499
507
2,650
NJ
2,078
242
242
258
NV
2,544
0
0
0
NY
5,363
958
726
1,447
OH
18,000
3,105
1,031
4,006
OK
26,786
4,388
1,376
5,276
PA
14,919
2,184
1,656
4,550
TX
61,099
4,691
1,880
9,963
UT
4,232
252
52
615
VA
7,757
2,200
978
2,652
WV
6,318
1,649
408
2,100
Totals
302,425
44,616
16,786
67,728
a The 2019 ozone season emissions are calculated as 5/12 of the annual emissions from the following two emissions
inventory files: nonegu_SmokeFlatFile_2019NEI_POINT_20210721_controlupdate_13sep2021_v0 and
oilgas_SmokeFlatFile_2019NEIPOINT 2021072 lcontrolupdatel 3 sep202 lvO.
b Note that for some industries the more stringent alternative reflects assumed technologies (and estimated emissions
reductions) that are not widely demonstrated in practice in the U.S.
4.2 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. The EPA used IPM to project likely future electricity market conditions
with and without the Transport FIP for the 2015 ozone NAAQS.
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IPM, developed by 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. Due to lack of lead time,
the EPA does not allow IPM to build certain new capital investments such as new, unplanned
natural gas or renewable capacity or new SCR or SNCR through the 2023 run year in response to
the state emission budgets (i.e., retrofits, retirements or builds additional to those selected in the
baseline are not allowed in 2023). The compliance analysis of the final rule and alternatives
assumes new combustion controls in the 2023 analysis year (although the rule would require
these in 2024). After 2023, this limit is relaxed, and the model is no longer prevented from
undertaking these capital investments.
The 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 prospective environmental policies. The model is designed to reflect electricity
markets as accurately as possible. The 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.73
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.74
73 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/documentation-epas-power-sector-modeling-platform-v6-summer-2021-reference-
case.
74 See Chapter 8 of EPA's Baseline run using IPM v6 documentation, available at:
https://www.epa.gov/airmarkets/documentation-epas-power-sector-modeling-platform-v6-summer-2021-reference-
case.
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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.75
To estimate the annualized costs of additional capital investments in the power sector, the
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
capital.76 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.
The 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, 2011), the Mercury and Air Toxics Standards
(U.S. EPA, 201 la), the Clean Power Plan for Existing Power Plants (U.S. EPA, 2015), the
Carbon Pollution Standards for New Power Plants (U.S. EPA, 2015a), the Cross-State Air
Pollution Rule Update (U.S. EPA, 2016), the Affordable Clean Energy Rule (U.S. EPA, 2019),
the Clean Power Plan Repeal (U.S. EPA, 2019), and the Revised Cross-State Air Pollution
Update Rule (U.S. EPA, 2021). The EPA has also used IPM to estimate the air pollution
75 See Chapter 7 of the IPM v6 documentation. The documentation for EPA's power sector modeling platform v6 -
summer 2021 reference case consists of a comprehensive document for the Summer 2021 release of IPM v. 6.20 and
is available at: https://www.epa.gov/airmarkets/documentation-epas-power-sector-modeling-platform-v6-summer-
2021-reference-case.
76 See Chapter 10 of the documentation for EPA's power sector modeling platform v6 - summer 2021 reference case,
available at: https://www.epa.gov/airmarkets/documentation-epas-power-sector-modeling-platform-v6-summer-
2021-reference-case
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reductions and power sector impacts of water and waste regulations affecting EGUs, including
Cooling Water Intakes (316(b)) Rule (U.S. EPA, 2014), Disposal of Coal Combustion Residuals
from Electric Utilities (U.S. EPA, 2015b), Steam Electric Effluent Limitation Guidelines (ELG)
(U.S. EPA, 2015c), and Steam Electric Reconsideration Rule (U.S. EPA, 2020).
The model and the 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 review77 of EPA Baseline run version 5.13 using the
Integrated Planning Model. Additionally, and in the late 1990s, the Science Advisory Board
reviewed IPM as part of the CAA Amendments Section 812 prospective studies.78 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.
4.3 The EPA's Power Sector Modeling of the Baseline run and Three Regulatory Control
Alternatives
The IPM "baseline run" 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 run represents an element of the
baseline for this RIA.79 The EPA frequently updates the IPM baseline run to reflect the latest
77 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.
78 http://www2.epa.gov/clean-air-act-overview/benefits-and-costs-clean-air-act
79 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." (USEPA, 2010).
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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.
4.3.1 The EPA 's IPM Baseline run v. 6.20
For our analysis of the final Transport FIP for the 2015 ozone NAAQS, the EPA used an
updated version of the Summer 2021 release of IPM version 6.20 to provide power sector
emissions data for air quality modeling, as well as a companion updated database of EGU units
(the National Electricity Energy Data System, or NEEDS, Summer 202280) that is used in the
EPA's modeling applications of IPM. The IPM Baseline run includes the CSAPR, CSAPR
Update, and the Revised CSAPR Update, as well as the Mercury and Air Toxics Standards. The
Baseline run also includes the 2015 Effluent Limitation Guidelines (ELG) and the 2015 Coal
Combustion Residuals (CCR), and the finalized 2020 ELG and CCR rules.81 While finalized in
December 2021, the impacts of the 2023 and Later Model Year Light-Duty Vehicle GHG
Emissions Standards are not 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 also not captured in the baseline.82 Additionally, the
model was also updated to account for current elevated input fuel pricing, with natural gas prices
in the 2023 and 2025 run years hardwired based on futures prices,83 and coal prices escalated in
the 2023 run year. The model runs for the main RIA analysis do not capture the impacts of the
Inflation Reduction Act (IRA). Appendix 4A includes a representation of key IRA provisions in
the baseline and under a scenario that includes the final rule as modeled here, along with the
associated costs and emission reductions. The analysis of power sector cost and impacts
presented in this chapter is based on a single IPM Baseline run, and represents incremental
80 https://www.epa.gov/airmarkets/national-electric-energy-data-system-needs-v6.20
81 For a full list of modeled policy parameters, please see:
https://www.epa.gov/airmarkets/documentation-epas-power-sector-modeling-platform-v6-summer-2021-reference-
case
82 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
83 2023 and 2025 Henry Hub gas prices were exogenously input based on the average of the daily values of the
NYMEX Natural Gas Henry Hub Annual Strip over the 5/09/22 - 6/21/22 period, which reflected the most recent
set of values available at the time of this analysis. Hence the price of natural gas in these run years is derived based
on futures pricing and not a solved for output. Subsequent years reflect fundamentals-based pricing.
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impacts projected solely as a result of compliance with the emissions budgets presented in Table
4-3. above and the backstop emission rate.
4.3.2 Methodology for Evaluating the Regulatory Control Alternatives
To estimate the costs, benefits, and economic and energy market impacts of the Transport
FIP for the 2015 ozone NAAQS, the EPA conducted quantitative analysis of the three regulatory
control alternatives: the Transport FIP for the 2015 ozone NAAQS emission budgets and a more
and a less stringent alternative. Details about these regulatory control alternatives, including
state-specific EGUNOx ozone-season emissions budgets for each alternative as analyzed in this
RIA, are provided above in Section 4.1.
Before undertaking power sector analysis to evaluate compliance with the regulatory
control alternatives, the EPA first considered available EGU NOx mitigation strategies that could
be implemented for the 2023 ozone season. The EPA considered all widely-used EGU NOx
control strategies: optimizing84 NOx removal by existing operational selective catalytic reduction
(SCRs) and turning on and optimizing existing idled SCRs; optimizing existing idled selective
non-catalytic reduction (SNCRs); installation of (or upgrading to) state-of-the-art NOx
combustion controls; and installing new SCRs and SNCRs. The EPA determined that affected
EGUs within the 22 states could implement the NOx mitigation strategies based on optimization
of existing controls for the 2023 ozone season.85 (The final rule does not phase in reductions
associated with upgraded combustion controls until 2024, but the modeling for this RIA assumes
this control strategy in the 2023 run year.) After assessing the available NOx mitigation methods,
this RIA projects the system-wide least-cost strategies for complying with the annual budgets
and the backstop emission rate. Least-cost compliance may lead to the application of different
control strategies at a given source compared to the particular control measure assumed for that
source in the analysis used to calculate the budgets, which is in keeping with the cost-saving
compliance flexibility afforded by this allowance trading program.
Within IPM, units are assigned NOx emission rates based on historical data. To account
for changes in emission rates based on the seasonal operation of controls, each unit is assigned
84 Optimization of controls refers to the process of fully operating controls in order to meet the "widely achievable
emission rate" as outlined in the EGU NOx Mitigation Strategies Final Rule TSD.
85 The analysis assumes that SNCR and SCR optimization and state-of-the-art combustion control installation is
available starting in 2023 and is adopted by all units identified by the Engineering Analysis. This compliance choice
is an exogenous input into IPM.
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four modes of operation. When the model is run, IPM selects the appropriate mode for each
season based on historical data (i.e., how the unit operated in the past), whether the unit is
subject to any seasonal or annual NOx reduction requirements, and whether the unit installs any
additional controls.86 The rule's emission control requirements for EGUs only apply during the
program's ozone season (May 1 through September 30).
Many of these mitigation strategies are captured within IPM. However, due to limitations
on model size, IPMv.6.20 does not have the ability to endogenously determine whether to
operate existing EGU post-combustion NOx controls (i.e., SCR or SNCR), optimize existing
SCRs and SNCRs, and install combustion controls in response to a regulatory emissions
requirement.87 The treatment of these controls in the analyses are described in turn. The
operating status of existing post-combustion NOx controls at a particular EGU in a model
scenario is determined by the model user. In order to evaluate compliance with the regulatory
alternatives, the EPA determined outside of IPM the operation of existing controls that are idle in
the baseline that would be expected for compliance with each of the evaluated regulatory
alternatives and for which model years they can feasibly be applied. The EPA considers a unit to
have optimized use of an SCR if emissions rates are equal to (or below) the "widely achievable"
rate of 0.08 lbs/MMBtu for coal steam units, 0.03 lbs/MMBtu for oil/gas and combustion turbine
units, and 0.012 lb/MMBtu for combined cycle units.88 Within IPM, units with partially
operating or idled SCRs are defined as SCR-equipped units with ozone season NOx emission
rates exceeding the optimized rates in the baseline run. These units had their emission rates
lowered to the applicable "widely achievable" optimized emissions rate. These control options
(optimizing partially operating SCR controls or turning on idled SCR controls) are achievable in
2023 and have a uniform control cost of $900 per ton (2016$) for coal units that partially operate
their controls and $1,600 per ton (2016$) for coal units that have idled their controls, and $900
per ton (2016$) for the other identified sources. As explained below in Section 4.3.3, the costs
associated with this measure are accounted for outside of the model, and no further adjustments
were made inside the model to the variable and fixed operating cost of these units or to their
86 For details on the emission rate assumptions within the model, please refer to chapter 3 of the IPM documentation
available at: https://www.epa.gov/system/files/documents/2021-09/epa-platform-v6-summer-2021-reference-case-
09-ll-21-v6.pdf.
87 EGUs with idled SCR or SNCR in the Baseline run represent a small percentage (less than 10 percent) of the EGU
fleet that is equipped with NOx post-combustion controls.
88 For details on the derivation of this standard, please see preamble Section VLB. 1.
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modeled heat rates. Under the proposed rule, 261 units are projected to fully run existing SCR
controls in 2023 and in each year thereafter until the year the unit retires or at the end of the
model period.
The EPA considers a unit to have optimized use of an SNCR if NOx emissions rates are
equal to or less than the mode 2 rate from the NEEDS database (Summer 2021). As described in
Chapter 3 of the EPA's power sector IPM Modeling Documentation, these backstop NOx mode
rates are calculated from historical data and reflect operation of existing post-combustion
controls. Mode 2 for SNCR-controlled coal units is intended to reflect the operation of that unit's
post-combustion control based on prior years when that unit operated its control. Hence any units
with existing SNCRs with NOx emission rates greater than their mode 2 rates in the 22-state
region had their rates lowered to their mode 2 rates. These control options are achievable in 2023
and have a uniform control cost of $1,800 per ton (2016$). As explained below in Section 4.3.3,
the costs associated with this measure are accounted for outside of the model, and no further
adjustments were made inside the model to the variable and fixed operating cost of these units.
Under this rule, 44 units are projected to fully run existing SNCR controls in 2023 and in each
year thereafter until the year the unit retires or at the end of the model period.
Finally, unit combustion control configurations listed in NEEDS were compared against
Table 3-14 in the documentation for the EPA Power Sector Modeling Platform v.6.20 Summer
2021 Reference Case, which lists state-of-the-art combustion control configurations based on
unit firing type. This allowed the EPA to identify units that would receive state-of-the-art
combustion control upgrades in IPM. The EPA then followed the procedure in the EGU NOx
Mitigation Strategies Final Rule TSD to calculate each of these unit's new NOx emission rate.
These upgrades were assumed to occur in the 2023 run year (though the rule does not reflect
them until 2024) and have a uniform control cost of $1,600 per ton (2016$). As explained below
in Section 4.3.3, the costs associated with this measure are accounted for outside of the model,
and no further adjustments were made inside the model to the variable and fixed operating cost
of these units. Under this rule, nine units are projected to install state-of-the-art combustion
controls in 2023 and operate them in each year thereafter until the year the unit retires or at the
end of the model period. The book-life of the new combustion controls is assumed to be 15
years, hence the stream of costs from 2023-45 fully captures the cost of any incremental controls
under the rule. The EGU NOx mitigation strategies that are assumed to operate or are available
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to reduce NOx in response to each of the regulatory control alternatives are shown in Table 4-2
above; more information about the estimated costs of these controls can be found in the EGU
NOx Mitigation Strategies Final Rule TSD.
Under the final rule 8 GW of SCR installations are projected. Under the more stringent
alternative 15 GW of SCR installations are projected. Under the less stringent alternative 8 GW
of new SCR installations are projected. The book-life of the new SCRs is assumed to be 15
years, hence the stream of costs from 2023-45 fully captures the cost of any incremental controls
under the rule. Under the final rule and less stringent alternative an incremental 13 GW of coal
(63 units) retirements are projected by 2030. Under the more stringent alternative 8 GW of coal
retirements are projected by 2030. The associated costs of retirement are fully captured within
the total costs of this rule presented in the RIA.
In addition to the limitation on ozone season NOx emissions required by the EGU
emissions budgets for the 22 states and the backstop emission rate, there are four important
features of the allowance trading program represented in the model that may influence the level
and location of NOx emissions from affected EGUs, including: the ability of affected EGUs to
buy and sell NOx ozone season allowances from one another for compliance purposes; the
ability of affected EGUs to bank NOx ozone season allowances for future use; the effect of limits
on the total ozone season NOx emissions from affected EGUs in each state required by the
assurance provisions; and the treatment of banked pre-2023 vintage NOx ozone season
allowances issued under the Revised CSAPR Update now being revised under this rule. Each of
these features of the ozone season allowance trading program is described below. The analysis
does not explicitly capture the dynamic budget adjustments over time, but the forced operation of
controls during the ozone season over the forecast period (even in the absence of binding mass
limits) approximates this feature of the program design.
Affected EGUs are expected to choose the least-cost method of complying with the
requirements of the allowance trading program, and the distribution of ozone season NOx
emissions across affected EGUs is generally governed by this cost-minimizing behavior in the
analysis. The total ozone season NOx emissions from affected EGUs in this analysis are limited
to the amount allowed by the sum of the NOx budgets across the 22 states, the starting bank of
allowances, and any additional allowances that are banked for future use. The number of banked
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allowances is influenced by the determination of whether (i) existing controls that are idle in the
baseline run are turned on, (ii) it is less costly to abate ozone season NOx emissions in a current
ozone season than to abate emissions in a later ozone season, and (iii) the restriction on the total
size of the bank, which is 21 percent of the sum of the state emissions budgets for the current
control period until 2030 (at which point it declines to 10.5%). Affected EGUs are expected to
bank NOx ozone season allowances in the 2023 ozone season for use in a later ozone season.
The model starts with an assumed bank level in 2023 (described below) and endogenously
determines the bank in each subsequent year.
The rule allows pre-2023 vintage NOx ozone season allowances to be used for
compliance with this rule. The sources that would be participants in a revised Group 3 Trading
Program under this rule are transitioning from several different starting points - with some
sources already in the Group 3 Trading Program under its current regulations, some sources
coming from the Group 2 Trading Program, and some sources not currently participating in any
seasonal NOx trading program. As described in Section VI.B. 12 of the preamble, the EPA is
transitioning provisions that differ across the sets of potentially affected sources based on the
sources' different starting points. Based on the EPA's expectation of the size of the NOx
allowance bank after the one-time conversion carried out pursuant to the terms of this rule, the
treatment of these banked allowances is represented in the modeling as an additional 43,389 tons
of NOx allowances, the equivalent of one year of the variability limit associated with the
emission budgets, that may be used by affected EGUs during the 2023 ozone season or in later
ozone seasons under the Transport FIP for the 2015 ozone NAAQS and the more and less
stringent alternatives.
While there are no explicit limits on the exchange of allowances between affected EGUs
and on the banking of 2023 and future-year vintage NOx ozone season allowances, the assurance
provisions limit the amount of seasonal NOx emissions by affected EGUs in each of the 22
states. The assurance level limits affected EGU emissions over an ozone season to the state's
NOx ozone season emissions budget plus an increment equal to 21 percent of each state's
emissions budget. This increment is called the variability limit. See Section VI.B. 5 of the
preamble for a discussion of the purpose of the assurance provision and further detail about how
the variability limits and assurance levels are determined. If a state exceeds its assurance level in
a given year, sources within that state are assessed a 3-to-l allowance surrender penalty on the
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excess tons. Section VI.B.5 of the preamble also explains how the EPA then determines which
EGUs are subject to this surrender requirement. In the modeling, the assurance provisions are
represented by a limit on the total ozone season NOx emissions that may be emitted by affected
EGUs in each state, and thus the modeling does not permit affected EGUs to collectively emit
beyond their respective state's assurance levels and thus incur penalties.
4.3.3 Methodology for Estimating Compliance Costs
This section describes the EPA's approach to quantify estimated compliance costs in the
power sector associated with the three illustrative regulatory control alternatives. These
compliance costs include estimates projected directly by the model as well as calculations
performed outside of the model that use IPM model inputs and methods. The model projections
capture the costs associated with shifting generation to lower-NOx emitting EGUs. As discussed
in the previous subsection, the costs of increasing the use and optimizing the performance of
existing and operating SCRs and SNCRs,89 and for installing or upgrading NOx combustion
controls, were estimated outside of the model. The costs for these three NOx mitigation
strategies are calculated based on IPM emissions projections and use the same NOx control cost
equations used in IPM. Therefore, this estimate is consistent with modeled projections and
provides the best available quantification of the costs of these NOx mitigation strategies.
The following steps summarize the EPA's methodology for estimating the component of
compliance costs that are calculated outside of the model for the final rule alternative in 2023.
Similar calculations are performed for every year in the forecast horizon90:
(1) In the model projections, identify all EGUs in the 22 states that can adopt the following
NOx mitigation strategies (described in previous subsection):
• Fully operating existing SCRs
• Fully operating existing SNCRs
• Installing state-of-the-art combustion controls
(2) Estimate the total NOx reductions that are attributable to each of these strategies:
89 This includes optimizing the performance of SCRs that were not operating.
90 For more information on the derivation of costs and useful life of combustion controls, please see EGU NOx
Mitigation Strategies Final Rule TSD.
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• Fully operating existing SCRs at coal steam, oil/gas steam, combined cycle, and
combustion turbine units: 5,314 tons
• Fully operating existing SNCRs: 1,192 tons
• Installing state-of-the-art combustion controls: 6,288 tons
(3) Estimate the average cost (in 2016$) associated with each of these strategies:91
• Fully operating existing SCRs at coal steam units, oil/gas steam, combined
cycle, and combustion turbine units: $900/ton
• Fully operating existing SNCRs: $l,800/ton
• Installing state-of-the-art combustion controls: $l,600/ton
(4) Multiply (2) by (3) to estimate the total cost associated with each of these strategies.
Table 4-5 summarizes the results of this methodology for the final rule alternative in 2023.
Table 4-5. Summary of Methodology for Calculating Compliance Costs Estimated Outside
of IPM for the Transport FIP for the 2015 Ozone NAAQS, 2023 (2016$)
NOx Mitigation Strategy
NOx Ozone
Season
Emissions
(tons)
Average Cost
($/ton)
Total Cost
($MM)
Optimize existing SCRs at coal steam, oil/gas,
combined cycle, and combustion turbine units
5,341
900
5
Optimize existing SNCRs
1,192
1,800
2
Installing state- of-the-art combustion controls
2,251
1,600
4
The EPA exogenously updated the emissions rates for the identified EGUs within the 22
states consistent with the set of controls determined for 2023-2025 within IPM. The model was
updated to incorporate the emissions budgets identified for each case, and the first-year bank
adjustment as outlined in Section 4.3.2. The backstop emission rate was also imposed on affected
uncontrolled units as outlined in Table 4-2, either in 2025 (in the more stringent alternative) or in
2030 (in the final rule and less stringent alternatives), which forced units to choose to either
retrofit or retire in either of those years, respectively.
The change in the reported power system production cost between the rule alternative
model run and the baseline run was used to capture the cost of generation shifting and the cost of
91 See EGU NOx Mitigation Strategies Proposed Rule TSD for derivation of cost-per-ton estimates for fully
operating SCRs and upgrading to state-of-the-art combustion controls.
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new SCR installations. The total costs of compliance with the regulatory control alternatives are
estimated as the sum of the costs that are modeled within IPM and the costs that are calculated
outside the model.
4.4 Estimating Emissions Units, Emissions Reductions, and Costs for Non-EGUs
For non-EGUs, the EPA developed an analytical framework to facilitate decisions about
industries and emission unit types for inclusion in a proposed Transport FIP for the 2015 ozone
NAAQS transport obligations. A February 28, 2022 memorandum, titled Screening Assessment
of Potential Emissions Reductions, Air Quality Impacts, and Costs from Non-EGU Emissions
Units for 2026, documents the analytical framework used to identify industries and emission unit
types included in the proposed FIP.92 To further evaluate the industries and emissions unit types
identified and to establish the proposed emissions limits, the EPA reviewed Reasonably
Available Control Technology (RACT) rules, New Source Performance Standards (NSPS) rules,
National Emissions Standards for Hazardous Air Pollutants (NESHAP) rules, existing technical
studies, rules in approved state implementation plan (SIP) submittals, consent decrees, and
permit limits. That evaluation is detailed in the Non-EGU Sectors Technical Support Document
(TSD) prepared for the proposed FIP.93 The EPA is retaining the industries and many of the
emissions unit types included in the proposal in this final action. Below is a summary of the
adjustments and additions to the emissions requirements and limitations the EPA made between
the proposed FIP and this final rule.
• For Pipeline Transportation of Natural Gas, the EPA is finalizing the same emissions
limits as proposed; however, the EPA is adjusting the applicability criteria to exclude
emergency engines. Further, to allow for the industry to install controls on the engines
with the largest potential for emissions reductions at cost-effective thresholds, the final
regulations allow for the use of facility-wide emissions averaging for engines in the
industry.
• For Cement and Concrete Product Manufacturing, in the final rule the EPA has removed
the daily source cap limit, which could have resulted in an artificially restrictive NOx
92 The memorandum is available in the docket here: https://www.regulations.gov/document/EPA-HQ-OAR-2021-
0668-0150.
93 The TSD is available in the docket here: https://www.regulations.gov/document/EPA-HQ-OAR-2021-0668-0145.
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emissions limit for affected cement kilns due to lower operating periods resulting from to
the COVID-19 pandemic.
• For Iron and Steel and Ferroalloy Manufacturing, the EPA is only finalizing a test-and-set
requirement for reheat furnaces premised on the installation of low-NOx burners. By not
finalizing the other proposed emissions limits that were likely to require the installation
of SCR, the EPA has addressed the various concerns regarding the feasibility and cost-
effectiveness of installation of the other proposed controls at other unit types at these
facilities.
• For Glass and Glass Product Manufacturing, the EPA is finalizing alternative standards
that apply during startup, shutdown, and idling conditions.
• For boilers in Iron and Steel and Ferroalloy Manufacturing, Metal Ore Mining, Basic
Chemical Manufacturing, Petroleum and Coal Products Manufacturing, and Pulp, Paper,
and Paperboard Mills, the EPA is finalizing a low-use exemption to eliminate the need to
install controls on low-use boilers that would have resulted in relatively small reductions.
• For municipal waste combustors in Solid Waste Combustors and Incinerators, the EPA is
finalizing emissions limits, summarized in Table 4-1.
In the final rule, the EPA is requiring that controls be installed and operational by the
2026 ozone season, except where an individual source qualifies for a limited extension of time to
comply based on a specific demonstration of necessity. Where an individual source submits a
satisfactory demonstration that an extension of time to comply beyond 2026 is necessary, the
EPA may grant an extension of up to one year for that source to fully implement the controls,
after which the source may request and the EPA may grant an additional extension of up to two
additional years for full compliance, where specific criteria are met. The EPA's evaluation of
timing issues associated with this rule are further discussed in Section VI.A of the preamble.
Because it is not possible to currently know which sources or how many may seek or be granted
an extension of time to comply with the emissions limits, we assume in the RIA that all covered
non-EGUs comply with the rule beginning in 2026.
With the exception of Solid Waste Combustors and Incinerators for each industry and
emissions unit type, using a 2019 inventory prepared from the emissions inventory system (EIS)
the EPA first estimated a list of emissions units captured by the applicability criteria for the final
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rule. For Solid Waste Combustors and Incinerators, the EPA estimated the list for MWCs using
the 2019 inventory and the NEEDS-v6-summer-2021-reference-case workbook.94 Based on the
review of RACT, NSPS, NESHAP rules, as well as SIPs, consent decrees, and permits, we also
assumed certain control technologies could meet the final emissions limits.
Using the list of emissions units estimated to be captured by the applicability criteria, the
assumed control technologies that would meet the emissions limits (see Table 4-18 below), and
information on control efficiencies and default cost/ton values from the CMDB95, the EPA
estimated NOx emissions reductions and costs for the year 2026. For the final rule the EPA did
not run the Control Strategy Tool (CoST) to estimate emissions reductions and costs and
programmed the assessment using R.96 The EPA did not estimate emissions reductions of SO2,
PM2.5, CO2 and other pollutants that may be associated with controls on non-EGU emissions
units. We estimated emissions reductions using the actual emissions from the 2019 emissions
inventory. In the assessment, we matched emissions units by Source Classification Code (SCC)
from the inventory to the applicable control technologies in the CMDB. We modified SCC codes
as necessary to match control technologies to inventory records. For additional details about the
steps taken to estimate emissions units, emissions reductions, and costs, see the memorandum
titled Summary of Final Rule Applicability Criteria and Emissions Limits for Non-EGU
Emissions Units, Assumed Control Technologies for Meeting the Final Emissions Limits, and
Estimated Emissions Units, Emissions Reductions, and Costs available in the docket.97
The estimates using the 2019 inventory and information from the CMDB identify proxies
for emissions units, as well as emissions reductions, and costs associated with the assumed
control technologies that would meet the final emissions limits. Emissions units subject to the
final rule emissions limits may be different than those estimated in this assessment; the estimated
emissions reductions from and costs to meet the final rule emissions limits may be different than
those estimated in this assessment. The reported total costs do not include the costs of
94 Available here: https://www.epa.gov/power-sector-modeling/national-electric-energy-data-system-needs-v6.
95 More information about the Control Strategy Tool (CoST) and the control measures database (CMDB) can be
found at the following link: https://www.epa.gov/economic-and-cost-analysis-air-pollution-regulations/cost-
analysis-modelstools-air-pollution.
96 R is a free software environment for statistical computing and graphics. Additional information is available here:
https://www.r-project.org/. The R code that processed the data to estimate the emissions reductions and costs is
available upon request.
97 https://www.regulations.gov/document/EPA-HQ-OAR-2021-0668
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monitoring, recordkeeping, reporting, or testing. The EPA submitted an information collection
request (ICR) to OMB associated with the monitoring, calibrating, recordkeeping, reporting, and
testing activities required for non-EGU emissions units — ICR for the Final Rule, Federal Good
Neighbor Plan Addressing Regional Ozone Transport for the 2015 Primary Ozone National
Ambient Air Quality Standard: Transport Obligations for non-Electric Generating Units, EPA
ICR No. 2705.01. The ICR is summarized in Section X.B.2 of the final rule preamble. The EPA
estimates monitoring, recordkeeping, reporting, and testing costs of approximately $3.8 million
per year on average for the first three years. These costs are not reflected in the cost estimates in
Table 4-19 and Table 4-20 below.
4.5 Estimated Impacts of the Regulatory Control Alternatives
4.5.1 Emissions Reduction Assessment for EGUs
As indicated in Chapter 1, the EGU NOx emissions reductions are presented in this RIA
from 2023 through 2042 and are based on IPM projections. As outlined in Section 4.3.2 IPM is
operating existing and newly installed controls seasonally based on historical operation patterns
and seasonal and annual emission constraints within the model. Table 4-6 presents the estimated
reduction in power sector NOx emissions resulting from compliance with the evaluated
regulatory control alternatives (i.e., emissions budgets) in the 22 states, as well as the impact on
other states. The emission reductions follow an expected pattern: the less stringent alternative
produces smaller emissions reductions than the final rule emissions budgets, and the more
stringent alternative results in more NOx emissions reductions.
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Table 4-6. EGU Ozone Season NOx Emissions and Emissions Changes for the Baseline run
and the Regulatory Control Alternatives from 2023 - 204598
Ozone Season NOx
(thousand tons)
Total Emissions
Change from Baseline run
Baseline
run
Final Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
Final Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
22 States
230
220
220
220
-10
-10
-10
2023
Other States
143
143
143
143
0
0
0
Nationwide
373
363
363
363
-10
-10
-10
22 States
203
181
193
168
-22
-10
-35
2024
Other States
128
129
128
130
1
0
2
Nationwide
331
310
321
298
-21
-10
-33
22 States
176
143
167
116
-34
-9
-60
2025
Other States
113
115
113
117
2
0
4
Nationwide
289
258
279
233
-32
-10
-56
22 States
167
140
159
114
-27
-8
-53
2026
Other States
107
109
107
110
2
0
3
Nationwide
274
248
266
224
-25
-8
-49
22 States
157
137
151
111
-20
-6
-46
2027
Other States
101
103
101
104
2
0
3
Nationwide
258
239
252
215
-19
-6
-43
22 States
147
134
143
109
-14
-4
-39
2028
Other States
95
96
95
97
2
0
3
Nationwide
242
230
238
206
-12
-4
-36
22 States
137
101
102
103
-36
-35
-33
2030
Other States
91
93
94
94
2
3
3
Nationwide
228
194
195
197
-34
-33
-31
22 States
132
101
101
103
-30
-30
-29
2035
Other States
88
89
89
90
1
1
2
Nationwide
220
190
190
193
-29
-30
-27
22 States
119
89
89
91
-30
-30
-29
2040
Other States
79
79
79
79
0
0
0
Nationwide
198
169
168
170
-30
-30
-29
22 States
102
80
80
80
-22
-22
-22
2045
Other States
76
76
76
76
0
0
0
Nationwide
178
156
156
156
-22
-22
-22
Within the compliance modeling, in addition to compliance with the mass budgets,
emissions reductions are also driven by the assumption that units fully operate their controls
98 This analysis is limited to the geographically contiguous lower 48 states.
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during the ozone season. For units with existing controls, this is reflected in the achievement of
the "widely achievable" rate as outlined in Section 4.3.2. For units that lack existing SCR
controls, this is reflected in the decision to install new controls (which must be operated in the
ozone season) or retire. The final rule and more stringent alternative feature identical
Engineering Analysis derived budgets based on installation of SCRs in the 2025 run year in the
19-state region. However, the final rule alternative defers the backstop emission rate until the
2030 run year for units without SCRs, while the more stringent alternative assumes the backstop
emission rate is imposed in the 2025 run year. The less stringent alternative imposes Engineering
Analysis derived budgets based on installation of SCRs in the 2030 run year in the 19-state
region, and the backstop emission rate taking effect in the 2030 run year.
Hence emission reductions are lower under the less stringent alternative compared to the
final rule through 2030 (since the mass budget is less stringent). The more stringent alternative
features the backstop emission rate in effect in the 2025 run year, for which the model is set up to
constrain affected EGUs to retrofit or retire in the 2025 run year, driving higher abatement (and
more SCR retrofits) than the final rule before 2030. However, in 2030, the modeling of the final
rule and less stringent alternatives estimates more retirements relative to the more stringent
alternative. The more stringent alternative extends the operating life of plants that chose to
retrofit in 2025 rather than retire and therefore, in 2030 onwards, emissions reductions for the
final rule and less stringent alternative are slightly greater, since budgets are the same and the
backstop emission rate is also in effect in both scenarios. For details on the EGU emissions
controls assumed in each of the regulatory control alternatives, please see Table 4-2.
The results of the EPA's analysis show that, with respect to compliance with the EGU
NOx emission budgets in 2023, maximizing the use of existing operating SCRs provides the
largest amount of ozone season NOx emission reductions (54 percent, affecting 261 units),
installing state-of-the-art combustion controls provides the next highest levels of ozone season
reductions (22 percent, affecting 9 units), while optimizing existing SNCRs (12 percent,
affecting 44 units) and generation shifting (11 percent) make up the remaining ozone season
NOx reductions. (Although the budgets are not set using generation shifting, the IPM modeling
for the RIA allows generation shifting as a compliance strategy and thus some reductions
associated with generation shifting are observed in this analysis.) Based on this analysis of how
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EGUs are expected to comply with the Transport FIP for the 2015 ozone NAAQS, none of the
Group 3 states are projected to exceed their variability limits, nor use a substantial number of
allowances from the starting bank during the 2023-2042 period."
In addition to the ozone season NOx reductions, there will also be reductions of other air
emissions associated with EGUs burning fossil fuels (i.e., co-pollutants) that result from
compliance strategies to reduce seasonal NOx emissions. These other emissions include the
annual total changes in emissions of NOx, SO2, CO2, and direct PM2.5 emissions changes. The
emissions reductions are presented in Table 4-7.
99 As shown in Table 4-6, in 2023 and 2025 seasonal NOx emissions from affected EGUs in the Group 3 states are
projected to emit at levels equal to or below the aggregated state budgets, and therefore (i) will not bank additional
allowances, or (ii) on net, not use any banked allowances available at the end of the previous year or, in the case of
2023, from the starting bank.
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Table 4-7. EGU Annual Emissions and Emissions Changes for NOx, SO2, PM2.5, and CO2
for the Regulatory Control Alternatives for 2023-2045
Annual NOx
(thousand tons)
Total Emissions
Change from Baseline run
Baseline
run
Final Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
Final Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
22 States
561
546
546
546
-15
-15
-15
2023
Other
States
328
329
329
329
0
0
0
Nationwide
889
874
875
874
-15
-15
-15
22 States
491
464
476
429
-26
-15
-62
2024
Other
States
286
287
286
291
1
0
5
Nationwide
777
752
762
720
-25
-15
-57
22 States
420
383
406
312
-38
-14
-108
2025
Other
States
244
246
243
253
2
-1
9
Nationwide
664
629
649
566
-35
-15
-99
22 States
398
367
386
301
-31
-12
-96
2026
Other
States
232
234
231
240
2
-1
8
Nationwide
630
601
617
541
-29
-12
-88
22 States
375
351
366
290
-24
-9
-85
2027
Other
States
220
222
220
227
2
0
7
Nationwide
595
573
586
517
-22
-9
-78
22 States
353
336
346
279
-17
-7
-73
2028
Other
States
208
210
209
214
1
0
5
Nationwide
561
545
554
493
-16
-7
-68
22 States
324
261
262
270
-64
-62
-54
2030
Other
States
208
210
211
212
1
3
4
Nationwide
533
471
473
482
-62
-59
-50
22 States
304
254
254
259
-49
-49
-44
2035
Other
States
197
201
201
201
3
3
4
Nationwide
501
455
455
460
-46
-46
-41
22 States
267
221
221
225
-46
-46
-41
2040
Other
States
173
174
174
174
1
1
1
Nationwide
440
395
395
400
-45
-45
-40
22 States
218
195
195
197
-23
-23
-22
2045
Other
States
160
160
160
160
0
1
0
Nationwide
378
355
356
357
-23
-22
-21
151
-------
Annual SO2
(thousand tons)
Total Emissions
Change from Baseline run
Baseline
run
Final Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
Final Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
22 States
916
915
913
915
-1
-3
-1
2023
Other
States
279
279
279
279
0
0
0
Nationwide
1195
1194
1192
1194
-1
-3
-1
22 States
787
766
782
723
-21
-5
-64
2024
Other
States
239
240
239
243
1
0
4
Nationwide
1025
1006
1021
966
-19
-5
-59
22 States
657
617
651
531
-40
-6
-127
2025
Other
States
199
201
198
207
2
-1
8
Nationwide
856
818
849
738
-38
-7
-118
22 States
574
543
569
463
-31
-5
-111
2026
Other
States
181
183
181
188
2
0
7
Nationwide
755
726
750
651
-29
-5
-104
22 States
491
469
487
395
-22
-4
-96
2027
Other
States
163
164
163
168
1
0
5
Nationwide
654
633
650
563
-21
-4
-91
22 States
408
395
405
327
-13
-3
-80
2028
Other
States
145
145
146
149
0
0
4
Nationwide
553
540
551
476
-13
-2
-77
22 States
385
289
283
330
-95
-102
-54
2030
Other
States
147
150
151
151
2
4
3
Nationwide
532
439
434
481
-93
-98
-51
22 States
366
342
344
349
-24
-22
-16
2035
Other
States
135
138
138
137
3
3
2
Nationwide
501
480
482
486
-21
-19
-15
22 States
305
279
279
294
-26
-26
-12
2040
Other
States
126
127
127
127
1
1
1
Nationwide
432
406
406
420
-25
-25
-11
22 States
220
206
206
214
-15
-14
-6
2045
Other
States
128
128
128
128
0
0
0
Nationwide
349
334
334
342
-15
-15
-7
152
-------
Annual PM2.5
(thousand tons)
Total Emissions
Change from Baseline run
Baseline
run
Less-
More-
Less-
More-
Final Rule
Stringent
Alternative
Stringent
Alternative
Final Rule
Stringent
Alternative
Stringent
Alternative
22 States
63
63
63
63
0
0
0
2023
Other
States
40
40
40
40
0
0
0
Nationwide
103
103
103
103
0
0
0
22 States
57
56
56
55
-1
0
-2
2024
Other
States
36
36
36
37
0
0
1
Nationwide
93
92
93
92
-1
0
-1
22 States
51
49
50
47
-2
-1
-3
2025
Other
States
33
33
33
34
0
0
1
Nationwide
84
82
83
81
-2
-1
-2
22 States
49
48
49
46
-1
0
-3
2026
Other
States
33
33
33
34
0
0
1
Nationwide
82
81
81
80
-1
0
-2
22 States
48
47
48
46
-1
0
-2
2027
Other
States
32
32
32
33
0
0
1
Nationwide
80
80
80
79
-1
0
-2
22 States
47
46
47
45
0
0
-2
2028
Other
States
32
32
32
33
0
0
1
Nationwide
79
78
79
77
0
0
-1
22 States
45
43
43
44
-2
-2
0
2030
Other
States
32
32
32
32
0
0
0
Nationwide
76
75
75
76
-1
-1
0
22 States
46
44
44
45
-2
-2
-1
2035
Other
States
30
30
30
30
0
0
0
Nationwide
75
74
74
75
-1
-1
0
22 States
44
43
43
44
-2
-2
0
2040
Other
States
28
28
28
28
0
0
0
Nationwide
73
71
71
72
-2
-2
0
22 States
42
42
42
42
0
0
0
2045
Other
States
28
28
28
28
0
0
0
Nationwide
70
70
70
70
0
0
0
153
-------
Annual CO2
(million short tons)
Total Emissions
Change from Baseline run
Baseline
run
Final Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
Final Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
22 States
1033
1032
1032
1032
0
0
0
2023
Other
States
591
592
592
591
0
0
0
Nationwide
1624
1624
1624
1624
0
0
0
22 States
947
935
943
919
-12
-4
-28
2024
Other
States
539
541
540
548
2
0
8
Nationwide
1487
1476
1483
1467
-10
-4
-20
22 States
862
838
854
806
-24
-8
-56
2025
Other
States
488
491
488
504
3
0
17
Nationwide
1350
1329
1342
1310
-21
-8
-40
22 States
844
826
839
796
-18
-6
-48
2026
Other
States
477
480
477
492
3
0
15
Nationwide
1322
1306
1316
1288
-16
-6
-34
22 States
827
814
823
786
-13
-3
-41
2027
Other
States
467
469
467
480
2
0
13
Nationwide
1294
1284
1290
1266
-10
-3
-28
22 States
809
803
808
776
-7
-1
-33
2028
Other
States
457
459
457
468
2
0
12
Nationwide
1266
1261
1265
1244
-5
-1
-22
22 States
784
753
755
769
-31
-29
-16
2030
Other
States
450
455
456
458
5
6
7
Nationwide
1235
1209
1211
1227
-26
-23
-8
22 States
792
774
774
781
-19
-18
-12
2035
Other
States
436
438
438
439
2
3
3
Nationwide
1228
1212
1213
1220
-16
-15
-8
22 States
727
706
706
716
-21
-21
-11
2040
Other
States
411
411
412
412
1
1
1
Nationwide
1138
1117
1117
1128
-20
-20
-10
22 States
670
662
662
666
-9
-9
-4
2045
Other
States
400
400
400
400
0
0
0
Nationwide
1070
1061
1062
1066
-9
-8
-4
154
-------
4.5.2 Compliance Cost Assessment for EGUs
The estimates of the changes in the cost of supplying electricity for the regulatory control
alternatives are presented in Table 4-8.100 Since the final rule does not result in any additional
recordkeeping, monitoring or reporting requirements, the costs associated with compliance,
monitoring, recordkeeping, and reporting requirements are not included within the estimates in
this table.
Table 4-8. National Power Sector Compliance Cost Estimates (millions of 2016$) for the
Final Rule
More-
Stringent
Alternative
Less-
Stringent
Alternative
2023-2027 (Annualized)
14
677
-19
2023-2045 (Annualized)
449
645
446
2023 (Annual)
57
49
56
2024 (Annual)
-5
835
-35
2025 (Annual)
-5
835
-35
2026 (Annual)
-5
835
-35
2027 (Annual)
24
762
-47
2030 (Annual)
705
835
772
2035 (Annual)
817
592
847
2045 (Annual)
182
251
168
"2023-2027 (Annualized)" reflects total estimated annual compliance costs levelized over the period 2023 through
2027 and discounted using a 3.76 real discount rate.101 This does not include compliance costs beyond 2027. "2023-
2045 (Annualized)" reflects total estimated annual compliance costs levelized over the period 2023 through 2045
and discounted using a 3.76 real discount rate. This does not include compliance costs beyond 2045. "2023
(Annual)" through "2045 (Annual)" costs reflect annual estimates in each of those years.102
There are several notable aspects of the results presented in Table 4-8. One notable result is
that the estimated annual compliance costs for the final rule and less stringent alternative are
negative (i.e., a cost reduction) in 2023 through 2026, although this regulatory control alternative
reduces NOx emissions by 40 thousand tons as shown in Table 4-6. While seemingly
counterintuitive, estimating negative compliance costs in a single year is possible given the
100 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.
101 This table reports compliance costs consistent with expected electricity sector economic conditions. An NPV of
costs was calculated using a 3.76% real discount rate consistent with the rate used in IPM's objective function for
cost-minimization. The NPV of costs was then used to calculate the levelized annual value over a 5-year period
(2023-2027) and a 23-year period (2023-2045) using the 3.76% rate as well. Tables ES-15 and 8-7 report the NPV
of the annual stream of costs from 2023-2042 using 3% and 7% consistent with OMB guidance.
102 Cost estimates include financing charges on capital expenditures that would reflect a transfer and would not
typically be considered part of total social costs.
155
-------
assumption of perfect foresight. IPM's objective function is to minimize the discounted net
present value (NPV) of a stream of annual total cost of generation over a multi-decadal time
period.103 The specific reason for why costs are negative in these years for these two alternatives
follows.
Under the final rule and more stringent alternative budgets assume SCR/SNCR
optimization, state-of-the-art combustion control and SCR installations are selected by the 2025
run year. Under the less stringent alternative, budgets assume SCR/SNCR optimization, state-of-
the-art combustion control by the 2025 run year, but SCR installation is not assumed until the
2030 run year. Under the final rule and the less stringent alternative, the backstop emission rate
is imposed in the 2030 run year, while under the more stringent alternative, the backstop
emission rate is imposed in the 2025 run year. In the case of the final rule and less stringent
alternative, we see two waves of incremental coal retirement relative to the baseline - roughly 2
GW are retired in the 2025 run year (responding to tightening budgets), and an incremental 12
GW of retirements in the 2030 run year (responding to the backstop emission rate). In the case of
the more stringent alternative, we see a single wave of an incremental 12 GW relative to the
baseline in 2025.
The first wave of coal retirements reflects units that face challenging near-term conditions
in the baseline but would have been more economically valuable later in the baseline forecast
period, when demand growth and other firm retirements would improve their competitive
position. Hence early retirement of this capacity in the final rule and less stringent alternative
results in slightly lower near-term costs, but higher longer-term costs, and a point estimate of
negative costs in a single year.104 In the 2030 run year, the imposition of the backstop emission
rate under the final rule and the less stringent alternative results in a greater amount of coal
retirement reflective of projected economic preferences of unit owners/operators searching for
least-cost compliance strategies. Under the more stringent alternative, the backstop emission rate
103 For more information, please see Chapter 2 of the IPM documentation.
104 As a sensitivity, the EPA re-calculated costs assuming annual costs cannot be negative. This resulted in
annualized 2023-42 costs under the final rule increasing from $448.6 million to $449.5 million (less than 1%) and
did not change the conclusions of this RIA.
156
-------
is imposed in 2025, which results in a single wave of coal retirements and higher costs
throughout the forecast period.
Under the final rule, operating existing SCR and SNCR controls and upgrading to state-of-
the-art combustion controls provides a large share of the total emissions reductions in 2023. The
model is constrained in 2023 to builds and retrofits that occurred in the baseline and features
higher natural gas and coal prices reflecting near term trends. This means there is less flexibility
to respond to the mass budgets, and costs are higher in 2023 than in 2025 and 2028, when fuel
prices return to fundamentals and builds are not constrained to baseline levels. The imposition of
the deferred backstop emission rate in 2030 results in retrofit/retirement decisions being made in
that year as least-cost compliance strategies and fleet turnover as a result. Hence costs rise in
2030, and projected costs for the final rule peak in 2035 at $817 million (2016$) and annualized
costs for the 2023-2045 period are $449 million (2016$). To put these costs into context, the
incremental 2035 projected cost constitutes 0.6 percent of total projected baseline system
production costs.
Under the more stringent alternative, while budgets are unchanged from the final rule, the
backstop emission rate is imposed in the 2025 run year. In the model, affected units are required
to retrofit/retire sooner, and costs peak in 2025 at $835 million as a result. The annualized costs
over the 2023-2045 period are $645 million.
Under the less stringent alternative, the backstop emission rate is imposed in the 2030 run
year consistent with the final rule, but mass budgets in the 2025 and 2028 run years are less
stringent since they are based on Engineering Analysis that does not assume installation of new
SCRs. Hence costs are lower in the 2025 and 2028 run years, before converging to final rule
levels in 2030 and beyond. Costs peak in 2035 at $772 million as a result. The annualized costs
over the 2023-2045 period are $446 million.
In addition to evaluating annual compliance cost impacts, the EPA believes that a full
understanding of these three regulatory control alternatives benefits from an evaluation of
annualized costs over the 2023-2027 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
157
-------
annual cost associated with compliance with each regulatory control alternative.105 For this
analysis we first calculated the NPV of the stream of costs from 2023 through 2027106 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 NPV of the stream
of total costs of electricity generation. This discount rate is meant to capture the observed
equilibrium market rate at which investors are willing to sacrifice present consumption for future
consumption and is based on a Weighted Average Cost of Capital (WACC).107 After calculating
the NPV of the cost streams, the same 3.76 percent discount rate and 2023-2027 time period are
used to calculate the levelized annual (i.e., annualized) cost estimates shown in Table 4-8.108 The
same approach was used to develop the annualized cost estimates for the 2023-2045 timeframe.
Additionally, note that the 2023-2027 and 2023-2045 equivalent annualized compliance cost
estimates have the expected relationship to each other; the annualized costs are lowest for the
less stringent alternative, and highest for the more stringent alternative.
4.5.3 Impacts on Fuel Use, Prices and Generation Mix
The Transport FIP for the 2015 ozone NAAQS is expected to result in significant NOx
emissions reductions. It is also expected to have some impacts to the economics of the power
sector. While these impacts are relatively small in percentage terms, consideration of these
potential impacts is an important component of assessing the relative impact of the regulatory
control alternatives. 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 2023, 2025 and
2030 IPM model run years.
Table 4-9 and Table 4-10 present the percentage changes in national coal and natural gas
usage by EGUs in the 2023, 2025, and 2030 run years. These fuel use estimates reflect a modest
105 The XNPV() function in Microsoft Excel 2013 was used to calculate the NPV of the variable stream of costs, and
the PMT() function in Microsoft Excel 2013 is used to calculate the level annualized cost from the estimated NPV.
106 Consistent with the relationship between IPM run years and calendar years, EPA assigned 2023 compliance cost
estimates to both 2022 and 2023 in the calculation of NPV, and 2025 compliance cost to 2024 and 2025. For more
information, see Chapter 7 of the IPM Documentation.
107 The IPM Baseline run documentation (Section 10.4.1 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%."
108 The PMT() function in Microsoft Excel 2013 is used to calculate the level annualized cost from the estimated
NPV.
158
-------
shift to natural gas and renewables from coal in 2025 as a result of tightening budgets. In the
2025 run year, coal consumption reductions under the more stringent alternative are driven by
increasing coal EGU retirements and reduced coal dispatch as a result of tightening budgets and
the need to install SCR controls or retire uncontrolled units as shown in Table 4-14. To put these
reductions into context, under the Baseline, power sector coal consumption is projected to
decrease from 603 million tons in 2023 to 417 million tons in 2025 (15 percent annually),
whereas under the final rule coal consumption is projected to decrease from 603 million tons in
2023 to 402 million tons in 2025 (17 percent annually). Between 2015 and 2020, annual coal
consumption in the electric power sector fell between 8 and 19 percent annually.109
Under the more stringent alternative, the model projects a higher ratio of SCR retrofits to
retirements, and the bulk of these changes occur in the 2025 run year as compared to the final
rule and less stringent alternative when the majority of retirements and retrofits are projected to
occur in 2030. This in turn results in higher costs in run year 2025 under the more stringent
alternative, but comparatively lower costs in run year 2030. Under the less stringent alternative
and final rule, cost impacts are projected to be lower in 2025 and higher in 2030. This in turn
drives the differential impacts seen in the retail rate impacts.
Table 4-9. 2023, 2025 and 2030 Projected U.S. Power Sector Coal Use for the Baseline and
the Regulatory Control Alternatives
Million Tons
Percent Change from Baseline
Year
Baseline
Final Rule
Less-
Stringent
Alt.
More-
Stringent
Alt.
Final
Rule
Less-
Stringent
Alt.
More-
Stringent
Alt.
Appalachia
121
121
121
121
0%
0%
0%
Interior
96
96
96
96
0%
0%
0%
Waste Coal
2023
4
4
4
4
0%
0%
0%
West
382
382
382
382
0%
0%
0%
Total
603
603
603
603
0%
0%
0%
Appalachia
80
79
79
77
-2%
-2%
-4%
Interior
76
75
76
71
-1%
0%
-7%
Waste Coal
2025
4
4
4
4
0%
0%
0%
West
257
244
254
231
-5%
-1%
-10%
Total
417
402
412
382
-4%
-1%
-8%
109 US EIA Monthly Energy Review, Table 6.2, January 2022.
159
-------
Appalachia
49
47
47
48
-4%
-3%
-2%
Interior
51
49
49
52
-3%
-3%
2%
Waste Coal
2030
4
4
4
4
0%
0%
0%
West
170
154
155
160
-10%
-9%
-6%
Total
274
254
256
265
-7%
-7%
-4%
Table 4-10. 2023, 2025 and 2030 Projected U.S. Power Sector Natural Gas Use for the
Baseline and the Regulatory Control Alternatives
Trillion Cubic Feet
Percent Change from Baseline
Year
Baseline
Final
Rule
Less-
Stringent
Alt.
More-
Stringent
Alt.
Final Rule
Less More-Stringent
Stringent \
Alt.
2023
7.7
7.7
7.7
7.7
0%
0s-
O
0s-
O
2025
9.2
9.4
9.3
9.6
2%
0% 4%
2030
12.2
12.4
12.4
12.4
1%
1% 1%
Table 4-11 and Table 4-12 present the projected coal and natural gas prices in 2023, 2025
and 2030, as well as the percent change from the baseline run projected due to the regulatory
control alternatives. These minor impacts in 2023 are consistent with the small changes in fuel
use summarized above. The projected impacts in 2025 are larger in absolute value and consistent
with tightening budgets.
Table 4-11. 2023, 2025 and 2030 Projected Minemouth and Power Sector Delivered Coal
Price (2016$) for the Baseline and the Regulatory Control Alternatives
$/MMBtu
Percent Change from Baseline
Baseline
Final
Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
Final
Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
Minemouth
Delivered
2023
1.6
2.2
1.6
2.2
1.6
2.2
1.6
2.2
0%
0%
0%
0%
0%
0%
Minemouth
Delivered
2025
1.1
1.7
1.1
1.7
1.1
1.7
1.2
1.7
0%
-1%
0%
0%
1%
-1%
Minemouth
Delivered
2030
1.1
1.6
1.2
1.6
1.2
1.6
1.2
1.6
1%
-2%
1%
-2%
1%
-1%
160
-------
Table 4-12. 2023, 2025 and 2030 Projected Henry Hub and Power Sector Delivered
Natural Gas Price (2016$) for the Baseline and the Regulatory Control Alternatives
$/MMBtu
Percent Change from Baseline
p.. Less- More-
Baseline Stringent Stringent
Alternative Alternative
p.. Less- More-
Rute Stringent Stringent
Alternative Alternative
HemyHub 4.8 4.8 4.8 4.8
2023
Delivered 4.9 4.9 4.9 4.9
0% 0% 0%
0% 0% 0%
HemyHub 3.4 3.4 3.4 3.4
2025
Delivered 3.5 3.5 3.5 3.5
0% 0% 0%
0% 0% 0%
Hemy Hub 2.7 2.7 2.7 2.7
2030
Delivered 2.8 2.8 2.8 2.8
0% 1% 0%
0% 1% 0%
Table 4-13 presents the projected percentage changes in the amount of electricity
generation in 2023, 2025 and 2030 by fuel type. Consistent with the fuel use projections and
emissions trends above, the EPA projects an overall shift from coal to gas and renewables. The
projected impacts grow in 2025 reflecting the tightening budgets and are most pronounced in
2030 reflecting the imposition of the deferred backstop emission rate in the final rule.
Table 4-13. 2023, 2025 and 20230 Projected U.S. Generation by Fuel Type for the Baseline
and the Regulatory Control Alternatives
Generation (TWh)
Percent Change from Baseline
Year
Baseline
Final
Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
p.. Less- More-
Rufe Stringent Stringent
Alternative Alternative
Coal
1,133
1,133
1,133
1,133
0%
0%
0%
Natural Gas
1,090
1,090
1,090
1,090
0%
0%
0%
Nuclear
775
775
775
775
0%
0%
0%
Hydro
289
289
289
289
0%
0%
0%
2023
Non-Hydro RE
756
756
756
756
0%
0%
0%
Oil/Gas Steam
27
27
27
27
0%
0%
0%
Other
33
33
33
33
0%
0%
0%
Grand Total
4,103
4,103
4,103
4,103
0%
0%
0%
Coal
793
765
784
737
-4%
-1%
-7%
Natural Gas
1,311
1,332
1,314
1,356
2%
0%
3%
Nuclear
724
724
724
724
0%
0%
0%
Hydro
2025
294
295
295
295
0%
0%
0%
Non-Hydro RE
995
1,002
1,000
1,006
1%
1%
1%
Oil/Gas Steam
18
18
18
19
-1%
-2%
2%
Other
32
32
32
32
0%
0%
0%
161
-------
Generation (TWh)
Year
Baseline
Final
Rule
Less-
Stringent
Alternative
More-
Stringent
Alternative
p.. Less- More-
Rule Stringent Stringent
Alternative Alternative
Grand Total
4,167
4,168
4,168
4,168
0%
0%
0%
Coal
523
489
492
507
-7%
-6%
-3%
Natural Gas
1,691
1,710
1,709
1,708
1%
1%
1%
Nuclear
611
614
613
603
1%
0%
-1%
Hydro
2030
300
300
300
301
0%
0%
0%
Non-Hydro RE
1,111
1,122
1,121
1,116
1%
1%
0%
Oil/Gas Steam
22
22
22
23
0%
0%
4%
Other
32
32
32
32
0%
0%
0%
Grand Total
4,289
4,288
4,288
4,289
0%
0%
0%
Percent Change from Baseline
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind.
Table 4-14 presents the projected percentage changes in the amount of generating capacity
in 2023, 2025 and 2030 by primary fuel type. As explained above, the baseline run was
constrained to disallow endogenous retirement in 2023 to reflect near term limits. The policy
scenarios were limited to add no more capacity economically than was added under the baseline
in 2023 (also reflecting near term limits). These restrictions were removed in all subsequent run
years. As a result, none of the regulatory control alternatives are expected to have a net impact
on overall capacity by primary fuel type in 2023. By 2030 the rule is projected to result in an
additional 14 GW of coal retirements nationwide relative to the baseline, reflecting utilities
making least-cost decisions on how to achieve efficient compliance with the rule while
maintaining sufficient generating capacity to ensure grid reliability.110 This constitutes a
reduction of 13 percent of national coal capacity, partially reflecting some earlier retirement that
would otherwise have occurred later in the forecast period in the baseline. Under the baseline
run, total coal retirements between 2023 and 2030 are projected to be 74 GW (or 10.6 GW
annually). Under the final rule, total coal retirements between 2023 and 2030 are projected to be
89 GW (or 12.7 GW annually). This is compared to an average recent historical retirement rate
of 11 GW per year from 2015 - 2020.111
n° for further discussion of how the rule is anticipated to integrate into the ongoing power sector transition while
not impacting resource adequacy or grid reliability, see Section VLB of the preamble, and the Reliability
Assessment TSD included in the docket.
111 See EIA's Today in Energy: https://www.eia.gov/todayinenergy/detail.php?id=50838.
162
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Additionally, the rule is projected to incentivize an incremental 8 GW of SCR retrofit at
coal plants. The rule is also projected to result in an incremental 3 GW of renewable capacity
additions in 2025 (primarily consisting of solar capacity builds). These builds reflect early
action, i.e., builds that would otherwise have occurred later in the forecast period. By 2035-40
total solar capacity equilibrates between the baseline and final rule alternatives.
163
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Table 4-14. 2023, 2025 and 2030 Projected U.S. Capacity by Fuel Type for the Baseline run
and the Regulatory Control Alternatives
Capacity (GW)
Percent Change from Baseline run
Year
Baseline
run
Final
Rule
Less-
Stringent
Alt
More-
Stringent
Alt
Final
Rule
Less-
Stringent
Alt
More-
Stringent
Alt
Coal
187
187
187
187
0%
0%
0%
Natural Gas
441
441
441
441
0%
0%
0%
Nuclear
97
97
97
97
0%
0%
0%
Hydro
102
102
102
102
0%
0%
0%
2023
Non-Hydro RE
241
241
241
241
0%
0%
0%
Oil/Gas Steam
73
73
73
73
0%
0%
0%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,148
1,148
1,148
1,148
0%
0%
0%
Coal
140
138
138
128
-1%
-1%
-9%
Natural Gas
436
436
436
439
0%
0%
1%
Nuclear
91
91
91
91
0%
0%
0%
Hydro
102
102
102
102
0%
0%
0%
2025
Non-Hydro RE
301
304
303
305
1%
1%
1%
Oil/Gas Steam
60
60
60
62
0%
1%
4%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,135
1,137
1,136
1,133
0%
0%
0%
Coal
112
98
98
103
-13%
-13%
-8%
Natural Gas
468
477
477
474
2%
2%
1%
Nuclear
76
76
76
75
1%
0%
-1%
Hydro
103
103
103
103
0%
0%
0%
2030
Non-Hydro RE
339
343
342
343
1%
1%
1%
Oil/Gas Steam
62
64
64
64
2%
3%
2%
Other
7
7
7
7
0%
0%
0%
Grand Total
1,168
1,168
1,167
1,168
0%
0%
0%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind
The EPA estimated the change in the retail price of electricity (2016$) using the Retail
Price Model (RPM).112 The RPM was developed by ICF for the 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
112 See documentation available at: https://www.epa.gov/airmarkets/retail-price-model
164
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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).113
Table 4-15, Table 4-16, and Table 4-17 present the projected percentage changes in the
retail price of electricity for the three regulatory control alternatives in 2023, 2025 and 2030,
respectively. Consistent with other projected impacts presented above, average retail electricity
prices at both the national and regional level are projected to be small in 2023. In 2025, the EPA
estimates that this rule will result in a less than 0.2 percent increase in national average retail
electricity price, or by about 0.19 mills/kWh. In 2030, the EPA estimates that this rule will result
in a 0.9% increase in national average retail electricity price, or by about 0.80 mills/KWh.
113 See documentation available at:
https://www.eia.gov/outlooks/aeo/nems/documentation/electricity /pdf/m068(2020).pdf
165
-------
Table 4-15. Average Retail Electricity Price by Region for the Baseline and the Regulatory
Control Alternatives, 2023
All Sector
2023 Average Retail Electricity Price
(2016 mills/kWh)
Region
Baseline
Final
Rule
Less-
Stringent
Alt.
More-
Stringent
Alt.
Final
Rule
Less-
Stringent
Alt.
More-
Stringent
Alt.
TRE
77.5
77.5
77.5
77.5
0%
0%
0%
FRCC
109.1
109.1
109.1
109.1
0%
0%
0%
MISW
98.4
98.4
98.4
98.4
0%
0%
0%
MISC
93.2
93.2
93.2
93.2
0%
0%
0%
MISE
89.8
89.8
89.8
89.8
0%
0%
0%
MISS
84.6
84.6
84.6
84.6
0%
0%
0%
ISNE
151.3
151.4
151.3
151.9
0%
0%
0%
NYCW
680.1
684.4
683.4
696.4
1%
0%
2%
NYUP
148.1
148.1
148.1
148.3
0%
0%
0%
PJME
140.4
141.4
141.2
144.4
1%
1%
2%
PJMW
93.2
93.2
93.2
93.3
0%
0%
0%
PJMC
79.8
79.8
79.8
79.9
0%
0%
0%
PJMD
73.9
73.9
73.8
74.0
0%
0%
0%
SRCA
97.6
97.5
97.5
97.6
0%
0%
0%
SRSE
104.4
104.4
104.4
104.4
0%
0%
0%
SRCE
76.3
76.3
76.3
76.3
0%
0%
0%
SPPS
79.9
79.9
79.9
80.0
0%
0%
0%
SPPC
103.0
103.0
103.0
103.0
0%
0%
0%
SPPN
63.6
63.6
63.6
63.6
0%
0%
0%
SRSG
103.3
103.3
103.3
103.3
0%
0%
0%
CANO
153.0
153.0
153.0
153.0
0%
0%
0%
CASO
186.3
186.3
186.3
186.3
0%
0%
0%
NWPP
72.7
72.7
72.7
72.7
0%
0%
0%
RMRG
96.0
96.0
96.0
96.0
0%
0%
0%
BASN
90.8
90.9
90.9
90.9
0%
0%
0%
NATIONAL
113.0
113.2
113.1
113.6
0%
0%
0%
Percent Change from Baseline
166
-------
Table 4-16. Average Retail Electricity Price by Region for the Baseline and the Regulatory
Control Alternatives, 2025
All Sector
2025 Average Retail Electricity Price
(2016 mills/kWh)
Percent Change from Baseline
Final
Rule
Less-
More-
Final
Rule
Less-
More-
Region
Baseline
Stringent
Alt.
Stringent
Alt.
Stringent
Alt.
Stringent
Alt.
TRE
71.6
72.7
72.5
83.9
2%
1%
16%
FRCC
98.1
98.1
98.1
98.1
0%
0%
0%
MISW
94.7
94.7
94.7
95.3
0%
0%
1%
MISC
87.6
87.5
87.4
89.8
0%
0%
3%
MISE
79.1
79.9
79.8
84.8
1%
1%
6%
MISS
77.6
77.9
77.6
79.4
0%
0%
2%
ISNE
134.7
134.8
134.8
135.5
0%
0%
1%
NYCW
180.1
180.3
180.1
180.7
0%
0%
0%
NYUP
114.8
114.9
114.7
115.4
0%
0%
1%
PJME
116.3
116.4
116.1
117.0
0%
0%
1%
PJMW
86.3
86.7
86.4
90.6
0%
0%
5%
PJMC
76.2
75.4
75.6
83.0
-1%
-1%
10%
PJMD
67.2
67.5
67.3
71.4
0%
0%
6%
SRCA
92.3
92.3
92.3
92.3
0%
0%
0%
SRSE
95.4
95.4
95.4
95.0
0%
0%
0%
SRCE
69.8
69.7
69.7
70.4
0%
0%
1%
SPPS
76.7
77.1
76.8
79.4
0%
0%
3%
SPPC
100.2
100.5
100.4
102.6
0%
0%
2%
SPPN
63.0
62.7
62.9
61.6
0%
0%
-2%
SRSG
99.5
99.5
99.5
99.5
0%
0%
0%
CANO
152.1
152.1
152.1
152.7
0%
0%
0%
CASO
186.6
186.5
186.6
187.1
0%
0%
0%
NWPP
72.2
72.2
72.2
72.4
0%
0%
0%
RMRG
90.8
90.9
90.8
91.0
0%
0%
0%
BASN
89.0
89.1
89.0
90.3
0%
0%
1%
NATIONAL
95.6
95.7
95.6
98.0
0%
0%
2%
167
-------
Table 4-17. Average Retail Electricity Price by Region for the Baseline and the Regulatory
Control Alternatives, 2030
All Sector
2030 Average Retail Electricity Price
(2016 mills/kWh)
Percent Change from Baseline
Final
Rule
Less-
More-
Final
Rule
Less-
More-
Region
Baseline
Stringent
Alt.
Stringent
Alt.
Stringent
Alt.
Stringent
Alt.
TRE
79.2
83.0
83.1
78.4
5%
5%
-6%
FRCC
92.5
92.5
92.6
92.5
0%
0%
0%
MISW
90.6
90.6
90.7
90.6
0%
0%
0%
MISC
86.0
86.6
86.6
86.9
1%
1%
0%
MISE
102.1
102.0
102.1
102.0
0%
0%
0%
MISS
75.8
77.1
77.1
76.3
2%
2%
-1%
ISNE
144.6
145.2
145.2
145.8
0%
0%
0%
NYCW
190.3
192.1
192.2
194.1
1%
1%
1%
NYUP
117.0
118.7
118.9
120.4
2%
2%
1%
PJME
106.2
107.8
107.9
105.3
2%
2%
-2%
PJMW
91.9
92.5
92.5
92.0
1%
1%
-1%
PJMC
81.2
81.3
81.4
81.3
0%
0%
0%
PJMD
75.7
76.8
76.9
76.7
1%
2%
0%
SRCA
89.0
89.0
89.0
89.0
0%
0%
0%
SRSE
88.4
88.4
88.4
88.4
0%
0%
0%
SRCE
67.2
67.6
67.6
67.6
1%
1%
0%
SPPS
77.3
77.9
78.0
78.2
1%
1%
0%
SPPC
91.4
92.2
92.3
91.8
1%
1%
-1%
SPPN
63.3
63.0
63.0
63.2
-1%
-1%
0%
SRSG
91.6
91.5
91.4
91.7
0%
0%
0%
CANO
166.5
167.4
167.4
166.3
1%
1%
-1%
CASO
198.3
198.5
198.5
198.2
0%
0%
0%
NWPP
72.6
72.5
72.5
72.5
0%
0%
0%
RMRG
85.3
85.5
85.6
85.3
0%
0%
0%
BASN
86.4
87.3
87.3
87.6
1%
1%
0%
NATIONAL
96.1
96.9
97.0
96.3
1%
1%
-1%
168
-------
1(23
NWPP
MISWj
19
SPPN
NYUP
12
gjMCl
¦21B
[CANOj
NYCW
25
BASN
Pf24Y...
RMRG
MISC
K13TC
PJMD
22
CASO
||14B
[srca!
Figure 4-1. Electricity Market Module Regions
Source: EIA (http://Www.eia.gov/forecasts/faeo/pdf/herc_map.pdfi
4.5.4 Emissions Reductions and Compliance Cost Assessment for Non-EGUs for 2026
As stated in Section 4.4, using the list of emissions units estimated to be captured by the
applicability criteria, the assumed control technologies that would meet the emissions limits, and
information on control efficiencies and default cost/ton values from the CMDB, the EPA
estimated NOx emissions reductions and costs for the year 2026. We estimated emissions
reductions using the actual emissions from the 2019 emissions inventory. The EPA did not
estimate emissions reductions of SO2, PM2.5. CO2 and other pollutants that may be associated
with controls on non-EGU emissions units. Table 4-18 summarizes the industries, emissions unit
types, control technologies, and number of emissions units estimated to be subject to the rule.
The rule alternative includes an estimated 1,228 non-EGU emissions units. Table 4-19
summarizes the industries, emissions unit types, assumed control technologies, estimated annual
total annual costs (2016$), and estimated ozone season emissions reductions for the rule. Table
4-20 summarizes the industries, emissions unit types, assumed control technologies, and
estimated average annual costs (2016$). Lastly, Table 4-21 below summarizes the estimated
169
-------
reductions and estimated annual total and average annual costs (2016$) for the less and more
stringent alternatives.
Because the Transport FIP for the 2015 ozone NAAQS includes ozone season emissions
limits for the non-EGU emissions units and because we do not know if all affected sources will
run controls year-round or only during ozone season, we include estimates of ozone season NOx
emissions reductions and not annual estimates in Table 4-19 and Table 4-21. Note that some of
the EGU controls are assumed to run year-round. Also, because the Transport FIP for the 2015
ozone NAAQS includes emissions limits, and the non-EGU assessment does not account for
growth in the affected industries and capital turnover over time, the reductions are estimated to
be the same each year over the period from 2026 to 2042.
For additional 2026 non-EGU assessment results — including (i) by state and (ii) by state
and industry, estimated emissions reductions and costs, see the memorandum in the docket titled
Summary of Final Rule Applicability Criteria and Emissions Limits for Non-EGU Emissions
Units, Assumed Control Technologies for Meeting the Final Emissions Limits, and Estimated
Emissions Units, Emissions Reductions, and Costs.
Table 4-18. Non-EGU Industries, Emissions Unit Types, Assumed Control Technologies
that Meet Final Emissions Limits, Estimated Number of Control Installations
Industry/Industries
Emissions Unit Type
Assumed Control
Technologies that Meet Final
Emissions Limits
Estimated
Number of
Units Per
Assumed
Control
Pipeline Transportation of Natural Gas
Reciprocating Internal
Combustion Engines
NSCR or Layered Combustion
(Reciprocating)
Layered Combustion (2-cycle
Lean Burn)
SCR (4-cycle Lean Burn)
NSCR (4-cycle Rich Burn)
323
394
158
30
Cement and Concrete Product
Manufacturing
Kiln
SNCR
16
Iron and Steel Mills and Ferroalloy
Manufacturing
Reheat Furnaces
LNB
19
Glass and Glass Product Manufacturing
Furnaces
LNB
61
Iron and Steel Mills and Ferroalloy
Manufacturing
Boilers
LNB + FGR (Gas, No Coal or
Oil)
151
Metal Ore Mining
SCR (Any Coal, Any Oil)
15
Basic Chemical Manufacturing
170
-------
Industry/Industries
Emissions Unit Type
Assumed Control
Technologies that Meet Final
Emissions Limits
Estimated
Number of
Units Per
Assumed
Control
Petroleum and Coal Products
Manufacturing
Pulp, Paper, and Paperboard Mills
Solid Waste Combustors and
Incinerators3
Combustors or Incinerators
ANSCR
LN™ and SNCR
57
4
Total
1,228
a Twelve MWCs have existing controls, and we estimated these units will use more reagent in those controls to meet the final
emissions limits.
Table 4-19. Non-EGU Industries, Emissions Unit Types, Assumed Control Technologies,
Estimated Total Annual Costs (2016$), Estimated Ozone Season NOx Emissions
Reductions in 2026
Annual
Ozone
Assumed Control
Costs
Season
Industry/Industries
Emissions Unit
Type
Technologies that Meet
Final Emissions Limits
(million
2016$)
Emissions
Reductions
Reciprocating
Internal Combustion
NSCR or Layered
Combustion, Layered
Pipeline Transportation of Natural Gas
Engine
Combustion, SCR, NSCR
385
32,247
Cement and Concrete Product
Manufacturing
Kiln
SNCR
10.1
2,573
Iron and Steel Mills and Ferroalloy
Manufacturing
Reheat Furnaces
LNB
3.58
408
Glass and Glass Product Manufacturing
Furnaces
LNB
7.05
3,129
Iron and Steel Mills and Ferroalloy
Manufacturing
Boilers
SCR, LNB + FGR
8.84
440
Metal Ore Mining
0.621
18
Basic Chemical Manufacturing
Petroleum and Coal Products
49.7
1,748
Manufacturing
5.13
147
Pulp, Paper, and Paperboard Mills
62.3
1,836
Combustors or
Solid Waste Combustors and Incinerators
Incinerators
ANSCR or LN™ and SNCR
38.9
2,071
Totals
572
44,616
171
-------
Table 4-20. Summary of Non-EGU Industries, Emissions Unit Types, Assumed Control
Technologies, Estimated Average Cost/Ton (2016$)
Assumed Control Technologies
that Meet Final Emissions
Average
Cost/Ton
Values
Industry/Industries
Emissions Unit Type
Limits
(2016$)
NSCR or Layered Combustion,
Pipeline Transportation of Natural Gas
Reciprocating Internal
Combustion Engine
Layered Combustion, SCR,
NSCR
4,981
Cement and Concrete Product
Manufacturing
Kiln
SNCR
1,632
Iron and Steel Mills and Ferroalloy
Manufacturing
Reheat Furnaces
LNB
3,656
Glass and Glass Product Manufacturing
Furnaces
LNB
939
Iron and Steel Mills and Ferroalloy
Manufacturing
Boilers
SCR or LNB + FGR
8,369
Metal Ore Mining
14,595
Basic Chemical Manufacturing
Petroleum and Coal Products
11,845
Manufacturing
14,582
Pulp, Paper, and Paperboard Mills
14,134
Solid Waste Combustors and Incinerators
Combustors or Incinerators
ANSCR or LN™ and SNCRa
7,836
Overall Average Cost/Ton
5,339
a Covanta has developed a proprietary low NOx combustion system (LNTM) that involves staging of combustion
air. The system is a trademarked system and Covanta has received a patent for the technology.
Table 4-21. Estimated Emissions Reductions for 2026-2042 (ozone season tons) and
Estimated Annual Total Costs for the Less and More Stringent Alternatives
Ozone Season NOx
Annual Total Cost (million 2016$)
Alternative
Emissions Reductions
(Average Annual Cost/Ton)
Less Stringent Alternative
16,786
$144 ($3,573)
More Stringent Alternative
67,958
$1,280 ($7,852)
4.5.5 Total Emissions Reductions and Compliance Costs for EGUs and Non-EGUs
For select years between 2023 and 2042, Table 4-22 below summarizes the total
estimated emissions reductions and undiscounted compliance costs for EGUs and non-EGUs for
the final rule and the less and more stringent alternatives. For a complete stream of undiscounted
cost values, please see Chapter 8, Table 8-6.
Table 4-23 below summarizes the present value (PV) and equivalent annualized value
(EAV) of the total national compliance cost estimates for EGUs and non-EGUs for the final rule
and the less and more stringent alternatives. We present the PV of the costs over the twenty-year
period 2023 to 2042. We also present the EAV, which represents a flow of constant annual
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values that, had they occurred in each year from 2023 to 2042, would yield a sum equivalent to
the PV. The EAV represents the value of a typical cost for each year of the analysis.
Table 4-22. Total Estimated NOx Emissions Reductions (ozone season, thousand tons) and
Compliance Costs (million 2016$), 2023-2042
Final
Rule
Less
More
Final
Rule
Less
More
Stringent
Alternative
Stringent
Alternative
Stringent
Alternative
Stringent
Alternative
Emissions Reductions
Compliance Costs
(ozone season, thousand tons)
(million 2016$)
2023
EGUs
Non-EGUs
10
10
10
57
56
49
Total
10
10
10
57
56
49
2026
EGUs
27
8
53
(5)
(35)
840
Non-EGUs
45
17
68
570
140
1,300
Total
72
25
121
570
110
2,100
2027
EGUs
20
6
46
24
(47)
760
Non-EGUs
45
17
68
570
140
1,300
Total
65
23
114
600
97
2,000
2030
EGUs
36
35
33
710
770
840
Non-EGUs
45
17
68
570
140
1,300
Total
81
52
101
1,300
920
2,100
2035
EGUs
30
30
29
820
850
590
Non-EGUs
45
17
68
570
140
1,300
Total
75
47
97
1,400
990
1,900
2042
EGUs
30
30
29
820
830
600
Non-EGUs
45
17
68
570
140
1,300
Total
75
47
97
1,400
970
1,900
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Table 4-23. Total National Compliance Cost Estimates (millions of 2016$) for the Final
Rule and the Less and More Stringent Alternatives
Final Rule
Less Stringent
Alternative
More Stringent
Alternative
3 Percent
7 Percent
3 Percent
7 Percent
3 Percent
7 Percent
Present Value
EGU 2023-2042
$6,800
$3,900
$6,800
$3,900
$9,500
$6,500
Present Value
Non-EGU 2023-2042
$6,700
$4,300
$1,700
$1,100
$15,000
$9,500
Present Value
Total 2023-2042
$13,000
$8,200
$8,500
$5,000
$24,000
$16,000
EGU
Equivalent Annualized
Value
$460
$370
$460
$370
$640
$620
Non-EGU
Equivalent Annualized
Value
$450
$400
$110
$100
$1,000
$900
Total
Equivalent Annualized
Value
$910
$770
$570
$470
$1,600
$1,500
Note: Values have been rounded to two significant figures
4.6 Social Costs
As discussed in the EPA's Guidelines for Preparing Economic Analyses, social costs are
the total economic burden of a regulatory action (U.S. EPA, 2010). 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 this regulatory action will not necessarily be equal to the expenditures
by the electricity sector and other affected industries to comply with the final rule. Nonetheless,
here we use total national compliance costs for EGUs and non-EGUs as a proxy for social
costs. Table above presents the total annual estimated compliance costs for EGUs for 2023 and
EGUs and non-EGUs for 2026-2042.
The compliance cost estimates for EGUs in the rule and more or less stringent regulatory
control alternatives presented above are the change in expenditures by the electricity generating
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sector required by the power sector for compliance under each alternative. The change in the
expenditures required by the power sector to achieve and maintain compliance reflect the
changes in electricity production costs resulting from application of NOx control strategies
necessary to comply with the emissions budgets and the backstop emission rate. The production
cost changes include changes in fuel expenditures.
Ultimately, depending on the market structure and the demand and supply price
elasticities for electricity, some compliance costs may be borne by electricity consumers through
higher electricity prices. Furthermore, the share of compliance costs ultimately borne by owners
of electricity generating capacity and other capital may be borne unevenly, with some firms
becoming more profitable as a result of the regulation. These asset owners and electricity
consumers include U.S. citizens and residents as well as non-residents (e.g., foreign owners of
electricity-consuming commercial enterprises). For additional discussion of impacts on fuel use
and electricity prices, see Section 4.5.3 above.
The compliance cost estimates for non-EGUs in the rule and more or less stringent
regulatory control alternatives are the change in expenditures by the industries required for
compliance under each alternative. The change in the expenditures required by the industries to
maintain compliance reflect the changes in production costs resulting from application of NOx
control technologies or measures. As in the power sector, ultimately, depending on market
structure and the demand and supply price elasticities for these industrial products, some part of
the compliance costs may be borne by consumers through higher prices, and these costs are
distributed among U.S. citizens and residents and foreign asset owners.
For non-EGUs the estimated compliance costs in Table 4-22 are derived using the control
measures database, and for EGUs the estimated compliance costs are generated using the
Integrated Planning Model (IPM). 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, and other
constraints. However, potential effects outside of the electricity, coal and natural gas sectors are
not evaluated within IPM.
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Changes in production in a directly regulated sector may have indirect effects on a
myriad of other markets when output from that sector - for this rule electricity and certain
industrial products - is used as an input in the production of many 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 particular goods and services due to changes
in the price of electricity and other final goods prices.
When new regulatory requirements are expected to result in effects outside of regulated
and closely related sectors, a key challenge is determining whether they are of sufficient
magnitude to warrant explicit evaluation (Hahn and Hird 1990). It is not possible to estimate the
magnitude and direction of these potential effects outside of the regulated sector(s) without an
economy-wide modeling approach. For example, studies of air pollution regulations for the
power sector have found that the social costs and benefits may be greater or lower than when
secondary market impacts are considered, and that the direction of the estimates may depend on
the form of the regulation (e.g., Goulder et al. 1999, Williams 2002, Goulder et al. 2016).
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. It is structured around the assumption that, for some discrete period
of time, an economy can be characterized by a set of equilibrium conditions in which supply
equals demand in all markets. When the imposition of a regulation alters conditions in one
market, a general equilibrium approach will determine a new set of prices for all markets that
will return the economy to equilibrium. These prices in turn determine the outputs and
consumption of goods and services in the new equilibrium. In addition, a new set of prices and
demands for the factors of production (labor, capital, and land), the returns to which compose the
income of businesses and households, will be determined in general equilibrium. The social cost
of the regulation can then be estimated by comparing the value of variables in the pre-regulation
"baseline" equilibrium with those in the post-regulation, simulated equilibrium.
In 2015, the 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
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economic impacts in regulatory development. In its final report (U.S. EPA 2017), the SAB
recommended that the EPA begin to integrate CGE modeling into regulatory analysis to offer a
more comprehensive assessment of the effects of air regulations. The SAB noted that CGE
models can provide insight into the likely social costs of a regulation even when they do not
include a characterization of the likely social benefits of the regulation. CGE models may also
offer insights into the ways costs are distributed across regions, sectors, or households.
The SAB also noted that the case for using CGE models to evaluate a regulation's effects
is strongest when the costs of compliance are expected to be large in magnitude and the sector
has strong linkages to the rest of the economy. The report also noted that the extent to which
CGE models add value to the analysis depends on data availability. CGE models provide
aggregated representations of the entire economy and are designed to capture substitution
possibilities between production, consumption, and trade; interactions between economic
sectors; and interactions between a policy shock and pre-existing distortions, such as taxes.
However, one also needs to adequately represent a regulation in the model to estimate its effects.
In response to the SAB's recommendations, the EPA built a new CGE model called
SAGE. A second SAB panel performed a peer review of SAGE, and the reviewed concluded in
2020.114 While the EPA now has a peer reviewed CGE model for analyzing the potential
economy-wide effects of regulations, we have not used the model in the RIA for this rule due to
the expedited rulemaking timeline. However, the EPA continues to be committed to the use of
CGE models to evaluate the economy-wide effects of its regulations.
4.7 Limitations
The 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 rule for
EGUs, as quantified here, is the EPA's best assessment of the cost of implementing the rule for
114 See U.S. EPA (2020). The model peer review and other SAB reports can be downloaded at:
https://sab.epa.gov/ords/sab/f?p=100:12:15036376991605::: 12::
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the power sector. These costs are generated from rigorous economic modeling of changes in the
power sector due to implementation of the rule.
The IPM-projected annualized cost estimates of private compliance costs provided in this
analysis are meant to show the increase in production (generating) costs to the power sector in
response to the rule. To estimate these annualized costs, as discussed earlier in this chapter, the
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 to calculate annual costs. The CRF is derived from estimates of the cost of capital
(private discount rate), the amount of insurance coverage required, local property taxes, and the
life of capital. The private compliance costs presented earlier are the EPA's best estimate of the
direct private compliance costs of the rule.
In addition, 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. To the
extent electric demand is higher and 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 reflected through an
increase in natural gas price inputs for 2023 and 2025 model run years, and coal price
inputs in the 2023 model run year. 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, at which time the
modeling in support of this rule was in an advanced stage and timing considerations did
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not allow for incorporation of the effects of this legislation. In order to illustrate the
impact of the IRA on this rulemaking, the EPA included a baseline that incorporates key
provisions of the IRA as well as imposing the final rule as modeled in this RIA on that
baseline. The results from these scenarios are compared with the non-IRA scenarios and
provided in Appendix 4A. The analysis quantifies total costs and emission changes but
does not quantify the benefits associated with these emission changes.
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 affect the modeling of the baseline and illustrative policy
alternatives similarly, and therefore the impact on the incremental projections (reflecting the
potential costs/benefits of the illustrative final rule alternative) would be more limited and are not
likely to result in notable changes to the assessment of the Transport FIP for the 2015 ozone
NAAQS found in this chapter. While it is important to recognize these key areas of uncertainty,
they do not change the EPA's overall confidence in the estimated impacts of the illustrative final
rule alternative presented in this chapter. The 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.
The baseline includes modeling to capture the finalized 2020 Effluent Limitation
Guidelines (ELG), 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 relative to the baseline scenario, which would - all else equal -
reduce the modeled costs and benefits of the rule 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.
As discussed in section 4.3.2, IPM v.6.20 does not have the capacity to endogenously
determine whether to maximize the use of existing EGU post-combustion NOx controls (i.e.,
SCR), or install/upgrade combustion controls in response to a regulatory control requirement.
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These decisions were imposed exogenously on the model, as documented in section 4.3.2. While
the emissions projections reflect operation of these controls, the projected compliance costs were
supplemented with exogenously estimated costs of optimizing SCR operation, optimizing SNCR
operation, and installing/upgrading combustion controls (see section 4.3.3). As a result of this
modeling approach, the dispatch decisions made within the model do not take into consideration
the additional operating costs associated with these three types of compliance strategies (the
operating costs of the units on which these strategies are imposed do not reflect the additional
costs of these strategies). The effect of changes in facility and system-wide emissions from these
changes in operating costs are also not accounted for in the air quality modeling for the
regulatory alternatives described in Chapter 3.
The impacts of the Later Model Year Light-Duty Vehicle GHG Emissions Standards115 is
not captured in the baseline. This rule is projected to increase the total demand for electricity by
0.5% in 2030 and 1% in 2040 relative to 2020 levels.116 This translates into a 0.4% increase in
electricity demand in 2030 and a 0.8% increase in electricity demand in 2040 relative to the
baseline electricity demand projections assumed in this analysis. 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 Climate Review117 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 these two programs
would likely result in a modest increase in the total cost of compliance for this rule.
Lastly, the EPA estimated the non-EGU emissions units subject to the final rule using the
2019 inventory from the emissions inventory system (EIS) and supplemented the information by
reviewing online permits for the estimated emissions units in the Cement and Concrete Product
Manufacturing, Glass and Glass Product Manufacturing, and Iron and Steel Mills and Ferroalloy
Manufacturing industries. Because the number of estimated emissions units for reciprocating
115 Available at: https://www.federalregister.gov/documents/2021/08/10/2021-16582/revised-2023-and-later-model-
year-light-duty-vehicle-greenhouse-gas-emissions-standards
116 Regulatory Impact Analysis available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1012ONB.pdf
117 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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internal combustion engines and boilers was larger, the EPA did a limited permit review for
those units. For boilers, the EPA also reviewed the database used in the July 2022 revised Boiler
MACT. Using the list of emissions units estimated to be captured by the applicability criteria, the
assumed control technologies that would meet the emissions limits, and information on control
efficiencies and default cost/ton values from the CMDB, the EPA estimated NOx emissions
reductions and costs for the year 2026. The estimates using the 2019 inventory and information
from the CMDB identify proxies for emissions reductions and costs associated with the assumed
control technologies that would meet the final emissions limits.118 The control cost estimates
assume an average level of retrofit difficulty for control applications, and do not include
monitoring, recordkeeping, reporting, or testing costs. It is not possible to determine whether this
approach leads to an overestimate or underestimate of the costs, NOx, and other pollutant
emissions changes, benefits, and other impacts, including the effect on downwind receptors, of
the rule and the analyzed alternatives. Between proposal and the final rule, based on comments
received and additional research about whether a unit already had an existing control, the EPA
updated the estimated emissions reductions and costs reflecting this information. For the final
rule, if the EPA was aware of the presence of a control, in many cases it then assumed that the
unit did not need additional control. And, if it was not aware of the presence of a control, it
assumed that a control was required, and the costs and benefits were accounted for based on this
approach.
We are not able to project potential changes in the number of existing and new units
resulting from industry growth or capital turnover, over time in the baseline. The effects of the
uncertainty in these changes on costs, emissions reductions and benefits of the final rule are
ambiguous. We are also not able to project whether the emissions limitations would require
further NOx emissions reductions at new units relative to what is required of them in the
baseline.
Also, we are not able to project whether non-EGU units will make operational changes for
compliance with the final rule and whether those changes will lead to changes in emissions other
118 The EPA did not run the Control Strategy Tool to estimate emissions reductions and costs and programmed the
assessment using R. R is a free software environment for statistical computing and graphics. Additional information
is available here: https://www.r-project.org/. The R code that processed the data to estimate the emissions reductions
and costs is available upon request.
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than NOx. For example, if the non-EGUs respond to this final rule by replacing an old unit with
a newer, more efficient unit, emissions of other pollutants from non-EGUs may also decrease.
Furthermore, certain non-EGUs may choose compliance approaches for the final rule that also
incidentally reduce NOx emissions outside of the ozone season, which would yield additional
benefits from reduced PM2.5 exposure. If ultimate compliance with this final rule incidentally
reduces NOx and other pollutants emissions outside of the ozone season, the benefits from non-
EGUs, all else equal, are likely underestimated.
4.8 References
Goulder, L., Parry, I., Williams, R., and Burtraw, D. 1999. "The Cost-Effectiveness of
Alternative Instruments for Environmental Protection in a Second-Best Setting." Journal of
Public Economics, 72(3): 329-360. 144
Goulder, L., M. Hafstead, and R. Williams III 2016. "General Equilibrium Impacts of a Federal
Clean Energy Standard." American Economic Journal - Economic Policy 8(2): 186-218.
Hahn, R., and J. Hird. 1990. "The Costs and Benefits of Regulation: Review and Synthesis."
Yale Journal of Regulation 8: 233-278.
U.S. Energy Information Administration (EIA). 2020. The Electricity Market Module of the
National Energy Modeling System: Model Documentation 2020.
U.S. EPA. 2021. Revised Cross-State Air Pollution Rule (CSAPR) Update.
https://www.epa.gov/csapr/revised-cross-state-air-pollution-rule-update.
U.S. EPA. 2020. Steam Electric Reconsideration Rule, https://www.epa.gov/eg/2020-steam-
el ectri c-reconsi derati on-rul e.
U.S. EPA. 2019. Affordable Clean Energy Rule and Repeal of the Clean Power Plan.
https://www.epa.gov/stationary-sources-air-pollution/affordable-clean-energy-rule.
U.S. EPA. 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.
U.S. EPA. 2016. Cross-State Air Pollution Rule (CSAPR) Update.
https://www.epa.gov/csapr/final-cross-state-air-pollution-rule-update.
U.S. EPA, 2015. Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric
Utility Generating Units (FinalRule), http://www2.epa.gov/cleanpowerplan/clean-power-
plan-existing-power-plants.
U.S. EPA, 2015 a. Standards of Performance for Greenhouse Gas Emissions from New,
Modified, and Reconstructed Stationary Sources: Electric Utility Generating Units (Final
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Rule), http://www2.epa.gov/cleanpowerplan/carbon-pollution-standards-new-modified-
and-reconstructed-power-plants.
U.S. EPA, 2015b. Disposal of Coal Combustion Residuals from Electric Utilities (Final Rule).
http ://www2. epa. gov/coal ash/coal-ash-rule.
U.S. EPA, 2015c. Steam Electric Power Generating Effluent Guidelines (Final Rule).
http://www2.epa.gov/eg/steam-electric-power-generating-effluent-guidelines-2015-final-
rule.
U.S. EPA, 2014. Final Rule for Existing Power Plants and Factories.
http://www2.epa.gov/cooling-water-intakes.
U.S. EPA, 2011. Cross-State Air Pollution Rule.
http://www3.epa.gov/airtransport/CSAPRyindex.html
U.S. EPA, 201 la. Mercury and Air Toxics Standards (MATS), http://www3.epa.gov/mats/.
U.S. EPA. 2010. EPA Guidelines for Preparing Economic Analyses. Available at:
https://www.epa.gov/environmental-economics/guidelines-preparing-economic-analyses
U.S. EPA, 2005. Clean Air Interstate Rule,
http://archive.epa.gov/airmarkets/programs/cair/web/html/index.html.
Williams III, R. 2002. "Environmental tax interactions when pollution affects health or
productivity." Journal of Environmental Economics and Management 44(2): 261-270.
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APPENDIX 4A: INFLATION REDUCTION ACT EGU SENSITIVITY RUN RESULTS
In this appendix we describe the EGU compliance behavior, costs, and emissions
reductions that include adjustments made to the IPM baseline for the Inflation Reduction Act
(IRA) of 2022. The IRA includes significant additional new generation incentives targeting more
efficient and lower-emitting sources of generation that is likely to meaningfully affect the U.S.
generation mix in the future and increase the pace of new lower-emitting generation replacing
some of older higher-emitting generating capacity. This supplementary analysis quantifies the
incremental impacts of the Federal Good Neighbor Plan Addressing Regional Ozone Transport
for the 2015 Ozone National Ambient Air Quality Standards (Transport FIP for the 2015 ozone
NAAQS) under the alternative baseline characterization and compares impacts with the main
analysis described in Chapter 4. As described in Chapter 4, the model runs that inform air quality
do not include the IRA due to time limitations. However, for completeness this appendix seeks to
quantify the effect on the expected power sector outcomes of the final rule with this alternative
baseline.
4A.1 Modeling the IRA in IPM
This supplementary analysis incorporates several key aspects of the IRA that influence
EGU behavior in the IPM baseline. The analysis addresses aspects of the IRA to the extent
possible given overall timing limitations in the production of this RIA and uncertainties around
some of the final rule's potential impacts. The main IPM model updates are included in Table
4A. 1. No adjustments are made to electricity demand to reflect the impact of incremental
electrification, since this parameter is subject to a significant amount of uncertainty and is more
likely to drive results later in the forecasted period.
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Table 4A-1. IRA Provisions Modeled in IPM
PTC/ITC and Clean Energy Tax Credits
• Wage and apprenticeship requirements are assumed to be met.
• Extended to include stand-alone storage and new nuclear resources.
• All storage assumed to qualify for 10% bonus energy tax credit.
• All other technologies assumed to qualify for a prorated bonus energy tax credit based on the share of
energy community land area to total land area within an IPM zone.
• Credits remain in place until later of 2032 or the year in which power sector emissions are 25% or less of
2021 historical levels (used as a proxy for 2022 emissions).
Capital Cost Step Adder Adjustment
• The short-term capital cost adder step widths for solar, wind, geothermal, hydro, and nuclear
technologies are relaxed to reflect the IRA's impact on improvements to manufacturing capability. The
scalars are linearly interpolated in between 2023 and 2035. However, a scalar of 1.0 is also used for
2025 to reflect near term limitations.
45(q) Tax Credits for CCUS
• A CO2 storage tax credit of $60/metric tonne for EOR sites and $85/metric tonne for non EOR sites is
provided to the CCS investments made in the 2030 and 2035 run years.
Other
• Nuclear endogenous retirements are disabled. Nuclear units are retired per a predetermined retirement
schedule. Exceptions are made if a specific unit's age based on its license expiration date is greater than
60 years.
• Lower price steps are added to the 2045 and 2050 natural gas supply curves to reflect lower gas
consumption.
• The CO2 financing uncertainty adder is removed from fossil builds.
Throughout the rest of this appendix, costs and emissions outcomes are provided for the
Baseline and final rule with and without the IRA active to provide a comparison between
compliance with the final rule under each baseline characterization.
4A.1.1 Compliance Cost Assessment for EGUs
The estimates of incremental costs of supplying electricity for the final rule with and
without IRA provisions are presented in Table 4A-2. Since the final rule generally does not result
in significant, additional recordkeeping, monitoring or reporting requirements for EGUs, the
costs associated with compliance, monitoring, recordkeeping, and reporting requirements are not
included within the estimates in this table.
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Table 4A-2. National Power Sector Compliance Cost Estimates (millions of 2016$) for the
Final Rule With and Without the IRA
Final Rule +
IRA
Final Rule
2023-2027 (Annualized)
13
14
2023-2045 (Annualized)
196
449
2023 (Annual)
47
57
2024 (Annual)
-17
-5
2025 (Annual)
-17
-5
2026 (Annual)
-17
-5
2027 (Annual)
67
24
2030 (Annual)
577
705
2035 (Annual)
297
817
2045 (Annual)
163
182
"2023-2027 (Annualized)" reflects total estimated annual compliance costs levelized over the period 2023 through
2027 and discounted using a 3.76 real discount rate.119 This does not include compliance costs beyond 2027. "2023-
2045 (Annualized)" reflects total estimated annual compliance costs levelized over the period 2023 through 2045
and discounted using a 3.76 real discount rate. This does not include compliance costs beyond 2045. "2023
(Annual)" through "2045 (Annual)" costs reflect annual estimates in each of those years.120
The impact of the IRA is to increase the economic competitiveness of lower emitting and
renewable technologies relative to the higher emitting technologies that this rule seeks to
regulate. Since the IRA incentives persist over the forecast period, we do not see the "rush to
build" that characterizes modeling of incentives that will expire in the near future. As such the
impact of the IRA is felt to a greater extent over the medium and longer term when the incentives
are further aided by sector cost declines and performance improvements assumed over time. As a
result, compliance costs are projected to be similar to the scenario without the IRA over the five-
year period (2023-27) but are less than half the costs over the 2023-2045 period ($449 million
2016$ without the IRA and $196 million 2016$ including the IRA). Moreover, the costs peak in
2030 at $577 million 2016$ with the IRA as compared to peaking in 2035 at $817 million 2016$
under the no IRA scenario.
119 This table reports compliance costs consistent with expected electricity sector economic conditions. An NPV of
costs was calculated using a 3.76% real discount rate consistent with the rate used in IPM's objective function for
cost-minimization. The NPV of costs was then used to calculate the levelized annual value over a 5-year period
(2023-2027) and a 20-year period (2023-2042) using the 3.76% rate as well.
120 Cost estimates include financing charges on capital expenditures that would reflect a transfer and would not
typically be considered part of total social costs.
186
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4A.1.2 Emissions Reduction Assessment for EGUs
As indicated in Chapter 1, the NOx emissions reductions are presented in this RIA from
2023 through 2045 and are based on IPM projections. As outlined in Section 4.3.2 IPM is
operating existing and newly installed controls seasonally based on historical operation patterns
and seasonal and annual emission constraints within the model. Table 4A-3 presents the
estimated reduction in power sector NOx emissions resulting from compliance with the final rule
in the 22 states, as well as the impact on other states both with and without the IRA. The
emission reductions follow an expected pattern: near term NOx emissions reductions are similar
with and without the IRA in place, while longer-term reductions are lower in the presence of the
IRA, reflecting a lower emitting baseline as a result of the greater levels of clean energy
incentives modeled. Differences in emissions reductions after 2030 suggest that some units that
are projected to retire in 2030 due to the final rule reported in Chapter 4 have already been
retired due to the IRA by this point. Further, the EPA observes that the differences in estimated
costs and emissions reductions in the IRA sensitivity suggests that there would also be
differences in estimated health and climate benefits under this scenario, although the Agency did
not have time under this rulemaking schedule to quantify those differences.
Table 4A-3. EGU Ozone Season NOx Emissions and Emissions Changes (thousand tons)
for the Baseline run and Final Rule with and without IRA from 2023 - 2045
Ozone Season NOx
(thousand tons)
Total Emissions
Change from
Baseline run
Baseline
run +
IRA
Final
Rule +
IRA
Baseline
Final
With
Without
run
Rule
IRA
IRA
22 States
229
220
230
220
-10
-10
2023
Other States
144
144
143
143
0
0
Nationwide
373
363
373
363
-10
-10
22 States
201
182
203
181
-20
-22
2024
Other States
127
129
128
129
2
1
Nationwide
329
311
331
310
-18
-21
22 States
173
144
176
143
-30
-34
2025
Other States
111
114
113
115
3
2
Nationwide
284
258
289
258
-26
-32
22 States
158
135
167
140
-23
-27
2026
Other States
104
106
107
109
2
2
Nationwide
262
241
274
248
-20
-25
22 States
142
126
157
137
-16
-20
187
-------
Ozone Season NOx
(thousand tons)
Total Emissions
Change from
Baseline run
Baseline
run +
IRA
Final
Rule +
IRA
Baseline
Final
With
Without
run
Rule
IRA
IRA
2027
Other States
97
98
101
103
2
2
Nationwide
239
225
258
239
-15
-19
22 States
127
117
147
134
-10
-14
2028
Other States
90
90
95
96
1
2
Nationwide
217
208
242
230
-9
-12
22 States
110
82
137
101
-28
-36
2030
Other States
84
85
91
93
0
2
Nationwide
195
167
228
194
-28
-34
22 States
58
51
132
101
-8
-30
2035
Other States
50
50
88
89
-1
1
Nationwide
108
100
220
190
-8
-29
22 States
56
45
119
89
-11
-30
2040
Other States
38
38
79
79
0
0
Nationwide
94
84
198
169
-11
-30
22 States
46
41
102
80
-5
-22
2045
Other States
36
36
76
76
0
0
Nationwide
82
77
178
156
-5
-22
In addition to the ozone season NOx reductions, there will also be reductions of other air
emissions associated with EGUs burning fossil fuels (i.e., co-pollutants) that result from
compliance strategies to reduce seasonal NOx emissions. These other emissions include the
annual total changes in emissions of NOx, SO2, CO2, and direct PM2.5 emissions changes. The
emissions reductions are presented in Table 4A-4.
Table 4A-4. EGU Annual Emissions and Emissions Changes for Annual NOx, SO2, PM2.5,
and CO2 for the Baseline run and Final Rule with and without IRA from 2023 - 2045
Annual NOx
(thousand tons)
Total Emissions
Change from
Baseline run
Baseline
run +
IRA
Final
Rule +
IRA
Baseline
Final
With
Without
run
Rule
IRA
IRA
22 States
560
545
561
546
-15
-15
2023
Other States
329
329
328
329
0
0
Nationwide
889
874
889
874
-15
-15
22 States
490
467
491
464
-23
-26
2024
Other States
284
286
286
287
2
1
188
-------
Nationwide
774
753
777
752
-21
-25
22 States
419
388
420
383
-31
-38
2025
Other States
239
243
244
246
4
2
Nationwide
659
631
664
629
-27
-35
22 States
381
357
398
367
-24
-31
2026
Other States
225
228
232
234
3
2
Nationwide
606
585
630
601
-21
-29
22 States
342
326
375
351
-17
-24
2027
Other States
211
213
220
222
2
2
Nationwide
553
539
595
573
-15
-22
22 States
304
295
353
336
-9
-17
2028
Other States
197
198
208
210
1
1
Nationwide
500
492
561
545
-8
-16
22 States
261
199
324
261
-63
-64
2030
Other States
186
187
208
210
1
1
Nationwide
447
386
533
471
-62
-62
22 States
131
110
304
254
-21
-49
2035
Other States
102
103
197
201
1
3
Nationwide
233
213
501
455
-20
-46
22 States
100
87
267
221
-13
-46
2040
Other States
80
80
173
174
0
1
Nationwide
180
167
440
395
-13
-45
22 States
82
79
218
195
-4
-23
2045
Other States
68
69
160
160
0
0
Nationwide
151
148
378
355
-3
-23
Annual SO2
(thousand tons)
Total Emissions
Change from
Baseline run
Baseline
run +
IRA
Final
Rule +
IRA
Baseline
run
Final
Rule
With
IRA
Without
IRA
22 States
908
912
916
915
4
-1
2023
Other States
280
280
279
279
0
0
Nationwide
1188
1192
1195
1194
4
-1
22 States
778
765
787
766
-13
-21
2024
Other States
235
236
239
240
2
1
Nationwide
1012
1001
1025
1006
-11
-19
22 States
647
618
657
617
-29
-40
2025
Other States
189
192
199
201
3
2
Nationwide
837
810
856
818
-26
-38
22 States
540
520
574
543
-20
-31
189
-------
2026
Other States
169
172
181
183
2
2
Nationwide
710
692
755
726
-18
-29
22 States
433
423
491
469
-10
-22
2027
Other States
150
151
163
164
1
1
Nationwide
583
574
654
633
-9
-21
22 States
326
326
408
395
-1
-13
2028
Other States
130
130
145
145
0
0
Nationwide
456
455
553
540
-1
-13
22 States
247
158
385
289
-88
-95
2030
Other States
126
128
147
150
2
2
Nationwide
373
286
532
439
-87
-93
22 States
109
61
366
342
-47
-24
2035
Other States
49
50
135
138
1
3
Nationwide
157
111
501
480
-46
-21
22 States
64
44
305
279
-20
-26
2040
Other States
34
34
126
127
0
1
Nationwide
98
78
432
406
-20
-25
22 States
36
34
220
206
-2
-15
2045
Other States
22
22
128
128
0
0
Nationwide
58
56
349
334
-2
-15
Annual PM2.5
(thousand tons)
Total Emissions
Change from
Baseline run
Baseline
run +
IRA
Final
Rule +
IRA
Baseline
run
Final
Rule
With
IRA
Without
IRA
22 States
75
75
63
63
0
0
2023
Other States
47
47
40
40
0
0
Nationwide
122
122
103
103
0
0
22 States
67
66
57
56
-1
-1
2024
Other States
42
42
36
36
0
0
Nationwide
109
108
93
92
-1
-1
22 States
58
57
51
49
-2
-2
2025
Other States
37
37
33
33
0
0
Nationwide
96
94
84
82
-1
-2
22 States
55
54
49
48
-1
-1
2026
Other States
36
36
33
33
0
0
Nationwide
91
90
82
81
-1
-1
22 States
51
51
48
47
0
-1
2027
Other States
35
35
32
32
0
0
Nationwide
87
86
80
80
0
-1
190
-------
22 States
48
48
47
46
0
0
2028
Other States
34
34
32
32
0
0
Nationwide
82
82
79
78
0
0
22 States
45
39
45
43
-6
-2
2030
Other States
33
33
32
32
0
0
Nationwide
78
72
76
75
-5
-1
22 States
30
28
46
44
-2
-2
2035
Other States
21
21
30
30
0
0
Nationwide
51
49
75
74
-2
-1
22 States
26
25
44
43
-1
-2
2040
Other States
18
18
28
28
0
0
Nationwide
44
43
73
71
-1
-2
22 States
23
23
42
42
0
0
2045
Other States
17
17
28
28
0
0
Nationwide
40
40
70
70
0
0
Annual CO2
(million short tons)
Total Emissions
Change from
Baseline run
Baseline
run +
IRA
Final
Rule +
IRA
Baseline
run
Final
Rule
With
IRA
Without
IRA
22 States
1030
1030
1033
1032
0
0
2023
Other States
592
592
591
592
0
0
Nationwide
1622
1622
1624
1624
0
0
22 States
950
941
947
935
-10
-12
2024
Other States
538
540
539
541
3
2
Nationwide
1488
1481
1487
1476
-7
-10
22 States
870
851
862
838
-19
-24
2025
Other States
483
488
488
491
5
3
Nationwide
1354
1340
1350
1329
-14
-21
22 States
825
813
844
826
-13
-18
2026
Other States
467
471
477
480
4
3
Nationwide
1292
1283
1322
1306
-9
-16
22 States
780
774
827
814
-7
-13
2027
Other States
450
454
467
469
3
2
Nationwide
1231
1227
1294
1284
-3
-10
22 States
735
735
809
803
-1
-7
2028
Other States
434
436
457
459
3
2
Nationwide
1169
1171
1266
1261
2
-5
22 States
660
611
784
753
-49
-31
2030
Other States
390
397
450
455
7
5
191
-------
Nationwide
1050
1008
1235
1209
-42
-26
22 States
416
397
792
774
-19
-19
2035
Other States
240
241
436
438
1
2
Nationwide
656
638
1228
1212
-18
-16
22 States
352
342
727
706
-11
-21
2040
Other States
211
211
411
411
0
1
Nationwide
563
553
1138
1117
-10
-20
22 States
330
327
670
662
-3
-9
2045
Other States
205
205
400
400
0
0
Nationwide
535
532
1070
1061
-3
-9
4A. 1.3 Impacts on Fuel Use and Generation Mix
The Transport FIP for the 2015 ozone NAAQS is expected to result in significant NOx
emissions reductions. It is also expected to have some impacts to the power sector. While these
impacts are relatively small in percentage terms, consideration of these potential impacts is an
important component of assessing the relative impact of the regulatory control alternatives. In
this section we discuss the estimated changes in fuel use, fuel prices, generation by fuel type, and
capacity by fuel type for the 2023, 2025 and 2030 IPM model run years with and without the
IRA.
As outlined in Table 4A-5 coal consumption remains similar in 2023 between the two
baselines. In 2025 and beyond, the baseline with IRA results in lower coal consumption, with the
result that the reduction in total coal consumption is lower in the presence of the IRA than in its
absence. However, reductions still occur, demonstrating that the policy constraints are binding.
Table 4A-5. 2023, 2025 and 2030 Projected U.S. Power Sector Coal Use for the Baseline
and the Final Rule with and without IRA
Million Tons
Percent Change from
Baseline
Year
Baseline
Run +
IRA
Final
Rule +
IRA
Baseline
Final
With
Without
Run
Rule
IRA
IRA
Appalachia
121
121
121
121
0%
0%
Interior
96
96
96
96
0%
0%
Waste Coal
2023
4
4
4
4
0%
0%
West
381
381
382
382
0%
0%
Total
602
602
603
603
0%
0%
192
-------
Million Tons
Percent Change from
Baseline
Year
Baseline
Run +
IRA
Final
Rule +
IRA
Baseline
Final
With
Without
Run
Rule
IRA
IRA
Appalachia
75
74
80
79
-2%
-2%
Interior
77
77
76
75
0%
-1%
Waste Coal
2025
4
4
4
4
0%
0%
West
255
244
257
244
-4%
-5%
Total
411
399
417
402
-3%
-4%
Appalachia
32
31
49
47
-2%
-4%
Interior
46
35
51
49
-24%
-3%
Waste Coal
2030
4
4
4
4
0%
0%
West
133
112
170
154
-16%
-10%
Total
214
182
274
254
-15%
-7%
As outlined in Table 4A-6 gas consumption remains similar in 2023 between the two
baselines. In 2025 gas consumption is elevated in the scenario with the IRA in place, reflecting
greater levels of coal retirements and lower financing costs for new gas technology. In 2030,
total gas consumption is lower in the IRA baseline since energy storage and renewables become
more cost competitive relative to fossil fuels, and nuclear retirements are lower. The reduced
coal dispatch due to the policy results in similar increases in gas consumption under both
baselines.
Table 4A-6. 2023, 2025 and 2030 Projected U.S. Power Sector Natural Gas Use for the
Baseline and the Final Rule with and without I
RA
Trillion Cubic Feet
Percent Change
from Baseline
Baseline Final
Year Run + Rule +
IRA IRA
Baseline Final
Run Rule
With Without
IRA IRA
2023 7.7 7.7
7.7 7.7
0% 0%
2025 9.6 9.8
9.2 9.4
2% 2%
2030 11.4 11.5
12.2 12.4
1% 1%
As outlined in Table 4A-7 and Table 4A-8 coal and gas prices remain similar in 2023 and
2025 between the two baselines. Gas prices reflect the current elevated fuel price environment
through 2025, before returning to fundamentals by 2030. Coal prices reflect elevated levels in
193
-------
2023, before returning to fundamentals by 2025. The result is that through 2025 the two
baselines show similar price trends. By 2030, the gas prices in the IRA baseline are lower, since
total gas consumption has fallen, reflecting decreased nuclear retirements, increasing renewable
penetration, and falling coal dispatch. Increases in gas price as a result of the policy are similar
between the two cases.
Table 4A-7. 2023, 2025 and 2030 Projected Minemouth and Power Sector Delivered Coal
Price (2016$) for the Baseline and the Final Rule with and without IRA
$/MMBtu
Percent Change
from Baseline
Baseline
Run +
IRA
Final
Rule +
IRA
Baseline
Final
With
Without
Run
Rule
IRA
IRA
Minemouth
2023
1.6
1.6
1.6
1.6
0%
0%
Delivered
2.2
2.2
2.2
2.2
0%
0%
Minemouth
2025
1.1
1.1
1.1
1.1
0%
0%
Delivered
1.7
1.7
1.7
1.7
-1%
-1%
Minemouth
2030
1.1
1.1
1.1
1.2
2%
1%
Delivered
1.4
1.4
1.6
1.6
-1%
-2%
Table 4A-8. 2023, 2025 and 2030 Projected Henry Hub and Power Sector Delivered
Natural Gas Price (2016$) for the Baseline and the Final Rule with and without IRA
$/MMBtu
Percent Change
from Baseline
Baseline Final
Run + Rule +
IRA IRA
Baseline Final
Run Rule
With Without
IRA IRA
Henry Hub 4.8 4.8
2023
Delivered 4.9 4.9
4.8 4.8
4.9 4.9
0% 0%
0% 0%
Henry Hub 3.4 3.4
2025
Delivered 3.5 3.5
3.4 3.4
3.5 3.5
0% 0%
0% 0%
Henry Hub 2.5 2.6
J 2030
Delivered 2.6 2.6
2.7 2.7
2.8 2.8
1% 0%
1% 0%
As outlined in Table 4A-9 the generation mix remains similar between the two baselines in
2023. By 2025, gas generation rises relative to coal generation, and increases in nuclear
generation driven by reduced levels of nuclear retirement. Total non-hydro RE generation is
lower, reflecting the fact that in the absence of the IRA the Production Tax Credit (PTC) for
shore wind and the Investment Tax Credit (ITC) for solar PV builds are assumed to phase out
through 2025. This results in a 'rush to build' in order to take advantage of the tax credits before
194
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they expire. Under the IRA scenario, the tax credits are both more valuable and extend
throughout the forecast period, as such renewable additions accelerate over the forecast period,
taking advantage of cost declines that occur later in the horizon. Hence gas generation peaks in
2025 and then declines over the rest of the forecast period under the IRA baseline, while gas
generation grows throughout the forecast period under the non-IRA baseline.
Tightening mass budgets in the 2025 run year (representing the 2026 compliance year in
the rule) lead to erosion of coal dispatch under the policy scenario under both cases. In 2030,
imposition of the deferred backstop emission rate results in higher levels of coal retirement,
driving coal generation lower under both scenarios.
Table 4A-9. 2023, 2025 and 20230 Projected U.S. Generation by Fuel Type for the Baseline
and the Final Rule with and without IRA
Generation (TWh)
Percent Change
from Baseline
Year
Baseline
Run +
IRA
Final
Rule +
IRA
Baseline
Final
With
Without
Run
Rule
IRA
IRA
Coal
1,131
1,131
1,133
1,133
0%
0%
Natural Gas
1,091
1,091
1,090
1,090
0%
0%
Nuclear
775
775
775
775
0%
0%
Hydro
2023
289
289
289
289
0%
0%
Non-Hydro RE
757
757
756
756
0%
0%
Oil/Gas Steam
27
27
27
27
0%
0%
Other
33
33
33
33
0%
0%
Grand Total
4,103
4,103
4,103
4,103
0%
0%
Coal
777
755
793
765
-3%
-4%
Natural Gas
1,376
1,397
1,311
1,332
1%
2%
Nuclear
747
747
724
724
0%
0%
Hydro
2025
293
293
294
295
0%
0%
Non-Hydro RE
910
912
995
1,002
0%
1%
Oil/Gas Steam
18
18
18
18
0%
-1%
Other
32
32
32
32
0%
0%
Grand Total
4,154
4,154
4,167
4,168
0%
0%
Coal
397
347
523
489
-13%
-7%
Natural Gas
1,635
1,653
1,691
1,710
1%
1%
Nuclear
725
725
611
614
0%
1%
Hydro
Non-Hydro RE
2030
305
1,192
305
1,224
300
1,111
300
1,122
0%
3%
0%
1%
Oil/Gas Steam
12
11
22
22
-6%
0%
Other
32
31
32
32
0%
0%
Grand Total
4,296
4,296
4,289
4,288
0%
0%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind.
195
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As outlined in Table 4A-10 the capacity mix follows similar trends to those seen under
the generation mix table. Coal capacity in 2023 remains identical across cases, reflecting the
limitation on retirements. In 2023 gas capacity is higher, reflecting incremental builds as a result
of the removal of the carbon uncertainty adder. Non-Hydro RE builds are lower through 2025
under the IRA scenario and then higher thereafter, as described earlier. By 2030 total coal
retirements as a result of the policy are 14 GW in the absence of IRA, and 17 GW in the
presence of IRA. This is driven by the weaker competitive position of fossil fired EGUs under
the IRA scenario, making SCR retrofits on existing coal plants less economic. As a result, there
are 2.7 GW of SCR retrofits under the Final Rule with IRA scenario as compared to 8 GW of
retrofits in the Final Rule scenario without IRA.
Table 4A-10. 2023, 2025 and 2030 Projected U.S. Capacity by Fuel Type for the Baseline
and the Final Rule with and without IRA
Capacity (GW)
Percent Change from
Baseline run
Year
Baseline
Run + IRA
Final Rule +
IRA
Baseline
Run
Final Rule
With IRA
Without
IRA
Coal
187
187
187
187
0%
0%
Natural Gas
441
441
441
441
0%
0%
Nuclear
97
97
97
97
0%
0%
Hydro
2023
102
102
102
102
0%
0%
Non-Hydro RE
241
241
241
241
0%
0%
Oil/Gas Steam
73
73
73
73
0%
0%
Other
7
7
7
7
0%
0%
Grand Total
1,163
1,163
1,163
1,163
0%
0%
Coal
138
137
140
138
0%
-1%
Natural Gas
440
441
436
436
0%
0%
Nuclear
93
93
91
91
0%
0%
Hydro
2025
102
102
102
102
0%
0%
Non-Hydro RE
278
278
301
304
0%
1%
Oil/Gas Steam
60
59
60
60
0%
0%
Other
7
7
7
7
0%
0%
Grand Total
1,136
1,136
1,154
1,155
0%
0%
Coal
100
82
112
98
-17%
-13%
Natural Gas
454
458
468
477
1%
2%
Nuclear
91
91
76
76
0%
1%
Hydro
2030
104
104
103
103
0%
0%
Non-Hydro RE
357
365
339
343
2%
1%
Oil/Gas Steam
61
64
62
64
5%
2%
Other
7
7
7
7
0%
0%
Grand Total
1,203
1,204
1,189
1,189
0%
0%
Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind
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CHAPTER 5: BENEFITS
Overview
The Final Federal Good Neighbor Plan Addressing Regional Ozone Transport for the
2015 Ozone National Ambient Air Quality Standards (Transport FIP for the 2015 Ozone
NAAQS) is expected to reduce emissions of nitrogen oxides (NOx) transported from states that
contribute significantly to nonattainment or interfere with maintenance of the 2015 Ozone
National Ambient Air Quality Standards (NAAQS) in downwind states. Implementing the
Transport FIP for the 2015 Ozone NAAQS_is expected to reduce emissions of NOx, which will
in turn reduce concentrations of ground-level ozone and fine particles (PM2.5); the rule is also
projected to reduce sulfur dioxide (SO2), direct PM2.5 emissions, carbon dioxide (CO2) emissions
as well as water effluents, and potentially reduce mercury (Hg) emissions. This chapter reports
the estimated monetized health benefits from reducing concentrations of ozone and PM2.5 for
each of three regulatory control alternatives described in prior chapters.121 The chapter also
reports the estimated monetized climate benefits from reducing CO2 emissions. Though the rule
is likely to also yield positive benefits associated with reducing pollutants other than ozone and
PM2.5, limited time, resource and data limitations prevented us from characterizing the value of
those reductions.
This chapter describes the methods used to estimate the benefits to human health of
reducing concentrations of ozone from affected EGUs (electrical generating units) and non-
EGUs (non-electric generating units, or other stationary source emissions sources) and PM2.5
from affected EGUs. The analysis quantifies health benefits resulting from changes in ozone
concentrations in 2023 and changes in ozone and PM2.5 in 2026 for each of the three regulatory
control alternatives (i.e., final rule, less stringent alternative, and more stringent alternative). The
methods for quantifying the number and value of air pollution-attributable premature deaths and
illnesses are described in the Technical Support Document (TSD) for the 2022 PM NAAQS
121 A comprehensive approach to benefit-cost analysis (BCA) is required to assess whether it is conceivable for
those who experience a net gain from a regulatory action to potentially compensate those who experience a net loss.
As such, a BCA should aim to evaluate all benefits and costs resulting from the regulation, which includes welfare
effects from all changes in externalities due to changes in environmental contaminants as well as any other
externalities. This requires evaluating changes in pollutant concentrations induced beyond the contaminant(s)
targeted by the action.
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Reconsideration Proposal RIA titled Estimating PM2.5- and Ozone-Attributable Health
Benefits122 (U.S. EPA 2023).
Analyses were also run for each year between 2023 and 2042, using the model surfaces
as described below, but accounting for the change in population size in each year, income
growth, and baseline mortality incidence rates at five-year increments. However, due to
additional uncertainties associated with baseline air quality projections beyond 2026, annual
health benefits beyond 2026 presented in Tables 5-7 and 5-8 are based on 2026 air quality
changes. Additionally, within each 12 km grid cell we assumed the 2023 ozone concentration
change until 2025 and the 2026 ozone and PM2.5 concentration change until 2042. As we do not
account fully for changes in the size or distribution of the population beyond the year 2026, and
the changes in the level and location of NOx emissions attributable to this rule, this may
introduce uncertainty to the analysis and is described below in Section 5.1.3.
Data, resource, and methodological limitations prevent the EPA from monetizing health
benefits of reducing direct exposure to NO2 and SO2, ecosystem effects and visibility impairment
associated with these pollutants, ozone and PM2.5, as well as benefits from reductions in other
pollutants, such as water effluents. We qualitatively discuss these unquantified benefits in this
chapter.
5.1 Estimated Human Health Benefits
The final rule is expected to reduce ozone season and annual NOx emissions. In the
presence of sunlight, NOx and volatile organic compounds (VOCs) can undergo a chemical
reaction in the atmosphere to form ozone. Reducing NOx emissions generally reduces human
exposure to ozone and the incidence of ozone-related health effects, though the degree to which
ozone is reduced will depend in part on local concentration levels of VOCs. In addition to NOx,
the rule is also expected to reduce emissions of direct PM2.5 and SO2 throughout the year.
Because NOx and SO2 are also precursors to secondary formation of ambient PM2.5, reducing
122 The Agency recently asked the Science Advisory Board to evaluate the approach EPA takes to identifying,
selecting and parametrizing endpoints to quantify and monetize health benefits; this approach is detailed in a
Technical Support Document (TSD) noted above (U.S. EPA, 2023). Additional information regarding the
composition of the SAB panel, the schedule for the review and the charge questions may be found at
https://sab.epa.gov/ords/sab/f?p=l 14:18:11364624237840:: :RP, 18:P18_ID:2617
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these emissions would reduce human exposure to ambient PM2.5 throughout the year and would
reduce the incidence of PIVh.s-attributable health effects.
In this Transport FIP for the 2015 Ozone NAAQS regulatory impact analysis (RIA), as
discussed above, the EPA quantifies benefits of changes in ozone and PM2.5 concentrations. In
particular, we incorporate evidence reported in the most recent completed PM and Ozone
Integrated Science Assessments (ISAs) and account for recommendations from the Science
Advisory Board (U.S. EPA 2019a, U.S. EPA 2020b, U.S. EPA-SAB 2019, U.S. EPA-SAB
2020a). When updating each health endpoint, the 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. Detailed descriptions of these updates are available in the TSD
for the 2022 PM NAAQS Reconsideration Proposal RIA titled Estimating PM2.5- and Ozone-
Attributable Health Benefits (U.S. EPA 2023).
The Estimating PM2.5- and Ozone-Attributable Health Benefits TSD describes fully 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 uncertainty.123
As structured, the rule would affect the distribution of ozone and PM2.5 concentrations in
much of the U.S.; this includes locations both meeting and exceeding the NAAQS for ozone and
particulate matter (PM). 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, 2015e). 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
123 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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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, estimated emissions control measures. As shown and described in Chapter 3, we project
most levels of ozone and PM2.5 to decrease, primarily in and downwind of the states included in
this final rule.124 The ozone and PM-related benefit estimates are based on these modeled
changes in summer season average ozone concentrations and changes in average annual PM2.5
concentrations.
5.1.1 Health Impact Assessment for Ozone and PM2.5
The benefits analysis presented in this chapter incorporates science-policy and technical
changes that the Agency adopted and documented in the benefits chapter of the RIA
accompanying the 2022 PM NAAQS Reconsideration Proposal (U.S. EPA 2022a), based on the
2019 PM ISA (U.S. EPA 2019a), Supplement to the 2019 PM ISA (U.S. EPA 2022b), and 2020
ozone ISA (U.S. EPA, 2020c).
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 those 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 change in
risk, 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 across all of the affected individuals residing in the U.S.125 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) selecting air pollution health
endpoints to quantify; (2) calculating counts of air pollution effects using a health impact
124 In a small number of areas in the northwest, we project ozone to increase slightly compared to the baseline.
125 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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function; and (3) specifying the health impact function with concentration-response parameters
drawn from the epidemiological literature.
5.1.2 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 2020b) and the Integrated Science Assessment and Supplement for
Particulate Matter (PM ISA) (U.S. EPA 2019a, U.S. EPA 2022b). These three documents
synthesize the toxicological, clinical and epidemiological evidence to determine whether each
pollutant is causally related to an array of adverse human health outcomes 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. The Agency
estimates the incidence of air pollution effects for those health endpoints above where the ISA
has classified them as either causal or likely-to-be-causal.
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
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.
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Table 5-1 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 5.3 and
Table 5-14 below report other omitted health and environmental benefits expected from the
emissions and water effluent changes as a result of this rule, such as health effects associated
with NO2 and SO2, and any welfare effects such as acidification and nutrient enrichment.
Specifically, for ozone-related benefits, for EGUs and non-EGUs we conducted a full health
benefits analysis that includes premature deaths and illnesses attributable to photochemical
modeled changes in summer season average ozone concentrations for the years 2023 and 2026.
For PM-related benefits for EGUs, we conducted a full health benefits analysis that includes
premature deaths and illnesses attributable to photochemical modeled changes in average PM2.5
concentrations for the year 2026.
Consistent with economic theory, the 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
(U.S. EPA-SAB 2020b, Kivi and Shogren, 2010). 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
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identified as having a "causal" or "likely to be causal" relationship with the pollutant of interest
is to estimate the pollutant-attributable human health benefits in which we are most confident.126
All else equal, this approach may underestimate the benefits of ozone and PM2.5 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.127 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.
126 This decision criterion for selecting health effects to quantify and monetize ozone and PM2 5 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
chapter 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.
127 The 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 (.Estimating PM2.5- and Ozone-Attributable Health Benefits TSD section 6.2.3).
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Table 5-1. Health Effects of Ambient Ozone and PM2.5
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)
Other respiratory effects (e.g., premature aging of
lungs)
—
—
Ozone ISA2
Ozone ISA2
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Cardiovascular and nervous system effects — — Ozone ISA2
Reproductive and developmental effects — — Ozone ISA2
'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.
5.1.1.2 Calculating Counts of Air Pollution Effects Using the Health Impact Function
We use EPA's Benefits Mapping and Analysis Program - Community Edition (BenMAP-
CE) to quantify counts of premature deaths and illnesses attributable to photochemical modeled
changes in summer season average ozone concentrations for the years 2023 and 2026 using
health impact functions. The program is also used to estimate counts of premature deaths and
illnesses attributable to photochemical modeled changes in annual average PM2.5 concentrations
from changes in NOx, SO2 and PM2.5 emissions for the year 2026.
BenMAP quantifies counts of attributable effects using a health impact function, which
combines 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 (i=l,.. .,1
where I is the total number of years analyzed) among adults aged 30 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— 2a yija
yija = moija x(eP-ACij-l) X Pija, Eq[l]
where moija is the baseline all-cause mortality rate for adults aged a=30-99 in county j in year i
stratified in 10-year age groups, P is the risk coefficient for all-cause mortality for adults
associated with annual average PM2.5 exposure, Cij is the annual mean PM2.5 concentration in
county j in year i, and Pija is the number of county adult residents aged a=30-99 in county j in
year i stratified into 5-year age groups.128
128 In this illustrative example, the air quality is resolved at the county level. For this RIA, we simulate air quality
concentrations at 12 by 12 km grids. The BenMAP-CE tool assigns the rates of baseline death and disease stored at
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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. Changes in ozone and PM2.5 concentrations are taken from the air pollution spatial
surfaces for the analytic years 2023 (ozone only) and 2026 described in Chapter 3.
5.1.1.3 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
above, the Estimating PM2.5- and Ozone-Attributable 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 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 the 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
the county level to the 12 by 12 km grid cells 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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total mortality (Bell et al. 2004), two multi-city studies of cardiopulmonary and total mortality
(Huang et al. 2004; 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 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.E. EPA, 2020b). As a result, we use two
estimates of ozone-attributable respiratory deaths from short-term exposures are estimated using
the risk estimate parameters from Zanobetti et al. (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 et al. (2008) as June-August. Instead, we apply this risk estimate to our standard warm
season of May-September.
5.1.1.4 Quantifying Cases of PM2.5-Attributable Premature Death
When quantifying PM-attributable cases of adult mortality, we use the effect coefficients
from two epidemiology studies examining two large population cohorts: the American Cancer
Society cohort (Turner et al. 2016) and the Medicare cohort (Di et al. 2017). The Integrated
Science Assessment for Particulate Matter (PM ISA) (U.S. EPA 2019a) and Supplement to the
2019 PM ISA (U.S. EPA 2022b) concluded that the analyses of the ACS and Medicare cohorts
provide strong evidence of an association between long-term PM2.5 exposure and premature
mortality with support from additional cohort studies. There are distinct attributes of both the
ACS and Medicare cohort studies that make them well-suited to being used in a PM benefits
assessment and so here we present PM2.5 related effects derived using relative risk estimates from
both cohorts.
The PM ISA, which was reviewed by the Clean Air Scientific Advisory Committee of
EPA's Science Advisory Board (SAB-CASAC) (EPA-SAB 2020a), 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. The PM ISA also concluded that the scientific literature
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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 "no-threshold" model and that "little evidence
was observed to suggest that a threshold exists" (U.S. EPA 2009) (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 2010c, 2010d, 2011c, 201 Id, 2012, 2013b, 2014a, 2014b, 2014c,
2015a, 2015b, 2015c, 2015d, 2015e, 2016b).
5.1.2 Economic Valuation Methodology for Health Benefits
We next quantify the economic value of the ozone and PIVh.s-related deaths and illnesses
estimated above. Changes in ambient concentrations of air pollution generally yield small
changes in the risk of future adverse health effects for a large number of people. The appropriate
economic measure of the value of these small changes in risk of a health effect for the purposes
of a benefit-cost analysis is WTP. For some health effects, such as hospital admissions, WTP
estimates are not generally available, so we use the cost of treating or mitigating the effect. These
cost-of-illness (COI) estimates are typically a lower bound estimate of the true value of reducing
the risk of a health effect because they reflect the direct expenditures related to treatment, but not
the value of avoided pain and suffering. The unit values applied in this analysis are provided in
Table 21 of the Estimating PM2.5- and Ozone-Attributable Health Benefits TSD.
The value of avoided premature deaths generally account for over 95 percent of
monetized ozone-related benefits and over 98 percent of monetized PIVh.s-related benefits. The
economics literature concerning the appropriate method for valuing reductions in premature
mortality risk is still developing. The value for the projected reduction in the risk of premature
mortality is the subject of continuing discussion within the economics and public policy analysis
community. Following the advice of the SAB's Environmental Economics Advisory Committee
(SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating
estimates of mortality benefits, because we believe this calculation provides the most reasonable
208
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single estimate of an individual's willingness to trade off money for changes in the risk of death
(U.S. EPA-SAB 2000a). The VSL approach is a summary measure for the value of small
changes in the risk of death experienced by a large number of people.
The EPA continues work to update its guidance on valuing mortality risk reductions, and
the Agency consulted several times with the SAB-EEAC on this issue. Until updated guidance is
available, the Agency determined that a single, peer-reviewed estimate applied consistently, best
reflects the SAB-EEAC advice it has received. Therefore, EPA applies the VSL that was vetted
and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA 2016a)
while the Agency continues its efforts to update its guidance on this issue. This approach
calculates a mean value across VSL estimates derived from 26 labor market and contingent
valuation studies published between 1974 and 1991. The mean VSL across these studies is $4.8
million (1990$). We then adjust this VSL to account for the currency year and to account for
income growth from 1990 to the analysis year. Specifically, the VSL applied in this analysis in
2016$ after adjusting for income growth is $10.7 million for 2026.
The Agency is committed to using scientifically sound, appropriately reviewed evidence
in valuing changes in the risk of premature death and continues to engage with the SAB to
identify scientifically sound approaches to update its mortality risk valuation estimates. In 2016,
the Agency proposed new meta-analytic approaches for updating its estimates (U.S. EPA-SAB
2017), which were subsequently reviewed by the SAB-EEAC. The EPA is reviewing the SAB's
formal recommendations.
In valuing PM2.5-related premature mortality, we discount the value of premature
mortality occurring in future years using rates of 3 percent and 7 percent (U.S. Office of
Management and Budget 2003). We assume that there is a multi-year "cessation" lag between
changes in PM exposures and the total realization of changes in health effects. Although the
structure of the lag is uncertain, the EPA follows the advice of the SAB-HES to use a segmented
lag structure that assumes 30 percent of premature deaths are reduced in the first year, 50 percent
over years 2 to 5, and 20 percent over the years 6 to 20 after the reduction in PM2.5 (U.S. EPA-
SAB 2004). Changes in the cessation lag assumptions do not change the total number of
estimated deaths but rather the timing of those deaths.
209
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Because estimated counts of short-term ozone-related premature mortality occur within
each analysis year, these estimated ozone-related benefits are identical for all discount rates.
When valuing changes in long-term ozone-attributable respiratory deaths using the Turner et al.
(2015) study, we follow advice provided by the Health Effects Subcommittee of the SAB, which
found that. .there is no evidence in the literature to support a different cessation lag between
ozone and particulate matter. The HES therefore recommends using the same cessation lag
structure and assumptions as for particulate matter when utilizing cohort mortality evidence for
ozone" (U.S. EPA-SAB 2010).
These estimated health benefits do not account for the influence of future changes in the
climate on ambient concentrations of pollutants (USGCRP 2016). For example, recent research
suggests that future changes to climate may create conditions more conducive to forming ozone;
the influence of changes in the climate on PM2.5 concentrations are less clear (Fann et al. 2015).
The estimated health benefits also do not consider the potential for climate-induced changes in
temperature to modify the relationship between ozone and the risk of premature death (Fann et
al. 2021, Jhun et al. 2014; Ren et al. 2008a, 2008b).
5.1.3 Characterizing Uncertainty in the Estimated Benefits
This analysis includes many data sources as inputs that are each subject to uncertainty.
Input parameters include projected emission inventories, projected emissions and emissions
changes from the electricity planning model, projected baseline emission and emissions
reductions from non-EGUs, air quality data from models (with their associated parameters and
inputs), population data, population estimates, health effect estimates from epidemiology studies,
economic data, and assumptions regarding the future state of the world (i.e., regulations,
technology, and human behavior). When compounded, even small uncertainties can greatly
influence the size of the total quantified benefits.
Our estimate of the total monetized ozone and PM2.5-attributable benefits is based on the
EPA's interpretation of the best available scientific literature and methods and supported by the
SAB-HES and the National Academies of Science (NRC 2002). Below are key assumptions
underlying the estimates for ozone-related premature deaths, followed by key uncertainties
associated with estimating the number and value of PM2.5-related premature mortality.
210
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The estimated number and value of avoided ozone-attributable deaths are subject to
uncertainty. When estimating the economic value of avoided premature mortality from long-term
exposure to ozone, we use a 20-year segment lag (as used for PM2.5) as there is no alternative
empirical estimate of the cessation lag for long-term exposure to ozone. The 20-year segmented
lag accounts for the onset of cardiovascular related mortality, an outcome which is not relevant
to the long-term respiratory mortality estimated here. We use a log-linear impact function
without a threshold in modeling short-term ozone-related mortality. Thus, the estimates include
health benefits from reducing ozone in areas with varied concentrations of ozone down to the
lowest modeled concentrations. However, we acknowledge reduced confidence in specifying the
shape of the concentration-response relationship in the range of < 40ppb and below (2020 Ozone
ISA, section 6.2.6).
We assume that all fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality. This is an important assumption, the PM ISA concluded
that "many constituents of PM2.5 can be linked with multiple health effects, and the evidence is
not yet sufficient to allow differentiation of those constituents or sources that are more closely
related to specific outcomes" (U.S. EPA 2009).
As noted above, we quantify health impacts of fine particles using a log-linear no-
threshold model. Thus, some portion of the air quality and health benefits from the regulatory
control alternatives will occur in areas not attaining the ozone or PM NAAQS. Expected changes
in the ambient concentrations of both ozone and PM2.5 pollutants may lead to states changing
their NAAQS compliance approaches. However, we do not simulate how states would account
for this rule when complying with the NAAQS, which introduces uncertainty in the estimated
benefits (and costs).
Also, as noted above, 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-
SAB 2004), which affects the valuation of mortality benefits at different discount rates. The
above assumptions are subject to uncertainty.
211
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In general, we are more confident in the magnitude of the risks we estimate from
simulated PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in
the epidemiological studies that are used to estimate the benefits. Likewise, we are less confident
in the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the
observed data in these studies. There are uncertainties inherent in identifying any particular point
at which our confidence in reported associations decreases appreciably, and the scientific
evidence provides no clear dividing line. This relationship between the air quality data and our
confidence in the estimated risk is represented below in Figure 5-1.
Less confident More
Figure 5-1 Stylized Relationship between the PM2.5 Concentrations Considered in
Epidemiology Studies and our Confidence in the Estimated PM-related Premature Deaths
For Turner et al. 2016, the LRL is 2.8 |ig/m3 and for Di et al. 2017, the LRL is 0.02
|ig/m3. Additional information on low concentration exposures in Turner et al. 2016 and Di et al.
2017 can be found in section 6.1.2.1 of the Estimating PM2.5- and Ozone-Attributable Health
Benefits TSD. These results are sensitive to the annual mean PM2.5 concentration the air quality
model predicted in each 12 km by 12 km grid cell. The air quality modeling predicts PM2.5
concentrations to be at or below the current annual mean PM2.5 NAAQS (12 |ig/m3) in nearly all
locations. The photochemical modeling we employ accounts for the suite of local, state and
federal policies expected to reduce PM2.5 and PM2.5 precursor emissions in future years. The
results should be viewed in the context of the air quality modeling technique we used to estimate
PM2.5 concentrations. We are more confident in our ability to use the air quality modeling
techniques described above to estimate changes in annual mean PM2.5 concentrations than we are
in our ability to estimate absolute PM2.5 concentrations.
confident
Below LRL of PM2.5 data
in epidemiology study
(extrapolation)
I standard deviation below the
mean PM2.5 observed in
epidemiology study
Mean of PM2.5 data in
epidemiology study
212
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5.1.4 Estimated Number and Economic Value of Health Benefits
Below we report the estimated number of reduced premature deaths and illnesses in each
year relative to the baseline along with the 95% confidence interval (Table 5-2, Table 5-3 and
Table 5-4) for ozone-attributable health benefits in 2023 and 2026 and PM-attributable health
benefits in 2026. The number of reduced estimated deaths and illnesses from the final rule and
more and less stringent alternatives are calculated from the sum of individual reduced mortality
and illness risks across the population. Table 5-5 and Table 5-6 report the estimated economic
value of avoided premature deaths and illness in each year relative to the baseline along with the
95% confidence interval. We also report the stream of benefits from 2023 through 2042 for the
final rule, more-, and less- stringent alternatives, using the monetized sums of long-term ozone
and PM2.5 mortality and morbidity impacts (Table 5-7 and Table 5-8.).129
129 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.
213
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Table 5-2. Estimated Avoided Ozone-Related Premature Respiratory Mortalities and
Illnesses for the Final Rule and More and Less Stringent Alternatives for 2023 (95%
Confidence Interval) a'b
Final Rule
More Stringent
Alternative
Less Stringent
Alternative
Avoided premature respiratory mortalities
Long-
term
Turner etal. (2016)°
78
80
78
exposure
(54 to 100)
(56 to 100)
(54 to 100)
Short-
term
Katsouyanni el al.
(2009)cd and Zanobetti el
3.5
3.6
3.5
exposure
al. (2008)d pooled
(1.4 to 5.6)
(1.5 to 5.7)
(1.4 to 5.5)
Morbidity effects
Long-
term
exposure
Asthma onset6
640
(550 to 720)
650
(560 to 740)
640
(550 to 720)
Allergic rhinitis
symptoms®
3,600
(1,900 to 5,200)
3,700
(1,900 to 5,400)
3,600
(1,900 to 5,200)
Hospital admissions—
9.3
9.6
9.3
respiratoryd
(-2.4 to 21)
(-2.5 to 21)
(-2.4 to 20)
ED visits—respiratoryf
200
(54 to 410)
200
(56 to 420)
200
(54 to 410)
Short-
110,000
term
Asthma symptoms
110,000
120,000
(-14,000 to
exposure
(-14,000 to 240,000)
(-14,000 to 240,000)
240,000)
Minor restricted-activity
54,000
55,000
54,000
daysd-f
(22,000 to 85,000)
(22,000 to 87,000)
(21,000 to 85,000)
School absence days
41,000
(-5,800 to 86,000)
42,000
(-5,900 to 88,000)
41,000
(-5,700 to 85,000)
a Values rounded to two significant figures.
b We estimated ozone benefits for changes in NOx for the ozone season for EGUs in 2023. This table does not
include benefits from emissions reductions for non-EGUs because emissions reductions from these sources are not
expected prior to 2026 when the final standards would apply to these sources.
0 Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
d Converted ozone risk estimate metric from maximum daily 1-hour average (MDA1) to maximum daily 8-hour
average (MDA8).
e Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
f Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
g Converted ozone risk estimate metric from daily 24-hour average (DA24) to MDA8.
214
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Table 5-3. Estimated Avoided Ozone-Related Premature Respiratory Mortalities and
Illnesses for the Final Rule and More and Less Stringent Alternatives for 2026 (95%
Confidence Interval) a'b
More Stringent
Less Stringent
Final Rule
Alternative
Alternative
Exposure
Duration
Study
Affected Facility
Avoided premature respiratory mortalities
310
560
98
Long-
term
EGUs
(220 to 400)
(380 to 720)
(68 to 130)
Turner el al.
Non-EGUs
580
890
220
(2016)°
(400 to 750)
(620 to 1,200)
(160 to 290)
exposure
EGUs + Non-
1,400
EGUs
890
(620 to 1,200)
(1,000 to
1,900)
320
(220 to 420)
Short-
Katsouyanni el
EGUs
14
(5.7 to 22)
25
(10 to 40)
4.4
(1.8 to 7.0)
term
al. (2U(jy)' and ""
Zanobetti et al.
Non-EGUs
26
40
10
exposure
(11 to 41)
(16 to 64)
(4.1 to 16)
(zuuoj pooiea
EGUs + Non-
40
66
15
EGUs
(16 to 64)
(26 to 100)
(5.9 to 23)
Morbidity effects
4,200
2,300
(3,600 to
730
EGUs
(1,900 to 2,600)
4,700)
(630 to 830)
Non-EGUs
6,900
Long-
term
exposure
Asthma onset6
4,400
(3,800 to 5,000)
(6,000 to
7,900)
1,800
(1,500 to 2,000)
EGUs + Non-
EGUs
6,600
(5,700 to 7,500)
11,000
(9,500 to
13,000)
2,500
(2,100 to 2,800)
24,000
13,000
(13,000 to
4,200
EGUs
(6,800 to 19,000)
35,000)
(2,200 to 6,100)
Allergic rhinitis
symptoms®
Non-EGUs
25,000
(13,000 to 37,000)
40,000
(21,000 to
58,000)
10,000
(5,300 to 15,000)
EGUs + Non-
64,000
EGUs
38,000
(20,000 to 55,000)
(34,000 to
92,000)
14,000
(7,500 to 21,000)
38
67
12
Hospital
EGUs
(-9.9 to 84)
(-17 to 150)
(-3.1 to 26)
admissions—
Non-EGUs
70
110
27
Short-
term
respiratoryd
(-18 to 160)
(-28 to 240)
(-7.0 to 60)
EGUs + Non-
110
170
39
EGUs
(-28 to 240)
(-46 to 390)
(-10 to 86)
exposure
ED visits—
EGUs
720
(200 to 1,500)
1,300
(370 to 2,800)
240
(65 to 490)
respiratoryf
Non-EGUs
1,400
(390 to 3,000)
2,200
(610 to 4,600)
560
(150 to 1,200)
215
-------
Asthma
symptoms
Minor
restricted-
activity daysd-f
School absence
days
EGUs + Non-
EGUs
2,100
(590 to 4,500)
3,600
(980 to 7,500)
790
(220 to 1,700)
EGUs
Non-EGUs
EGUs + Non-
EGUs
EGUs
Non-EGUs
EGUs + Non-
EGUs
EGUs
Non-EGUs
EGUs + Non-
EGUs
420,000
(-51,000 to
870,000)
770,000
(-95,000 to
1,600,000)
130,000
(-17,000 to 280,000)
810,000
(-100,000 to
1,700,000)
1,300,000
(-160,000 to
2,700,000)
320,000
(-40,000 to 670,000)
1,200,000
(-150,000 to
2,500,000)
2,000,000
(-250,000 to
4,200,000)
460,000
(-56,000 to 950,000)
190,000
(77,000 to
300,000)
350,000
(140,000 to
560,000)
62,000
(25,000 to 98,000)
380,000
(150,000 to
590,000)
600,000
(240,000 to
940,000)
150,000
(61,000 to 240,000)
570,000
(230,000 to
900,000)
950,000
(380,000 to
1,500,000)
210,000
(85,000 to 340,000)
150,000
(-21,000 to
310,000)
270,000
(-38,000 to
570,000)
48,000
(-6,700 to 100,000)
290,000
(-41,000 to
600,000)
450,000
(-64,000 to
950,000)
110,000
(-16,000 to 240,000)
430,000
(-61,000 to
910,000)
720,000
(-100,000 to
1,500,000)
160,000
(-23,000 to 340,000)
a Values rounded to two significant figures.
b We estimated ozone benefits for changes in NOx for the ozone season and changes in PM2 5 and PM2 5 precursors
for EGUs in 2026.
0 Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September
warm season.
d Converted ozone risk estimate metric from MDA1 to MDA8.
e Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm
season.
f Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm
season.
g Converted ozone risk estimate metric from DA24 to MDA8.
216
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Table 5-4. Estimated Avoided PM-Related Premature Respiratory Mortalities and Illnesses
for the Final Rule and More and Less Stringent Alternatives for 2026 (95% Confidence
Interval)
Avoided Mortality
Final Rule
More Stringent
Less Stringent
Pope III et al., 2019 (adult mortality ages 18-99
440
1,400
120
years)
(320 to 570)
(1,000 to 1,800)
(84 to 150)
Wu et al., 2020 (adult mortality ages 65-99
200
640
53
years)
(180 to 230)
(570 to 720)
(46 to 59)
Woodruff et al., 2008 (infant mortality)
0.64
(-0.40 to 1.6)
1.9
(-1.2 to 4.9)
0.19
(-0.12 to 0.49)
Avoided Morbidity
Final Rule
More Stringent
Less Stringent
Hospital admissions—cardiovascular (age >
29
92
7.5
18)
(21 to 36)
(66 to 120)
(5.4 to 9.5)
Hospital admissions—respiratory
4.7
15
1.2
(0.18 to 9.0)
(0.55 to 28)
(0.047 to 2.4)
ED visits-cardiovascular
64
200
17
(-25 to 150)
(-78 to 470)
(-6.7 to 41)
ED visits—respiratory
130
420
37
(26 to 270)
(82 to 870)
(7.2 to 77)
Acute Myocardial Infarction
6.8
21
1.7
(3.9 to 9.5)
(12 to 30)
(0.97 to 2.4)
Cardiac arrest
3.1
10
0.84
(-1.3 to 7.1)
(-4.1 to 23)
(-0.34 to 1.9)
Hospital admissions-Alzheimer's Disease
120
340
32
(92 to 150)
(250 to 420)
(24 to 40)
Hospital admissions-Parkinson's Disease
13
41
3.2
(6.3 to 18)
(21 to 60)
(1.6 to 4.7)
Stroke
12
39
3.2
(3.1 to 21)
(10 to 66)
(0.82 to 5.5)
Lung cancer
14
44
3.6
(4.2 to 23)
(13 to 74)
(1.1 to 6.1)
Hay Fever/Rhinitis
3,300
10,000
930
(790 to 5,700)
(2,500 to 18,000)
(220 to 1,600)
Asthma Onset
520
1,600
150
(490 to 540)
(1,600 to 1,700)
(140 to 150)
Asthma symptoms - Albuterol use
69,000
220,000
19,000
(-9,400 to 47,000)
(-33,000 to
170,000)
(-110,000 to
530,000)
Lost work days
25,000
(21,000 to
28,000)
79,000
(66,000 to 91,000)
6,800
(5,700 to 7,800)
Minor restricted-activity days
140,000
(120,000 to
170,000)
460,000
(380,000 to 550,000)
40,000
(32,000 to 47,000)
217
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Table 5-5. Estimated Discounted Economic Value of Avoided Ozone-Related Premature
Mortality and Illness for the Final Rule and the Less and More Stringent Alternatives in
2023 (95% Confidence Interval; millions of 20]
L6$)a'b
Disc.
Rate
Pollutant
Final Rule
More Stringent
Alternative
Less Stringent
Alternative
3%
Ozone
Benefits
$100($27 $820($91
to $220)° to $2,100)d
$110 $840
($28 to and ($94 to
$230)° $2,200)d
$100 $810
($27 to and ($91 to
$220)° $2,100)d
7%
Ozone
Benefits
$93($17 $730 ($75
to 210)° to $l,900)d
$96 $750
($18 to and ($77 to
$210)° $2,000)d
$93 $730
($17 to and ($75 to
$210)° $l,900)d
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should
not be summed.
b We estimated ozone benefits for changes in NOx for the ozone season. This table does not include benefits from
reductions for non-EGUs because reductions from these sources are not expected prior to 2026 when the final
standards would apply to these sources.
0 Using the pooled short-term ozone exposure mortality risk estimate.
d Using the long-term ozone exposure mortality risk estimate.
Table 5-6. Estimated Discounted Economic Value of Avoided Ozone and PM2.5-Attributable
Premature Mortality and Illness for the Final Rule and the Less and More Stringent
Alternatives in 2026 (95% Confidence Interval; millions of 2016$)a'b
Disc
Rate
Polluta
nt
Final Rule
More Stringent Alternative
Less Stringent Alternative
3%
Ozone
Benefits
$1,100
($280 to
$2,400)
and
$9,400
($1,000 to
$25,000)
$1,900
(470 to
$4,000)
and
$15,000
($1,700 to
$40,000)
$420
($110 to
$900)
and
$3,400
($380 to
$8,900)
PM
Benefits
$2,000
($220 to
$5,300)
and
$4,400
($430 to
$12,000)
$6,400
($690 to
$17,000)
and
$14,000
($1,300 to
$37,000)
$530
($57 to
$1,400)
and
$1,100
($110 to
$3,100)
Ozone
plus PM
Benefits
$3,200
($500 to
$7,700)°
and
$14,000
($1,500 to
$36,000)d
$8,300
($1,200
to
$21,000)
C
and
$29,000
($3,000 to
$77,000)d
$950
($160 to
$2,300)°
and
$4,600
($490 to
$12,000)d
7%
Ozone
Benefits
$1,000
($180 to
$2,300)
and
$8,400
($850 to
$22,000)
$1,700
($300 to
$3,800)
and
$14,000
($1,400 to
$36,000)
$380
($68 to
$850)
and
$3,100
($310 to
$8,100)
PM
Benefits
$1,800
($190 to
$4,700)
and
$3,900
($380 to
$11,000)
$5,800
($600 to
$15,000)
and
$12,000
($1,200 to
$33,000)
470
($50 to
$1,200)
and
$1,000
($100 to
$2,800)
Ozone
plus PM
Benefits
$2,800
($370 to
$7,000)°
and
$12,000
($1,200 to
$33,000)d
$7,500
($910 to
$19,000)
C
and
$26,000
($2,600 to
$69,000)d
$850
($120 to
$2,100)°
and
$4,100
($410 to
$ll,000)d
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify
that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should
not be summed.
b We estimated changes in NOx for the ozone season and annual changes in PM2 5 and PM2 5 precursors in 2026.
0 Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al.
(2020) long-term PM2 5 exposure mortality risk estimate.
d Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2016) long-term PM2 5
exposure mortality risk estimate.
218
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Table 5-7. Stream of Human Health Benefits from 2023 through 2042: Monetized
Benefits Quantified as Sum of Long-Term Ozone Mortality for EGUs and Non-EGUs
and Long-Term PM2.5 Mortality for EGUs (Discounted at 3%; millions of 2016$)a
2023*
2024
2025
2026*
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
Final Rule
More Stringent Alternative
Less Stringent Alternative
820
840
810
810
840
810
8,600
14,000
3,100
13,000
27,000
4,200
13,000
26,000
4,200
12,000
25,000
4,000
12,000
25,000
4,000
12,000
25,000
4,000
12,000
25,000
3,900
12,000
25,000
3,900
11,000
24,000
3,800
11,000
24,000
3,800
11,000
24,000
3,700
11,000
24,000
3,700
11,000
23,000
3,700
11,000
23,000
3,600
10,000
22,000
3,500
10,000
22,000
3,500
10,000
22,000
3,400
10,000
21,000
3,400
200,000
420,000
69,000
Net Present Value
*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. 2016 study); Ozone-attributable deaths (quantified using a
concentration-response relationship from the Turner et al. 2017 study); and ozone and PM2 5-related morbidity
effects.
a For the years 2023-2025, there are no non-EGU emissions reductions. As such, there are no estimated benefits
from non-EGU reductions for 2023-2025.
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Table 5-8. Stream of Human Health Benefits from 2023 through 2042: Monetized
Benefits Quantified as Sum of Short-Term Ozone Mortality for EGUs and Non-EGUs
and Long-Term PM2.5 Mortality for EGUs (Discounted at 7%; millions of 2016$)a
Final Rule
More Stringent Alternative
Less Stringent Alternative
2023*
730
750
730
2024
700
720
700
2025
7,100
12,000
2,600
2026*
10,000
21,000
3,300
2027
9,700
20,000
3,200
2028
8,900
19,000
2,900
2029
8,500
18,000
2,800
2030
8,200
17,000
2,700
2031
7,800
17,000
2,600
2032
7,500
16,000
2,500
2033
7,000
15,000
2,300
2034
6,700
14,000
2,200
2035
6,400
14,000
2,100
2036
6,100
13,000
2,000
2037
5,800
12,000
1,900
2038
5,400
11,000
1,800
2039
5,100
11,000
1,700
2040
4,900
10,000
1,600
2041
4,600
9,800
1,500
2042
4,400
9,300
1,500
Net Present Value
130,000
260,000
43,000
*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. 2016 study); Ozone-attributable deaths (quantified using a
pooled estimate of results quantified using concentration-response relationships two short-term exposure mortality
studies); and ozone and PM2 5-related morbidity effects.
a For the years 2023-2025, there are no non-EGU emissions reductions. As such, there are no estimated benefits
from non-EGU reductions for 2023-2025.
5.2 Climate Benefits from Reducing CO2
We estimate the climate benefits for this final rulemaking using estimates of the social
cost of greenhouse gases (SC-GHG), specifically the social cost of carbon (SC-CO2). The SC-
CO2 is the monetary value of the net harm to society associated with a marginal increase in CO2
emissions in a given year, or the benefit of avoiding that increase. In principle, SC-CO2 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 natural disasters, disruption of energy systems, risk of conflict,
environmental migration, and the value of ecosystem services. The SC-CO2, therefore, reflects
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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
CO2 emissions. In practice, data and modeling limitations naturally restrain the ability of SC-
CO2 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. The EPA and other
Federal agencies began regularly incorporating SC- CO2 estimates in their benefit-cost analyses
conducted under Executive Order (E.O.) 12866130 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
130 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. Many statutes also require agencies to conduct at least some of the same analyses
required under E.O. 12866, such as the Energy Policy and Conservation Act, which mandates the setting of fuel
economy regulations. 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 change in
GHG emissions and the monetized benefits associated with those changes play no part in the record basis for this
action, which is taken to implement the good neighbor provision, CAA section 110(a)(2)(D)(i)(I), for the 2015
ozone NAAQS.
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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 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 updated SC-GHG
methodology within a sensitivity analysis in the regulatory impact analysis of EPA's November
2022 supplemental proposal for oil and gas standards that is currently undergoing external peer
review and a public comment process.131
The 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 TSD
and is likewise using them in this RIA. We have evaluated the SC-GHG estimates in the
February 2021 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 final rule
and alternative standards. 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 TSD, and the issues
and studies discussed therein, the EPA concludes 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 the 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
131 See https://www.epa.gov/environmental-economics/scghg
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were ran 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.132 In
August 2016 the IWG published estimates of the 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-
CO2 GHGs has undergone multiple stages of peer review. The SC-CH4 and SC-N2O estimates
were developed by Marten, Kopits, Griffiths, Newbold, and Wolverton (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.133 The 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, the
EPA began using the estimates in the primary benefit-cost analysis calculations and tables for a
number of proposed rulemakings in 2015 (EPA 2015f, 2015g). The EPA considered and
responded to public comments received for the proposed rulemakings before using the estimates
in final regulatory analyses in 20 1 6.134 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-CO2 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
132 Dynamic Integrated Climate and Economy (DICE) 2010 (Nordhaus 2010), Climate Framework for Uncertainty,
Negotiation, and Distribution (FUND) 3.8 (Anthoff and Tol 2013a, 2013b), and Policy Analysis of the Greenhouse
Gas Effect (PAGE) 2009 (Hope 2013).
133 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, 2015).
134 See IWG (2016b) for more discussion of the SC-CH4 and SC-N20 and the peer review and public comment
processes accompanying their development.
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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). Benefit-
cost analyses following E.O. 13783 used SC-CO2 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
reflect many pathways by which climate impacts affect the welfare of U.S. citizens and
residents) and were calculated using two default discount rates recommended by Circular A-4, 3
percent and 7 percent.135 All other methodological decisions and model versions used in SC-
CO2 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 SC-CO2 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- CO2 estimates developed by the
IWG prior to the group being disbanded in 2017, adjusted for inflation (IWG, 2021). As
discussed in the February 2021 TSD, the IWG's selection of these interim estimates reflected the
immediate need to have SC- CO2 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, the EPA participated in the IWG but has also independently evaluated
the interim SC-CO2 estimates published in the February 2021 TSD and determined they are
appropriate to use to estimate climate benefits for this action. The EPA and other agencies intend
to undertake a fuller update of the SC- CO2 estimates that takes into consideration the advice of
the National Academies (2017) and other recent scientific literature. The EPA has also evaluated
135 The 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%. 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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the supporting rationale of the February 2021 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-CO2 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
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, the EPA agrees with this assessment and, therefore, in this RIA, the EPA centers attention
on a global measure of SC-CO2. This approach is the same as that taken in EPA regulatory
analyses over 2009 through 2016. A robust estimate of climate damages only 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
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
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economic impacts of climate change recognized in the climate change literature, as discussed
further below. The 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.
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-CO2. 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 (IWG, 2010; IWG, 2013;
IWG, 2016a; IWG, 2016b), and recommended that discount rate uncertainty and relevant aspects
of intergenerational ethical considerations be accounted for in selecting future discount rates.136
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. The
EPA agrees with this assessment and will continue to follow developments in the literature
pertaining to this issue. The 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
136 GHG emissions are stock pollutants, where damages are associated with what has accumulated in the atmosphere
over time, and they are long lived such that subsequent damages resulting from emissions today occur over many
decades or centuries depending on the specific greenhouse gas under consideration. In calculating the SC-GHG, the
stream of future damages to agriculture, human health, and other market and non-market sectors from an additional
unit of emissions are estimated in terms of reduced consumption (or consumption equivalents). Then that stream of
future damages is discounted to its present value in the year when the additional unit of emissions was released.
Given the long time horizon over which the damages are expected to occur, the discount rate has a large influence
on the present value of future damages.
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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, the 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 RIA. In this analysis, to calculate the present
and annualized values of climate benefits, the 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 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 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 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 provide information on potentially higher-than-expected economic impacts
from climate change, conditional on the 3 percent estimate of the discount rate. As explained in
the February 2021 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
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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
2013.137
Table 5-9 summarizes the interim SC-CO2 estimates for the years 2020 to 2050. These
estimates are reported in 2016 dollars 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 2021 TSD emphasizes the importance of considering all four of the SC-CO2 values.
The SC-CO2 increases 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.
137 At the time of proposal of this rule, a preliminary injunction was in place that prevented the Agency from
displaying the February 2021 TSD-based Interim Estimates. That injunction was subsequently stayed on appeal. The
Agency then prepared an addendum to the RIA for the proposed rule presenting the monetized climate benefits of
the proposed rule and placed this in the docket and on our website. The EPA invited comment on that analysis. As
that document explained, and as remains true for this final rule, "the monetized climate benefits ... are not a part of
the technical or legal basis of the proposed action for which the RIA was prepared." See Addendum to the
Regulatory Impact Analysis: Monetizing Climate Benefits for the Proposed FIP for Addressing Regional Ozone
Transport for the 2015 Ozone NAAQS, available at https://www.epa.gov/system/files/documents/2022-04/2015-fip-
climate-benefits-technical-memo_04052022.pdf.
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Table 5-9. Interim Social Cost of Carbon Values, 2020-2050 (2016$/Metric Tonne CO2)
Emissions
Discount Rate and Statistic
Year
5%
3%
2.5%
3%
Average
Average
Average
95th Percentile
2020
$13
$47
$71
$140
2025
$15
$52
$77
$160
2030
$18
$57
$83
$170
2035
$20
$63
$90
$190
2040
$23
$67
$95
$210
2045
$26
$73
$100
$220
2050
$29
$78
$110
$240
Note: These SC-CO2 values are identical to those reported in the 2016 TSD (IWG 2016a) adjusted for inflation to
2016 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 2021). The values are stated in $/metric tonne CO2 (1 metric tonne equals
1.102 short tons) and vary depending on the year of CO2 emissions. This table displays the values rounded to the
nearest dollar; the annual unrounded values used in the calculations in this RIA are available on OMB's website:
https://www.whitehouse.gOv/omb/information-regulatory-affairs/regulatory-matters/#scghgs.
Source: Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates
under Executive Order 13990 (IWG 2021)
There are a number of limitations and uncertainties associated with the SC-CO2 estimates
presented in Table 5-9. 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 5-2 presents the
quantified sources of uncertainty in the form of frequency distributions for the SC-CO2 estimates
for emissions in 2030. The distributions of SC-CO2 estimates reflect 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
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 2021
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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5% Average = $18
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100 120 140 160 180 200 220 240 260 280 300
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Social Cost of Carbon in 2030 [2016$ / metric ton C02]
Figure 5-2. Frequency Distribution of SC-CO2 Estimates for 2030138
The interim SC-CO2 estimates presented in Table 5-8 have a number of 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 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
138 Although the distributions and numbers in Figure 5-2 are based on the full set of model results (150,000
estimates for each discount rate), for display purposes the horizontal axis is truncated with 0.78 percent of the
estimates falling below the lowest bin displayed and 3.64 percent of the estimates falling above the highest bin
displayed.
230
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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-CO2 estimates. However, as discussed in the February 2021 TSD, the IWG has
recommended that, taken together, the limitations suggest that the SC-CO2 estimates used in this
RIA likely underestimate the damages from CO2 emissions. EPA concurs that the values used in
this RIA conservatively underestimate the rule's climate benefits. In particular, the
Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (IPCC 2007),
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. 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) (e.g., IPCC 2018, 2019a, 2019b; U.S. Global Change
Research Program (USGCRP) 2016, 2018; and National Academies 2016, 2019). 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 (IPCC 2007). 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-
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 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 TSD and
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other input from experts and diverse stakeholder groups (National Academies 2017). While that
process continues, the 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,
the EPA presented a draft set of updated SC-GHG estimates within a sensitivity analysis in the
regulatory impact analysis of the 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. The 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 Gas
rule. The EPA is also embarking on an external peer review of this technical report. More
information about this process and public comment opportunities is available on the EPA's
website.139 EPA's draft technical report will be among the many technical inputs available to the
IWG as it continues its work.
Table 5-10 shows the estimated monetary value of the estimated changes in CO2
emissions expected to occur over 2021-2040 for this rule, the more-stringent alternative, and the
less-stringent alternative. The EPA estimated the dollar value of the C02-related effects for each
analysis year between 2021 and 2040 by applying the SC-CO2 estimates, shown in Table 5-9, to
the estimated changes in CO2 emissions in the corresponding year under the regulatory options.
The EPA then calculated the present value and annualized benefits from the perspective of 2020
by discounting each year-specific value to the year 2020 using the same discount rate used to
calculate the SC-CO2.140
139 See https://www.epa.gov/environmental-economics/scghg
140 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 "[d]ifferent regulations may call for different emphases in the analysis, depending on the
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Table 5-10. Estimated Climate Benefits from Changes in C02 Emissions 2023 - 2040
(Millions of 2016$)a
Discount Rate and Statistic
Regulatory Alternative
Year
5%
Average
3%
Average
2.5%
Average
3%
95th Percentile
2023
1
5
7
14
2024
319
1,075
1,586
3,218
2025
329
1,096
1,611
3,286
Final Rule
2026
338
1,117
1,637
3,354
2030
474
1,512
2,191
4,572
2035
335
1,015
1,448
3,095
2040
474
1,378
1,941
4,234
2023
1
5
7
14
More-Stringent
2024
605
2,040
3,009
6,104
Alternative
2025
623
2,079
3,057
6,234
2026
642
2,119
3,105
6,363
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 C02 (2016 dollars) using a 3 percent discount rate for emissions occurring in 2023 to
$9 per metric ton C02 using a 3 percent discount rate for emissions occurring in 2040. Applying the same estimate
(based on a 3% discount rate) to the CO2 emissions reduction expected under the finalized option in this final rule
would yield benefits from climate impacts within U.S borders of $0.8 million in 2023, increasing to $138 million in
2035. 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). The 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 5-10. Estimated Climate Benefits from Changes in C02 Emissions 2023 - 2040
(Millions of 2016$)a
Discount Rate and
Statistic
2030
150
479
694
1,447
2035
175
530
757
1,618
2040
231
671
945
2,062
2023
1
4
6
12
2024
120
405
598
1,213
2025
124
413
608
1,239
Less-Stringent
Alternative
2026
128
421
617
1,265
2030
422
1,346
1,950
4,070
2035
319
967
1,380
2,949
2040
471
1,367
1,925
4,200
5.3 Total Human Health and Climate Benefits
Tables 5-11 through 5-13 present the total health and climate benefits for the final rule
and the more and less stringent alternatives for 2023, 2026, and 2030.
Table 5-11. Combined Health Benefits and Climate Benefits for the Final Rule and More
and Less Stringent Alternatives for 2023 (millions of 2016$)
Climate Benefits
Only3
Health and Climate Benefits
SC-CO2 Discount (Discount Rate Applied to Health
Rate and Statistic Benefits)
3% 7%
Final Rule
5% (average) $100 and $820 $94 and $730 $1
3% (average) $100 and $820 $98 and $740 $5
2.5% (average) $110 and $820 $100 and $740 $7
3% (95th percentile) $110 and $830 $110 and $750 $14
Less Stringent Alternative
5% (average) $100 and $810 $94 and $730 $1
3% (average) $100 and $820 $97 and $730 $4
2.5% (average) $110 and $820 $99 and $730 $6
3% (95th percentile) $110 and $830 $100 and $740 $12
More Stringent Alternative
5% (average) $110 and $840 $97 and $750 $1
3% (average) $110 and $840 $100 and $760 $5
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SC-CO2 Discount
Rate and Statistic
Health and Climate Benefits
(Discount Rate Applied to Health
Benefits)
3%
7%
2.5% (average)
3% (95th percentile)
$120 and $850
$120 and $850
$100 and $760
$110 and $770
Climate Benefits
Only3
$7
$14
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the social cost of carbon (SC-CO2) (model average at 2.5 percent, 3 percent, and 5 percent discount
rates; 95th percentile at 3 percent discount rate).
Table 5-12. Combined Health Benefits and Climate Benefits for the Final Rule and More
and Less Stringent Alternatives for 2026 (millions of 2016$)
SC-CO2 Discount
Rate and Statistic
Health and Climate Benefits
(Discount Rate Applied to Health Benefits)
Climate
Benefits
Only3
3%
7%
Final Rule
5% (average)
$3,500 and $14,000
$3,100 and $13,000
$340
3% (average)
$4,300 and $15,000
$3,900 and $13,000
$1,100
2.5% (average)
$4,800 and $15,000
$4,400 and $14,000
$1,600
3% (95th percentile)
$6,600 and $17,000
$6,200 and $16,000
$3,400
Less Stringent Alternative
5% (average)
$1,100 and $4,700
$980 and $4,200
$130
3% (average)
$1,400 and $5,000
$1,300 and $4,500
$420
2.5% (average)
$1,600 and $5,200
$1,500 and $4,700
$620
3% (95th percentile)
$2,200 and $5,800
$2,100 and $5,400
$1,300
More Stringent Alternative
5% (average)
$8,900 and $30,000
$13,000 and $27,000
$640
3% (average)
$10,000 and $31,000
$14,000 and $28,000
$2,100
2.5% (average)
$11,000 and $32,000
$15,000 and $29,000
$3,100
3% (95th percentile)
$15,000 and $35,000
$18,000 and $32,000
$6,400
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the social cost of carbon (SC-CO2) (model average at 2.5 percent, 3 percent, and 5 percent discount
rates; 95th percentile at 3 percent discount rate).
Table 5-13. Combined Health Benefits and Climate Benefits for the Final Rule and More
and Less Stringent Alternatives for 2030 (millions of 2016$)
Health and Climate Benefits Climate
SC-CO2 Discount ,-!>• . t, . A n ... D .... . Benefits
. . (Discount Rate Applied to Health Benefits) „ . a
Rate and Statistic Only3
3% 7%
Final Rule
5% (average) $3,900 and $15,000 $3,500 and $14,000 $470
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3% (average) $4,900 and $16,000 $4,500 and $15,000 $1,500
2.5% (average) $5,600 and $17,000 $5,200 and $15,000 $2,200
3% (95th percentile) $8,000 and $19,000 $7,600 and $18,000 $4,600
Less Stringent Alternative
5% (average) $1,400 and $5,300 $1,300 and $4,800 $420
3% (average) $2,300 and $6,200 $2,300 and $5,700 $1,300
2.5% (average) $3,000 and $6,800 $2,900 and $6,300 $2,000
3% (95th percentile) $5,100 and $8,900 $5,000 and $8,400 $4,100
More Stringent Alternative
5% (average) $9,200 and $31,000 $8,300 and $28,000 $150
3% (average) $9,500 and $31,000 $8,600 and $28,000 $480
2.5% (average) $9,700 and $32,000 $8,800 and $28,000 $700
3% (95th percentile) $10,000 and $32,000 $9,500 and $29,000 $1,400
a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different
estimates of the social cost of carbon (SC-CO2) (model average at 2.5 percent, 3 percent, and 5 percent discount
rates; 95th percentile at 3 percent discount rate).
5.4 Additional Unquantified Benefits
Data, time, and resource limitations prevented the EPA from quantifying the estimated
health impacts or monetizing estimated benefits associated with direct exposure to NO2 and SO2
(independent of the role NO2 and SO2 play as precursors to ozone and PM2.5), as well as
ecosystem effects, and visibility impairment due to the absence of air quality modeling data for
these pollutants in this analysis. 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 ozone, PM2.5, NO2 or SO2.141 In this section, we provide a qualitative description of
these and water quality benefits, which are listed in Table 5-14.
Table 5-14. 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
Reduced incidence of
morbidity from exposure
to NO2
Respiratory emergency department visits
—
—
NO2 ISA1
Asthma exacerbation
—
—
NO2 ISA1
Acute respiratory symptoms
—
—
NO2 ISA1
Premature mortality
—
—
NO2 ISA1'2'3
141 While not quantified in this RIA, we anticipate that the final rule may produce public health and welfare benefits
for populations living in Canada and Mexico.
236
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Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
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
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
exposure to ozone
Carbon sequestration in terrestrial
ecosystems
—
—
Ozone ISA1
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
—
—
NOxSOxISA1
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Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Tree mortality and decline
—
—
NOxSOxISA2
Commercial fishing and forestry effects
—
—
NOxSOxISA2
Reduced effects from acid
deposition
Recreational demand in terrestrial and
aquatic ecosystems
—
—
NOxSOxISA2
Other non-use effects
NOxSOxISA2
Ecosystem functions (e.g., biogeochemical
cycles)
—
—
NOxSOxISA2
Species composition and biodiversity in
terrestrial and estuarine ecosystems
—
—
NOxSOxISA2
Reduced effects from
nutrient enrichment from
deposition.
Coastal and liminal eutrophication
—
—
NOxSOxISA2
Recreational demand in terrestrial and
estuarine ecosystems
—
—
NOxSOxISA2
Other non-use effects
NOxSOxISA2
Ecosystem functions (e.g., biogeochemical
cycles, fire regulation)
—
—
NOxSOxISA2
Reduced vegetation effects
Injury to vegetation from SO2 exposure
—
—
NOxSOxISA2
from ambient exposure to
SO2 and NOx
Injury to vegetation from NOx exposure
—
—
NOxSOxISA2
Improved water aesthetics
from reduced effluent
discharges.
Improvements in water clarity, color, odor in
residential, commercial and recreational
settings.
—
—
SE ELG BCA4
Protection of Threatened and Endangered
(T&E) species from changes in habitat and
potential population effects.
—
—
SE ELG BCA4
Effects on aquatic
organisms and other
wildlife from reduced
effluent discharges
Other non-use effects
—
—
SE ELG BCA4
Changes in sediment contamination on
benthic communities and potential for re-
entrainment.
—
—
SE ELG BCA4
Quality of recreational fishing and other
recreational use values.
—
—
SE ELG BCA4
Commercial fishing yields and harvest
quality.
—
—
SE ELG BCA4
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
Climate effects
Climate impacts from carbon dioxide (CO2)
—
—
Section 5.2
discussion
Other climate impacts (e.g., ozone, black
carbon, aerosols, other impacts)
IPCC,
Ozone ISA,
PMISA
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.
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5.4.1 N02 Health Benefits
In addition to being a precursor to ozone and PM2.5, NOx emissions are also linked to a
variety of adverse health effects associated with direct exposure. We were unable to estimate the
health benefits associated with reduced NO2 exposure in this analysis. Following a
comprehensive review of health evidence from epidemiologic and laboratory studies, the
Integrated Science Assessment for Oxides of Nitrogen —Health Criteria (NOx ISA) (U.S. EPA,
2016c) concluded that there is a likely causal relationship between respiratory health effects and
short-term exposure to NO2. 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.
5.4.2 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. We were unable to estimate the health benefits
associated with reduced SO2 in this analysis. Therefore, this analysis only quantifies and
monetizes the PM2.5 benefits associated with the reductions in SO2 emissions. Following an
extensive evaluation of health evidence from epidemiologic and laboratory studies, the
Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO2 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
preexisting 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
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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 SO2ISA 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.
5.4.3 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, 2020b). 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 Technical Support Document (TSD) for the Proposed
Federal Implementation Plan Addressing Regional Ozone Transport for the 2015 Ozone
National Ambient Air Quality Standard, Ozone Transport Policy Analysis Proposed Rule TSD
for a summary of an assessment of risk of ozone-related growth impacts on selected forest tree
species.
5.4.4 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) (U.S. EPA, 2020d),
NOx and SO2 emissions also contribute to a variety of adverse welfare effects, including those
associated with acidic deposition, visibility impairment, and nutrient enrichment. Deposition of
nitrogen and sulfur causes acidification, which can cause a loss of biodiversity of fishes,
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
240
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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, 2008b)
Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment.
In lake and 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, 2008b)
5.4.5 Visibility Impairment Benefits
Reducing secondary formation of PM2.5 under the Regional Haze Rule would improve
levels of visibility in the U.S. because suspended particles and gases degrade visibility by
scattering and absorbing light (U.S. EPA, 2009). 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 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
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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, 2009). Previous
analyses (U.S. EPA, 201 la) show that visibility benefits can be a significant welfare benefit
category. Without air quality modeling, we are unable to estimate visibility-related benefits, and
we are also unable to determine whether the emissions reductions associated with the final
emission guidelines would be likely to have a significant impact on visibility in urban areas or
Class I areas.
Reductions in emissions of NO2 will improve the level of visibility throughout the United
States because these gases (and the particles of nitrate and sulfate formed from these gases)
impair visibility by scattering and absorbing light (U.S. EPA, 2009). 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, 2009). 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, 2009).
5.4.6 Water Quality and Availability Benefits
As described in Chapter 4, this rule is expected to lead to shifts in electricity production
away from fossil-fired steam generation towards renewable and natural gas generation. There are
several negative health, ecological, and productivity effects associated with water effluent and
intake from coal generation that will be avoided, and the benefits are qualitatively described
below.142 For additional discussion of these effects and their consequent effect on welfare, see
the Benefit and Cost Analysis for Revisions to the Effluent Limitations Guidelines and Standards
for the Steam Electric Power Generating Point Source Category (U.S. EPA 2020a).
5.4.6.1 Potential Water Quality Benefits of Reducing Coal-Fired Power Generation
Discharges of wastewater from coal-fired power plants can 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
142 While natural gas combined cycle units also emit wastewater effluents and withdrawal demands, which offset
some of the benefits of reduced fossil steam generation, the scale of these waste streams is much smaller than for
other fossil steam generator types.
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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 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.
Reducing coal-fired power generation affects 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 benefits from
surface water quality improvements may include drinking water benefits, fish consumption
benefits, and other complimentary measures.
In addition, reducing coal-fired power generation can affect the ecological condition and
recreation use effects from surface water quality changes. The EPA expects the ecological
impacts from reducing 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.
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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.
5.4.6.2 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 to bring the finished water into compliance with EPA's National Secondary
Drinking Water Treatment Standards can significantly increase the cost of public water supply.
Likewise, public drinking water supplies are subject to National Primary Drinking Water
Standards that have set legally enforceable maximum contaminant levels (MCLs), for a number
of pollutants, like metals, discharged from coal-fired power plants. Drinking water systems
downstream from these power plants may be required to treat source water to remove the
contaminants to levels below the MCL in the finished water. This treatment will also increase
costs at drinking water treatment plants. Episodic releases from coal fired power plants, may be
detected only after the completion of a several-month round of compliance monitoring at
drinking water treatment plants and there could also by a lag between detection of changes in
source water contaminants and the system implementing treatment to address the issue. This lag
may result in consumers being exposed to these contaminants through ingestion, inhalation, and
skin absorption. The constituents found in the power plant discharge may also interact with
drinking water treatment processes and contribute to the formation of disinfection byproducts
that can have adverse human health impacts.
5.4.6.3 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 the Benefit and Cost Analysis for Revisions to the
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Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point
Source Category (U.S. EPA 2020a) 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.
5.4.6.4 Changes in Surface Water Quality
Reducing 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.
5.4.6.5 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.
5.4.6.6 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
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levels can bio-magnify through higher trophic levels, posing a threat to the food chain at large
(Ruhletal., 2012).
5.4.6.7 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 (Clark et al., 1985; 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. The 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.
5.4.6.8 Changes in Water Consumption and Withdrawals
A reduction in water consumption 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. In a study completed,
in 2011, by the U.S. Department of Energy's National Renewable Energy Laboratory (U.S. DOE
2011), water consumption, which is defined as water removed from the immediate water
environment and can include cooling water evaporation, cleaning, and process related water use
including flue gas desulfurization, was found to range from 100- 1,100 gal/MWh at generic coal
power plants. This study also found that water withdraws, defined as the amount of water
removed from the ground or diverted from a water source for use, ranged from 300 - 50,000
gal/MWh at a generic coal power plant. Reductions in water consumption and withdraws will
lower the number of aquatic organisms impinged and entrained by the power plant's water
filtration and cooling systems.
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5.4.7 Hazardous Air Pollutant Impacts
The rule is expected to reduce fossil-fired EGU generation and consequentially is
expected to lead to reduced HAP emissions. HAP emissions from EGUs create risks of
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 are 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.
5.5 References
Alex L. Marten, et al. 201". "Incremental CH4 and N20 mitigation benefits consistent with the
US Government's SC-C02 estimates." Climate Policy 15 (2): 272-298.
doi:l 0.1080/14693062.201.
Anthoff, D. and Tol, R.S.J. 2013a. The uncertainty about the social cost of carbon: a
decomposition analysis using FUND. Climatic Change 117: 515-530.
Anthoff, D. and Tol, R.S.J. 2013b. Erratum to: The uncertainty about the social cost of carbon: A
decomposition analysis using FUND. Climatic Change 121(2): 413.
Bell, M.L., A. McDermott, S.L. Zeger, J.M. Sarnet, and F. Dominici. 2004. "Ozone and Short-
Term Mortality in 95 U.S. Urban Communities, 1987-2000." Journal of the American
Medical Association. 292(19):2372-8.
Bell, M.L., F. Dominici, and J.M. Samet. 2005. "A Meta-Analysis of Time-Series Studies of
Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air
Pollution Study." Epidemiology. 16(4):436-45.
Blunden, J., and D.S. Arndt, Eds. 2020. State of the Climate in 2019. Bull. Amer. Meteor. Soc,
S1-S429. Available at:
https://journals.ametsoc.Org/view/journals/bams/101/8/2020BAMSStateoftheClimate.xml
Clark, E. H., Haverkamp, J. A., and Chapman, W. 1985. Eroding soils. The off-farm impacts:
Conservation Foundation.
Di, Q, Wang, Y, Zanobetti, A, Wang, Y, Koutrakis, P, Choirat, C, Dominici, F and Schwartz, JD
(2017). Air pollution and mortality in the Medicare population. New Engl J Med 376(26):
2513-2522.
247
-------
Executive Order (E.O.) 13783. Promoting Energy Independence and Economic Growth March
28, 2017. Available at: https://www.federalregister.gov/documents/2017/03/31/2017-
06576/promoting-energy-independence-and-economic-growth (accessed February 5,
2021).
Executive Order (E.O.) 13990. Protecting Public Health and the Environment and Restoring
Science to Tackle the Climate Crisis. January 20, 2021. Available at:
https://www.federalregister.gOv/documents/2021/01/25/2021-01765/protecting-public-
health-and-the-environment-and-restoring-science-to-tackle-the-climate-crisis (accessed
February 5, 2021).
Fann, N.; Coffman, E.; Jackson, M.; Jhun, I.; Lamichhane, A. P.; Nolte, C. G.; Roman, H.;
Sacks, J. D. The Role of Temperature in Modifying the Risk of Ozone-Attributable
Mortality under Future Changes in Climate: A Proof-of-Concept Analysis. Environ. Sci.
Technol. 2021. https://doi.org/10.1021/acs.est.lc05975.
Fann N, Nolte CG, Dolwick P, Spero TL, Brown AC, Phillips S, et al. 2015. The geographic
distribution and economic value of climate change-related ozone health impacts in the
United States in 2030. J Air Waste Manag Assoc 65; doi: 10.1080/10962247.2014.996270.
Graf, W. L., Wohl, E., Sinha, T., & Sabo, J. L. 2010. Sedimentation and sustainability of western
American reservoirs. Water Resources Research, 46(12).
Graf, W. L., Wohl, E., Sinha, T., & Sabo, J. L. 2011. Correction to "Sedimentation and
sustainability of western American reservoirs." Water Resources Research, 47(8).
Hargrove, W. L., Johnson, D., Snethen, D., & Middendorf, J. 2010. From dust bowl to mud
bowl: sedimentation, conservation measures, and the future of reservoirs. Journal of Soil
and Water Conservation, 65(1):14A-17A.
Hope, C. 2013. Critical issues for the calculation of the social cost of CO2: why the estimates
from PAGE09 are higher than those from PAGE2002. Climatic Change, 117: 531-543.
Huang Y, Dominici F, Bell M. 2005. Bayesian Hierarchical Distributed Lag Models for Summer
Ozone Exposure and Cardio-Respiratory Mortality. Johns Hopkins Univ Dept Biostat
Work Pap Ser. Environmetrics; 16: 547-562.
Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis
Report, Contribution of Working Groups I, II, and II to the Fourth Assessment Report of
the Intergovernmental Panel on Climate Change. Vols. ISBN 92-9169-122-4. IPCC.
[Core Writing Team; Pachauri, R.K; and Reisinger, A. (eds.)]
Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2014: Synthesis
Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change. Edited by Core Writing Team, R.K.
Pachauri and L.A. Meyer. Geneva.
248
-------
Intergovernmental Panel on Climate Change (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 [Masson-Delmotte, V., P. Zhai, H.-O. Po' rtner, D. Roberts, J. Skea, P.R. Shukla,
A. Pirani, W. Moufouma-Okia, C. Pe'an, R. Pidcock, S. Connors, J.B.R. Matthews, Y.
Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)].
Intergovernmental Panel on Climate Change (IPCC). 2019a. Climate Change and Land: an IPCC
special report on climate change, desertification, land degradation, sustainable land
management, food security, and greenhouse gas fluxes in terrestrial ecosystems [P.R.
Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Po' rtner, D.C. Roberts, P.
Zhai, R. Slade, S. Connors, R. van Diem en, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M.
Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J.
Malley, (eds.)]
Intergovernmental Panel on Climate Change (IPCC). 2019b. IPCC Special Report on the Ocean
and Cryosphere in a Changing Climate [H.-O. Po "rtner, D.C. Roberts, V. Masson-
Delmotte, P. Zhai M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegn'a, M. Nicolai, A.
Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)].
Interagency Working Group on Social Cost of Carbon (IWG). 2010. Technical Support
Document: Social Cost of Carbon for Regulatory Impact Analysis under Executive Order
12866. February. United States Government.
Interagency Working Group on Social Cost of Carbon (IWG). 2013. Technical Support
Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis
Under Executive Order 12866. May. United States Government.
Interagency Working Group on the Social Cost of Carbon (IWG). 2015. Technical Support
Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis
Under Executive Order 12866.
Interagency Working Group on the Social Cost of Carbon (IWG). 2016a. Addendum to
Technical Support Document on Social Cost of Carbon for Regulatory Impact Analysis
under Executive Order 12866: Application of the Methodology to Estimate the Social Cost
of Methane and the Social Cost of Nitrous Oxide. Available at:
https://www.epa.gov/sites/default/files/2016-12/documents/addendum_to_sc-
ghg_tsd_august_2016.pdf.
Interagency Working Group on the Social Cost of Carbon (IWG). 2016b. Technical Support
Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis
Under Executive Order 12866. Retrieved from Washington, DC. Available at:
https://www.epa.gov/sites/default/files/2016-12/documents/sc_co2_tsd_august_2016.pdf
249
-------
Interagency Working Group on the Social Cost of Carbon (IWG). 2021. Technical Support
Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under
Executive Order 13990. Accessed 2023. Available at: https://www.whitehouse.gov/wp-
content/uploads/2021/02/TechnicalSupportDocument_SocialCostofCarbonMethaneNitrous
Oxide.pdf.
Ito, K., S.F. De Leon, and M. Lippmann. 2005. "Associations Between Ozone and Daily
Mortality: Analysis and Meta-Analysis." Epidemiology. 16(4):446-57.
Jerrett M, Burnett RT, Pope CA, Ito K, Thurston G, Krewski D, et al. 2009. Long-term ozone
exposure and mortality. NEngl J Med 360:1085-95; doi:10.1056/NEJMoa0803894.
Jhun I, Fann N, Zanobetti A, Hubbell B. 2014. Effect modification of ozone-related mortality
risks by temperature in 97 US cities. Environment International. 73:128-34.
Katsouyanni, K, Samet, JM, Anderson, HR, Atkinson, R, Le Tertre, A, Medina, S, Samoli, E,
Touloumi, G, Burnett, RT, Krewski, D, Ramsay, T, Dominici, F, Peng, RD, Schwartz, J,
Zanobetti, A and Committee, HEIHR (2009). Air pollution and health: a European and
North American approach (APHENA). Res Rep Health Eff Inst (142): 5-90.
Kivi, PA and Shogren, JF (2010). Second-order ambiguity in very low probability risks: food
safety valuation. J Agric Resour Econ: 443-456.
Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. 2009. Extended Follow-Up
and Spatial Analysis of the American Cancer Society Study Linking Particulate Air
Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.
Lepeule, J., F. Laden, D. Dockery, and J. Schwartz. 2012. "Chronic Exposure to Fine Particles
and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to
2009." Environmental Health Perspectives. 120(7):965-70.
Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. 2005. "Ozone Exposure and Mortality: An
Empiric Bayes Metaregression Analysis." Epidemiology. 16(4):458-68.
McGartland, A, Revesz, R, Axelrad, DA, Dockins, C, Sutton, P and Woodruff, TJ. 2017.
Estimating the health benefits of environmental regulations. Science 357(6350): 457-458.
Miranda, L. E. (2017). Section 3: Sedimentation. In: Reservoir Fish Habitat Management.
Totowa, New Jersey: Lightning Press.
National Academies of Sciences, Engineering, and Medicine. 2016. Attribution of Extreme
Weather Events in the Context of Climate Change. Washington, DC: The National
Academies Press, https://doi.org/10.17226/21852.
National Academies of Sciences, Engineering, and Medicine (National Academies). 2017.
Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide.
Washington, D.C.: National Academies Press.
250
-------
National Academies of Sciences, Engineering, and Medicine. 2017. Valuing Climate Damages:
Updating Estimation of the Social Cost of Carbon Dioxide. Washington, DC: The National
Academies Press.
Available at: https://doi.org/10.17226/24651.
National Academies of Sciences, Engineering, and Medicine. 2019. Climate Change and
Ecosystems. Washington, DC: The National Academies Press. Available at:
https://doi.org/10.17226/25504.
National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air
Pollution Regulations. National Academies Press. Washington, DC.
National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic
Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington,
DC.
Nordhaus, W. 2010. Economic aspects of global warming in a post-Copenhagen environment.
Proceedings of the National Academy of Sciences, 107(26): 11721-11726.
Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis.
Washington, DC. Available at: https://www.whitehouse.gov/wp-
content/uploads/legacy_drupal_files/omb/circulars/A4/a-4,pdf>
Ren, C., G.M. Williams, K. Mengersen, L. Morawska, and S. Tong. 2008a. "Does Temperature
Modify Short-Term Effects of Ozone on Total Mortality in 60 Large Eastern U.S.
Communities? An Assessment Using the NMMAPS Data." Environment International.
34:451-458.
Ren, C., G.M. William, L. Morawska, K. Mengensen, and S. Tong. 2008b. "Ozone Modifies
Associations between Temperature and Cardiovascular Mortality: Analysis of the
NMMAPS Data." Occupational and Environmental Medicine. 65:255-260.
Ribaudo, M., and Johansson, R. 2006. Water Quality: Impacts on Agriculture. In Keith Wiebe &
Noel Gollehon (Eds.), Agricultural Resources and Environmental Indicators, 2006 Edition
(EIB-16). Washington DC: Economic Research Service, U.S. Department of Agriculture.
Ruhl, L., Vengosh, A., Dwyer, G. S., Hsu-Kim, H., Schwartz, G., Romanski, A., and Smith, S.
D. 2012. The impact of coal combustion residue effluent on water resources: a North
Carolina example. Environmental Science & Technology, 46(21), 12226-12233.
Sisler, J.F. 1996. Spatial and Seasonal Patterns and Long-Term Variability of the Composition of
the Haze in the United States: An analysis of data from the IMPROVE network. CIRA
Report, ISSN 0737-5352-32, Colorado State University.
Schwartz, J. 2005. "How Sensitive is the Association between Ozone and Daily Deaths to
Control for Temperature?" American Journal of Respiratory and Critical Care Medicine.
171(6): 627-31.
251
-------
Smith RL, Xu B, Switzer P. 2009. Reassessing the relationship between ozone and short-term
mortality in U.S. urban communities. Inhal Toxicol 21 Suppl 2:37-61;
doi: 10.1080/08958370903161612.
Turner, MC, Jerrett, M, Pope, A, III, Krewski, D, Gapstur, SM, Diver, WR, Beckerman, BS,
Marshall, JD, Su, J, Crouse, DL and Burnett, RT (2016). Long-term ozone exposure and
mortality in a large prospective study. Am J Respir Crit Care Med 193(10): 1134-1142.
U.S. Department of Defense (DOD), Office of the Undersecretary for Policy (Strategy, Plans,
and Capabilities). 2021. Department of Defense Climate Risk Analysis. Report Submitted
to National Security Council. Available at:
https://media.defense.gOv/2021/Oct/21/20028773 53/-1/-1/O/DOD-CLIMATE-RISK-
ANALYSIS-FINAL.PDF.
U.S. Department of Energy (U.S. DOE) National Renewable Energy Laboratory. 2011. A
Review of Operational Water Consumption and Withdrawal Factors for Electricity
Generating Technologies. Technical Report NREL/TP-6A20-50900.
https://www.nrel.gov/docs/fyllosti/50900.pdf
U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for
Oxides of Nitrogen and Sulfur-Ecological Criteria National (Final Report). National Center
for Environmental Assessment - RTP Division, Research Triangle Park, NC. EPA/600/R-
08/139. December. Available at:
http://cfpub. epa.gov/ncea/cfm/recordisplay. cfm?deid=201485.
U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for
Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for Environmental
Assessment - RTP Division, Research Triangle Park, NC. December. Available at:
http://cfpub. epa.gov/ncea/cfm/recordisplay. cfm?deid=216546.
U.S. Environmental Protection Agency (U.S. EPA). 2010b. Technical Support Document:
Summary of Expert Opinions on the Existence of a Threshold in the Concentration-
Response Function for PM2.5-related Mortality. Research Triangle Park, NC. June.
Available at: http://www.epa.gov/ttn/ecas/regdata/Benefits/thresholdstsd.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2010c. Regulatory Impact Analysis (RIA)
for Existing Stationary Compression Ignition Engines NESHAP Final Draft.
U.S. Environmental Protection Agency (U.S. EPA). 2010d. Regulatory Impact Analysis for the
Proposed Federal Transport Rule.
U.S. Environmental Protection Agency (U.S. EPA). 201 la. Policy Assessment for the Review of
the Particulate Matter National Ambient Air Quality Standards. EPA-452/D-11-003. Office
of Air Quality Planning and Standards, Health and Environmental Impacts Division. April.
Available at: http://www.epa.gov/ttn/naaqs/standards/pm/data/20110419pmpafinal.pdf.
252
-------
U.S. Environmental Protection Agency (U.S. EPA). 201 lc. 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.
U.S. Environmental Protection Agency (U.S. EPA). 201 Id. Regulatory Impact Analysis for the
Final Mercury and Air Toxics Standards.
U.S. Environmental Protection Agency (U.S. EPA). 2012. Regulatory Impact Analysis for the
Final Revisions to the National Ambient Air Quality Standards for Particulate Matter.
EPA-452/R-12-003. Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division, Research Triangle Park, NC.
U.S. Environmental Protection Agency (U.S. EPA). 2013a. Integrated Science Assessment of
Ozone and Related Photochemical Oxidants (Final Report). EPA/600/R-10/076F. National
Center for Environmental Assessment - RTP Division, Research Triangle Park. Available
at: http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download.
U.S. Environmental Protection Agency (U.S. EPA). 2013b. Regulatory Impact Analysis for the
Final Revisions to the National Ambient Air Quality Standards for Particulate Matter.
EPA-452/R-12-005. Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division, Research Triangle Park, NC.
U.S. Environmental Protection Agency (U.S. EPA). 2014a. Regulatory Impact Analysis (RIA)
for Proposed Residential Wood Heaters NSPS Revision.
U.S. Environmental Protection Agency (U.S. EPA). 2014b. Regulatory Impact Analysis for the
Proposed Carbon Pollution Guidelines for Existing Power Plants and Emission Standards
for Modified and Reconstructed Power Plants.
U.S. Environmental Protection Agency (U.S. EPA). 2014c. Regulatory Impact Analysis of the
Proposed Revisions to the National Ambient Air Quality Standards for Ground-Level
Ozone.
U.S. Environmental Protection Agency (U.S. EPA). 2015a. Regulatory Impact Analysis for
Residential Wood Heaters NSPS Revision: Final Report.
U.S. Environmental Protection Agency (U.S. EPA). 2015b. Regulatory Impact Analysis for the
Clean Power Plan Final Rule.
U.S. Environmental Protection Agency (U.S. EPA). 2015c. Regulatory Impact Analysis for the
Proposed Cross-State Air Pollution Rule (CSAPR) Update for the 2008 Ozone National
Ambient Air Quality Standards (NAAQS).
U.S. Environmental Protection Agency (U.S. EPA). 2015d. Regulatory Impact Analysis for the
Proposed Federal Plan Requirements for Greenhouse Gas Emissions from Electric Utility
Generating Units Constructed on or Before January 8, 2014; Model Trading Rules;
Amendments to Framework Regulations. 4-52
253
-------
U.S. Environmental Protection Agency (U.S. EPA). 2015e. Regulatory Impact Analysis of the
Final Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone,
EPA-452/R-15-07. EPA-452/R-12-003. Office of Air Quality Planning and Standards,
Health and Environmental Impacts Division, Research Triangle Park, NC.
(https ://www. epa.gov/ sites/production/files/2016-02/documents/20151001 ria.pdf).
U.S. Environmental Protection Agency (U.S. EPA). 2015f. Regulatory Impact Analysis for the
Proposed Revisions to the Emission Guidelines for Existing Sources and Supplemental
Proposed New Source Performance Standards in the Municipal Solid Waste Landfills
Sector. Available at: https://www.epa.gov/sites/default/files/2020-
07/documents/landfills_ria_proposed-nsps_2015-08.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2015g. Regulatory Impact Analysis of the
Proposed Emission Standards for New and Modified Sources in the Oil and Natural Gas
Sector. Available at: https://www.epa.gov/sites/default/files/2020-
07/documents/oilgas_ria_proposed-nsps_2015-08.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Guidelines for Preparing Economic
Analyses.
U.S. Environmental Protection Agency (U.S. EPA). 2016b. Regulatory Impact Analysis of the
Cross-State Air Pollution Rule (CSAPR) Update for the 2008 National Ambient Air
Quality Standards for Ground-Level Ozone.
U.S. Environmental Protection Agency (U.S. EPA). 2016c. Integrated Science Assessment for
Oxides of Nitro-en - Health Criteria (Final Report). National Center for Environmental
Assessment, Research Triangle Park, NC. July. Available at:
https://cfpub. epa.gov/ncea/isa/recordisplay. cfm?deid=310879.
U.S. Environmental Protection Agency (U.S. EPA). 2017. Integrated Science Assessment for
Sulfur Oxides—Health Criteria (Final Report). National Center for Environmental
Assessment - RTP Division, Research Triangle Park, NC. September. Available at:
https://cfpub. epa.gov/ncea/isa/recordisplay. cfm?deid=338596..
U.S. Environmental Protection Agency (U.S. EPA). 2022. Environmental Benefits Mapping and
Analysis Program - Community Edition. User's Manual. Office of Air Quality Planning
and Standards, Health and Environmental Impacts Division, Research Triangle Park, NC.
Available at: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-
ce_user_manual_march_2015 .pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2019a. Integrated Science Assessment (ISA)
for Particulate Matter (Final Report, 2019). U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R-19/188, 2019.
U.S. Environmental Protection Agency (U.S. EPA). 2020a. Benefit and Cost Analysis for
Revisions to the Effluent Limitations Guidelines and Standards for the Steam Electric
Power Generating Point Source Category. U.S. Environmental Protection Agency,
Washington, DC, EPA-821-R-20-003. Available at:
254
-------
https://www.epa.gov/sites/default/files/2020-
08/documents/steam_electric_elg_2020_final_reconsideration_rule_benefit_and_cost_anal
ysis.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 2020b. Integrated Science Assessment (ISA)
for Ozone and Related Photochemical Oxidants (Final Report). U.S. Environmental
Protection Agency, Washington, DC, EPA/600/R-20/012, 2020.
U.S. Environmental Protection Agency (U.S. EPA). 2020c. Regulatory Impact Analysis for the
Proposed Cross-State Air Pollution Rule (CSAPR) Revised Update for the 2008 Ozone
National Ambient Air Quality Standards (NAAQS).
U.S. Environmental Protection Agency (U.S. EPA). 2020d. Integrated Science Assessment (ISA)
for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter Ecological Criteria (Final
Report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-20/278,
2020.
U.S. Environmental Protection Agency (U.S. EPA). 2021a. Regulatory Impact Analysis for
the Final Revised Cross-State Air Pollution Rule (CSAPR) Update for the 2008 Ozone
National Ambient Air Quality Standards (NAAQS). U.S. Environmental Protection
Agency, Research Triangle Park, NC, EPA-452/R-21-002, March 2021.
U.S. Environmental Protection Agency (U.S. EPA). 2021b. Technical Support Document:
Estimating the Benefit per Ton of Reducing Directly-Emitted PM2.5, PM2.5 Precursors
and Ozone Precursors from 21 Sectors. U.S. Environmental Protection Agency, Research
Triangle Park, NC, December 2021. Available at: https://www.epa.gov/benmap/estimating-
benefit-ton-reducing-pm25-precursors-21-sectors
U.S. Environmental Protection Agency (U.S. EPA). 2022a. Regulatory Impact Analysis for the
Proposed Reconsideration of the National Ambient Air Quality Standards (NAAQS) for
Particulate Matter. U.S. Environmental Protection Agency, Research Triangle
Park, NC, EPA-452/P-22-001, December 2022.
U.S. Environmental Protection Agency (U.S. EPA). 2022b. Supplement to the 2019 Integrated
Science Assessment for Particulate Matter (Final Report). U.S. Environmental Protection
Agency, Office of Research and Development, Center for Public Health and Environmental
Assessment. Research Triangle Park, NC. U.S. EPA. EPA/600/R-22/028. May 2022.
Available at: https://www.epa.gov/isa/integrated-science-assessment-isa-particulate-matter.
U.S. Environmental Protection Agency (U.S. EPA). 2023. Technical Support Document:
Estimating PM2.5- and Ozone-Attributable Health Benefits. U.S. Environmental Protection
Agency, Research Triangle Park, NC, January 2023. Available at:
https://www.epa.gOv/system/files/documents/2023-01/Estimating%20PM2.5-
%20and%200zone-Attributable%20Health%20Benefits%20TSD_0.pdf
255
-------
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2000a. An
SAB Report on EPA's White Paper Valuing the Benefits of Fatal Cancer Risk Reduction.
EPA-SAB-EEAC-00-013. July.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004.
Advisory Council on Clean Air Compliance Analysis Response to Agency Request on
Cessation Lag. EPA-COUNCIL-LTR-05-001. December.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010.
Review of EPA's Draft Health Benefits of the Second Section 812 Prospective Study of the
Clean Air Act
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2017. Letter
from Peter S. Thorne, Chair, Science Advisory Board, and Madhu Khanna, Chair, SAB
Environmental Economics Advisory Committee, to Administrator Scott Pruitt. Re: SAB
Review of EPA's Proposed Methodology for Updating Mortality Risk Valuation Estimates
for Policy Analysis. February 23, 2017. EPA-SAB-17-005. Office of the Administrator,
Science Advisory Board U.S. EPA HQ, Washington DC.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2019. Letter
from Louis Anthony Cox, Jr., Chair, Clean Air Scientific Advisory Committee, to
Administrator Andrew R. Wheeler. Re: CASAC Review of the EPA's Integrated Science
Assessment for Particulate Matter (External Review Draft - October 2018). April 11, 2019.
EPA-CASAC-19-002. Office of the Administrator, Science Advisory Board U.S. EPA HQ,
Washington DC.
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2020a.
Letter from Louis Anthony Cox, Jr., Chair, Clean Air Scientific Advisory Committee, to
Administrator Andrew R. Wheeler. Re: CASAC Review of the EPA's Integrated Science
Assessment for Ozone and Related Photochemical Oxidants (External Review Draft -
September 2019). February 19, 2020. EPA-CASAC-20-002. Office of the Administrator,
Science Advisory Board Washington, DC
U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2020b.
Letter from Michael Honeycutt Chair, Scientific Advisory, to Administrator Lisa Jackson.
Re: Science Advisory Board (SAB) Consideration of the Scientific and Technical Basis of
EPA's Proposed Rule titled "Increasing Consistency and Transparency in Considering
Benefits and Costs in the Clean Air Act Rulemaking Process". September 30, 2020. EPA-
SAB-20-012. Office of the Administrator, Science Advisory Board U.S. EPA HQ,
Washington DC.
U.S. Global Change Research Program (USGCRP). 2016. The Impacts of Climate Change on
Human Health in the United States: A Scientific Assessment. Crimmins, A., J. Balbus, J.L.
Gamble, C.B. Beard, J.E. Bell, D. Dodgen, R.J. Eisen, N. Fann, M.D. Hawkins, S.C.
Herring, L. Jantarasami, D.M. Mills, S. Saha, M.C. Sarofim, J. Trtanj, and L. Ziska, Eds.
U.S. Global Change Research Program, Washington, DC, 312 pp.
256
-------
U.S. Global Change Research Program (USGCRP). 2018. Impacts, Risks, and Adaptation in the
United States: Fourth National Climate Assessment, Volume II [Reidmiller, D.R., C.W.
Avery, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C. Stewart
(eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 1515 pp. doi:
10.7930/NCA4.2018
Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 2008. "Air pollution and postneonatal infant
mortality in the United States, 1999-2002." Environmental Health Perspectives. 116(1):
110-115
Woods & Poole (2015). Complete Demographic Database. -
https://www.woodsandpoole.com/our-databases/united-states/cedds/.
Zanobetti A, Schwartz J. 2008. Mortality displacement in the association of ozone with
mortality: an analysis of 48 cities in the United States. Am J Respir Crit Care Med
177:184-9; doi: 10.1164/rccm.200706-8230C.F
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CHAPTER 6: ECONOMIC IMPACTS
Overview
Economic impact analyses focus on changes in market prices and output levels. If
changes in market prices and output levels in the primary markets are significant enough,
impacts on other markets may also be examined. Both the magnitude of costs needed to comply
with a rule and the distribution of these costs among affected facilities can have a role in
determining how the market will change in response to a rule. This chapter analyzes the potential
impacts on small entities and the potential labor impacts associated with this rulemaking. For
additional discussion of impacts on fuel use and electricity prices, see Chapter 4, Section 4.5.7
6.1 Small Entity Analysis
For the final rule, the EPA performed a small entity screening analysis for impacts on all
affected EGUs and non-EGU facilities 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 the 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 the 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 the EPA on compliance with the Regulatory Flexibility Act (RFA)143 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 (SBA, 2017).
6.1.1 EGU Small Entity Analysis and Results
This section presents the methodology and results for estimating the impact of the rule on
small EGU entities in 2026 based on the following endpoints:
• annual economic impacts of the rule on small entities, and
143 The RFA compliance guidance to the EPA rule writers can be found at
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• ratio of small entity impacts to revenues from electricity generation.
In this analysis, the EPA considered EGUs that are subject to the FIP and meet the
following five criteria: 1) EGU is represented in NEEDS v6; 2) EGU is fossil fuel-fired; 3) EGU
is located in a state covered by this rule; 4) EGU is neither a cogeneration unit nor solid waste
incineration unit; and 5) EGU capacity is 25 Megawatt (MW) or larger. EPA next refined this list
of EGUs, narrowing it to those that exhibit at least one of the following changes, in comparison
to the baseline. Please see Chapter 4, Section 4.3 for more discussion of the power sector
modeling.
• Summer fuel use (BTUs) changes by +/- 1 percent or more
• Summer generation (GWh) changes by +/- 1 percent or more
• NOx summer emissions (tons) changes by +/- 1 percent or more
Based on these criteria, the EPA identified a total of 436 potentially affected EGUs
warranting examination in 2026 in this RFA analysis. Next, we determined power plant
ownership information, including the name of associated owning entities, ownership shares, and
each entity's type of ownership. We primarily used data from Ventyx, supplemented by limited
research using publicly available data.144 Majority owners of power plants with affected EGUs
were categorized as one of the seven ownership types.145 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.
144 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.
145 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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6. State: Utility owned by the state.
7. Federal: Utility owned by the federal government.
Next, the EPA used both the D&B Hoovers online database and the Ventyx database to
identify the ultimate owners of power plant owners identified in the Ventyx 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 Hoovers).146 In these cases, the ultimate parent
entity was identified via D&B Hoovers, whether domestically or internationally owned.
The 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.147 SBA guidelines list all industries, along with their
associated North American Industry Classification System (NAICS) code148 and SBA size
standard. Therefore, it was necessary to identify the specific NAICS code associated with each
ultimate parent entity to understand the appropriate size standard to apply. Data from D&B
Hoovers 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 6-1. 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 EPA's IPM base case.
Table 6-1. 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
146 The D&B Hoovers 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.
147 SBA's table of size standards can be located here: https://www.sba.gov/document/support--table-size-
standards.
148 North American Industry Classification System can be accessed at the following link:
https://www.census.gov/naics/
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NAICS
Codes
NAICS U.S. Industry Title
Size
Standards
(Millions of
dollars)
Size
Standards
(Number of
employees)
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: SB A, 2022
The EPA compared the relevant entity size criterion for each ultimate parent entity to the
SBA size standard noted in Table 6-1. 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 Hoovers database as the
primary source for information on ultimate parent entity employee numbers, revenue, and
assets.149 In parallel, EPA also considered estimated revenues from affected EGUs based
on analysis of IPM parsed file150 estimates for the baseline run for 2023 and 2026. 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
50,000.151 EPA primarily relied on data from the Ventyx database and the U.S. Census
Bureau to inform this determination.
149 Estimates of sales were used in lieu of revenue estimates when revenue data was unavailable.
150 IPM output files report aggregated results for "model" plants (i.e., aggregates of generating units with similar
operating characteristics). Parsed files approximate the IPM results at the generating unit level.
151 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
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.
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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.
In 2026, EPA identified 436 potentially affected EGUs, owned by 75 entities. Of these, the
EPA identified 71 potentially affected EGUs owned by 19 small entities included in the power
sector baseline.
In 2023, an entity can comply with the Final Good Neighbor Plan Addressing Regional
Ozone Transport for the 2015 Ozone National Ambient Air Quality Standards (Transport FIP for
the 2015 ozone NAAQS) through some combination of the following: optimizing existing SCRs,
optimizing existing SNCR controls, installing state-of-the-art combustion controls, using
allocated allowances, purchasing allowances, or reducing emissions through a reduction in
generation. Additionally, units with more allowances than needed can sell these allowances in
the market. 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. In addition to
the 2023 compliance options, in 2026 an entity can comply with the Transport FIP for the 2015
ozone NAAQS by installing SCR or SNCR retrofits.
To attempt to account for each potential control strategy, EPA estimates compliance costs as
follows:
Ccompliance A Coperating+Retrofit A CFuel A CAllowances A (^Transaction A R
where C represents a component of cost as labeled152, 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 2026.
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,
some units will forgo some level of electricity generation (and thus revenues) to comply, 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
152 Retrofit costs include the costs of fully operating existing controls, as well as the installation of state-of-the-art
combustion controls, SCRs and SNCRs.
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compliance costs (or increased profit). Overall, small entities are not projected to install
relatively costly emissions control retrofits if it can be avoided while still complying with the
rule but may choose to do so in some instances. 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 actually a measure of the net
economic impact of the rule on small entities.
For this analysis, the EPA used IPM-parsed 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, sale or purchase of allowances, and the change in fuel costs or electricity
generation revenues under the Transport FIP for the 2015 ozone NAAQS relative to the base
case. These individual components of compliance costs were estimated as follows:
(1) Operating and retrofit costs (A Copemting+Retrofit)'. Using engineering analytics,
EPA identified which compliance option would be selected by each EGU in 2023
(i.e., SCR/SNCR optimization and/or installing state-of-the-art combustion
controls) and applied the appropriate cost to this choice (for details, please see
Chapter 4 of this RIA). For 2026, IPM projected retrofit costs were also included
in the calculation.
(2) Sale or purchase of allowances (A CAllowances)'. To estimate the value of
allowance holdings, allocated allowances were subtracted from projected
emissions, and the difference was then multiplied by model projected allowance
costs. Units were assumed to purchase or sell allowances to exactly cover their
projected emissions under the Transport FIP for the 2015 ozone NAAQS.
(3) Fuel costs (A Cpuet): The change in fuel expenditures under the Transport FIP for
the 2015 ozone NAAQS was estimated by taking the difference in projected fuel
expenditures between the IPM estimates for the Transport FIP for the 2015 ozone
NAAQS and the baseline.
(4) Value of electricity generated: To estimate the value of electricity generated, the
projected level of electricity generation is multiplied by the regional-adjusted
retail electricity price ($/MWh) estimate, for all entities except those categorized
as private in Ventyx. See Chapter 4, Section 4.5.3 for a discussion of the Retail
263
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Price Model, which was used to estimate the change in the retail price of
electricity. For private entities, EPA used the wholesale electricity price instead of
the retail electricity price because most of the private entities are independent
power producers (IPP). IPPs sell their electricity to wholesale purchasers and do
not own transmission facilities. Thus, their revenue was estimated with wholesale
electricity prices.
(5) Administrative costs (A CTransaction)'. Because most affected units are already
monitored as a result of other regulatory requirements, EPA considered the
primary administrative cost to be transaction costs related to purchasing or selling
allowances. EPA assumed that transaction costs were equal to 1.5 percent of the
total absolute value of the difference between a unit's allocation and projected
NOx emissions. This assumption is based on market research by ICF.
As indicated above, the use of a sales test for estimating small business impacts for a
rulemaking is consistent with guidance offered by the 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 Transport
FIP for the 2015 ozone NAAQS on small entities are summarized in Table 6-2. All costs are
presented in 2016$. EPA estimated the annual net compliance cost to small entities to be
approximately $18 million in 2026.
Table 6-2. Projected Impact of the Transport FIP for the 2015 Ozone NAAQS on Small
Entities in 2026
Total Net
EGU
Number of Potentially
Compliance Cost
Number of Small Entities with
Ownership
Affected Entities
($2016 millions)
Compliance Costs >1% of Generation
Type
Revenues
Municipal
6
1.0
0
Private
5
0.5
0
Co-op
8
16.6
0
Total
19
18.1
0
Source: IPM analysis
The 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 19 entities considered in this
264
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analysis, none are projected to experience compliance costs greater than 1% of generation
revenues in 2026.
6.1.2 Non-EGU Small Entity Impacts and Results
We identified 1,228 emissions units, discussed in Chapter 4, owned by 137 parent
companies, using information from D&B Hoovers,153 that could be affected by the final rule. Of
the parent companies, 10 companies, or seven percent, are small entities. We also used
information from D&B Hoovers for the parent company revenues. We identified the NAICS
code for all parent companies and applied the most current version of SBA's table of size
standards to determine which of the companies were small entities. Table 6-3 below includes the
ranges NAICS codes and SB A entity size guidelines for small entity parent companies.
Table 6-3. Non-EGU SBA Size Standards by NAICS Code
NAICS
Codes
NAICS U.S. Industry Title
Size
Standards
(millions)
Size
Standards
(Number of employees)
212290
Other Metal Ore Mining
750
327211
Flat Glass Manufacturing
1,000
327212
Other Pressed and Blown Glass and Glassware
1,250
Manufacturing
327213
Glass Container Manufacturing
1,250
327310
Cement Manufacturing
1,000
331110
Iron and Steel Mills and Ferroalloy Manufacturing
1,500
486210
Pipeline Transportation of Natural Gas
$36.5
322110
Pulp Mills
750
322120
Paper (except Newsprint) Mills
1,250
322130
Paperboard Mills
1,250
324110
Petroleum Refineries
1,500
324199
All Other Petroleum and Coal Products
500
Manufacturing
325110
Petrochemical Manufacturing
1,000
325180
Other Basic Inorganic Chemical Manufacturing
1,000
325199
All Other Basic Organic Chemical Manufacturing
1,250
562213
Solid Waste Combustors and Incinerators
$41.5
153 D&B Hoovers is a subscription-based database that compiles publicly available information and can be found at
https://www.dnb.com/products/marketing-sales/dnb-hoovers.html.
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In addition, we identified several waste combustors owned by government entities at the
county or city level. When evaluating the small entity impact to a government-owned facility the
size of the population served by that government should be used as the basis for the small entity
screening. In our analysis we identified 17 emissions units owned by five separate jurisdictions.
None of the populations served by those governments are below the threshold for inclusion as a
small entity.
We calculated the cost-to-sales ratios for all of the affected entities to determine (i) the
magnitude of the costs of the rule, and (ii) whether there would be a significant impact on small
entities compared to large entities. Non-EGUs do not operate in a price-regulated environment,
like EGUs, where they are able to recover expenses through rate increases. As presented in Table
6-4 for all firms the average cost-to-sales ratio is approximately 0.2 percent; the median cost-to-
sales ratio is less than 0.1 percent; and the maximum cost-to-sales ratio is approximately 2.4
percent. For large firms, the average cost-to-sales ratio is approximately 0.1 percent; the median
cost-to-sales ratio is less than 0.1 percent; and the maximum cost-to-sales ratio is approximately
1.1 percent. For small firms, the average cost-to-sales ratio is approximately 0.8 percent, the
median cost-to-sales ratio is 0.7 percent, and the maximum cost-to-sales ratio is 2.4 percent.
Table 6-4. Summary of Sales Test Ratios for 2026 for Firms Affected by Proposed Rule
Firm Size
No. of Known
% of Total
Mean Cost-
Median Cost-
Min. Cost-
Max. Cost-
Affected
Known
to-Sales
to-Sales
to-Sales
to-Sales
Firms
Affected
Ratio
Ratio
Ratio
Ratio
Firms
Small
10
7.3%
0.8%
0.7%
<0.0%
2.4%
Large
127
92.7%
0.1%
<0.0%
<0.0%
1.1%
All
137
100.0%
0.2%
<0.0%
<0.0%
2.4%
As mentioned above, we compare annual compliance costs to annual revenues at the
ultimate parent company level. Table 6-5 below includes the small parent companies and their
projected cost-to-sales ratio, NAICS code, and small business size standards. The costs for the
small parent companies ranged from $12 thousand to $2.3 million annually (2016$).
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Table 6-5. Summary of Small Parent Company Small Business Size Standards
SBA Business Small Size
Standards
Small
Parent Annual
Cost to
Number of
Revenue
Number of
Small Parent Company
NAICS
Sales Ratio
Employees
(millions)
Employees
ND Fairmont LLC
322110
0.96%
250
750
Cobra Pipeline Company3
486210
2.40%
13
36.5
Angus Chemical Company
325199
1.76%
500
1,250
Cstn Holdings
325199
0.86%
600
1,250
Empire Pipeline Corp3
486210
0.22%
8
36.5
FutureFuel Chemical
325199
0.51%
460
1,250
Bear Island Paper Wb LLC
322120
0.73%
190
1,250
Deltech LLC
325110
0.61%
100
1,000
American Eagle Paper Mills
322120
0.42%
240
1,250
Savant Inc.
327212
0.03%
927
1,250
a These small entity parent companies were evaluated using the size standard for annual revenues.
6.1.3 Conclusion
Making a no SISNOSE (significant economic impacts on a substantial number of small
entities) determination reflects an assessment of whether an estimated economic impact is
significant and whether that impact affects a substantial number of small entities. We prepared
an analysis of small entity impacts for EGUs and non-EGUs in 2026 separately and combined
the 2026 results for a SISNOSE determination for the rule.
For EGUs in 2026, the analysis indicates that 19 small entities see a +/- 1 percent change
in either summer NOx emissions, summer generation or summer fuel use, and none of these are
projected to have a cost impact of greater than 1 percent of their revenues.
In 2026, the EPA identified 71 possibly affected EGU entities. Of these, the EPA
identified 19 small entities affected by the rule, and of these no small entities may experience
costs of greater than 1 percent of revenues. The EPA's decision to exclude units smaller than 25
MW capacity from the FIP, and exclusion of uncontrolled units smaller than 100 MW from the
backstop emission rate has already significantly reduced the burden on small entities by reducing
the number of affected small entity-owned units. Further, in 2026 for non-EGUs, there are 10
small entities, and two small entities are estimated to have a cost-to-sales impact of more than
one percent of their revenues.
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Based on this analysis, for this rule overall we conclude that the estimated costs for the
final rule will not have a significant economic impact on a substantial number of small entities
(SISNOSE).
6.2 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 due to 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. To
simplify, 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
(Graff, 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 it at all (Morgenstern et al. 2002, Deschenes 2018, Berman
and Bui 2001). 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),
Greenstone (2002), Ferris, Shadbegian and Wolverton (2014), and Curtis (2018, 2020).
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. Employment impacts in related sectors are often difficult to measure.
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Consequently, we focus our labor impacts analysis primarily on the directly regulated facilities
and other EGUs and related fuel markets and in the different non-EGU industry sectors.
6.2.1 EGULabor Impacts
This section discusses and projects potential employment impacts for the utility power,
coal and natural gas production sectors that may result from the rule. EPA has a long history of
analyzing the potential impacts of air pollution regulations on changes in the amount of labor
needed in the power generation sector and directly 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.154
The results presented in this section are based on a methodology that estimates the impact
on employment based on the 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 on employment in this sector. As discussed in Chapter 4, there is uncertainty related to
the future baseline projections, in part due to unknown impacts of the Inflation Reduction Act.
Because the incremental employment estimates presented in this section are based on projections
discussed in Chapter 4, it is important to highlight the relevance of the Chapter 4 uncertainty
discussion to the analysis presented in this section.
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 ripple effects include the secondary job
impacts in both upstream and downstream sectors. The analysis includes impacts on upstream
sectors including coal, natural gas, and uranium. However, the approach does not analyze
impacts on other fuel sectors, nor does it analyze potential impacts related to transmission,
distribution, or storage. 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
154 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.
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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).
6.2.2 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.155 For employment
related to other electric power sector generating and pollution control technologies, the second
approach utilizes information available in the U.S. Economic Census.
Detailed employment inventory data is available regarding recent employment related to
coal, hydro, 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).
Where detailed employment data is unavailable, it is possible to estimate labor impacts
using labor intensity ratios. These factors provide a relationship between employment and
155 https://www.usenergyjobs.org/
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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.
6.2.3 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 that are most
relevant to the projected impacts of the rule. Other segments not discussed here include other
fuels, energy efficiency, and transmission, distribution, and storage. The statistics presented here
are based on the 2020 USEER, which reports data from 2019.156
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. These losses 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%). Other components of the electric power
generation workforce include utility workers (20%), professional and business service employees
(20%), manufacturing (13%), wholesale trade (8%), and other (5%). In 2019, jobs related to
solar and wind generation represent 31% and 14% of total jobs, respectively, and jobs related to
coal generation represent 10% of total employment.
In addition to generation-related employment we also look at employment related to coal
and natural gas use 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%). The natural gas fuel sector employed about 276,000 employees in
2019. About 60%) of those jobs were related to mining and extraction.
156 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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6.2.4 Projected Sectoral Employment Changes due to the Final Rule
Affected EGUs may respond to the rule through a number of means including optimizing
existing controls, upgrading to state-of-the-art combustion controls, shifting generation from
higher emitting to lower emitting sources, and installing new SCRs and SNCRs. Under the
modeling of the final rule, 8 GW of SCR installations are projected by the 2030 run year, and an
incremental 14 GW of coal retirements are projected by 2030. Additionally, EPA's modeling of
this rule projects an incremental 3 GW of non-hydro renewable additions, by 2025, and an
additional 1 GW of non-hydro renewable and 9 GW of natural gas capacity by the 2030 run year.
Based on these power sector modeling projections, we estimate an increase in
construction-related job-years related to the installation of new pollution controls under the rule,
as well as the construction of new generating capacity (largely natural gas and solar PV). In 2025
and 2030, we estimate an increase of over 15,000 and 20,000 construction-related job-years,
respectively, consistent with the projected increase in construction of new renewable and natural
gas capacity in those years. 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 6-6 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 jobs for that project).
Table 6-6. Changes in Labor Utilization: Construction-Related (Number of Job-Years of
Employment in a Single Year)
2023 2025 2030
New Pollution Controls <100 <100 2,800
New Capacity <100 15,400 20,500
Note: "<100" denotes an increase or decrease of less than 100 job-years
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-
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construction jobs. The rule is also projected to result in a small increase in recurring employment
related to fuel extraction. The total net estimated decrease in recurring employment is less than
4,000 job-years in 2030, 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). Note that the projected decreases related to
operation of existing pollution controls is consistent with the projected retirements of existing
capacity. Table 6-7 provide detailed estimates of recurring non-construction employment
changes.
Table 6-7. Changes in Labor Utilization: Recurring Non-Construction (Number of Job-
Years of Employment in a Single Year)
2023
2025
2030
Pollution Controls
<100
<100
<100
Existing Capacity
<100
-1,000
-6,700
New Capacity
<100
1,000
2,600
Fuels (Coal, Natural
Gas, Uranium)
<100
<100
200
Coal
<100
<100
-200
Natural Gas
<100
<100
400
Uranium
<100
<100
<100
Note: "<100" denotes an increase or decrease of less than 100 job-years; Numbers may not sum due to
rounding
6.2.5 Non-EGULabor Impacts
This section begins with a description of baseline conditions in non-EGU industries
affected by the rule, focusing on the directly regulated industries and groups of affected workers.
Table 6-8 shows the industry definitions and the NAICS codes used to categorizes the data for
the relevant industries. The cement and concrete product manufacturing industry (NAICS 3273)
by far is the largest regulated industry in terms of the number of people employed. BLS Current
Employment Statistics show that the industry employs 186,000 people nationally. The iron and
steel mills and ferroalloy manufacturing industry (NAICS 3311) and glass and glass product
manufacturing industry (NAICS 3772) are similarly sized with 81,400 and 79,900 people
employed, respectively. Each of the non-EGU industries has seen different trends in employment
over the past decade. Both the pipeline transportation of natural gas (NAICS 4862) and cement
and concrete product manufacturing industries saw sizable increases in employment over the past
decade, but cement and concrete product manufacturing contracted in 2020 from the COVID-19
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pandemic. The iron and steel mills and ferroalloy manufacturing industry has seen steady decline
in total employment, while the glass and glass product manufacturing industry has remained
relatively constant over the last decade.157
Table 6-8. Relevant Industry Employment (2020)
NAICS
Employment
Percent Change
(Thousands)
2011 - 2020
Pipeline Transportation of Natural Gas
4862
49.1
19%
Cement and Concrete Product Manufacturing
3273
186.4
17%
Iron and Steel Mills and Ferroalloy
3311
81.4
-10%
Manufacturing
Glass and Glass Product Manufacturing
3772
79.9
-1%
Basic Chemical Manufacturing
3251
150.1
5%
Petroleum and Coal Products Manufacturing
3241
106.5
-5%
Pulp, Paper, and Paperboard Mills
3221
92.6
-15%
Waste Treatment and Disposal
5622
101
4%
Metal Ore Mining
2122
41.7
11%
Source: BLS
These industries are capital intensive. We rely on two public sources to get a range of
estimates of employment per output by sector: the Economic Census (EC), and the Annual
Survey of Manufacturers (ASM), both provided by the U.S. Census Bureau. The EC is
conducted every 5 years, most recently in 2017. The ASM is an annual subset of the EC and is
based on a sample of establishments. The latest set of data from the ASM is from 2019. Both sets
of U.S. Census Bureau data provide detailed industry data, providing estimates at the 4-digit
NAICS level. They provide separate estimates of the number of employees and the value of
shipments at the 4-digit NAICS, which we convert to a ratio in this employment analysis.
Table 6-9 provides estimates of employment per $1 million of products sold by the
industry for each data source in 2017$. While the ratios are not the same, they are similar across
time for both surveys. Glass and glass product manufacturing seems to be the most labor-
intensive industry followed by waste treatment and disposal.
157 Bureau of Labor Statistics. BLS Employment, Hours, and Earnings from the Current Employment Statistics
survey (National), All-employees, May 2021
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Table 6-9. Employment per $1 million Output
Sector
Economic
Census
ASM 2019
Pipeline Transportation of Natural Gas
Cement and Concrete Product Manufacturing
Iron and Steel Mills and Ferroalloy Manufacturing
Glass and Glass Product Manufacturing
Basic Chemical Manufacturing
Petroleum and Coal Products Manufacturing
Pulp, Paper, and Paperboard Mills
Waste Treatment and Disposal
Metal Ore Mining
1.21
2.80
0.97
3.34
0.68
0.20
1.24
3.25
1.33
N/A
3.05
0.91
3.35
0.75
0.18
1.30
N/A
N/A
6.2.6 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 economic changes, including the impact of the coronavirus pandemic on labor markets and
the state of the macroeconomy generally. For EGUs, the Transport FIP for the 2015 ozone
NAAQS may result in a sizable increase in construction-related jobs related to the installation of
new pollution controls, as well as the construction of new generating 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.
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.158 The Inflation Reduction Act also provides
incentives to encourage investment in communities affected by coal mine and coal power plant
closures.159
158 See "Initial Report to the President on Empowering Workers Through Revitalizing Energy Communities" April
2021 at https://energycommunities.gOv/wp-content/uploads/2021/l 1/Initial-Report-on-Energy-
Communities_Apr2021 .pdf
159 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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For the non-EGU industries, the employment trends over the last decade vary by
industry. Without more detailed information on the labor required for installing pollution
controls in these specific industries and other potential compliance approaches, we are not able
to determine the potential effect of employment changes in the non-EGU industries.
6.3 References
Berman, E. and L. T. M. Bui. 2001. "Environmental Regulation and Labor Demand: Evidence
from the South Coast Air Basin." Journal of Public Economics. 79(2): 265-295;
Curtis, E. M. 2018. "Who loses under cap-and-trade programs? The labor market effects of the
NOx budget trading program," Review of Economics and Statistics 100 (1): 151-66;
Curtis, E.M. 2020. "Reevaluating the ozone nonattainment standards: Evidence from the 2004
expansion," Journal of Environmental Economics and Management, 99: 102261;
Deschenes, O. 2018. "Environmental regulations and labor markets," IZA World of Labor: 22:
1-10;
Ferris, A. E., R. Shadbegian, A. Wolverton. 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 Economics 1(4): 521-553
Graff Zivin, J. and M. Neidell. 2018. "Air pollution's hidden impacts". Science. 359(6371). 39-
40.
Greenstone, Michael, "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 110, no. 6 (2002): 1175-1219
Morgenstern, R.D., W.A. Pizer, and J. Shih. 2002. Jobs Versus the Environment: An Industry-
Level Perspective. Journal of Environmental Economics and Management 43: 412-436.
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CHAPTER 7: ENVIRONMENTAL JUSTICE IMPACTS
7.1 Introduction
Executive Order 12898 directs the 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
chapter. Additionally, Executive Order 13985 was signed to advance racial equity and support
underserved communities through Federal government actions (86 FR 7009, January 20, 2021).
The 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. The 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".160 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.161 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
160 See, e.g., "Environmental Justice." Epa.gov, U.S. Environmental Protection Agency, 4 Mar. 2021,
https://www.epa.gov/environmentaljustice.
161 See https://www.epa.gov/environmentaljustice/technical-guidance-assessing-environmental-justice-regulatory-
analysis.
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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.
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 Executive Order 13563,
federal agencies may consider equity, human dignity, fairness, and distributional considerations,
where appropriate and permitted by law. For purposes of analyzing regulatory impacts, the EPA
relies upon its June 2016 "Technical Guidance for Assessing Environmental Justice in
Regulatory Analysis,"162 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.
162 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.
The 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.
7.2 Analyzing EJ Impacts in This Final Rule
In addition to the benefits assessment (Chapter 5), the EPA considers potential EJ
concerns associated with this final rulemaking. 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" (U.S. EPA, 2015). 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 action" (U.S. EPA, 2015). Although EJ concerns for
each rulemaking are unique and should be considered on a case-by-case basis, the EPA's EJ
Technical Guidance (U.S. EPA, 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?
(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 rulemaking, as well as the nature of known and potential exposures
across various demographic groups. For example, while we recognize that the final rule is
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focused on reducing NOx emissions to implement obligations for 23 states under the "Good
Neighbor" provision of the Clean Air Act to eliminate significant contribution to nonattainment
and interference with maintenance of the 2015 ozone National Ambient Air Quality Standards
(NAAQS) in other states, this rulemaking may also reduce other pollutant emissions, such as
nitrogen dioxide (NO2).
Like other oxides of nitrogen, NO2 can contribute to the formation of ozone and PM2.5
downwind of sources; however, direct emissions of NO2 can also lead to localized exposures that
may be associated with respiratory effects in nearby populations at sufficiently high
concentrations. In addition, people with asthma, children (especially ages 0-14 years), and older
adults (especially ages 65 years and older) are identified as being at increased risk of N02-related
health effects (U.S. EPA 2016). While NO2 exposures and concentrations were not evaluated as
part of this rule, proximity analyses of affected EGU and non-EGU facilities were performed as
local exposures may be relevant to the baseline and/or change due to this action (Section 7.3).163
In contrast, proximity analyses should not be used to interpret ozone and PM2.5 exposure impacts
due to this rulemaking, as ozone is secondarily formed and both pollutants can undergo long-
range transport.
To directly assess EJ ozone and PM2.5 exposure impacts, the EPA conducts an analysis of
reductions in modeled ozone and PM2.5 concentrations nationwide resulting from the NOx
emissions reductions projected to occur under the rule, characterizing aggregated and
distributional exposures both prior to and following implementation of the three regulatory
alternatives in 2023 and 2026 (Section 7.4).
Unique limitations and uncertainties are specific to each type of analysis, which are
described prior to presentation of analytic results in the subsections below.
7.3 Demographic Proximity Analyses
Demographic proximity analyses allow one to assess the potentially vulnerable
populations residing nearby 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
163 EPA is considering if and how to incorporate NO2 health benefits into rulemakings. The ISA states that a key
uncertainty in understanding the relationship between non-respiratory health effects and short- or long-term
exposure to NO2 is co-pollutant confounding, particularly by other traffic pollutants.
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location including noise, odors, traffic, and emissions such as NO2, covered under this EPA
action and not modeled elsewhere in this RIA.
Although baseline proximity analyses are presented here, several important caveats
should be noted. In most areas, emissions are not expected to increase from the rulemaking, so
most communities nearby affected facilities should experience decreases in exposure from
directly emitted pollutants. However, 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 question from
EPA's EJ Technical Guidance.
Demographic proximity analyses were performed for two subsets of affected facilities:
• Electricity Generating Unit (EGU): Comparison of the percentage of various populations
(race/ethnicity, age, education, poverty status, income, and linguistic isolation) living
nearby covered EGU sources to average national levels.
• Non-EGU (non-electric generating units, or other stationary emissions sources):
Comparison of the percentage of various populations (race/ethnicity, age, education,
poverty status, income, and linguistic isolation) living nearby covered non-EGU sources
to average national levels.
7.3.1 EGU Proximity Assessments
The current analysis identified all census blocks with centroids within a 5 km, 10 km and
50 km radius of the latitude/longitude location of each facility, and then linked each block with
census-based demographic data.164 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
164 Five km and 50 km radii are the default distances currently used for proximity analyses. The 5 km distance is the
shortest distance that should be chosen to avoid excessive demographic uncertainty and provides information on
near-field populations. The 50 km distance offers a sub-regional perspective. The 10 km distance was added to this
analysis as few to no people were within 5 km of some affected facilities.
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each block's population provided by the decennial Census.165 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 2015-2019. 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 in different
age groups (<18, 18-64, and >64), the percentage without a high school diploma, the percentage
that are below 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., 50 km) for all facilities as a whole was also
computed (e.g., all EGUs or all non-EGU facilities). 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., 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., 5 km, 10 km, and 50 km) to the demographic
composition of the nationwide population.
For this action, a demographic analysis was conducted for 711 EGU facilities at the 5 km,
10 km, and 50 km radius distances (Table 7-1). Approximately 158 million people live within 50
km of the EGU facilities, representing roughly 48% of the 328 million total population of the
U.S. The percent demographic make-up of the population within 50 km of the EGU facilities is
very similar to the national average for each demographic investigated. Approximately 18.1
million and 48.1 million people live within 5 km and 10 km of the EGU facilities, respectively.
The demographic make-up of the population within 5 km and 10 km of EGU facilities are very
similar. Within 5 km and 10 km of EGU facilities, there is a higher Hispanic/Latino population
(about 3 to 5% above national average) and a higher African American population (about 5 to
6% above national average). The age distribution for the population within 5 km and 10 km of
EGU facilities is similar to the national average. The percent of people living below the poverty
level is about 3% higher within 5 km and 10 km of the EGU facilities than the national average.
165 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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About 7% to 8% of the population within 5 km and 10 km of the EGU facilities is living in
linguistic isolation, this is higher than the national average of 5%.
Table 7-1. Population Demographics for EGU Facilities
Percent of Population Within Each Distance Compared to the
Demographic Group National Average1
5km
10km
50km
National Average
White
49.6%
50.7%
62.7%
60.1%
Race/
Ethnicity
African American
17.0%
18.3%
14.6%
12.2%
Native American
0.4%
0.4%
0.4%
0.7%
Other and Multiracial
9.3%
8.6%
7.1%
8.2%
Hispanic or Latino2
23.7%
21.9%
15.2%
18.8%
0-17 Years Old
21.9%
22.5%
22.5%
22.6%
Age
18-64 Years Old
63.9%
62.9%
61.9%
61.7%
>=65 Years Old
14,2%
14.6%
15.6%
15.7%
Income
People Living Below the
Poverty Level
16.8%
15.9%
13.2%
13.4%
Education
>= 25 Years Old Without
a High School Diploma
15.2%
14.3%
11.7%
12.1%
Language
People Living in
Linguistic Isolation
8.1%
7.3%
4.5%
5.4%
Total Population
18,094,722
48,062,338
157,740,319
328,016,242
1 Demographic percentage is based on the Census' 2015-2019 American Community Survey 5-year averages, at the block
group level, and include the 50 states, District of Columbia, and Puerto Rico. Total population is based on block level data
from the 2010 Decennial Census.
2 To avoid double counting, the "Hispanic or Latino" category is treated as a distinct demographic category for these analyses.
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.
7.3.2 Non-EGUProximity Analysis
For this action, a demographic analysis was also conducted for 482 non-EGU facilities at
the 5 km, 10 km, and 50 km radius distances (Table 7-2). Approximately 130 million people live
within 50 km of the non-EGU facilities, representing roughly 40% of the 328 million total
population of the U.S. The percent demographic make-up of the population within 50 km of the
non-EGU facilities is very similar to the national average for each demographic investigated.
Approximately 5.7 million and 19.3 million people live within 5 km and 10 km of the non-EGU
facilities, respectively. The demographic make-up of the population within 5 km and 10 km of
non-EGU facilities are similar. Within 5 km and 10 km of non-EGU facilities, the African
American population is 6% higher than the national average. The age distribution for the
population within 5 km and 10 km of non-EGU facilities is similar to the national average. The
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percent of people living below the poverty level within 5 km and 10 km of the non-EGU
facilities is about 2 to 4% higher than the national average. The percent of the population within
5 km and 10 km of the non-EGU facilities living in linguistic isolation is about the same as the
national average (about 5%).
Table 7-2. Population Demographics for Non-EGU Facilities
Percent of Population Within Each Distance Compared to
Demographic Group the National Average1
National
5km
10km
50km
Average
White
55.6%
56.8%
59.0%
60.1%
African American
18.2%
18.2%
14.1%
12.2%
Race/
Ethnicity
Native American
0.5%
0.4%
0.4%
0.7%
Other and Multiracial
6.1%
7.1%
8.9%
8.2%
Hispanic or Latino2
19.7%
17.4%
17.6%
18.8%
0-17 Years Old
22.9%
22.2%
22.1%
22.6%
Age
18-64 Years Old
62.5%
62.4%
62.2%
61.7%
>=65 Years Old
14.6%
15.3%
15.7%
15.7%
Income
People Living Below the
Poverty Level
17.7%
15.3%
13.5%
13.4%
Education
>= 25 Years Old Without a
High School Diploma
15.7%
13.5%
12.8%
12.1%
Language
People Living in Linguistic
Isolation
5.4%
4.8%
5.4%
5.4%
Total Population
5,743,473
19,284,115
130,446,759
328,016,242
1 Demographic percentage is based on the Census' 2015-2019 American Community Survey 5-year averages, at the block
group level, and include the 50 states, District of Columbia, and Puerto Rico. Total population is based on block level data
from the 2010 Decennial Census.
2 To avoid double counting, the "Hispanic or Latino" category is treated as a distinct demographic category for these analyses.
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.
For additional information on the EGU or non-EGU proximity analyses, see the
memorandum Analysis of Demographic Factors For Populations Living Near EGU and Non-
EGU Facilities, in the rulemaking docket.
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7.4 EJ Ozone and PM2.5 Exposure Impacts
This EJ air pollutant exposure166 analysis aims to evaluate the potential for EJ concerns
related to PM2.5 and ozone exposures167 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
the EPA's 2016 EJ Technical Guidance,168 which ask if there are potential EJ concerns associated
with stressors affected by the regulatory action for population groups of concern in the baseline
and if those potential EJ concerns in the baseline are exacerbated, mitigated, or unchanged under
the regulatory options being considered.169
To address these questions with respect to the air pollutants ozone and PM2.5, the EPA
developed an analytical approach that considers the purpose and specifics of this final
rulemaking, as well as the nature of known and potential exposures and impacts. Specifically, as
1) this final rule 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 as 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 baseline and policy PM2.5 and ozone air
quality surfaces, we conduct an analysis of changes in PM2.5 and ozone concentrations resulting
from the emission changes projected by the Integrated Planning Model (IPM) to occur under the
final rule as compared to the baseline scenario, characterizing average and distributional
exposures following implementation of the regulatory alternatives in 2023 and 2026. However,
several important caveats of this analysis are as follows:
166 The term exposure is used here to describe estimated ozone and PM2 5 concentrations and not individual dosage.
167 Air quality surfaces used to estimate exposures are based on 12 km x 12 km grids. Additional information on air
quality modeling can be found in the air quality modeling information section.
168 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
169 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 7.6).
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• Modeling of post-policy air quality concentration changes are based on state-level
emission data paired with facility-level baseline emissions. The air quality surfaces will
capture expected air quality changes that result from state-to-state emissions change but
will not capture heterogenous changes in emissions from multiple facilities within a
single state.
• Air quality simulation input information are at a 12km x 12km 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 8-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.
• In the source apportionment modeling we aggregate emissions from point sources on all
Tribal lands into a single nationwide source tag. Using a single nationwide Tribal tag will
affect the spatial distribution pollutant impacts. In this respect, the NOx reductions at the
Bonanza power plant in the 2026 final rule policy and more stringent alternatives impact
pollutant concentrations in and around all Tribal lands. This is most evident in and
around the Four Corners Generating Station in northwestern New Mexico where there are
predicted pollutant reductions even though there are no controls applied to units at this
facility.
• 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.
• Relative to the proposed rule, the final rule defers the backstop daily NOx emission rate
from 2027 to no later than 2030 for those EGUs that do not have an SCR. In this analysis,
we capture ozone and PM2.5 exposure impacts in 2026 across the final, less stringent, and
more stringent alternative for EGUs, but do not account for impacts of projected exposure
changes in 2030 due to the backstop. However, given the IPM modeling in Chapter 4, we
expect exposure reductions to be greater in 2030 for the final rule relative to the more
stringent alternative.
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 7-3).170
170 Population projections stratified by race/ethnicity, age, and sex are based on economic forecasting models
developed by Woods and Poole (Woods and 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.
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Table 7-3. Demographic Populations Included in the Ozone and PM2.5 EJ Exposure
Analyses
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
7.4.1 Ozone Exposure Analysis
To evaluate the potential for EJ concerns among potentially vulnerable populations
resulting from exposure to ozone under the baseline and regulatory control alternatives in this
rule, we assess the impact of NOx emissions reductions on downwind ozone concentrations.
EPA presents an analysis of ozone concentrations associated with upwind NOx emissions,
characterizing the distribution of exposures both prior to and following implementation of the
final rule, as well as of the more and less stringent regulatory alternatives, in 2023 and 2026.
Under the final rule and more stringent regulatory alternative, the year of full compliance is 2026
for both EGUs and non-EGUs, except for the EGU backstop emission rate on coal units greater
than 100 MW within the 19-state region that lack SCR controls, which occurs in 2030 in the final
rule (emissions budgets in 2026 are commensurate with a backstop rate in place). Under the less
stringent scenario the year of full compliance is 2030 for EGUs and 2026 for non-EGUs.171
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).
171 We did not evaluate or bring in stratified baseline incidence rates or concentration-response functions relating to
potentially evaluating at-risk populations. As results of a risk analysis lacking stratified concentration-response
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As this analysis is based on the same ozone spatial fields as the benefits assessment (see
Chapter 3 for a discussion of the spatial fields), it is subject to similar types of uncertainty (see
Chapter 5, Section 5.1.3 for a discussion of the uncertainty). 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 (see Chapter 3, Section 3.4).
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 8-hour average ozone concentrations (AS-M03),
consistent with the health impact functions used in the benefits assessment (Chapter 5). 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 8-hour concentrations.172 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 (8-
hour) exceedances of the ozone NAAQS. Relatedly, the 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, the 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
and/or baseline incidence rates would not provide additional information regarding population group impacts
beyond exposure differences and age-related difference in baseline incidence, this EJ analysis was limited to
exposure only.
172 Level of 70 ppb with an annual fourth-highest daily maximum 8-hour concentration, averaged over 3 years.
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ozone exposure assessment. To further support the use of the AS-M03 spatial fields in this
ozone analysis, we compared baseline AS-M03 spatial fields with average baseline maximum
daily 1-hour average (MDA1) ozone concentrations spatial fields in the proposal for this
rulemaking, also over the April-September warm season, and found that average population
ozone concentration trends within populations were similar when considering either the AS-
M03 or the MDA1 spatial fields. Therefore, in this final rulemaking, we performed ozone
analyses using only the AS-M03 metric over the April-September warm season.
The metric and averaging season are also relevant inputs to consider when interpreting
the results as they can affect the sharpness of pollutant gradients, an important factor when
associating exposure for different demographic populations. Figure 3-2 and Figure in Chapter 3
of this RIA show maps of the baseline 12 km gridded AS-M03 concentrations in 2023 and 2026,
respectively. As the AS-M03 seasonal metric is based on the average of concentrations over
more than 180 days in the spring and summer, the resulting spatial fields are relatively smooth
and do not display sharp gradients, compared to what might be expected when looking at the
spatial patterns of the average maximum daily 8-hour average ozone concentrations on
individual high ozone episode days.
The ozone exposure analyses begin with heat maps of national- and state-level
aggregated results (Section 7.4.1.1) and then examine spatially resolved distributional results via
figures (Section 7.4.1.3).
7.4.1.1 Aggregated Results
Results aggregated to the national and state levels provide an overview of the average
impacts within each population group. We provide baseline results in absolute terms (i.e., total
AS-M03 concentrations) and regulatory alternative results in relative terms (i.e., the change in
AS-M03 concentrations).
As inclusion of additional "on the books" regulations could impact the pre-policy
scenario, it is important to begin by evaluating the baseline, or pre-regulatory, conditions.
Average baseline AS-M03 concentrations in parts per billion (ppb) in the two modeled future
years, 2023 and 2026, are shown in the colored columns of the below heat maps (Figure 7-1 and
Figure 7-2). Concentrations in the "baseline" column represent the total estimated ozone
exposure burden averaged over the 6-month warm season each year and are colored to more
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easily visualize differences in average concentrations, with lighter green coloring representing
lower average concentrations and darker green coloring representing higher average
concentrations.
Average ozone concentrations are estimated to increase slightly across the overall
reference population (top row) between 2023 and 2026 by approximately 0.5 ppb. While many
of the average ozone concentrations within the individual population groups are estimated to be
similar to or below average concentrations of the overall reference group (i.e., the total
population of contiguous U.S.), certain populations are estimated to experience higher average
ozone concentrations in the baseline in both future years. Populations with national average
ozone concentrations higher than the reference population in both 2023 and 2026 ordered from
most to least difference were: American Indians, Hispanics, linguistically isolated, Asians, the
less educated, and children. These populations live in areas with seasonal average baseline ozone
concentrations of up to 2.1 ppb higher than the national average concentrations.173 In contrast,
national average baseline ozone concentrations in the Black population are estimated to be about
1.2 ppb less than the reference group in both 2023 and 2026. However, it is important to note
that these are aggregate results across broad areas and large numbers of people, which may
underestimate the impact in individual locations where there is both an ozone nonattainment
issue and a disproportionately large racial/ethnic population. Additionally, while average AS-
M03 exposures across all groups are relatively low (-40-43 ppb), these seasonal estimates do
not necessarily indicate that individual locations do not experience exceedances of the NAAQS.
Thus, it is difficult to draw conclusions from this analysis about whether some population
subgroups experience hyperlocal higher daily maximum exposures than others in the baseline.
Overall, the national-level baseline assessment of ozone concentrations suggests that
there may be potential EJ exposure concerns for certain population groups of concern in the
baseline. Specifically, the data indicate that some population subgroups evaluated may
experience slightly elevated seasonal average ozone concentrations in the baseline as compared
to the reference group nationally.
173 Differences in both 2023 and 2026 were calculated and averaged to generate these estimates, as differences
between the air quality in the two future years were similar.
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The right sides of Figure 7-1 and Figure 7-2 provide information regarding how the final
rulemaking will impact ozone concentrations across various populations.174 Figure 7-3 shows
how ozone concentrations may change in 2023 (from EGU controls only) and in 2026 (from
EGUs controls, non-EGU controls, and EGU and non-EGU controls combined) under the rule,
the less stringent alternative, and the more stringent alternative. Under the final rule, the
population-weighted seasonal average ozone reduction in the overall reference group is
approximately 0.03 ppb in 2023 and 0.3 ppb in 2026. The relative population-weighted AS-M03
ozone concentration reduction contributions from EGUs and non-EGUs can be directly
compared in 2026. 0.1 ppb of ozone concentration reductions are attributable to affected EGUs
and 0.2 ppb are attributable to non-EGU affected facilities. Hispanics, Asians, American Indians,
and linguistically isolated populations are estimated to experience reductions in AS-M03 that are
slightly less than the reference group in both 2023 and 2026. Pairing these results with the
national baseline ozone concentrations suggests that although this rule lessens overall ozone
concentrations within each population as compared with the baseline levels, reductions are
smallest in populations with higher baseline ozone concentrations. However, the relative
differences in the policy impacts are small (e.g., on the order of -0.1 ppb less reduction in ozone
among these subpopulations as compared to the reference group) and substantially smaller than
the baseline differences across these subpopulations (~2 ppb). Conversely, Black and non-
Hispanic individuals, who on average experience lower ozone concentrations than the reference
group under the baseline, are estimated to experience average ozone concentration reductions
slightly greater than the reference group in 2023 and 2026. Again, these differences are small
relative to the overall reduction in ozone concentrations across all populations.175
174 The final rule and less stringent scenario defer the backstop emission rate for certain EGUs until the 2030 run
year, while the more stringent alternative imposes the backstop emission rate in the 2025 run year. Retirements that
may be undertaken by EGU source owners/operators as a least-cost compliance strategy are therefore delayed in the
final rule and less stringent alternative relative to the more stringent alternative. Since the power sector model is
forward looking, it has an incentive to run units harder before they retire. This incentive is lower in the final rule and
less stringent alternative relative to the more stringent alternative due to delayed retirements. As such, emissions are
slightly lower in 2023 in some states in the less stringent alternative and final rule relative to the more stringent
alternative, leading to slightly greater emissions reductions.
175 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.
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Under the less stringent regulatory alternative in 2023 there are similar magnitudes of
ozone concentration reductions in the reference group as in the rule, and a greater reduction in
average ozone concentration in the more stringent regulatory alternative, within all population
groups. In 2026 the less stringent and more stringent alternatives are estimated to result in
smaller and larger reductions in ozone concentrations, respectively, as compared to the final rule.
Notably, the less stringent alternative has smaller ozone concentration reductions from EGUs
than from non-EGUs, whereas the more stringent alternative has slightly larger ozone
concentration reductions from both EGUs and non-EGUs.
The national-level assessment of ozone before and after implementation of this final
rulemaking 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. In other words, the data indicate that all population subgroups
evaluated may experience similar seasonal average ozone concentration changes after
implementation of this rule as compared to the reference group nationally.
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2023
Population
Group
Population
Count
Baseline
Final
EGU
Less
More
Reference
Reference (0-99)
343M
41.30
0.03
0.03
0.03
Race
White (0-99)
270M
41.39
0.03
0.03
0.03
American Indian (0-99)
4M
43.41
0.04
0.04
0.04
Asian (0-99;
22M
42.44
0.02
0.02
0.02
Black (0-99)
47 M
40.13
0.03
0.03
0.04
Ethnicity
Non-Hispanic (0-99)
275M
40.83
0.03
0.03
0.04
Hispanic (0-99)
68M
43.22
0.02
0.02
0.02
Linguistic
English "well or better" (0-99)
327M
41.24
0.03
0.03
0.03
Isolation
English < "well" (0-99)
16 M
42.55
0.02
0.02
0.02
Poverty
Povertyline (0-99)
288M
41.30
0.03
0.03
0.03
Educational
More educated (>24: HS or more) 201M
41.13
0.03
0.03
0.03
Attainment
Less educated (>24; no HS)
33M
41.70
0.03
0.03
0.03
Employment Employed (0-99)
9M
41.71
0.03
0.03
0.03
Status
Unemployed (0-99)
343M
41.30
0.C3
0.03
0.03
Not in the labor force (0-99)
174M
41.27
0.03
0.03
0.03
Insurance
Insured (0-64)
251M
41.44
0.03
0.03
0.03
Status
Unisured (0-64)
30 M
41.00
0.03
0.03
0.03
Age
Children (0-17)
78M
41.54
0.03
0.03
0.03
Adults (18-64)
204M
41.34
0.03
0.03
0.03
Older Adults (65-99)
61M
40.89
0.03
0.03
0.03
Sex
Females (0-99)
174M
41.29
0.03
0.03
0.03
Males (0-99)
169M
41.31
0.03
0.03
0.03
Figure 7-1. Heat Map of the National Average AS-M03 Ozone Concentrations in the
Baseline and Reductions in Concentrations Due to this Rulemaking Across Demographic
Groups in 2023 (ppb)
2026
Baseline
Final
Less
More
Population
Group
Population
Count
-
EGU
NonEGU
EGU+Non
EGU
EGU
NonEGU
EGU+Non
EGU
EGU
NonEGU
EGU+Non
EGU
Reference
Reference (0-99)
352M
41.8
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Race
White (0-99)
276M
41.9
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.3
0.6
American Indian (0-99)
4M
43.9
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.3
0.5
Asian (0-99)
24M
42.8
0.1
0.2
0.3
0.0
0.1
0.1
0.1
0.3
0.5
Black (0-99)
49 M
40.6
0.1
0.3
0.4
0.0
0.1
0.1
0.2
0.4
0.6
Ethnicity
Non-Hispanic (0-99)
279M
41.3
0.1
0.2
0.4
0.0
0.1
0.1
0.2
0.4
0.6
Hispanic (0-99)
73M
43.6
0.1
0.2
0.2
0.0
0.1
0.1
0.2
0.3
0.5
Linguistic
English "well or better" (0-99)
336M
41.7
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Isolation
English < "well" (0-99)
16 M
42.9
0.1
0.2
0.2
0.0
0.1
0.1
0.1
0.3
0.5
Poverty
Poverty ine (0-99)
296M
41.8
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Educational
More educated (>24: HS or more) 207M
41.6
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.3
0.6
Attainment
Less educated (>24; no HS)
34M
42.2
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Employment
Employed (0-99)
9M
42.2
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Status
Unemployed (0-99)
352M
41.8
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Not in the labor force (0-99)
179M
41.8
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Insurance
Insured (0-64)
255M
41.9
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Status
Unisured (0-64)
31M
41.5
0.1
0,2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Age
Children (0-17)
80M
42.0
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Adults (18-64)
206M
41.8
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Older Adults (65-99)
67 M
41.4
0.1
0,2
0.3
0.0
0.1
0.1
0.2
0.3
0.5
Sex
Females (0-99)
178M
41.8 |
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Males (0-99)
174M
41.8
0.1
0.2
0.3
0.0
0.1
0.1
0.2
0.4
0.6
Figure 7-2. Heat Map of the National Average AS-M03 Ozone Concentrations in the
Baseline and Reductions in Concentrations Due to this Rulemaking Across Demographic
Groups in 2026 (ppb)
293
-------
7.4.1.2 State Aggregated Results
The goal of this action is to require NOx emissions reductions that will eliminate
significant contribution to nonattainment or interference with maintenance of the 2015 ozone
NAAQS in downwind areas.176 As upwind emissions reductions necessary to achieve this goal
will not affect ozone concentrations uniformly within each state, we provide AS-M03 ozone
concentration changes by state and demographic population for the two future years (Figure 7-3
and Figure 7-4). Figure 7-3 shows the EGU impacts in 2023 and Figure 7-4 shows the combined
EGU and non-EGU impacts in 2026 for the 48 states in the contiguous U.S, for the policy
scenario only. In these heat maps darker green indicates larger AS-M03 reductions and red
colors show AS-M03 increases, although the demographic groups are now shown as columns
and each state as a row. On average, the state-specific reference populations are projected to
experience reductions in AS-M03 concentrations by up to 0.16 ppb in Missouri in 2023 and 1.2
ppb in Arkansas in 2026. In 2023 there are also predicted to be AS-M03 increases up to 0.06
ppb in West Virginia; these increases are very small, however, and by 2026, West Virginia is
projected to experience substantially greater reductions in AS-M03 concentrations, on the order
of 0.8 ppb. In most states, populations potentially of concern are projected to experience similar
AS-M03 concentration changes as the state-level reference population.
An important limitation of this state-level analysis is that the influence of the number of
people in the state is not reflected in the results, whereas the national-level results above weight
air quality changes by population. For example, even though there is only a small reduction in
AS-M03 concentration from this action in California, the state's large population will contribute
substantially to the national averages. Conversely, while the largest AS-M03 concentration
reductions in 2026 occur in Arkansas and Louisiana, as of 2022, they are the 34th and 25th most
populated states, respectively, and will contribute less to the national population-weighted AS-
M03 information than more populated states, such as California.
Therefore, whereas ozone exposure impacts vary considerably across states, the small
magnitude of differential impacts expected by the final rule is not likely to meaningfully
exacerbate or mitigate EJ concerns within individual states.
176 See Section 1 of the rule preamble for a discussion of the states included in the rule and their requirements for
EGUs and non-EGUs.
294
-------
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0.06 0.06
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.06
0.01
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire 0.01
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 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 0.01 0.01
0.01
0.03
0.01
0.07
0.06
0.07
0.09
0.08
0.06
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01
0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
0.03 0.03
0.01 0.01 0.01 0.01 0.01 0.01
0.07 0.07 0.06 0.07
0.06 0.06 0.06 0.06
0.07 0.06 0.07 0.07
0.09 0.10 0.09 0.09
0.08 0.08 0.08 0.09
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.07 0.07 0.07 0.07 0.07 0.07 0.07
0.03 0.03
0.01 0.01
0.07 0.07
0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
0.07 0.07 0.07
0.09 0.09 0.09
0.07 0.07 0.07 0.07 0.07 0.07
0.09 0.09 0.09 0.09
0.07 0.08 0.08 0.07 0.08 0.08 0.07
0.06 0.06 0.06 0.06 0.06
0.09 0.09
0.08 0.07
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
0.08 0.06 0.07 0.06
0.06 0.06 0.06 0.06
0.07 0.07 0.07 0.07
0.09 0.08 0.09 0.08
0.08 0.08 0.08 0.09
0.06 0.06 0.07 0.06 0.06 0.07 0.06 0.07
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
0,08 0.08 0.06 0.08 0.08 0.08 0.08 0.08 0.08 0.07 0.08 0.08 0.07 0.07
0.10 0.10 0.09 0.13 0.08 0.10 0.11 0.10 0.10 0.09 0.10 0.10_ 0.09 0.10
0.14 0.14 0.12 0.16 0.15 0.14 0.13 0.14 0.14 0.13 0.14 0.14 0.13 0.14
0.01 0.01 0.00 0.01 0.00 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.00 0.01
0.05 0.05 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
0.06 0.06 0.06 0.05 0.06 0.05 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
0.01 0.C1 0.01 0.01 0.01 0.01 0.01 0.C1 0.01 0.01 0.01 0.01 0.01
0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
0.02 0.03 0.02 0.02 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03
0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
0.01 0.01 0.01 0.02 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 0.01
0.03 0.03 0.03 0.04 0.04 0.03 0.03 0.03 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
0.02
0.03
0.02
0.02
0.06 0.06 0.06 0.06
0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01
0.04 0.04 0.04 0.04
0.08 0,07 0.08 0.07
0.10J3.10 0.10 0.10
0.14 0.14 0.14 0.13
0.01 0.01 0.01 0.00
0.05 0.05 0.05 0.04
0.06 0.06 0.06 0.06
0.01 0.01 0.01 0.01
0.02 0.02 0.02 0.02
0.03 0.03 0.03 0.03
0.01
0.01
0.03
0.01
0.07
0.06
0.07
0.09
0.08
0.06
0.01
0.01
0.01
0.04
0.08
0.10
0.14
o.qi
0.05
0.06
0.01
0.02
0.03
0.02
0.02
0.01
0.04
0.06 0.06
0.01 0.01
0.08 0.08
0.00 0.00
0.04 0.04
0.02 0.02
0.01 0.01
0.01 0.01
0.03 0.03
0.01 0.01
0.07 0.07
0.06 0.06
0.07 0.07
0.09 0.09
0.08 0.08
0.06 0.06
0.01 0.01
0.01 0.01
0.01 0.01
0.04 0.04
0.08 0.07
0.10
0.14
0.01
0.05
0.06
0.09
0.14
0.01
0.04
0.05
0.01 0.01
0.02 0.02
0.03 0.02
0.02 0.02
0.02 0.02
0.01 0.01
0.03 0.03
0.14 0.14 0.13 0.15 0.14 0.14 0.14 0.14 0.15 0.14 0.14 0.14 0.13 0.14 0.14 0.13 0.14 0.13 0.14 0.14 0.13
0.02
0.03
0.06
0.05
0.00 0.00 0.00 0.0010.00 0.00 10.00 0.00 .0.00 0.00 0.00 0.00 0.00 0.00
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
0.05 0.04 0.05 0.04 0.04 0.05 0.05 0.04 0.05 0.05 0.05 0.05 0.04 0.05
0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 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 0.0010.00 0.00 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
-0.04 -0.04 -0.04 -0.06 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0 04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04
0.07 0.07 0.06 0.07 0.08 0.07 0.08 0.07 0.08 0.07 0.07 0.07 0.07 0:07 0.07 0.07 0.07 0.07 0.07
0.03 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
0.00 0.00 0.00 0.00 0.00
0.01 0.01 0.01 0.01 0.01
0.01 0.01 0.01 0.01 0.01
0.02 0.02 0.02 0.02
0.03 0.02 0.03 0.03
0.05 0.06 0.06 0.07
0.05 0.05 0.05 0.05
0.07 0.07 0.07 0.07 0.07
0.01
0.01
Wyoming
Figure 7-3. Heat Map of State Average AS-M03 Ozone Concentration Reductions
and Increases (Red) by Demographic Group for EG Us in 2023 (ppb)
0.00 0.00
0.01 0.01
0.01 0.01
0.02 0.02
0.03 0.03
0.06 0.05
0.05 0.05
0.07 0.07
0.01 0.01
0.01 0.01
0.00 0.00
•0.04 -0.04
0.07 0.07
0.03 0.02
(Green)
295
-------
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
Ref..
Race
Ethnicity
E
<
Linguistic
Isolation
2026
EGU+NonEGU
Poverty Educational'
Status Attainment
«a "
5 S
Employment
Status
E o
Insurance
Status
Age
2 3
t 2
aj id
0.36 0.37 0.38 0.36 0.35 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36
0.09 0.09 0.11 0.09 0.09 0.10 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
1.02 0.99 0.89 0.92]i.20 1.03 0.91 1.02 0.92 1.03 1.02 1.03 1.00 1.04 1.02 1.02 1.03 0.98 1.02 1.02 1.02
1.04 1.02 1.02 1.03 0.98 1.02 1.02
0.11 0.11 0.11 0.11 0.11 0.10 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
0.15 0.15 0.16 0.15 0.15 0.16 0.15 0.15 0.15 0.16 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.16 0.15 0.15
0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22
0.35 0.35 0.35 0.36 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35
0.07 0.07 0.08 0.07 0.07 0.08 0.05 0.07 0.05 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
0.24 0.25 0.25 0.26 0.23 0.24 0.25 0.24 0.26 0.23 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24
0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
0.55 0.55 0.52 0.50 0.56 0.56 0.50 0.55 0 49 0.56 0.55 0.55 0.54 0.54 0.55 0.55 0.55 0.53 0.55 0 55
0.64 0.64 0.62 0.63 0.61 0.64 0.60 0.64 0.60 0.63 0.64 0.64 0.63 0.63 0.64 0.63 0.64 0.63 0.63 0.64
0.36 0.36 0.34 0.37 0.38 0.37 0.35 0.36 0.35 0.37 0.36 0.36 0.36 0.37 0.36 0.37 0.36 0.36 0.36 0.36
0.53 0.53 0.58 0.54 0.55 0.54 0.51 0.53 0.50 0.54 0.53 0.54 0.53 0.55 0.53 0.54 0.53 0.53 0.53 0.53
0.83 0.82 0.84 0.86 0.89 0.83 0.85 0.83 0.87 0.80 0.84 0.84 0.80 0.83 0.83 0.82 0.83 0.81 0.83 0.83
1.02 1.02 0.90 1.03 1.04 1.02 1.03 1.02 1.02 1.01 1.03 1.03 1.00 1.03 1.02 1.02 1.03 1.01 1.02 1.03
0.09 0.09 0.08 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
0.41 0.42 0.41 0.42 0.40 0.42 0.41 0.41 0.41 0.41 0.41 0.42 0.41 0.41 0.41 0.42 0.41 0.41 0.41 0.41
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
0.47 0.47 0.42 0.48 0.49 0.47 0.49 0.47 0.49 0.48 0.47 0.47 0.48 0.47 0.47 0.47 0.47 0.47 0.47 0.47
0.22 0.22 0.17 0.23 0.23 0.22 0.23 0.22 0.23 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22 0.22
0.11
0.16
0.22
0.35
0.07
0.24
0.02
0.56
0.64
0.36
0.53
0.83
1.02
0.09
0.42
0.16
0.47
0.22
0.70
0.81
0.69 0.64 0.68 0.71
0.80 0.73 0.88 0.87
0.70 0.69
0.81 0.73
0.70 0.71
0.81 0.78
0.70 0.70
0.78 0.81
0.70 0.70
0.81 0.80
0.70 0.70 0.70
0.81 0.81 0.80
0.70 0.70
0.81 0.78
0.70 0.70
0.80 0.81
0.70
0.81
0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
0.26 0.26 0.24 0.28 0.28 0.26 0.26 0.26 0.27 0.27 0.26 0.26 0.26 0.27 0.26 0.26 0.27 0.26 0.26 0.27
0.09 0.08 0.07 0.09 0.09 0.08 0.09 0.08 0.09 0.09 0.08 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.08 0.09
New Hampshire 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
0.30 0.30 0.30 0.30 0.30 0.30 0.29 0.30 0.29 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30
0.17 0.16 0.24 0.16 0.16 0.18 0.16 0.17 0.15 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.18 0.17 0.17
0.27 0.27 0.27 0.26 0.26 0.27 0.26 0.27 0.25 0.27 0.27 0.27 0.26 0.27 0.27 0.27 0.27 0.26 0.27 0.27
0.28 0.28 0.25 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28
0.06 0.06 0.04 0.07 0.06 0.06 0.05 0.06 0.06 0.06 0.06 0.06 0.05 0.05 0.06 0.05 0.06 0.05 0.05 0.06
0.02
0.26
0.08
0.14
0.30
0.17
0.27
0.28
0.05
0.69
0.89
0.69 0.69 0.70 0.68
0.88 0.90 0.92 0.92
0.69 0.66
0.89 0.89
0.69 0.67
0.89 0.91
0.69 0.69
0.89 0.89
0.69 0.69
0.89 0.88
0.68 0.69 0.69 0.69 0.69
0.90 0.89 0.89 0.89 0.88
0.69 0.69
0.89 0.89
0.68
0.88
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.46 0.46 0.42 0.42 0.42 0.46 0.40 0.46 0.40 0.45 0.46 0.46 0.44 0.46 0.46 0.46 0.45 0.48 0.45 0.45 0.46
0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17
0.20 0.20 0.20 0.20 0.19 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
0.14 0.14 0.10 0.15 0.16 0.14 0.14 0.14 0.17 0.13 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.14 0.14 0.13
0.52 0.50 0.52 0.54 0.62 0.52 0.54 0.52 0.53 0.54 0.52 0.52 0.52 0.53 0.52 0.52 0.53 0.53 0.53 0.52 0.51
0.44 0.43 0.45 0.48 0.50 0.48 0.38 0.44 0.40 0.41 0.44 0.45 0.42 0.44 0.44 0.43 0.44 0.42 0.43 0.44 0.44
0.28 0.28 0.28 0.29 0.29 0.28 0.29 0.28 0.29 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.27
0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18
0.44 0.44 0.43 0.43 0.41 0.44 0.43 0.44 0.43 0.44 0.43 0.44 0.44 0.43 0.44 0.44 0.43 0.44 0.43 0.43 0.44
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.83 0.84 0.83 0.85 0.80 0.84 0.77 0.83 0.76 0.84 0.83 0.83 0.82 0.84 0.83 0.83 0.84 0.82 0.83 0.83 0.83
0.31 0.31 0.27 0.31 0.35 0.31 0.34 0.31 0.33 0.32 0.31 0.31 0.32 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31
0.08 0.08 0.07 0.09 0.10 0.08 0.09 0.08 0.08 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
Figure 7-4. Heat Map of State Average AS-M03 Ozone Concentration Reductions by
Demographic Group for EGUs and Non-EGUs in 2026 (ppb)
296
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7.4.1.3 Distributional Results
While aggregated national- and state-level average ozone concentration results (Section
7.4.1.1) provide an overview of potential exposure differences across populations, detailed
information on the distribution of AS-M03 ozone exposures within populations, and specifically
the portions of each population experiencing ozone concentration changes due to the rule, can
provide a more comprehensive understanding of analytical results. Figures in this section present
cumulative counts of each population exposed to ascending levels of AS-M03 ozone
concentrations across the contiguous U.S. Results allow evaluation of what percentage of each
subpopulation (e.g., Hispanics) in the contiguous U.S. experience average baseline ozone
concentrations at or below certain AS-M03 ozone concentrations (e.g., 40 ppb) compared to
what percentage of the overall reference group (i.e., the total population of contiguous U.S.)
experiences ozone concentrations. More specifically, to permit the direct comparison of
demographic populations with different absolute numbers (e.g., the large overall reference
population with the much smaller number of Asians), we plot the running sum of each
population as a percentage against the ozone concentration changes from NOx emissions
reductions under the regulatory alternatives.
This distributional EJ analysis is also subject to additional uncertainties related to more
highly resolved input parameters and additional assumptions (U.S. EPA 2021d, Section 6). For
example, this analysis does not account for potential difference in underlying susceptibility,
vulnerability, or risk factors across populations expected to experience post-policy AS-M03
exposure changes. We also did not evaluate whether concentration reductions/increases occurred
in areas of higher/lower baseline burden exposures. Nor could we include information about
differences in other factors that could affect the likelihood of adverse impacts (e.g., exercise
patterns) across groups. Therefore, this analysis should not be used to conclusively assert that
there are meaningful differences in ozone exposure impacts in either the baseline or the rule
across population groups.
As the baseline scenario is similar to that of the rule, we focus on the policy-specific
ozone changes of this final rulemaking.177 Distributions of 12 km gridded ozone concentration
177 Briefly, the rule concluded that approximately 80% of the overall reference population resides in areas of AS-
M03 ozone concentrations at or less than about 45 ppb in 2023 and 2026. Most of this population experiences AS-
297
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reductions from NOx emissions reductions of affected facilities under the three regulatory
alternatives analyzed in this final rulemaking in 2023 (EGU controls only) and 2026 (EGU and
non-EGU controls, combined) are shown in Figure 7-5 and Figure 7-6, respectively. For clarity,
only above/below the poverty line and those who speak English "well or better'V'less than well"
are shown and sex and the overall reference group are excluded from the cumulative distribution
figures.
The vast majority of ozone concentration changes are less than 0.1 ppb in 2023 and less
than 1 ppb in 2026. As was observed in the national average ozone concentration analysis
(Section 7.4.1.1), there are slight differences in the ozone concentration changes across
population demographics and regulatory alternatives in 2023 and 2026 (Figure 7-5 and Figure
7-6, respectively). Proportionally, Hispanics, Asians, American Indians, and those linguistically
isolated populations experience smaller ozone concentration reductions under the regulatory
alternatives than the overall reference population in 2023, by a very small amount. Alternatively,
the distribution of ozone concentration reductions for Black populations is greater than the
reference population only in the smallest half of ozone concentration reductions.
The magnitude of ozone concentration reductions from affected EGU sources is
estimated to be roughly 10-fold greater in 2026 compared to 2023. Approximately 90% of the
overall reference population experiences a fairly linear distribution of ozone concentration
reductions, although the steepness of the distribution varies by regulatory alternative and facility
type.
Distributions of ozone concentration changes across population demographics and
affected facility types are reasonably similar across the three regulatory alternatives, although to
differing magnitudes. Individuals who identify as Hispanic, Asian, American Indian, and those
linguistically isolated experience proportionally smaller ozone concentration reductions from
EGU and non-EGU NOx emissions reductions under the regulatory alternatives than the overall
reference population in 2026.
As such, the very small difference shown in the distributional analyses of ozone
concentration changes under the various regulatory alternatives in 2023 and 2026 provides
M03 ozone concentrations between 30-44 ppb. In contrast, the 20% of the overall reference population residing in
areas of the highest baseline ozone concentrations experiences concentrations of between about 45-70 ppb.
298
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additional evidence that the final rule is not likely to meaningfully exacerbate or mitigate EJ
concerns for population groups evaluated
Population
Final
2023
EGU
Less
More
c 100%'
O
Race J -5 50%-
Q.
a 0%.
1 SjT
\ fjr
J 1 White (0-99)
Jff 1 American Indian (0-99)
jl m Asian (0-99)
V ¦ Black (0-99)
r
r
c 100%"
O
Ethnicity J -5 50%-
0.
0
a 0%.
j / ¦ Non-Hispanic (0-99)
y ¦ Hispanic (0*99)
r
f
c 100%'
.0
Linguistic Is ft ,no/.
Isolation # §. 0
* 0%.
f 1 English "well or better" (0-99)
ft ¦ English < "well" (0-99)
r _J
r
f .
c 100%"
0
Educational o ro
. . vp *5 50%-
Attainment 3s
0
0%.
\S~ \jT
( rm More educated (>24. HS or more) r
1 / ¦ Less educated (>24. no HS) f
J u
1 j
I y
1 F
i /
j/
c 100%"
O
Employment ^ ft
— vp 3 50 /o
Status 5- 5.
* 0%.
J i y
i jp i jr
f ¦ Employed (0-99) J f
f ¦ Unemployed (0-99) j /
w ¦ Not in the labor force (0-99) jf
^—
1 JF
1 A
1 f
1 /
1/
c 100%'
,0
Insurance 0 ft
. nO 3 DvJ 70"
Status 6- q.
s 0%.
y
/
f ¦ insured (0-64)
1 f ¦ Unisured (0-64)
^
i y
i y
i J
i i
i f
/
1 jf
1 /
1 f
c 100%"
0
Poverty | |
Status S q_
0
a 0%.
y
/
i f
/ ¦ >Poverty line (0-99)
J ¦
-------
2026
EGU NonEGU EGU+NonEGU
Ozone(ppb)* Ozone(ppb) * Ozone(ppb) * Ozone(ppb) * Ozone(ppb) * Ozone(ppb) * Ozone(ppb) * Ozone(ppb)* Ozone(ppb) *
Figure 7-6. Distributions of Ozone Concentration Changes Across Populations, Affected
Facilities, and Regulatory Alternatives in 2026
7.4.2 PM2.5 Exposure Analysis
7.4.2.1 National Aggregated Results
While ozone is the targeted air pollutant of this final rulemaking, PM2.5 reductions are a
predicted co-pollutant reduction. PM2.5 EJ exposure impacts of the policy options were not
evaluated in the rule proposal as air quality spatial fields were unavailable. However, surfaces
were developed for this final rulemaking, so PM2.5 EJ impacts are provided here for EGU
emission reductions in 2026.178
National average baseline PM2.5 concentrations in micrograms per cubic meter (|.ig/nr) in
2026 are shown in the colored column labelled "baseline" the heat map in Figure 7-7.1 '9
178 Spatial fields of PM2.5 concentration changes are predicted only from affected EGU sources in 2026.
179 The 2026 baseline EGU SO2 and, to some extent, PM2.5 emissions were notably higher in the final case compared
to the proposal, especially for units in Oklahoma. In Oklahoma, annual total EGU SO: emissions in the final 2026
baseline scenario were 14,595 tons per year compared to only 2 tons per year in the proposal 2026 baseline scenario
which produced unrealistically high PM;. concentrations in Oklahoma. The unrealistic PM2.5 concentrations were
300
-------
Concentrations in the "baseline" column represent the total estimated PM2.5 exposure burden
averaged over the 12-month calendar year and is colored to more easily visualize differences in
average concentrations, with lighter blue coloring representing smaller average concentrations
and darker blue coloring representing larger average concentrations. Average national disparities
observed in the baseline of this rule are similar to those described by recent rules (e.g., the PM
NAAQS Proposal), that is, populations with national average PM2.5 concentrations higher than
the reference population in 2026 ordered from most to least difference were: individuals who are
linguistically isolated, Hispanic individuals, Asian individuals, Black individuals, the less
educated, and children.
The three columns on the right side of Figure 7-7 provide information regarding how the
final rulemaking will impact PM2.5 concentrations across various populations from EGU controls
under the rule, the less stringent alternative, and the more stringent alternative. Under the final
rule in 2026, the difference in population-weighted seasonal average PM2.5 reductions across
demographic groups are relatively small and consistent.
The national-level assessment of PM2.5 before and after implementation of this final
rulemaking 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.
removed from the spatial fields for the final rule 2026 alternatives by replacing the final rule EGU SO2 and PM2 5
emissions in Oklahoma with the corresponding 2026 baseline SO2 and PM2 5 emissions from the proposal. This
impacts the magnitude of baseline PM2 5 concentrations but should not impact changes due to the policy alternatives.
301
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Population
Group
Population
Count
Baseline
2026
EGU
Final Less
More
Reference
Reference (0-99)
352M
7.34
0.01
0.00
0.04
Race
White (0-99)
276M
7.24
0.01
0.00
0.04
American Indian (0-99)
4M
6.83
0.01
0.00
0.03
Asian (0-99)
24M
7.91
0.01
0.00
0.03
Black (0-99)
49 M
7.64
0.02
0.00
0.04
Ethnicity
Non-Hispanic (0-99)
279M
7.13
0.01
0.00
0.04
Hispanic (0-99)
73M
8.13
0.01
0.00
0.03
Linguistic
English "well or better" (0-99)
336M
7.30
0.01
0.00
0.04
isolation
English < "well" (0-99)
16 M
8.26
0.01
0.00
0.03
Poverty
>Povertyline (0-99)
296M
7.31
0.01
0.00
0.04
Status
24: HS or more)
207M
7.24
0.01
0.00
0.04
Attainment
Less educated (>24; no HS)
34 M
7.67
0.01
0.00
0.04
Employment
Employed (0-99)
9M
7.49
0.01
0.00
0.04
Status
Unemployed (0-99)
352M
7.34
0.01
0.00
0.04
Wot in the laborforce (0-99)
179M
7.34
0.01
0.00
0.04
Insurance
Insured (0-64)
255M
7.38
0.01
0.00
0.04
Status
Unisured (0-64)
31M
747
0.01
0.00
0.04
Age
Children (0-17)
80M
7.41
0.01
0.00
0.04
Adults (18-64)
206M
7.38
0.01
0.00
0.04
Older Adults (65-99)
67M
7.12
0.01
0.00
0.04
Sex
Females (0-99)
178M
7.35
0.01
0.00
0.04
Males (0-99)
174M
7.33
0.01
0.00
0.04
Figure 7-7. Heat Map of the National Average PM2.5 Concentrations in the Baseline and
Reductions in Concentrations Due to this Rulemaking Across Demographic Groups in 2026
(fig/1113)
7.4.2.2 State Aggregated Results
We also provide PM2.5 concentration reductions by state and demographic population in
2026 for the 48 states in the contiguous U.S, for the policy scenario only. In this heat map darker
blue again indicates larger PM2.5 reductions, with demographic groups shown as columns and
each state as a row. On average, the state-specific reference populations are projected to
experience reductions in PM2.5 concentrations by up to 0.07 |.ig/nr in Arkansas and Louisiana. In
all 48 states, populations potentially of concern are projected to experience similar PM2.5
concentration reductions as the state-level reference population. 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.
302
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Therefore, whereas PM2.5 exposure impacts vary considerably across states, the small
magnitude of differential impacts expected by the final rule is not likely to meaningfully
exacerbate or mitigate EJ concerns within individual states.
303
-------
EGU
2026
Linguistic Poverty Educational Employment Insurance
Ret.. | Race Ethnicity | Isolation Status |Attainment| Status Status | Age
State 5 | || |
Alabama 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Arizona 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Arkansas 0.06 0.06 0.06 0.06 0.0/ 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06
California |0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Colorado 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 O.Oolo.OO 0.00 0.00
Connecticut 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Delaware 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Florida 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.01 0.00 0.01 0.01 0.01 0.00 0.00 0.01 0.01 0.01 0.00 0.01 0.01 0.01
Georgia 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Idaho 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Illinois 0.02 0.02 0.02 0.01 0.02 0.02 0.01 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Indiana 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Iowa 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Kansas 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Kentucky 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Louisiana
Maine 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Maryland 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Massachusetts 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Michigan 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Minnesota 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Mississippi 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
Missouri 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
Montana 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Nebraska 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Nevada 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
New Hampshire 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
New Jersey 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
New Mexico 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
New York 0.01 0.01 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.01
North Carolina 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah 0.02 0.02 0.01 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.01 0.02 0.02 0.01
Vermont 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Virginia 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Washington 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
West Virginia 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Wisconsin 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Wyoming 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Figure 7-8. Heat Map of State Average PM2.5 Concentration Reductions by Demographic
Group for EGUs and Non-EGUs in 2026 (jig/m3)
0.00
0
0
0
0
0
0
0
0
0
0
0
0
0
b
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.00 0.00
0.00 0.00 0.00
0.00 0.00
0.00 0.00 0.00
0.02
0.03
0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
0.02 0.02
0.03 0.03
0.02 0.02 0.02
0.03 0.03 0.03
0.02 0.02
0.03 0.03
0.02 0.02 0.02
0.03 0.03 0.03
0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00
0.00 0.00 0.00
0.00 0.00
0.00 0.00 0.00
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 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 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 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
0.00 0.00
0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00
0.03
0.03
0.02 0.02 0.03 0.03
0.03 0.03 0.03 0.03
0.02 0.03
0.03 0.03
0.03 0.03
0.03 0.03
0.03 0.02
0.03 0.03
0.02 0.03 0.03 0.03 0.03
0.03 0.03 0.03 0.03 0.03
0.03 0.03 0.03 0.03 0.02
0.03 0.03 0.03 0.03 0.03
304
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7.4.2.3 Distributional Results
We also present cumulative counts of each population exposed to ascending levels of
PM2.5 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 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 reductions under the three regulatory alternatives in 2026.
This distributional EJ analysis is also subject to additional uncertainties related to more
highly resolved input parameters and additional assumptions (U.S. EPA 2021d, Section 6). 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. Therefore, this analysis should not be used to assert that
there are meaningful differences in PM2.5 exposures associated with either the baseline or the
rule.
As the baseline scenario is similar to that described by other RIAs, we focus on the PM2.5
changes due to this final rulemaking. Distributions of 12 km gridded PM2.5 concentration
reductions from EGU control strategies of affected facilities under the three regulatory
alternatives analyzed in this final rulemaking in 2026 are shown in Figure 7-9. For clarity, only
above/below the poverty line and those who speak English "well or better'V'less than well" are
shown and sex and the overall reference group are excluded from the cumulative distribution
figures.
The vast majority of PM2.5 concentration changes are less than 0.1 |ig/m3 in 2026. As was
observed in the national average PM2.5 concentration analysis (Section 7.4.2.1), there are slight
differences in the PM2.5 concentration changes across population demographics and regulatory
alternatives in 2026 (Figure 7-9).
Distributions of PM2.5 concentration changes across population demographics are
reasonably similar across the three regulatory alternatives, although to differing magnitudes. As
such, the very small difference shown in the distributional analyses of PM2.5 concentration
changes under the various regulatory alternatives in 2026 provides additional evidence that the
305
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final rule is not likely to meaningfully exacerbate or mitigate EJ concerns for population groups
evaluated.
Affected Facilities / Year / Policy Option
EGU
2026
Population Final 1 Less More
7D
0)
n
(t>
% of
Population
M
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000
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0
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^ CL
o
a 0%.
f
i
—
f
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¦ Hispanic (0-99)
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c 100%'
o
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r. . xp 3 50 /o_
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f
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o
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_ -e "5 50%-
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o
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j
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¦ Unisured (0-64)
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o
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Status ^ q.
O
a 0%.
i
i
S
¦ >Poverty line (0-99)
¦
-------
7.4.2.4 Qualitative Assessment of PM2.5 Health Impacts
Health studies have shown a significant association between exposure to particle
pollution and health risks, including premature death (U.S. EPA 2019 and Chapter 5). PM2.5
reductions are expected from this action, but demographic-specific health impacts were not
assessed for baseline or regulatory alternatives under this rulemaking, due to the small magnitude
of predicted changes. However, in general, both recent publications and analyses by the EPA
suggest that the burden of PM2.5 exposures and impacts may disproportionately affect certain
groups, such as Black and Hispanic populations (e.g., Bell 2012, Bravo 2016, Kelly 2021, U.S.
EPA 2020, U.S. EPA 2021a, U.S. EPA 2021c).
7.5 Qualitative Assessment of CO2
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
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),180'181 the Intergovernmental Panel on Climate Change
180 USGCRP, 2018: Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment,
Volume II [Reidmiller, D.R., C.W. Aveiy, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C.
Stewart (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 1515 pp. doi: 10.7930/NCA4.2018.
181 USGCRP, 2016: The Impacts of Climate Change on Human Health in the United States: A Scientific
Assessment. Crimmins, A., J. Balbus, J.L. Gamble, C.B. Beard, J.E. Bell, D. Dodgen, R.J. Eisen, N. Fann, M.D.
Hawkins, S.C. Herring, L. Jantarasami, D.M. Mills, S. Saha, M.C. Sarofim, J. Trtanj, andL. Ziska, Eds. U.S. Global
Change Research Program, Washington, DC, 312 pp. http://dx.doi.org/10.7930/J0R49NQX
307
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(IPCC),182'183'184'185 and the National Academies of Science, Engineering, and Medicine186'187
add more evidence that the impacts of climate change raise potential environmental justice
concerns. 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 United States. In particular,
the 2016 scientific assessment on the Impacts of Climate Change on Human Health188 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.
182 Oppenheimer, M., M. Campos, R.Warren, J. Birkmann, G. Luber, B. O'Neill, and K. Takahashi, 2014: Emergent
risks and key vulnerabilities. In: 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 [Field, C.B., 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, and
L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039-
1099.
183 por(Cr j r l Xie, A.J. Challinor, K. Cochrane, S.M. Howden, M.M. Iqbal, D.B. Lobell, andM.I. Travasso,
2014: Food security and food production systems. In: 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 [Field, C.B., 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, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA, pp. 485-533.
184 Smith, K.R., A.Woodward, D. Campbell-Lendrum, D.D. Chadee, Y. Honda, Q. Liu, J.M. Olwoch, B. Revich,
and R. Sauerborn, 2014: Human health: impacts, adaptation, and co-benefits. In: 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 [Field, C.B., 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, and L.L.White (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, pp. 709-754.
185 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 [Masson-
Delmotte, V., 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, M. Tignor, and T.
Waterfield (eds.)]. In Press.
186 National Research Council. 2011. America's Climate Choices. Washington, DC: The National Academies Press.
https://doi.org/10.17226/12781.
187 National Academies of Sciences, Engineering, and Medicine. 2017. Communities in Action: Pathways to Health
Equity. Washington, DC: The National Academies Press, https://doi.org/10.17226/24624.
188 USGCRP, 2016: The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment
308
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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.189 The report found that Black
and African American populations are approximately 40% more likely to live in areas of the U.S.
projected to experience the highest increases in mortality rates due to changes in extreme
temperatures. Additionally, Hispanic and Latino individuals in weather-exposed industries were
found to be 43% more likely to currently live in areas with the highest projected labor hour
losses due to extreme temperatures. American Indian and Alaska Native individuals are
projected to be 48% 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 disproportionate risks of experiencing the most
damaging impacts of climate change.
These findings suggest that CO2 reductions may benefit disproportionately impacted
populations. However, as we have not conducted the wide-ranging analyses that would be
needed to assess the specific impacts of this rule on the multiple climate-EJ interactions
described above, we cannot analyze the potential impacts of the final rule quantitatively.
7.6 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. For
example, here we provide a quantitative EJ assessment of ozone and PM2.5 concentration
changes from this rule but can only qualitatively discuss EJ impacts of CO2 emission reductions.
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, so results similar to those presented here should not be assumed for other
rulemakings.
189 U.S. EPA 2021e. Climate Change and Social Vulnerability in the United States: A Focus on Six Impacts. U.S.
Environmental Protection Agency, EPA 430-R-21-003.
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For the rule, we quantitatively evaluate the proximity of affected facilities to potentially
disadvantaged populations for consideration of local pollutants impacted by this rule but not
modeled here (Section 7.3). We also quantitatively evaluate the potential for disproportionate
pre- and policy-policy ozone and PM2.5 exposures across different demographic groups (Section
7.4). 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, local NO2 emitted
from sources affected by the regulatory action for certain population groups of (Section 7.3). The
baseline demographic proximity analyses suggest that larger percentages of Hispanic individuals,
African American individuals, people below the poverty level, people with less educational
attainment, and people linguistically isolated are living within 5 km and 10 km of an affected
EGU, compared to national averages. It also finds larger percentages of African American
individuals, people below the poverty level, and with less educational attainment living within 5
km and 10 km of an affected non-EGU facility. Relating these results to question 1 from Section
7.2, we conclude that there may be potential EJ concerns associated with directly emitted
pollutants that are affected by the regulatory action (e.g., NO2) for certain population groups of
concern in the baseline (question 1). However, as proximity to affected facilities does not capture
variation in baseline exposure across communities, nor does it indicate that any exposures or
impacts will occur, these results should not be interpreted as a direct measure of exposure or
impact.
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 rule as well as a snapshot of air pollution concentrations in the near future.
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
of the environmental stressor primarily affected by the regulatory action (Section 7.4). Baseline
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ozone and PM2.5 exposure analyses show that certain populations, such as Hispanic individuals,
Asian individuals, those linguistically isolated, those less educated, and children may experience
disproportionately higher ozone and PM2.5 exposures as compared to the national average.
Individuals who identify as American Indian 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 post-policy regulatory alternatives of this final 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
disparities in the ozone and PM2.5 concentration burdens are likely to remain after
implementation of the regulatory action or alternatives under consideration due to the small
magnitude of the concentration changes associated with this rulemaking across demographic
populations relative to baseline burden disparities (question 2). Also, due to the very small
differences observed in the distributional analyses of post-policy ozone and PM2.5 exposure
impacts across populations, we do not find evidence that potential EJ concerns related to ozone
or PM2.5 exposures will be meaningfully exacerbated or mitigated in the regulatory alternatives
under consideration, compared to the baseline (question 3). Importantly, the action described in
this rule is expected to lower ozone and PM2.5 in many areas, including those areas that struggle
to attain or maintain the ozone 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 final rulemaking (EJ question 2). This EJ assessment also
suggests that this action will neither mitigate nor exacerbate disparities across populations of EJ
concern analyzed (EJ question 3) at the national scale in a meaningful way.
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7.7 References
Bell, M.L. and Ebisu, K., (Bell 2012). Environmental inequality in exposures to airborne
particulate matter components in the United States. Environmental health perspectives,
120(12), pp.1699-1704.
Bravo, M.A., Anthopolos, R., Bell, M.L. and Miranda, M.L., (Bravo 2016). Racial isolation and
exposure to airborne particulate matter and ozone in understudied US populations:
Environmental justice applications of downscaled numerical model output. Environment
international, 92, pp.247-255.
Kelly, J.T., Jang, C., Timin, B., Di, Q., Schwartz, J., Liu, Y., van Donkelaar, A., Martin, R.V.,
Berrocal, V. and Bell, M.L (Kelly 2021). Examining PM2.5 concentrations and exposure
using multiple models. Environmental Research, 196, 110432.
U.S. Environmental Protection Agency (U.S. EPA 2016). Integrated Science Assessment for
Oxides of Nitrogen -Health Criteria. U.S. EPA. Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Research and Development, National Center
for Environmental Assessment. EPA/600/R-15/068. January 2016. Available at:
https://cfpub. epa.gov/ncea/isa/recordisplay. cfm?deid=310879
U.S. Environmental Protection Agency (U.S. EPA 2019). Integrated Science Assessment (ISA)
for Particulate Matter (Final Report). U.S. EPA. Research Triangle Park, NC: U.S.
Environmental Protection Agency, Office of Research and Development, Center for Public
Health and Environmental Assessment.
U.S. Environmental Protection Agency (U.S. EPA 2020). Integrated Science Assessment (ISA)
for Ozone and Related Photochemical Oxidants (Final Report). U.S. Environmental
Protection Agency, Washington, DC, EPA/600/R-20/012, 2020.
U.S. Environmental Protection Agency (U.S. EPA 2021a). Regulatory Impact Analysis for the
Final Revised Cross-State Air Pollution Rule (CSAPR) Update for the 2008 Ozone
National Ambient Air Quality Standards (NAAQS). U.S. Environmental Protection
Agency, Research Triangle Park, NC, EPA-452/R-21-002, March 2021.
U.S. Environmental Protection Agency (U.S. EPA 2021b). Supplement to the 2019 Integrated
Science Assessment for Particulate Matter (External Review Draft). Center for Public
Health and Environmental Assessment. Office of Research and Development, U.S.
Environmental Protection Agency, Research Triangle Park, NC. EPA/600/R-21/198,
September 2021. Available at:
https://cfpub. epa.gov/ncea/isa/recordisplay. cfm?deid=3 52823
U.S. Environmental Protection Agency (U.S. EPA 2021c). Policy Assessment for the
Reconsideration of the National Ambient Air Quality Standards for
Particulate Matter, External Review Draft. U.S. Environmental Protection Agency, Office of Air
Quality Planning and Standards, Health and Environmental Impacts Division. Research
Triangle Park, NC. U.S. EPA. EPA-452/P-21-001. October 2021.
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U.S. Environmental Protection Agency (U.S. EPA 2021d). Technical Support Document (TSD)
for the Final Revised Cross-State Air Pollution Rule Update for the 2008 Ozone Season
NAAQS. Estimating PM2.5- and Ozone-Attributable Health Benefits. U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-HQ-OAR-
2020-0272. March 2021. Available at: https://www.epa.gov/sites/default/files/2021-
03/documents/estimating_pm2.5-_and_ozone-attributable_health_benefits_tsd.pdf
U.S. Environmental Protection Agency (U.S. EPA). 2021e. Climate Change and Social
Vulnerability in the United States: A Focus on Six Impacts. U.S. Environmental Protection
Agency, EPA 43 O-R-21-003.
Woods & Poole (2015). Complete Demographic Database. -
https://www.woodsandpoole.com/our-databases/united-states/cedds/.
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CHAPTER 8: COMPARISON OF BENEFITS AND COSTS
Overview
The EPA performed an analysis to estimate the costs and benefits of compliance with the
Federal Good Neighbor Plan Addressing Regional Ozone Transport for the 2015 Ozone National
Ambient Air Quality Standards (Transport FIP for the 2015 ozone NAAQS) and more and less
stringent alternatives.
Consistent with OMB Circular A-4 and EPA's Guidelines for Preparing Economic
Analyses (2010), this RIA presents the benefits and costs of the final rule from 2023 through
2042. The estimated health benefits are expected to arise from reduced PM2.5 and ozone
concentrations, and the estimated climate benefits are from reduced greenhouse gas (GHG)
emissions. The estimated costs for EGUs are the costs of installing and operating controls and
the increased costs of producing electricity. The estimated costs for non-EGUs are the costs of
installing and operating controls to meet the ozone season emissions limits. The estimated costs
for non-EGUs do not include monitoring, recordkeeping, reporting, or testing costs.
Unquantified benefits and costs are described qualitatively.
The more and less stringent alternatives differ from the final rule in that they set different
NOx ozone season emission budgets for the affected EGUs and different dates for compliance
with the backstop emission rate. All three scenarios use emission budgets that were developed
using uniform control stringency represented by $1,800 per ton of NOx (2016$) in 2023 and
$11,000 per ton of NOx (2016$) in 2026. The final rule and less-stringent alternative defer the
backstop emission rate for certain EGUs until the 2030 run year,190 while the more stringent
alternative imposes the backstop emission rate in the 2025 run year (reflective of imposition in
the 2026 calendar year). The backstop emission rate is imposed beginning in the relevant run
190IPM 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. For this analysis, IPM maps the calendar year 2023 to run year
2023, calendar years 2024-2026 to run year 2025 and calendar years 2027-2029 to run year 2028. For model details,
please see Chapter 2 of the IPM documentation, available at:
https://www.epa.gov/system/files/documents/2021 -09/epa-platform-v6-summer-2021 -reference-case-09-11-21-
v6.pdf
314
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year (2025 or 2030, depending on scenario), on all coal units within the 19-state region that are
greater than 100 MW and lack SCR controls (excepting circulating fluidized bed (CFB) units).191
The rule also includes NOx emissions limitations with an initial compliance date of 2026
applicable to certain non-EGU stationary sources in 20 states. The rule establishes NOx
emissions limitations during the ozone season for the following unit types: reciprocating internal
combustion engines in Pipeline Transportation of Natural Gas; kilns in Cement and Cement
Product Manufacturing; reheat furnaces in Iron and Steel Mills and Ferroalloy Manufacturing;
furnaces in Glass and Glass Product Manufacturing; boilers in Iron and Steel Mills and
Ferroalloy Manufacturing, Metal Ore Mining, Basic Chemical Manufacturing, Petroleum and
Coal Products Manufacturing, and Pulp, Paper, and Paperboard Mills; and combustors or
incinerators in Solid Waste Combustors or Incinerators.
In order to implement the OMB Circular A-4 guidance for fulfilling Executive Order
(E.O.) 12866 to assess one less stringent and one more stringent alternative to the rule, we
analyzed a less stringent non-EGU alternative that would require less stringent control
technologies for the reciprocating internal combustion engines in Pipeline Transportation of
Natural Gas and boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore
Mining, Basic Chemical Manufacturing, Petroleum and Coal Products Manufacturing, and Pulp,
Paper, and Paperboard Mills. We analyzed a more stringent non-EGU alternative that would
require more stringent control technologies for the kilns in Cement and Concrete Products
Manufacturing, the furnaces in Glass and Glass Products Manufacturing, and the natural gas-
fired boilers in Iron and Steel Mills and Ferroalloy Manufacturing, Metal Ore Mining, Basic
Chemical Manufacturing, Petroleum and Coal Products Manufacturing, and Pulp, Paper, and
Paperboard Mills. A summary of the emissions limits can be found in Section I.B. of the
preamble.
8.1 Results
This RIA evaluates how EGUs and non-EGUs covered by the rule are expected to reduce
their emissions in response to the requirements and flexibilities provided by the remedy
implemented by the Transport FIP for the 2015 ozone NAAQS and the benefits, costs, and
191 The 19 states are: Arkansas, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri,
Nevada, New Jersey, New York, Ohio, Oklahoma, Pennsylvania, Texas, Utah, Virginia, and West Virginia.
315
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impacts of their expected compliance behavior. This chapter summarizes these results. Table 8-1
shows the ozone season NOx emissions reductions expected from the rule as well as the more
and less stringent alternatives analyzed from 2023 through 2030, and for 2035 and 2042. In
addition, Table 8-1 shows the ozone season and annual NOx, as well as annual SO2, PM2.5, and
CO2 emissions reductions expected nationwide from the rule as well as the more and less
stringent alternatives analyzed from 2023 through 2027, and for 2030, 2035 and 2042.
Table 8-2 below provides a summary of the 2019 ozone season emissions for non-EGUs
for the 20 states subject to the Transport FIP for the 2015 ozone NAAQS in 2026, along with the
estimated ozone season reductions for the rule and the less and more stringent alternatives.
For 2023, total ozone season NOx emissions reductions of 10,000 tons are from EGUs;
for 2026 total ozone season NOx emissions reductions of 70,000 tons are from EGUs and non-
EGUs, and for 2030 total ozone season NOx emissions reductions of 79,000 tons are from EGUs
and non-EGUs.
Table 8-1. EGU Ozone Season NOx Emissions Changes and Annual Emissions Changes
for NOx, SO2, PM2.5, and CO2 for the Regulatory Control Alternatives from 2023 - 2042
Final Rule
Less Stringent
Alternative
More Stringent
Alternative
2023
NOx (ozone season)
10,000
10,000
10,000
NOx (annual)
15,000
15,000
15,000
SO2 (annual)*
1,000
3,000
1,000
CO2 (annual, thousand metric)
-
-
-
PM2.5 (annual) ...
2024
NOx (ozone season)
21,000
10,000
33,000
NOx (annual)
25,000
15,000
57,000
SO2 (annual)
19,000
5,000
59,000
CO2 (annual, thousand metric)
10,000
4,000
20,000
PM2.5 (annual)
1,000
-
1,000
2025
NOx (ozone season)
32,000
10,000
56,000
NOx (annual)
35,000
15,000
99,000
SO2 (annual)
38,000
7,000
118,000
CO2 (annual, thousand metric)
21,000
8,000
40,000
PM2.5 (annual)
2,000
1,000
2,000
2026
NOx (ozone season)
25,000
8,000
49,000
NOx (annual)
29,000
12,000
88,000
SO2 (annual)
29,000
5,000
104,000
CO2 (annual, thousand metric)
16,000
6,000
34,000
316
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Final Rule
Less Stringent
Alternative
More Stringent
Alternative
PM2 5 (annual)
1,000
-
2,000
2027
NOx (ozone season)
19,000
6,000
43,000
NOx (annual)
22,000
9,000
78,000
SO2 (annual)
21,000
4,000
91,000
CO2 (annual, thousand metric)
10,000
3,000
28,000
PM2.5 (annual)
1,000
-
2,000
2030
NOx (ozone season)
34,000
33,000
31,000
NOx (annual)
62,000
59,000
50,000
SO2 (annual)
93,000
98,000
51,000
CO2 (annual, thousand metric)
26,000
23,000
8,000
PM2.5 (annual)
1,000
1,000
-
2035
NOx (ozone season)
29,000
30,000
27,000
NOx (annual)
46,000
46,000
41,000
SO2 (annual)
21,000
19,000
15,000
CO2 (annual, thousand metric)
16,000
15,000
8,000
PM2.5 (annual)
1,000
1,000
-
2042
NOx (ozone season)
22,000
22,000
22,000
NOx (annual)
23,000
22,000
21,000
SO2 (annual)
15,000
15,000
7,000
CO2 (annual, thousand metric)
9,000
8,000
4,000
PM2.5 (annual)
-
-
-
317
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Table 8-2. Non-EGU Ozone Season NOx Emissions and Emissions Reductions for the Final
Rule and the Less and More Stringent Alternatives
State
2019 Ozone
Season
Emissions"
Final Rule -
Ozone Season
NOx Reductions
Less Stringent -
Ozone Season
NOx Reductions
More Stringent -
Ozone Season
NOx Reductions
AR
8,790
1,546
457
1,690
CA
16,562
1,600
1,432
4,346
IL
15,821
2,311
751
2,991
IN
16,673
1,976
1,352
3,428
KY
10,134
2,665
583
3,120
LA
40,954
7,142
1,869
7,687
MD
2,818
157
147
1,145
MI
20,576
2,985
760
5,087
MO
11,237
2,065
579
4,716
MS
9,763
2,499
507
2,650
NJ
2,078
242
242
258
NV
2,544
0
0
0
NY
5,363
958
726
1,447
OH
18,000
3,105
1,031
4,006
OK
26,786
4,388
1,376
5,276
PA
14,919
2,184
1,656
4,550
TX
61,099
4,691
1,880
9,963
UT
4,232
252
52
615
VA
7,757
2,200
978
2,652
WV
6,318
1,649
408
2,100
Totals
302,425
44,616
16,786
67,728
a The 2019 ozone season emissions are calculated as 5/12 of the annual emissions from the following two emissions
inventory files: nonegu_SmokeFlatFile_2019NEI_POINT_20210721_controlupdate_13sep2021_v0 and
oilgas_SmokeFlatFile_2019NEIPOINT 2021072 lcontrolupdatel 3 sep202 lvO.
As shown in Chapter 4, the estimated annual compliance costs to implement the rule, as
described in this RIA, are approximately $57 million in 2023 and $570 million in 2026 (2016$).
This RIA uses compliance costs as a proxy for social costs as mentioned in Chapter 4. As shown
in Chapter 5, the estimated monetized health benefits from reduced PM2.5 and ozone
concentrations from implementation of the rule are approximately $100 and $820 million in
2023 (2016$, based on a real discount rate of 3 percent). As shown in Chapter 5, the estimated
monetized climate benefits from reduced GHG emissions are approximately $5 million in 2023
(2016$, based on a real discount rate of 3 percent). For 2026, the estimated monetized health
benefits from implementation of the rule are approximately $3,200 and $14,000 million (2016$,
based on a real discount rate of 3 percent). The estimated monetized climate benefits from
318
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reduced GHG emissions are approximately $830 million in 2026 (2016$, based on a real
discount rate of 3 percent).
The EPA calculates the monetized net benefits of the rule by subtracting the estimated
monetized compliance costs from the estimated monetized health and climate benefits in 2023,
2026, and 2030. The benefits include those to public health associated with reductions in PM2.5
and ozone concentrations, as well as those to climate associated with reductions in GHG
emissions. The annual monetized net benefits of the rule in 2023 (in 2016$) are approximately
$48 and $760 million using a 3 percent real discount rate. The annual monetized net benefits of
the rule in 2026 are approximately $3,700 and $14,000 million using a 3 percent real discount
rate. The annual monetized net benefits of the rule in 2030 are approximately $3,600 and
$15,000 million using a 3 percent real discount rate. Table 8-3 presents a summary of the
monetized health and climate benefits, costs, and net benefits of the rule and the more and less
stringent alternatives for 2023. Table 8-4 presents a summary of these impacts for the rule and
the more and less stringent alternatives for 2026.
Table 8-5 presents a summary of these impacts for the rule and the more and less stringent
alternatives for 2030. These results present an incomplete overview of the effects of the rule
because important categories of benefits — including benefits from reducing other types of air
pollutants, and water pollution - were not monetized and are therefore not reflected in the cost-
benefit tables. We anticipate that taking non-monetized effects into account would show the rule
to be more net beneficial than this table reflects.
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Table 8-3. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2023 for the U.S. (millions of 2016$) a'b'c
, _ . Less Stringent More Stringent
Final Rule ... ,? ... ,.s
Alternative Alternative
Health Benefits0 $100 and $820 $100 and $810 $110 and $840
Climate Benefits $5 $4 $5
Total Benefits $100 and $820 $100 and $820 $110 and $840
Costs'1 $57 $56 $49
Net Benefits $48 and $760 $48 and $760 $66 and $800
a We focus results to provide a snapshot of costs and benefits in 2023, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Rows may not appear to add correctly due to rounding.
0 The benefits are associated with two point estimates from two different epidemiologic studies. For the purposes of
presenting the values in this table the health and climate benefits are discounted at 3%.
d The costs presented in this table are 2023 annual estimates for each alternative analyzed. For EGUs, an NPV of
costs was calculated using a 3.76% real discount rate consistent with the rate used in IPM's objective function for
cost-minimization. For further information on the discount rate use, please see Chapter 4, Table 4-8.
Table 8-4. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2026 for the U.S. (millions of 2016$) a'b'c
, _ . Less Stringent More Stringent
Final Rule ... .? ... ..s
Alternative Alternative
Health Benefits0 $3,200 and $14,000 $950 and $4,600 $8,300 and $29,000
Climate Benefits $1,100 $420 $2,100
Total Benefits $4,300 and $15,000 $1,400 and $5,000 $10,000 and $31,000
Costs'1 $570 $110 $2,100
Net Benefits $3,700 and $14,000 $1,300 and $4,900 $8,300 and $29,000
a We focus results to provide a snapshot of costs and benefits in 2026, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Rows may not appear to add correctly due to rounding.
0 The benefits are associated with two point estimates from two different epidemiologic studies. For the purposes of
presenting the values in this table the health and climate benefits are discounted at 3%.
d The costs presented in this table are 2026 annual estimates for each alternative analyzed. For EGUs, an NPV of
costs was calculated using a 3.76% real discount rate consistent with the rate used in IPM's objective function for
cost-minimization. For further information on the discount rate use, please see Chapter 4, Table 4-8.
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Table 8-5. Monetized Benefits, Costs, and Net Benefits of the Final Rule and Less and More
Stringent Alternatives for 2030 for the U.S. (millions of 2016$) a'b'c
Final Rule
Less Stringent
Alternative
More Stringent
Alternative
Health Benefits0
$3,400 and $15,000
$1,000 and $4,900
$9,000 and $31,000
Climate Benefits
$1,500
$1,300
$500
Total Benefits
$4,900 and $16,000
$2,300 and $6,200
$9,500 and $31,000
Costs'1
$1,300
$920
$2,100
Net Benefits
$3,600 and $15,000
$1,400 and $5,300
$7,400 and $29,000
a We focus results to provide a snapshot of costs and benefits in 2030, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
b Rows may not appear to add correctly due to rounding.
0 The benefits are associated with two point estimates from two different epidemiologic studies. For the purposes of
presenting the values in this table the health and climate benefits are discounted at 3%.
d The costs presented in this table are 2030 annual estimates for each alternative analyzed. For EGUs, an NPV of
costs was calculated using a 3.76% real discount rate consistent with the rate used in IPM's objective function for
cost-minimization. For further information on the discount rate use, please see Chapter 4, Table 4-8.
As part of fulfilling analytical guidance with respect to E.O. 12866, the EPA presents
estimates of the present value (PV) of the monetized benefits and costs over the twenty-year
period 2023 to 2042. To calculate the present value of the social net-benefits of the rule, annual
benefits and costs are discounted to 2023 at 3 percent and 7 discount rates as recommended by
OMB's Circular A-4. The EPA also presents the equivalent annualized value (EAV), which
represents a flow of constant annual values that, had they occurred in each year from 2023 to
2042, would yield a sum equivalent to the PV. The EAV represents the value of a typical cost or
benefit for each year of the analysis, in contrast to the year-specific estimates mentioned earlier
in the RIA. Table 8-6 below includes the undiscounted streams of health benefits, climate
benefits, costs, and net benefits from 2023 to 2042. Table 8-7 below provides the comparison of
benefits and costs in PV and EAV terms for the rule. Estimates in the table are presented as
rounded values. For the twenty-year period of 2023 to 2042, the PV of the net benefits, in 2016$
and discounted to 2023, is $200,000 million when using a 3 percent discount rate and $140,000
million when using a 7 percent discount rate. The EAV is $13,000 million per year when using a
3 percent discount rate and $12,000 million when using a 7 percent discount rate.
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Table 8-6. Undiscounted Streams Health Benefits, Climate Benefits, Costs, and Net Benefits
for 2023 - 2042 (millions of 2016$)
Health Benefits" „ h Costs Net Benefits
Benefits
3%
7%
3%
3%
7%
2023
$820
$730
$5
$57
$770
$680
2024
$840
$750
$1,100
($5)
$1,400
$1,300
2025
$9,100
$8,100
$1,100
($5)
$10,000
$9,200
2026
$14,000
$12,000
$1,100
$570
$14,000
$12,000
2027
$14,000
$13,000
$260
$600
$14,000
$13,000
2028
$14,000
$12,000
$270
$600
$14,000
$12,000
2029
$14,000
$13,000
$270
$600
$14,000
$13,000
2030
$15,000
$13,000
$1,500
$1,300
$15,000
$13,000
2031
$15,000
$13,000
$1,500
$1,300
$15,000
$13,000
2032
$15,000
$14,000
$960
$1,400
$15,000
$14,000
2033
$15,000
$14,000
$980
$1,400
$15,000
$14,000
2034
$16,000
$14,000
$1,000
$1,400
$16,000
$14,000
2035
$16,000
$14,000
$1,000
$1,400
$16,000
$14,000
2036
$16,000
$15,000
$1,000
$1,400
$16,000
$15,000
2037
$17,000
$15,000
$1,000
$1,400
$17,000
$15,000
2038
$17,000
$15,000
$1,300
$1,400
$17,000
$15,000
2039
$17,000
$15,000
$1,400
$1,400
$17,000
$15,000
2040
$17,000
$15,000
$1,400
$1,400
$17,000
$15,000
2041
$17,000
$16,000
$1,400
$1,400
$17,000
$16,000
2042
$18,000
$16,000
$1,400
$1,400
$18,000
$16,000
a We assume that there is a cessation lag between the change in exposures and the total realization of changes in
mortality effects. Specifically, we assume that some of the incidences of premature mortality related to exposures
occur in a distributed fashion over the 20 years following exposure, which affects the valuation of mortality benefits
at different discount rates. The table reflects the benefits associated with the higher of the two point estimates from
two different epidemiologic studies. We present the benefits calculated at real discount rates of 3 and 7 percent.
b We include the climate benefits calculated at a 3 percent discount rate.
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Table 8-7. Summary of Present Values and Equivalent Annualized Values for the 2023-2042 Timeframe for Estimated
Monetized Compliance Costs, Benefits, and Net Benefits for the Final Rule (millions of 2016$, discounted to 2023)a'b
Health Benefits Climate Benefits Cost0 Net Benefits
3%
7%
3%
3%
7%
3%
7%
2023
$820
$730
$5
$57
$57
$770
$680
2024
$810
$700
$1,000
($5)
($5)
$1,300
$1,200
2025
$8,600
$7,100
$1,000
($5)
($4)
$9,600
$8,100
2026
$13,000
$10,000
$1,000
$520
$460
$13,000
$10,000
2027
$13,000
$9,700
$230
$530
$450
$13,000
$9,700
2028
$12,000
$8,900
$230
$510
$420
$12,000
$8,700
2029
$12,000
$8,500
$230
$500
$400
$12,000
$8,800
2030
$12,000
$8,200
$1,200
$1,000
$800
$12,000
$8,600
2031
$12,000
$7,800
$1,200
$1,000
$740
$12,000
$8,200
2032
$12,000
$7,500
$740
$1,100
$760
$12,000
$7,700
2033
$11,000
$7,000
$730
$1,000
$710
$11,000
$7,200
2034
$11,000
$6,700
$720
$1,000
$660
$11,000
$6,900
2035
$11,000
$6,400
$710
$970
$620
$11,000
$6,500
2036
$11,000
$6,100
$700
$950
$580
$11,000
$6,300
2037
$11,000
$5,800
$690
$920
$540
$11,000
$6,000
2038
$11,000
$5,400
$860
$890
$500
$11,000
$5,700
2039
$10,000
$5,100
$850
$870
$470
$9,900
$5,400
2040
$10,000
$4,900
$830
$840
$440
$10,000
$5,300
2041
$10,000
$4,600
$820
$820
$410
$9,900
$4,900
2042
$10,000
$4,400
$810
$790
$380
$9,800
$4,600
PV
2023-2042
$200,000
$130,000
$15,000
$14,000
$9,400
$200,000
$140,000
EAV
2023-2042
$13,000
$12,000
$970
$910
$770
$13,000
$12,000
aRows may not appear to add correctly due to rounding.
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-23-001
Environmental Protection Health and Environmental Impacts Division March 2023
Agency Research Triangle Park, NC
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