EO12866_CPP 2060 AR33 RIA Final_20150626

Regulatory Impact Analysis 

for the 

Clean Power Plan Final Rule
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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EPA-452/R-15-003

July 2015

Regulatory Impact Analysis for the Clean Power Plan Final Rule 

U.S. Environmental Protection Agency

Office of Air and Radiation

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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CONTACT INFORMATION

This document has been prepared by staff from the Office of Air Quality Planning and 

Standards, the Office of Atmospheric Programs, and the Office of Policy of the U.S. 

Environmental Protection Agency. Questions related to this document should be addressed to 

Alexander Macpherson, U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards, Research Triangle Park, North Carolina 27711 (email: 

macpherson.alex@epa.gov). 

ACKNOWLEDGEMENTS

Thank you to the many staff who worked on this document from EPA Offices including 

the Office of Air Quality Planning and Standards, the Office of Atmospheric Programs, and the 

Office of Policy. Contributions to this report were also made by ICF International and RTI 

International.
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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TABLE OF CONTENTS

LIST OF TABLES...............................................................................................................................x

LIST OF FIGURES ......................................................................................................................... xvi

ACRONYMS ................................................................................................................................ xviii

EXECUTIVE SUMMARY....................................................................................................................1

ES.1 Background and Context ........................................................................................1

ES.2 Summary of Clean Power Plan Final Rule .............................................................1

ES.3 Illustrative Plan Approaches Examined in RIA......................................................4

ES.4 Emissions Reductions.............................................................................................5

ES.5 Costs .......................................................................................................................7

ES.6 Monetized Climate Benefits and Health Co-benefits.............................................9

ES.6.1 Estimating Global Climate Benefits.................................................................13

ES 6.2 Estimating Air Quality Health Co-Benefits......................................................15

ES 6.3 Combined Benefits Estimates...........................................................................18

ES.7 Net Benefits..........................................................................................................20

ES.8 Economic Impacts ................................................................................................24

ES.9 Employment Impacts............................................................................................25

ES.10 References.............................................................................................................26

CHAPTER 1: INTRODUCTION AND BACKGROUND FOR THE CLEAN POWER PLAN ........................1

1.1 Introduction....................................................................................................................1

1.2 Legal, Scientific and Economic Basis for this Rulemaking ..........................................1

1.2.1 Statutory Requirement........................................................................................1

1.2.2 Health and Welfare Impacts from Climate Change............................................2

1.2.3 Market Failure ....................................................................................................3

1.3 Summary of Regulatory Analysis..................................................................................4

1.4 Background for the Final Emission Guidelines.............................................................5

1.4.1 Base Case and Years of Analysis.......................................................................5

1.4.2 Definition of Affected Sources...........................................................................5

1.4.3 Regulated Pollutant.............................................................................................6

1.4.4 Emission Guidelines...........................................................................................6

1.5 Organization of the Regulatory Impact Analysis...........................................................7

1.6 References......................................................................................................................7

CHAPTER 2: INDUSTRY PROFILE ....................................................................................................1

2.1 Introduction....................................................................................................................1

2.2 Power Sector Overview .................................................................................................1

2.2.1 Generation ..........................................................................................................1

2.2.2 Transmission.......................................................................................................8

2.2.3 Distribution.........................................................................................................9
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2.3 Sales, Expenses and Prices ............................................................................................9

2.3.1 Electricity Prices...............................................................................................10

2.3.2 Prices of Fossil Fuels Used for Generating Electricity.....................................15

2.3.3 Changes in Electricity Intensity of the U.S. Economy Between 2002 to 

2012 16

2.4 Deregulation and Restructuring ...................................................................................17

2.5 Emissions of Greenhouse Gases from Electric Utilities..............................................22

2.6 Carbon Dioxide Control Technologies........................................................................25

2.6.1 Carbon Capture and Storage.............................................................................26

2.6.2 Geologic and Geographic Considerations for Geologic Sequestration ................31

2.7.2 Availability of geologic sequestration in deep saline formations.....................35

2.7.3 Availability of CO2 storage via enhanced oil recovery (EOR) ........................35

2.8 GHG and Clean Energy Regulation in the Power Sector ............................................37

2.8.1 State Policies.....................................................................................................37

2.8.2 Federal Policies.................................................................................................39

2.9 Revenues and Expenses...............................................................................................42

2.10 Natural Gas Market...............................................................................................43

2.11 References.............................................................................................................47

CHAPTER 3: COST, EMISSIONS, ECONOMIC, AND ENERGY IMPACTS............................................1

3.1 Introduction....................................................................................................................1

3.2 Overview........................................................................................................................1

3.3 Power Sector Modelling Framework.............................................................................1

3.3.1 Recent Updates to EPA’s Base Case using IPM (v.5.15) ..................................4

3.4 State Goals in this Final Rule........................................................................................5

3.5 Illustrative Plan Approaches Analyzed..........................................................................7

3.6 Demand-Side Energy Efficiency .................................................................................11

3.6.1 Demand-Side Energy Efficiency Improvements (Electricity Demand 

Reductions) ..................................................................................................................11

3.6.2 Demand-Side Energy Efficiency Costs............................................................13

3.7 Projected Power Sector Impacts ..................................................................................14

3.7.1 Projected Emissions (in the Contiguous U.S.) .................................................14

3.7.2 Projected Compliance Costs (in the Contiguous U.S.).....................................17

3.7.3 Projected Compliance Actions for Emissions Reductions...............................19

3.7.4 Projected Generation Mix.................................................................................21

3.7.5 Projected Incremental Retirements...................................................................25

3.7.6 Projected Capacity Additions...........................................................................26

3.7.7 Projected Coal Production and Natural Gas Use for the Electric Power 

Sector 28

3.7.8 Projected Fuel Price, Market, and Infrastructure Impacts................................28

3.8 Projected Primary PM Emissions from Power Plants..................................................30

3.9 Limitations of Analysis................................................................................................31

3.10 Significant Energy Impacts..........................................................................................32

3.11 Monitoring, Reporting, and Recordkeeping Costs ......................................................33

3.12 Social Costs..................................................................................................................36

3.13 References....................................................................................................................39
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CHAPTER 4: ESTIMATED CLIMATE BENEFITS AND HUMAN HEALTH CO-BENEFITS....................1

4.1 Introduction....................................................................................................................1

4.2 Estimated Climate Benefits from CO2 ...........................................................................1

4.2.1 Climate Change Impacts.....................................................................................2

4.2.2 Social Cost of Carbon.........................................................................................3

4.3 Estimated Human Health Co-Benefits.........................................................................10

4.3.1 Health Impact Assessment for PM2.5 and Ozone..............................................12

4.3.2 Economic Valuation for Health Co-benefits....................................................18

4.3.3 Benefit-per-ton Estimates for PM2.5 .................................................................20

4.3.4 Benefit-per-ton Estimates for Ozone................................................................21

4.3.5 Estimated Health Co-Benefits Results .............................................................22

4.3.6 Characterization of Uncertainty in the Estimated Health Co-benefits.............36

4.4 Combined Climate Benefits and Health Co-benefits Estimates..................................42

4.5 Unquantified Co-benefits.............................................................................................46

4.5.1 HAP Co-benefits...............................................................................................48

4.5.2 Additional NO2 Health Co-Benefits.................................................................51

4.5.3 Additional SO2 Health Co-Benefits..................................................................52

4.5.4 Additional NO2 and SO2 Welfare Co-Benefits.................................................53

4.5.5 Ozone Welfare Co-Benefits..............................................................................54

4.5.6 Carbon Monoxide Co-Benefits.........................................................................54

4.5.7 Visibility Impairment Co-Benefits...................................................................55

4.6 References....................................................................................................................55

APPENDIX 4A: GENERATING REGIONAL BENEFIT-PER-TON ESTIMATES ....................................1

4A.1 Overview of Benefit-per-Ton Estimates.................................................................1

4A.2 Air Quality Modeling for the Proposed Clean Power Plan ....................................2

4A.3 Regional PM2.5 Benefit-per-Ton Estimates for EGUs Derived from Air 

Quality Modeling of the Proposed Clean Power Plan ..........................................................6

4A.4 Regional Ozone Benefit-per-Ton Estimates.........................................................15

4A.6 References.............................................................................................................18

CHAPTER 5: ECONOMIC IMPACTS – MARKETS OUTSIDE THE UTILITY POWER SECTOR ............1

5.1 Introduction....................................................................................................................1

5.2 Methods..........................................................................................................................2

5.3 Summary of Secondary Market Impacts of Energy Price Changes...............................3

5.3.1 Share of Total Production Costs.........................................................................5

5.3.2 Ability to Substitute between Inputs to the Production Process.........................5

5.3.3 Availability of Substitute Goods and Services...................................................5

5.4 Effect of Changes in Input Demand from Electricity Sector.........................................6

5.5 Conclusions....................................................................................................................6

5.6 References......................................................................................................................7

CHAPTER 6: EMPLOYMENT IMPACT ANALYSIS .............................................................................1
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6.1 Introduction....................................................................................................................1

6.2 Economic Theory and Employment ..............................................................................2

6.3 Current State of Knowledge Based on the Peer-Reviewed Literature...........................6

6.3.1 Regulated Sector.................................................................................................7

6.3.2 Economy-Wide.........................................................................................................8

6.3.3 Labor Supply Impacts.......................................................................................10

6.4 Recent Employment Trends.........................................................................................11

6.4.1 Electric Power Generation................................................................................11

6.4.2 Fossil Fuel Extraction.......................................................................................12

6.4.3 Clean Energy Employment Trends...................................................................14

6.5 Projected Sectoral Employment Changes due to the Final Emission Guidelines........18

6.5.1 Projected Changes in Employment in Electricity Generation and Fossil 

Fuel Extraction.............................................................................................................19

6.5.2 Projected Changes in Employment in Demand-Side Energy Efficiency

Activities......................................................................................................................25

6.6 Conclusion ...................................................................................................................33

6.7 References....................................................................................................................35

APPENDIX 6A: ESTIMATING SUPPLY SIDE EMPLOYMENT IMPACTS .............................................1

6A.1 General Approach...................................................................................................1

6A.1.1 Employment Effects Included In the Analysis...................................................2

6A.2 Employment Changes due to Heat Rate Improvements.........................................3

6A.2.1 Employment Changes Due to Building (or Avoiding) New Generation 

Capacity .........................................................................................................................5

6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements.............................8

6A.2.3 Employment Changes due to Coal and Oil/Gas Retirements.............................8

CHAPTER 7: STATUTORY AND EXECUTIVE ORDER ANALYSIS ......................................................1

7.1 Executive Order 12866: Regulatory Planning and Review, and Executive Order 

13563: Improving Regulation and Regulatory Review ........................................................1

7.2 Paperwork Reduction Act (PRA)...................................................................................3

7.3 Regulatory Flexibility Act (RFA)..................................................................................5

7.4 Unfunded Mandates Reform Act (UMRA) ...................................................................6

7.5 Executive Order 13132: Federalism ..............................................................................6

7.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments.......................................................................................................................11

7.7 Executive Order 13045: Protection of Children from Environmental Health Risks 

and Safety Risks..................................................................................................................13

7.8 Executive Order 13211: Actions Concerning Regulations That Significantly

Affect Energy Supply, Distribution, or Use........................................................................14

7.9 National Technology Transfer and Advancement Act ................................................14

7.10 Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations..........................................................15

7.11 Congressional Review Act (CRA)...............................................................................18
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CHAPTER 8: COMPARISON OF BENEFITS AND COSTS ....................................................................1

8.1 Comparison of Benefits and Costs.................................................................................1

8.2 Uncertainty Analysis......................................................................................................5

8.2.1 Uncertainty in Costs and Illustrative Plan Approaches......................................5

8.2.2 Uncertainty Associated with Estimating the Social Cost of Carbon..................6

8.2.3 Uncertainty Associated with PM2.5 and Ozone Health Co-Benefits 

Assessment.....................................................................................................................7

8.3 References......................................................................................................................9
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LIST OF TABLES

Table ES-1. Climate and Air Pollutant Emission Reductions for the Rate-Based 

Illustrative Plan Approach1,2................................................................................................6

Table ES-2. Climate and Air Pollutant Emission Reductions for the Mass-Based 

Illustrative Plan Appproach1,2..............................................................................................7

Table ES-3. Compliance Costs for the Illustrative Rate-Based and Mass-Based Plan 

Approaches ..........................................................................................................................8

Table ES-4. Quantified and Unquantified Benefits...................................................................11

Table ES-5. Combined Estimates of Climate Benefits and Health Co-Benefits for RateBased Approach (billions of 2011$)*................................................................................19

Table ES-6. Combined Estimates of Climate Benefits and Health Co-benefits for MassBased Approach (billions of 2011$)*................................................................................20

Table ES-7. Monetized Benefits, Compliance Costs, and Net Benefits Under the Ratebased Illustrative Plan Approach (billions of 2011$) a

......................................................22

Table ES-8. Monetized Benefits, Compliance Costs, and Net Benefits under the Massbased Illustrative plan approach (billions of 2011$) a

.......................................................23

Table 2-1. Existing Electricity Generating Capacity by Energy Source, 2002 and 2012........3

Table 2-2. Net Generation in 2002 and 2012 (Trillion kWh = TWh)......................................5

Table 2-3. Coal and Natural Gas Generating Units, by Size, Age, Capacity, and 

Thermal Efficiency (Heat Rate)...........................................................................................6

Table 2-4. Total U.S. Electric Power Industry Retail Sales in 2012 (billion kWh)...............10

Table 2-5. Domestic Emissions of Greenhouse Gases, by Economic Sector (million 

metric tonnes of CO2 equivalent).......................................................................................23

Table 2-6. Greenhouse Gas Emissions from the Electricity Sector (Generation, 

Transmission and Distribution), 2002 and 2012 (million metric tonnes of CO2

equivalent)..........................................................................................................................24

Table 2-7. Fossil Fuel Emission Factors in EPA Base Case 5.14 IPM Power Sector 

Modeling Application ........................................................................................................25

Table 2-8. Total CO2 Storage Resource.................................................................................33
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Table 2-9. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric 

Utilities for 2010 (nominal $millions)...............................................................................43

Table 3-1. Rate-Based and Mass-Based State Goals...............................................................6

Table 3-3. Demand-Side Energy Efficiency Plan Scenario: Net Cumulative Demand 

Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales)................................12

Table 3-4. Annualized Cost of Demand-Side Energy Efficiency Plan Scenario 

[Contiguous U.S.] (at discount rates of 3 percent and 7 percent, billions 2011$).............14

Table 3-5. Projected CO2 Emission Impacts, Relative to Base Case.....................................15

Table 3-6. Projected CO2 Emission Impacts, Relative to 2005 .............................................15

Table 3-7. Projected Non-CO2 Emission Impacts, 2020-2030......................................................16

Table 3-8. Annualized Compliance Costs (billions of 2011$)...............................................18

Table 3-9. Total Power Sector Generating Costs (IPM) (billions 2011$) .............................19

Table 3-10. Projected Capacity Factor of Existing Coal Steam and Natural Gas 

Combined Cycle Capacity .................................................................................................21

Table 3-11. Generation Mix (thousand GWh).........................................................................23

Table 3-12. Total Generation Capacity by 2020-2030 (GW) ..................................................26

Table 3-13. Projected Capacity Additions, Gas (GW).............................................................27

Table 3-14. Projected Capacity Additions, Renewable (GW).................................................27

Table 3-15. Coal Production for the Electric Power Sector, 2025...........................................28

Table 3-16. Power Sector Gas Use ..........................................................................................28

Table 3-17. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu) ......30

Table 3-19. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices 

(2011$/MMBtu).................................................................................................................30

Table 3-25. Years 2020, 2025 and 2030: Summary of State Annual Respondent Burden 

and Cost of Reporting and Recordkeeping Requirements (2011$) ...................................35

Table 3-26. Years 2020, 2025 and 2030: Summary of Industry Annual Respondent 

Burden and Cost of Reporting and Recordkeeping Requirements (2011$) ......................35

Table 3-27. Years 2020, 2025 and 2030: Summary of Territories Annual Respondent 

Burden and Cost of Reporting and Recordkeeping Requirements (2011$) ......................36
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Table 3-28. Annualized Compliance Costs Including Monitoring, Reporting and 

Recordkeeping Costs Requirements (billions of 2011$) ...................................................36

Table 4-1. Climate Effects .......................................................................................................2

Table 4-2. Social Cost of CO2, 2015-2050 (in 2011$)*...........................................................8

Table 4-3. Estimated Global Climate Benefits of CO2 Reductions for the Final 

Emission Guidelines in 2020 (billions of 2011$)* ..............................................................8

Table 4-4. Estimated Global Climate Benefits of CO2 Reductions for the Final 

Emission Guidelines in 2025 (billions of 2011$)* ..............................................................8

Table 4-5. Estimated Global Climate Benefits of CO2 Reductions for the Final 

Emission Guidelines in 2030 (billions of 2011$)* ..............................................................9

Table 4-6. Human Health Effects of Ambient PM2.5 and Ozone...........................................14

Table 4-7. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)*............................23

Table 4-8. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)*............................23

Table 4-9. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2030 (2011$)*............................24

Table 4-10. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Rate-based Scenario in 2020 (thousands of short tons)*...................................................24

Table 4-11. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Rate-based Scenario in 2025 (thousands of short tons)*...................................................24

Table 4-12. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Rate-based Scenario in 2030 (thousands of short tons)*...................................................25

Table 4-13. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Mass-based Scenario in 2020 (thousands of short tons)*..................................................25

Table 4-14. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Mass-based Scenario in 2025 (thousands of short tons)*..................................................25

Table 4-15. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Mass-based Scenario in 2030 (thousands of short tons)*..................................................25

Table 4-16. Summary of Estimated Monetized Health Co-Benefits for the Final 

Emission Guidelines Rate-based Scenario in 2020 (billions of 2011$) * .........................26
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Table 4-17. Summary of Estimated Monetized Health Co-Benefits for the Final 

Emission Guidelines Rate-based Scenario in 2025 (billions of 2011$) * .........................26

Table 4-18. Summary of Estimated Monetized Health Co-Benefits for the Final 

Emission Guidelines Rate-based Scenario in 2030 (billions of 2011$) * .........................27

Table 4-19. Summary of Estimated Monetized Health Co-Benefits for the Final 

Emission Guidelines Mass-based Scenario in 2020 (billions of 2011$) * ........................27

Table 4-20. Summary of Estimated Monetized Health Co-Benefits for the Final 

Emission Guidelines Mass-based Scenario in 2025 (billions of 2011$) * ........................28

Table 4-21. Summary of Estimated Monetized Health Co-Benefits for the Final 

Emission Guidelines Mass-based Scenario in 2030 (billions of 2011$) * ........................28

Table 4-22. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for the Final Emission Guidelines Rate-based Scenario in 2020*....29

Table 4-23. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Final Emission Guidelines Rate-based Scenario in 2025*..........30

Table 4-24. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Final Emission Guidelines Rate-based Scenario in 2030*.........31

Table 4-25. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for the Final Emission Guidelines Mass-based Scenario in 

2020* 32

Table 4-26. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Final Emission Guidelines Mass-based Scenario in 2025*.........33

Table 4-27. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Final Emission Guidelines Mass-based Scenario in 2030*........34

Table 4-28. Population Exposure in the Clean Power Plan Proposal Modeling (used to 

generate the benefit-per-ton estimates) Above and Below Various Concentrations 

Benchmarks in the Underlying Epidemiology Studies *...................................................40

Table 4-29. Combined Climate Benefits and Health Co-Benefits for Final Emission 

Guidelines in 2020 (billions of 2011$)* ............................................................................44

Table 4-30. Combined Climate Benefits and Health Co-Benefits for Final Emission 

Guidelines in 2025 (billions of 2011$)* ............................................................................44

Table 4-31. Combined Climate Benefits and Health Co-Benefits for Final Emission 

Guidelines in 2030 (billions of 2011$)* ............................................................................45

Table 4-32. Unquantified Health and Welfare Co-benefits Categories...................................46
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Table 4A-3. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)*............................10

Table 4A-4. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)*............................10

Table 4A-5. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2030 (2011$)*............................11

Table 4A-6. Summary of Regional PM2.5 Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2020*..........................................12

Table 4A-7. Summary of Regional PM2.5 Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2025*..........................................13

Table 4A-8. Summary of Regional PM2.5 Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2030*..........................................14

Table 4A-9. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)*............................16

Table 4A-10. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)*............................16

Table 4A-11. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2030 (2011$)*............................16

Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2020*..........................................17

Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2025*..........................................17

Table 4A-14. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2030*..........................................17

Table 5-1. Estimated Percentage Changes in Average Energy Prices by Energy Type 

for the Final Emission Guidelines, Rate-based and Mass-based Illustrative Plan 

Approaches ..........................................................................................................................4

Table 6-1. U. S. Green Goods and Services (GGS) Employment (annual average)..............16

Table 6-2. Renewable Electricity Generation-Related Employment.....................................17

Table 6-3. Energy and Resources Efficiency-Related Employment......................................18

Table 6-4. Engineering-Baseda Changes in Labor Utilization, Rate-based Scenario 

(Number of Job-Yearsbof Employment in a Single Year)................................................24
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Table 6-5. Engineering-Baseda Changes in Labor Utilization, Mass-Based Illustrative 

Plan Approach (Number of Job-Years of Employment in Year) ......................................25

Table 6-6. Estimated Demand-Side Energy Efficiency Employment Impacts: Target 1 

percent Growth in Energy Efficiency ................................................................................30

Table 6A-1. Labor Productivity Growth Rate due to Heat Rate Improvement ..........................4

Table 6A-2. Capital Charge Rate and Duration Assumptions....................................................6

Table 6A-3. Expenditure Breakdown due to New Generating Capacity....................................6

Table 6A-4. Labor Productivity due to New Generating Capacity.............................................7

Table 6A-5. Average FOM Cost Assumptions...........................................................................8

Table 6A-6. Labor Productivity due to New Generating Capacity.............................................9
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LIST OF FIGURES

Figure 2-1. New Build and Retired Capacity (MW) by Fuel Type, 2002-2012 .......................4

Figure 2-2. Cumulative Distribution in 2010 of Coal and Natural Gas Electricity

Capacity and Generation, by Age ........................................................................................7

Figure 2-3. Fossil Fuel-Fired Electricity Generating Facilities, by Size...................................8

Figure 2-4. Average Retail Electricity Price by State (cents/kWh), 2011...............................11

Figure 2-5. Nominal National Average Electricity Prices for Three Major End-Use 

Categories ..........................................................................................................................12

Figure 2-6. Relative Increases in Nominal National Average Electricity Prices for Major 

End-Use Categories, With Inflation Indices......................................................................13

Figure 2-7. Real National Average Electricity Prices (2011$) for Three Major End-Use 

Categories ..........................................................................................................................14

Figure 2-8. Relative Change in Real National Average Electricity Prices (2011$) for 

Three Major End-Use Categories ......................................................................................15

Figure 2-9. Relative Real Prices of Fossil Fuels for Electricity Generation; Change in 

National Average Real Price per MBtu Delivered to EGU...............................................15

Figure 2-10. Relative Growth of Electricity Generation, Population and Real GDP Since 

2002 16

Figure 2-11. Relative Change of Real GDP, Population and Electricity Generation 

Intensity Since 2002...........................................................................................................17

Figure 2-12. Status of State Electricity Industry Restructuring Activities................................19

Figures 2-13 and 2-14. Capacity and Generation Mix by Ownership Type, 2002 & 2012 ........21

Figures 2-15 and 2-16. Generation Capacity Built and Retired between 2002 and 2012 by 

Ownership Type 22

Figure 2-17. Domestic Emissions of Greenhouse Gases from Major Sectors, 2002 and 

2013 (million metric tonnes of CO2 equivalent)................................................................23

Figure 2-18. Post-Combustion CO2 Capture for a Pulverized Coal Power Plant .....................27

Figure 2-18. Pre-Combustion CO2 Capture for an IGCC Power Plant .....................................29

Figure 2-19. Geologic Sequestration in the Continental United States.....................................32
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Figure 2-20. Relative Change Nominal and Real (2011$) Prices of Natural Gas Delivered 

to the Power Sector ($/MMBtu) ........................................................................................44

Figure 2-21. Relative Change in Real (2011$) Prices of Fossil Fuels Delivered to the 

Power Sector ($/MMBtu) ..................................................................................................45

Figure 3-3. Illustrative Regions for EE/RE Procurement Used in this Analysis.......................9

Figure 3-4. Generation Mix with the Base Case and 111(d) Options, 2020-2030 

(thousand GWh).................................................................................................................24

Figure 4-1. Monetized Health Co-benefits of Rate-based and Mass-based Scenarios for 

the Final Emission Guidelines in 2025 * ...........................................................................35

Figure 4-2. Breakdown of Monetized Health Co-benefits by Precursor Pollutant at a 3% 

Discount Rate for Rate-based Scenario for Final Emission Guidelines in 2025* .............36

Figure 4-3. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure 

in the Clean Power Plan Proposal Modeling (used to generate the benefit-per-ton 

estimates) ...........................................................................................................................41

Figure 4-4. Cumulative Distribution of Adult Population (age 30+) by Annual Mean 

PM2.5 Exposure in the Clean Power Plan Proposal Modeling (used to generate the 

benefit-per-ton estimates) ..................................................................................................42

Figure 4-5. Breakdown of Combined Monetized Climate and Health Co-benefits of 

Final Emission Guidelines in 2025 for Rate-based and Mass-based Scenarios and 

Pollutant (3% discount rate)*.............................................................................................45

Figure 4A-1. Regional Breakdown ..............................................................................................7

Figure 6.1. Electric Power Industry Employment...................................................................12

Figure 6.2. Coal Production Employment...............................................................................13

Figure 6.3. Oil and Gas Production Employment...................................................................14

Figure 6.4. Demand-Side Energy Efficiency Employment: Jobs per One Million Dollars 

(2011$)...............................................................................................................................32
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xviii

ACRONYMS

ACS American Cancer Society

AEO Annual Energy Outlook 

AQ Air quality

ASM Annual Survey of Manufactures

ATSDR Agency for Toxic Substances and Disease Registry

BACT Best Available Control Technology 

BenMAP Benefits Mapping and Analysis Program

BPT Benefit-per-Ton 

BSER Best System of Emissions Reduction 

Btu British Thermal Units 

C Celsius 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CCR Coal Combustion Residuals 

CCS Carbon Capture and Sequestration or Carbon Capture and Storage 

CCSP Climate Change Science Program

CFR Code of Federal Regulations 

CH

4 

Methane 

CO Carbon Monoxide

CO

2 

Carbon Dioxide 

CRF Capital Recovery Factor 

CSAPR Cross State Air Pollution Rule 

CT Combustion Turbines 

CUA Climate Uncertainty Adder 

DICE Dynamic Integrated Climate and Economy Model 

DOE U.S. Department of Energy 

EAB Environmental Appeals Board 

EC Elemental carbon

ECS Energy Cost Share

EG Emissions guidelines

EGR Enhanced Gas Recovery 

EGU Electric Generating Unit 

EIA U.S. Energy Information Administration 

EMM Electricity Market Module 

EO Executive Order 

EOR Enhanced Oil Recovery 

EPA U.S. Environmental Protection Agency 

ER Enhanced Recovery 

FERC Federal Energy Regulatory Commission 

FGD Flue Gas Desulfurization 

FOAK First of a Kind 

FOM Fixed Operating and Maintenance 

FR Federal Register 

FRCC Florida Reliability Coordinating Council 

FUND Framework for Uncertainty, Negotiation, and Distribution Model v

GDP Gross Domestic Product 

GHG Greenhouse Gas 
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xix

GS Geologic Sequestration 

Gt Gigaton 

H

2

S Hydrogen Sulfide 

HAP Hazardous air pollutant

HCl Hydrogen chloride

HFC Hydrofluorocarbons 

HIA Health impact assessment

IARC International Agency for Research on Cancer

IAM Integrated Assessment Model 

ICR Information Collection Request 

IGCC Integrated Gasification Combined Cycle 

IOU Investor Owned Utility 

IPCC Intergovernmental Panel on Climate Change 

IPM Integrated Planning Model 

IRIS Integrated Risk Information System

IRP Integrated Resource Plan 

ISA Integrated Science Assessment

kWh Kilowatt-hour 

lbs Pounds 

LCOE Levelized Cost of Electricity 

LML Lowest measured level

LNB Low NO

X 

Burners 

MATS Mercury and Air Toxics Standards 

MEA Monoethanolamine

MECSA Manufacturing Energy Consumption Survey 

MeHg Methylmercury

MGD Millions of Gallons per Day 

mg/L Milligrams per Liter 

mmBtu Million British Thermal Units 

MW Megawatt 

MWh Megawatt-hour 

N

2

O Nitrous Oxide 

NAAQS National Ambient Air Quality Standards

NAICS North American Industry Classification System

NaOH Sodium Hydroxide 

NATCARB National Carbon Sequestration Database and Geographic Information System 

NEEDS National Electric Energy Data System 

NEMS National Energy Modeling System 

NERC North American Electric Reliability Corporation 

NETL National Energy Technology Laboratory 

NGCC Natural Gas Combined Cycle 

NMMAPS National Morbidity, Mortality Air Pollution Study

NOAK Next of a Kind or Nth of a Kind 

NO

X 

Nitrogen Oxide 

NRC National Research Council 

NSPS New Source Performance Standard 

NSR New Source Review 

NTTAA National Technology Transfer and Advancement Act 

OC Organic carbon
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xx

OFA Overfire Air 

OMB Office of Management and Budget 

PAGE Policy Analysis of the Greenhouse Gas Effect Model 

PFC Perfluorocarbons 

PM2.5 Fine Particulate Matter 

ppm Parts per Million 

PRA Paperwork Reduction Act 

PSD Prevention of Significant Deterioration 

RCSP Regional Carbon Sequestration Partnerships 

RADS Relative Airways Dysfunction Syndrome

RES Renewable Electricity Standards 

RFA Regulatory Flexibility Act 

RGGI Regional Greenhouse Gas Initiative 

RIA Regulatory Impact Analysis 

RPS Renewable Portfolio Standards 

SAB-CASAC Science Advisory Board Clean Air Scientific Advisory Committee

SAB-HES Science Advisory Board Health Effects Subcommittee of the Advisory Council on 

Clean Air Compliance

SAB-EEAC Science Advisory Board Environmental Economics Advisory Committee

SBA Small Business Administration

SBREFA Small Business Regulatory Enforcement Fairness Act 

SCC Social Cost of Carbon 

SCPC Super Critical Pulverized Coal 

SCR Selective Catalytic Reduction 

SF

6 

Sulfur Hexafluoride 

SIP State Implementation Plan 

SO2 Sulfur Dioxide 

Tcf Trillion Cubic Feet 

TDS Total Dissolved Solids 

TSD Technical Support Document 

TSM Transportation Storage and Monitoring 

UMRA Unfunded Mandates Reform Act 

U.S.C. U.S. Code 

USGCRP U.S. Global Change Research Program 

USGS U.S. Geological Survey 

USG SCC U.S. Government’s Social Cost of Carbon

U.S. NRC U.S. Nuclear Regulatory Commission 

VCS Voluntary Consensus Standards 

VOC Volatile Organic Compounds

VOM Variable Operating and Maintenance 

VSL Value of a statistical life

WTP Willingness to pay
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ES-1

EXECUTIVE SUMMARY

This Regulatory Impact Analysis (RIA) discusses potential benefits, costs, and economic 

impacts of the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: 

Electric Utility Generating Units (herein referred to as “final emission guidelines” or the “Clean 

Power Plan Final Rule”).

ES.1 Background and Context 

The emission of greenhouse gases (GHGs) threatens Americans' health and welfare by

leading to long-lasting changes in our climate. Carbon dioxide (CO2) is the primary greenhouse 

gas pollutant, accounting for roughly three-quarters of global greenhouse gas emissions in 2010 

and 82 percent of U.S. greenhouse gas emissions in 2013. Fossil fuel-fired electric generating 

units (EGUs) are, by far, the largest emitters of GHGs, primarily in the form of CO2, among 

stationary sources in the U.S.

In this action, the Environmental Protection Agency (EPA) is establishing final emission 

guidelines for states, U.S. territories, and tribes to follow in developing plans to reduce 

greenhouse gas emissions from existing fossil fuel-fired EGUs. Specifically, the EPA is 

establishing: 1) CO2 emission performance rates for two source categories of existing fossil fuelfired EGUs, fossil fuel-fired electric utility steam generating units and stationary combustion 

turbines, and 2) guidelines for the development, submittal and implementation of state plans that 

implement the CO2 emission performance rates. This final rule will continue progress already 

underway in the U.S. to reduce CO2 emissions from the utility power sector.

ES.2 Summary of Clean Power Plan Final Rule

Under CAA section 111(d), states must establish, in their state plans, emission standards 

that reflect the degree of emission limitation achievable through the application of the best 

system of emission reduction (BSER) that, taking into account the cost of achieving such 

reduction and any non-air quality health and environmental impacts and energy requirements, the 
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ES-2

Administrator determines has been adequately demonstrated.1 To determine the BSER for 

reducing CO2 emissions at affected EGUs, the EPA considered numerous measures that are 

already being implemented and can be implemented more broadly to improve emission rates and 

to reduce overall CO2 emissions from fossil fuel-fired EGUs. Overall, the BSER is based on a 

range of measures that fall into three main categories (or “building blocks”) that have been 

amply demonstrated via their current widespread use by utilities and states:

1. Reducing the carbon intensity of generation at individual affected coal-fired steam 

EGUs through heat rate improvements.

2. Substituting increased generation from lower-emitting existing natural gas combined 

cycle units for reduced generation from higher-emitting affected steam generating units.

3. Substituting increased generation from new low- and certain zero-emitting generating 

units, including renewable energy, for reduced generation from affected fossil fuel-fired 

generating units. 

Specifically, the EPA is establishing CO2 emission performance rates for two source 

categories of existing fossil fuel-fired EGUs, fossil fuel-fired electric utility steam generating 

units and stationary combustion turbines. Also, states, U.S. territories, and tribes with one or 

more affected EGUs will be required to develop and implement plans to ensure that their 

affected EGUs, individually, in aggregate, or in combination with other measures undertaken by 

the state, achieve the equivalent of the final CO2 emission performance rates by 2030.

The EPA derived statewide rate-based CO2 emissions performance goals as a weighted 

average of the uniform rate goals with weights based on baseline generation for the two types of 

units (fossil steam and NGCC) in the state. This blended rate reflects the aggregate emission rate 

a state may expect to achieve when its fleet is meeting its technology-specific emission 

 

1 Under CAA section 111(a)(1) and (d), the EPA is authorized to determine the BSER and to calculate the amount of 

emission reduction achievable through applying the BSER. The state is authorized to identify the standard or 

standards of performance that reflects that amount of emission reduction. In addition, the state is required to include 

in its state plan the standards of performance and measures to implement and enforce those standards. The state must 

submit the plan to the EPA, and the EPA must approve the plan if the standards of performance and implementing 

and enforcing measures are satisfactory. 
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ES-3

performance rates. The Clean Power Plan Final Rule also establishes an 8-year interim

compliance period that begins in 2022 with a glide path for meeting interim CO2 emission 

performance rates separated into three steps: 2022-2024, 2025-2027, and 2028-2029.This results 

in interim and final statewide goal values unique to each state’s historical blend of fossil steam 

and NGCC generation. Chapter 3 presents finalized state goals.

The EPA is providing equivalent mass-based statewide CO2 emission performance goals 

for each state, which are presented in Chapter 3. The mass-based statewide CO2 emission 

performance goals are based on the tonnage totals from the numerator of the statewide rate-based 

CO2 emission performance goals. These mass levels are then adjusted to account for the ability

of affected EGUs to increase utilization under rate-based compliance by engaging in emission 

reduction strategies that are in excess of what is necessary to demonstrate compliance if affected 

EGUs choose to replace their own generation.2

The final guidelines are structured so that states and affected EGUs are not required to use 

each and every one of the measures that the EPA determines constitute the BSER or to apply any

one of those measures to the same extent that the EPA determines is achievable at a reasonable 

cost on a national basis. Instead, in developing its plan, each state will have the flexibility to 

select the measure or combination of measures it prefers in order to achieve the CO2 emission 

performance rates for its affected EGUs or meet the equivalent statewide rate- or mass-based 

CO2 goal. Thus, a state could choose to achieve more reductions from one measure encompassed 

by the BSER and less from another, or it could choose to include measures such as demand-side 

energy efficiency programs that are not part of the EPA’s BSER determination. Expressing the 

states’ obligation in terms of a state-level goal that can be met in aggregate, including through 

the adoption of CO2 emission reduction measures that are not identical to the components of the 

BSER or are not included in the BSER, rather than exclusively through source-specific emission 

performance rates, is entirely permissible. This is precisely because the approach mirrors the 

foundation of the BSER approach itself, namely the interconnected nature of the electricity

system in which affected EGUs operate. Given the flexibilities afforded states in complying with 

 

2

For more detail on the translation methodology, please refer to the technical support document, CO2 Emission 

Performance Rate and Goal Computation TSD for Final Rule, which is available in the docket.
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ES-4

the emission guidelines, the benefits, cost and economic impacts reported in this RIA are not 

definitive estimates, but are instead illustrative of approaches that states may take.

ES.3 Illustrative Plan Approaches Examined in RIA

This RIA depicts two illustrative plan approaches designed to achieve these goals, which 

we term the “rate-based approach” and “mass-based approach”. In the rate-based illustrative plan 

approach, states within each interconnect region are assumed to collaborate in order meet the 

CO2 emission performance goals. In the mass-based illustrative plan approach, affected sources 

within each state are assumed to collaborate to meet the mass-based CO2 emission performance 

goals. The equilibrium distribution of the mass levels across the existing fossil fuel-fired EGUs 

is modeled to yield a least-cost outcome, such that the marginal compliance cost of reducing a 

ton of emissions is equal across all of the existing fossil fuel-fired EGUs within each region. This 

equilibrium outcome holds for all years of the interim period and for each period after 2030. 

The analysis for both illustrative plan approaches includes the assumptions regarding 

policy design noted in Chapter 3, but does not incorporate any additional flexibilities that states 

are able to use in developing policy mechanisms to achieve the state goals. Alternative 

compliance approaches other than those modelled are also possible, which may have different 

levels and distributions of emissions and electricity generation as well as costs. While IPM finds 

a least cost way to achieve the state goals implemented through the rate-based or mass-based 

emissions constraints imposed in the illustrative plan approaches, individual states or multi-state 

regional groups may develop alternate approaches to achieve their state goals. Meanwhile, 

because IPM models the power sector within the 48 contiguous states, including areas of Indian 

Country, and the District of Columbia, the EPA performed supplementary analysis to account for

the potential benefits and costs of the Clean Power Plan Final Rule for Alaska, Hawaii, and U.S. 

territories with affected EGUs.

To present a complete picture of costs and benefits of the final emission guidelines, this 

RIA presents results for the analysis years 2020, 2025, and 2030. While 2020 is before the first 

year of the interim compliance period (2022), the EPA expects states and affected EGUs to 

perform voluntary activities that that facilitate compliance with interim and final goals. These 

pre-compliance period activities might include investments in demand-side energy efficiency
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ES-5

projects, for example, that produce emissions reductions in the compliance period. Activities 

might also include preparatory investments in monitoring, reporting, and recordkeeping systems. 

As a result, there are likely to be benefits and costs in 2020, so these are reported in the 

illustrative analysis of this RIA. Meanwhile, cost and benefits are estimated in this RIA for 2025, 

which is intended to represent a central period of the interim compliance time-frame as states, 

U.S. territories, and tribes are on glide paths toward fully meeting the final CO2 emission 

performance rates. Lastly, the RIA presents costs and benefits for 2030, when the emission 

performance rates are fully achieved.

ES.4 Emissions Reductions

Table ES-1 shows the emission reductions associated with the modelled rate-based 

illustrative plan approach. In 2020, the EPA estimates that CO2 emissions will be reduced by 109

million short tons under the rate-based scenario compared to base case levels. In 2025, the EPA 

estimates that CO2 emissions will be reduced by 430 million short tons under the rate-based 

approach compared to base case levels. CO2 emission reductions increase to 559 million short

tons annually in 2030 when compared to the base case emissions. Table ES-1 also shows 

emission reductions for criteria air pollutants.3

 

3 Emissions estimates of PM2.5 require additional processing of IPM results. These results will be provided in the 

RIA released at the signature of the final rule. However, the SO2 and NOX reductions account for the large majority 

of the anticipated health co-benefits, so the temporary omission of PM2.5 reductions does not lead to a significant 

under-estimate of monetized health co-benefits.
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ES-6

Table ES-1. Climate and Air Pollutant Emission Reductions for the Rate-Based 

Illustrative Plan Approach1,2

CO2

(million short tons)

SO2

(thousands of tons)

Annual NOX 

(thousands of tons)

PM2.5 

(thousands of tons)

2020 Rate-Based Approach

Base Case 2,220 1,350 1,420 [X]

Final Guidelines 2,110 1,300 1,330 [X]

Emissions Change 109 49 85 [X]

2025 Rate-Based Approach

Base Case 2,300 1,410 1,430 [X]

Final Guidelines 1,870 1,080 1,090 [X]

Emissions Change 430 331 347 [X]

2030 Rate-Based Approach

Base Case 2,370 1,430 1,420 [X]

Final Guidelines 1,810 1,030 1,020 [X]

Emission Change 559 395 396 [X]

Source: Integrated Planning Model, 2015. 

1 CO2 emission reductions are used to estimate the climate benefits of the guidelines. SO2, NOx, and directly emitted 

PM2.5 emission reductions are relevant for estimating air quality health co-benefits of the final guidelines.

2 Emissions rounded to three significant digits. Emissions may not sum due to rounding.

Table ES-2 shows the emission reductions associated with the modeled mass-based 

illustrative plan approach. In 2020, the EPA estimates that CO2 emissions will be reduced by [X]

million short tons under the mass-based approach compared to base case levels. In 2025, the 

EPA estimates that CO2 emissions will be reduced by [X] million short tons under the massbased approach compared to base case levels. CO2 emission reductions increase to [X] million 

short tons annually in 2030 when compared to the base case emissions. Table ES-2 also shows 

emission reductions for criteria air pollutants. 
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ES-7

Table ES-2. Climate and Air Pollutant Emission Reductions for the Mass-Based 

Illustrative Plan Appproach1,2

CO2

(million short tons)

SO2

(thousands of tons)

Annual NOX 

(thousands of tons)

PM2.5 

(thousands of tons)

2020 Mass-Based Approach

Base Case [X] [X] [X] [X]

Final Guidelines [X] [X] [X] [X]

Emissions Change [X] [X] [X] [X]

2025 Mass-Based Approach

Base Case [X] [X] [X] [X]

Final Guidelines [X] [X] [X] [X]

Emissions Change [X] [X] [X] [X]

2030 Mass-Based Approach

Base Case [X] [X] [X] [X]

Final Guidelines [X] [X] [X] [X]

Emission Change [X] [X] [X] [X]

Source: Integrated Planning Model, 2015. 

1 CO2 emission reductions are used to estimate the climate benefits of the guidelines. SO2, NOx, and directly emitted 

PM2.5 emission reductions are relevant for estimating air quality health co-benefits of the final guidelines.

2 Emissions rounded to three significant digits. Emissions may not sum due to rounding.

Comparing the rate-based and mass-based illustrative plan approaches indicates...

ES.5 Costs

The compliance cost estimated for this final action are represented in this analysis as the 

change in electric power generation costs between the base case and illustrative plan approach

policy cases, inclusive of the cost of demand-side EE measures. The modelled implementation 

plan approaches reflect states pursuing strategies which are not limited to the technologies and 

measures included in the BSER to meet the final emission guidelines. Additionally, the 

compliance cost estimates include the costs of implementing demand-side energy efficiency

measures. In the rate-based approach, energy efficiency activities are modeled as being used by 

EGUs as a low-cost method of demonstrating compliance with their rate-based emissions 

standards. In the mass-based approach, energy efficiency activities are assumed to be adopted by 

states to lower demand, which in turn reduces the cost of achieving the mass limitations. The 

level of energy efficiency measures is determined outside of IPM and is assumed to be the same 

in the two illustrative plan approaches. The compliance assumptions, and therefore the projected 

“compliance costs” set forth in this analysis, are illustrative in nature and do not represent the 

full suite of compliance flexibilities states may ultimately pursue.
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ES-8

The annual incremental cost is the projected additional cost of complying with the final 

rule in the year analyzed and includes the net change in the annualized cost of capital investment 

in new generating sources and heat rate improvements at coal-fired steam generating units, the 

change in the ongoing costs of operating pollution controls, shifts between or amongst various 

fuels, demand-side energy efficiency measures, and other actions associated with compliance.

The total compliance cost estimates presented here include illustrative compliance cost estimates 

for Alaska, Hawaii, and U.S. territories, as well as estimates of the costs associated with 

monitoring, reporting, and recordkeeping (MR&R).4The costs for both illustrative plan 

approaches are reflected in Table ES-3 below and discussed more extensively in Chapter 3 of 

this RIA. All dollar estimates are in 2011 dollars.

The EPA estimates the annual incremental compliance cost for the rate-based approach 

for final emission guidelines to be $2.4 billion in 2020, $1.1 billion in 2025 and $8.4 billion in 

2030, including the costs associated with monitoring, reporting, and recordkeeping.5The EPA 

estimates the annual incremental compliance cost for the mass-based approach for final emission 

guidelines to be $[X] billion in 2020, $[X] billion in 2025 and $[X] billion in 2030, including the 

costs associated with monitoring, reporting, and recordkeeping. 

Table ES-3. Compliance Costs for the Illustrative Rate-Based and Mass-Based Plan 

Approaches

Incremental Cost from Base Case (billions of 2011$)

Rate-based Approach Mass-based Approach 

2020 $2.4 $[X]

2025 $1.1 $[X]

2030 $8.4 $[X]

Source: Integrated Planning Model, 2015, with post-processing to account for exogenous demand-side management 

energy efficiency costs. See Chapter 3 of this RIA for a more complete explanation. Compliance costs shown 

include monitoring, reporting, and recordkeeping costs. Costs currently do not include costs for Alaska, Hawaii, and 

U.S. Territories.

 

4 These costs are estimated outside of the IPM modelling framework as IPM only models the contiguous U.S. and 

does not incorporate monitoring, reporting, and recordkeeping requirements specific to the Clean Power Plan Final 

Rules.

5 The MR&R costs estimates are $68 million in 2020, $17 million in 2025 and $17 million in 2030 and are assumed 

to be the same for both rate-based and mass-based illustrative plan approaches. Note the MR&R costs in 2020 are 

related to facilities setting up net energy output monitoring and upgrading data acquisition systems.
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ES-9

The costs reported in Table ES-3 represent the estimated incremental electric utility

generating costs changes from the base case plus the estimates of demand-side energy efficiency

program costs (which are paid by electric utilities), demand-side energy efficiency participant 

costs (which are paid by electric utility consumers), cost estimates for Alaska, Hawaii, and U.S. 

Territories, and MR&R costs. For example, in 2030, under the rate-based approach, the

incremental electric utility generating costs decline by $17.9 billion from the base case. The costs 

associated with illustrative plan approaches for Alaska, Hawaii, and U.S. territories in 2030 are 

estimated at $[X]. MR&R requirements in 2030 are estimated at $17 million. Demand-side

energy efficiency costs in 2030 are estimated to be $26.3 billion, split equally between program 

and participants using a 3 percent discount rate (see Chapter 3 of this RIA for more details on 

these estimates). These cost estimates sum to the $8.4 shown in Table ES-3 and represent the 

total costs of the rate-based illustrative plan approach in 2030. The same approach applies in 

each year of analysis for the rate-based and mass-based illustrative plan approaches.

Comparing the rate-based and mass-based illustrative plan approaches indicates...

The compliance costs reported in Table ES-3 are not social costs. These costs represent the 

estimated expenditures incurred by EGUs and states to comply with the BSER goals for the

Clean Power Plan Final Rule. These compliance cost estimates are compared to estimates of 

social benefits to derive net benefits of the final emission guidelines, which are presented later in 

this Executive Summary. For a more extensive discussion of social costs and benefits, see 

Chapter 3 and Chapter 4, respectively, of this RIA. 

ES.6 Monetized Climate Benefits and Health Co-benefits

Implementing the final emission guidelines is expected to reduce emissions of CO2 and 

have ancillary emission reductions (i.e., co-benefits) of SO2, NO2, and directly emitted PM2.5, 

which would lead to lower ambient concentrations of PM2.5 and ozone. The climate benefits 

estimates have been calculated using the estimated values of marginal climate impacts presented 

in the Technical Support Document: Technical Update of the Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866 (May 2013, Revised June 2015), 
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ES-10

henceforth denoted as the current SC-CO2 TSD.6 Also, the range of combined benefits reflects 

different concentration-response functions for the air quality health co-benefits, but it does not 

capture the full range of uncertainty inherent in the health co-benefits estimates. Furthermore, we 

were unable to quantify or monetize all of the climate benefits and health and environmental cobenefits associated with the final emission guidelines, including reducing exposure to SO2, NOX, 

and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effects 

and visibility improvement. The omission of these endpoints from the monetized results should 

not imply that the impacts are small or unimportant. Table ES-4 provides the list of the 

quantified and unquantified health and environmental benefits in this analysis. 

 

6 Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis 

Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council 

of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, 

Department of Energy, Department of Transportation, Domestic Policy Council, Environmental Protection Agency, 

National Economic Council, Office of Management and Budget, Office of Science and Technology Policy, and 

Department of Treasury (May 2013, Revised June 2015). 
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ES-11

Table ES-4. Quantified and Unquantified Benefits

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information

Improved Environment

Reduced climate 

effects

Global climate impacts from CO2 —1  SC-CO2 TSD

Climate impacts from ozone and black carbon (directly 

emitted PM) — —

Ozone ISA, PM 

ISA2

Other climate impacts (e.g., other GHGs such as methane, 

aerosols, other impacts) — — IPCC2

Improved Human Health (co-benefits)

Reduced incidence of 

premature mortality 

from exposure to PM2.5

Adult premature mortality based on cohort study estimates

and expert elicitation estimates (age >25 or age >30)   PM ISA

Infant mortality (age <1)   PM ISA

Reduced incidence of 

morbidity from 

exposure to PM2.5

Non-fatal heart attacks (age > 18)   PM ISA

Hospital admissions—respiratory (all ages)   PM ISA

Hospital admissions—cardiovascular (age >20)   PM ISA

Emergency room visits for asthma (all ages)   PM ISA

Acute bronchitis (age 8-12)   PM ISA

Lower respiratory symptoms (age 7-14)   PM ISA

Upper respiratory symptoms (asthmatics age 9-11)   PM ISA

Asthma exacerbation (asthmatics age 6-18)   PM ISA

Lost work days (age 18-65)   PM ISA

Minor restricted-activity days (age 18-65)   PM ISA

Chronic Bronchitis (age >26) — — PM ISA2

Emergency room visits for cardiovascular effects (all ages) — — PM ISA2

Strokes and cerebrovascular disease (age 50-79) — — PM ISA2

Other cardiovascular effects (e.g., other ages) — — PM ISA3

Other respiratory effects (e.g., pulmonary function, nonasthma ER visits, non-bronchitis chronic diseases, other ages 

and populations)

— — PM ISA3

Reproductive and developmental effects (e.g., low birth 

weight, pre-term births, etc.) — — PM ISA3,4

Cancer, mutagenicity, and genotoxicity effects — — PM ISA3,4

Reduced incidence of 

mortality from 

exposure to ozone

Premature mortality based on short-term study estimates (all 

ages)   Ozone ISA

Premature mortality based on long-term study estimates (age 

30–99) — — Ozone ISA2

Reduced incidence of 

morbidity from 

exposure to ozone

Hospital admissions—respiratory causes (age > 65)   Ozone ISA

Hospital admissions—respiratory causes (age <2)   Ozone ISA

Emergency department visits for asthma (all ages)   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

Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA3

Cardiovascular and nervous system effects — — Ozone ISA3

Reproductive and developmental effects — — Ozone ISA3,4
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ES-12

Table ES-4. Continued

Reduced incidence of 

morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2ISA2

Chronic lung disease hospital admissions (age > 65) — — NO2ISA2

Respiratory emergency department visits (all ages) — — NO2ISA2

Asthma exacerbation (asthmatics age 4–18) — — NO2ISA2

Acute respiratory symptoms (age 7–14) — — NO2ISA2

Premature mortality — — NO2ISA2,3,4

Other respiratory effects (e.g., airway hyperresponsiveness 

and inflammation, lung function, other ages and populations) — — NO2

ISA3,4

Reduced incidence of 

morbidity from 

exposure to SO2

Respiratory hospital admissions (age > 65) — — SO2ISA2

Asthma emergency department visits (all ages) — — SO2ISA2

Asthma exacerbation (asthmatics age 4–12) — — SO2ISA2

Acute respiratory symptoms (age 7–14) — — SO2ISA2

Premature mortality — — SO2ISA2,3,4

Other respiratory effects (e.g., airway hyperresponsiveness 

and inflammation, lung function, other ages and populations) — — SO2

ISA2,3

Reduced incidence of 

morbidity from 

exposure to 

methylmercury

Neurologic effects—IQ loss — — IRIS; NRC, 20002

Other neurologic effects (e.g., developmental delays, 

memory, behavior) — — IRIS; NRC, 20003

Cardiovascular effects — — IRIS; NRC, 20003,4

Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 20003,4

Reduced incidence of 

morbidity from 

exposure to HAP

Effects associated with exposure to hydrogen chloride — — ATSDR, IRIS2,3

Improved Environment (co-benefits)

Reduced visibility 

impairment

Visibility in Class 1 areas — — PM ISA2

Visibility in residential areas — — PM ISA2

Reduced effects on 

materials

Household soiling — — PM ISA2,3

Materials damage (e.g., corrosion, increased wear) — — PM ISA3

Reduced PM 

deposition (metals and 

organics)

Effects on Individual organisms and ecosystems — — PM ISA3

Reduced vegetation 

and ecosystem effects 

from exposure to 

ozone

Visible foliar injury on vegetation — — Ozone ISA2

Reduced vegetation growth and reproduction — — Ozone ISA2

Yield and quality of commercial forest products and crops — — Ozone ISA2

Damage to urban ornamental plants — — Ozone ISA3

Carbon sequestration in terrestrial ecosystems — — Ozone ISA2

Recreational demand associated with forest aesthetics — — Ozone ISA3

Other non-use effects Ozone ISA3

Ecosystem functions (e.g., water cycling, biogeochemical 

cycles, net primary productivity, leaf-gas exchange, 

community composition)

— — Ozone ISA3

Reduced effects from 

acid deposition

Recreational fishing — — NOx SOxISA2

Tree mortality and decline — — NOx SOxISA3

Commercial fishing and forestry effects — — NOx SOxISA3

Recreational demand in terrestrial and aquatic ecosystems — — NOx SOxISA3

Other non-use effects NOx SOxISA3

Ecosystem functions (e.g., biogeochemical cycles) — — NOx SOxISA3

Table ES-4. Continued
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Reduced effects from 

nutrient enrichment

Species composition and biodiversity in terrestrial and 

estuarine ecosystems — — NOx SOx

ISA3

Coastal eutrophication — — NOx SOxISA3

Recreational demand in terrestrial and estuarine ecosystems — — NOx SOxISA3

Other non-use effects NOx SOxISA3

Ecosystem functions (e.g., biogeochemical cycles, fire 

regulation) — — NOx SOx

ISA3

Reduced vegetation 

effects from exposure 

to SO2 and NOx

Injury to vegetation from SO2 exposure — — NOx SOxISA3

Injury to vegetation from NOx exposure — — NOx SOxISA3

Reduced ecosystem 

effects from exposure 

to methylmercury

Effects on fish, birds, and mammals (e.g., reproductive 

effects) — — Mercury Study RTC3

Commercial, subsistence and recreational fishing — — Mercury Study RTC2

1 The global climate and related impacts of CO2 emissions changes, such as sea level rise, are estimated within each 

integrated assessment model as part of the calculation of the SC-CO2. The resulting monetized damages, which 

are relevant for conducting the benefit-cost analysis, are used in this RIA to estimate the welfare effects of 

quantified changes in CO2 emissions.

2 We assess these co-benefits qualitatively due to data and resource limitations for this analysis.

3 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods.

4 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association.

ES.6.1 Estimating Global Climate Benefits

We estimate the global social benefits of CO2 emission reductions expected from this 

rulemaking using the SC-CO2 estimates presented in the current SC-CO2 TSD. We refer to these 

estimates, which were developed by the U.S. government, as “SC-CO2 estimates” for the 

remainder of this document. The SC-CO2 is a metric that estimates the monetary value of 

impacts associated with marginal changes in CO2 emissions in a given year. It includes a wide 

range of anticipated climate impacts, such as net changes in agricultural productivity and human 

health, property damage from increased flood risk, and changes in energy system costs, such as 

reduced costs for heating and increased costs for air conditioning. It is typically used to assess 

the avoided damages as a result of regulatory actions (i.e., benefits of rulemakings that lead to an 

incremental reduction in cumulative global CO2 emissions). 

The SC-CO2 estimates used in this analysis have been developed over many years, using 

the best science available, and with input from the public. The EPA and other federal agencies 

have considered the extensive public comments on ways to improve SC-CO2 estimation received 

via the notice and comment period that was part of numerous rulemakings. In addition, OMB’s 

Office of Information and Regulatory Affairs recently issued a response to the public comments 
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it sought through a separate comment period on the approach used to develop the SC-CO2

estimates.

An interagency working group (IWG) that included the EPA and other executive branch 

entities used three integrated assessment models (IAMs) to develop SC-CO2 estimates and 

recommended four global values for use in regulatory analyses. The SC-CO2 estimates represent 

global measures because of the distinctive nature of the climate change problem. Emissions of 

greenhouse gases contribute to damages around the world, even when they are released in the 

United States, and the world’s economies are now highly interconnected. Therefore, the SC-CO2 

estimates incorporate the worldwide damages caused by carbon dioxide emissions in order to 

reflect the global nature of the problem, and we expect other governments to consider the global 

consequences of their greenhouse gas emissions when setting their own domestic policies. See 

RIA Chapter 4 for more discussion.

The IWG first released the estimates in February 2010 and updated them in 2013 using 

new versions of each IAM. The general approach to estimating the SC-CO2 values in 2010 and 

2013 was to run the three integrated assessment models (DICE, FUND, and PAGE)7using the 

following three inputs in each model: a probabilistic distribution for climate sensitivity; five 

approach s capturing economic, population, and emission trajectories; and constant annual 

discount rates. The 2010 SC-CO2 Technical Support Document (2010 SC-CO2 TSD) provides a 

complete discussion of the methodology and the current SC-CO2 TSD8presents and discusses the 

updated estimates. The four SC-CO2 estimates are as follows: $13, $45, $66, and $130 per metric 

ton of CO2 emissions in the year 2020 (2011$), and each estimate increases over time. These SCCO2 estimates are associated with different discount rates. The first three estimates are the model 

average at 5 percent discount rate, 3 percent, and 2.5 percent, respectively, and the fourth 

estimate is the 95th percentile at 3 percent. 

 

7 The full models names are as follows: Dynamic Integrated Climate and Economy (DICE); Climate Framework for 

Uncertainty, Negotiation, and Distribution (FUND); and Policy Analysis of the Greenhouse Gas Effect (PAGE).

8 The IWG published the updated TSD in 2013, then issued two minor corrections to it in June 2015. 
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The 2010 SC-CO2 TSD noted a number of limitations to the SC-CO2 analysis, including 

the incomplete way in which the IAMs capture catastrophic and non-catastrophic impacts, their 

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

damages to high temperatures, and assumptions regarding risk aversion. Currently integrated 

assessment models do not assign value to all of the important physical, ecological, and economic 

impacts of climate change recognized in the climate change literature because of a lack of 

precise information on the nature of damages and because the science incorporated into these 

models understandably lags behind the most recent research. In particular, the IPCC Fourth 

Assessment Report concluded that “It is very likely that [SC-CO2 estimates] underestimate the 

damage costs because they cannot include many non-quantifiable impacts.” Nonetheless, these 

estimates and the discussion of their limitations represent the best available information about 

the social benefits of CO2 emission reductions to inform the benefit-cost analysis.

In addition, after careful evaluation of the full range of comments submitted to OMB’s 

Office of Information and Regulatory Affairs, the IWG continues to recommend the use of these

SC-CO2 estimates in regulatory impact analysis. With the release of the response to comments, 

the IWG announced plans to obtain expert independent advice from the National Academy of 

Sciences’ National Research Council (NRC) to ensure that the SC-CO2 estimates continue to 

reflect the best available scientific and economic information on climate change. The NRC

process will be informed by the public comments received and focus on the technical merits and 

challenges of potential approaches to improving the SC-CO2 estimates in future updates.

ES 6.2 Estimating Air Quality Health Co-Benefits

The final emission guidelines would reduce emissions of precursor pollutants (e.g., SO2, 

NOx, and directly emitted particles), which in turn would lower ambient concentrations of PM2.5

and ozone. This co-benefits analysis quantifies the monetized benefits associated with the 

reduced exposure to these two pollutants.9 Unlike the global SC-CO2 estimates, the air quality

health co-benefits are only estimated for the contiguous U.S.10 The estimates of monetized PM2.5

 

9 We did not estimate the co-benefits associated with reducing direct exposure to SO2 and NOx.

10 We do not have emission reduction information or air quality modeling available to estimate the air pollution 

health co-benefits in Alaska and Hawaii anticipated from implementation of the final emission guidelines.
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co-benefits include avoided premature deaths (derived from effect coefficients in two cohort 

studies [Krewski et al. 2009 and Lepeule et al. 2012] for adults and one for infants [Woodruff et 

al. 1997]), as well as avoided morbidity effects for ten non-fatal endpoints ranging in severity 

from lower respiratory symptoms to heart attacks (U.S. EPA, 2012). The estimates of monetized 

ozone co-benefits include avoided premature deaths (derived from the range of effect 

coefficients represented by two short-term epidemiology studies [Bell et al. (2004) and Levy et 

al. (2005)]), as well as avoided morbidity effects for five non-fatal endpoints ranging in severity 

from school absence days to hospital admissions (U.S. EPA, 2008, 2011).

We use a “benefit-per-ton” approach to estimate the PM2.5 and ozone co-benefits in this 

RIA. Benefit-per-ton approaches apply an average benefit per ton derived from modeling of 

benefits of specific air quality scenarios to estimates of emissions reductions for scenarios where 

no air quality modeling is available. The benefit-per-ton approach we use in this RIA relies on 

estimates of human health responses to exposure to PM and ozone obtained from the peerreviewed scientific literature. These estimates are used in conjunction with population data, 

baseline health information, air quality data and economic valuation information to conduct 

health impact and economic benefits assessments. 

Specifically, in this analysis, we multiplied the benefit-per-ton estimates by the 

corresponding emission reductions that were generated from air quality modeling of the 

proposed Clean Power Plan. Similar to the co-benefits analysis conducted for the RIA for this 

rule at proposal, we generated regional benefit-per-ton estimates by aggregating the impacts in 

BenMAP11 to the region (i.e., East, West, and California) rather than aggregating to the nation. 

To calculate the co-benefits for the final emission guidelines, we then multiplied the regional 

benefit-per-ton estimates for the EGU sector by the corresponding emission reductions. All 

benefit-per-ton estimates reflect the geographic distribution of the modeled emissions, which 

may not exactly match the emission reductions in this rulemaking, and thus they may not reflect 

 

11 BenMAP is a computer program developed by the EPA that calculates the number and economic value of air 

pollution-related deaths and illnesses. The software incorporates a database that includes many of the concentrationresponse relationships, population files, and health and economic data needed to quantify these impacts.
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the local variability in population density, meteorology, exposure, baseline health incidence 

rates, or other local factors for any specific location. 

Our estimate of the monetized co-benefits is based on the EPA’s interpretation of the best 

available scientific literature (U.S. EPA, 2009) and methods and supported by the EPA’s Science 

Advisory Board and the NAS (NRC, 2002). Below are key assumptions underlying the estimates 

for PM2.5-related premature mortality, which accounts for 98 percent of the monetized PM2.5

health co-benefits: 

1. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The PM ISA 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, 2009b).

2. We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health co-benefits from 

reducing fine particles in areas with varied concentrations of PM2.5, including both 

areas that do not meet the National Ambient Air Quality Standard for fine particles 

and those areas that are in attainment, down to the lowest modeled concentrations. 

3. We assume that there is a “cessation” lag between the change in PM exposures and 

the total realization of changes in mortality effects. Specifically, we assume that some 

of the incidences of premature mortality related to PM2.5 exposures occur in a 

distributed fashion over the 20 years following exposure based on the advice of the 

SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality cobenefits at different discount rates.

Every benefits analysis examining the potential effects of a change in environmental 

protection requirements is limited, to some extent, by data gaps, model capabilities (such as 

geographic coverage) and uncertainties in the underlying scientific and economic studies used to 
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configure the benefit and cost models. In addition, given the flexibilities afforded states in 

complying with the emission guidelines, the co-benefits estimated presented in this RIA are not 

definitive estimates, but are instead illustrative of approaches that states may take. Despite these 

uncertainties, we believe this analysis provides a reasonable indication of the expected health cobenefits of the air quality emission reductions for the final emission guidelines under a set of 

reasonable assumptions. This analysis does not include the type of detailed uncertainty 

assessment found in the 2012 PM2.5 National Ambient Air Quality Standard (NAAQS) RIA (U.S. 

EPA, 2012) because we lack the necessary air quality input and monitoring data to conduct a 

complete benefits assessment. In addition, using a benefit-per-ton approach adds another 

important source of uncertainty to the benefits estimates. 

ES 6.3 Combined Benefits Estimates

The EPA has evaluated the range of potential impacts by combining all four SC-CO2

values with health co-benefits values at the 3 percent and 7 percent discount rates. Different 

discount rates are applied to SC-CO2 than to the health co-benefit estimates; because CO2

emissions are long-lived and subsequent damages occur over many years. Moreover, several 

discount rates are applied to SC-CO2 because the literature shows that the estimate of SC-CO2 is 

sensitive to assumptions about discount rate and because no consensus exists on the appropriate 

rate to use in an intergenerational context. The U.S. government centered its attention on the 

average SC-CO2 at a 3 percent discount rate but emphasized the importance of considering all 

four SC-CO2 estimates. Table ES-5 (rate-based illustrative plan approach) and Table ES-6 

(mass-based illustrative plan approach) provide the combined climate benefits and health cobenefits for the Clean Power Plan Final Rule estimated for 2020, 2025, and 2030 for each 

discount rate combination. All dollar estimates are in 2011 dollars.
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Table ES-5. Combined Estimates of Climate Benefits and Health Co-Benefits for RateBased Approach (billions of 2011$)*

SC-CO2 Discount Rate and Statistic**

Climate 

Benefits 

Only

Climate Benefits plus Health Co-benefits 

(Discount Rate Applied to Health Co-benefits)

3% 7%

In 2020 63 million metric tons CO2

5% $0.8 $1.5 to $2.6 $1.4 to $2.5

3% $2.8 $3.5 to $4.6 $3.4 to $4.5

2.5% $4.1 $4.8 to $5.9 $4.8 to $5.8

3% (95th percentile) $8.2 $8.9 to $10 $8.8 to $9.9

In 2025 210 million metric tons CO2

5% $3.1 $11 to $21 $10 to $19

3% $10 $18 to $28 $17 to $26

2.5% $15 $23 to $33 $22 to $31

3% (95th percentile) $31 $38 to $49 $38 to $47

In 2030 377 million metric tons CO2

5% $6.4 $21 to $40 $19 to $37

3% $20 $34 to $54 $33 to $51

2.5% $29 $43 to $63 $42 to $60

3% (95th percentile) $61 $75 to $95 $74 to $92

*Benefit estimates are for affected sources in contiguous states and have been rounded to two significant figures. 

Climate benefits are based on reductions in CO2 emissions. Co-benefits are based on regional benefit-per-ton 

estimates. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NOx, SO2, and HAP; 

ecosystem effects; or visibility impairment. See Chapter 4 for more information about these estimates and for more 

information regarding the uncertainty in these estimates.

**Unless otherwise specified, it is the model average.
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Table ES-6. Combined Estimates of Climate Benefits and Health Co-benefits for MassBased Approach (billions of 2011$)*

SC-CO2 Discount Rate and Statistic**

Climate 

Benefits 

Only

Climate Benefits plus Health Co-benefits 

(Discount Rate Applied to Health Co-benefits)

3% 7%

In 2020 [X] million metric tonnes CO2

5% $[X] $[X] to $[X] $[X] to $[X]

3% $[X] $[X] to $[X] $[X] to $[X]

2.5% $[X] $[X] to $[X] $[X] to $[X]

3% (95th percentile) $[X] $[X] to $[X] $[X] to $[X]

In 2025 [X] million metric tonnes CO2

5% $[X] $[X] to $[X] $[X] to $[X]

3% $[X] $[X] to $[X] $[X] to $[X]

2.5% $[X] $[X] to $[X] $[X] to $[X]

3% (95th percentile) $[X] $[X] to $[X] $[X] to $[X]

In 2030 [X] million metric tonnes CO2

5% $[X] $[X] to $[X] $[X] to $[X]

3% $[X] $[X] to $[X] $[X] to $[X]

2.5% $[X] $[X] to $[X] $[X] to $[X]

3% (95th percentile) $[X] $[X] to $[X] $[X] to $[X]

*All benefit estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2

emissions. Co-benefits are based on regional benefit-per-ton estimates. Ozone co-benefits occur in analysis year, so 

they are the same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits 

and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to 

Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health 

effects from direct exposure to NOX, SO2, and HAP; ecosystem effects; or visibility impairment. See Chapter 4 for 

more information about these estimates and for more information regarding the uncertainty in these estimates.

**Unless otherwise specified, it is the model average.

Comparing the rate-based and mass-based illustrative plan approaches indicates...

ES.7 Net Benefits

Table ES-7 and ES-8 provide the estimates of the climate benefits, health co-benefits, 

compliance costs and net benefits of the final emission guidelines for rate-based and mass-based 

approaches, respectively. Comparing the rate-based and mass-based illustrative plan approaches 

indicates...

There are additional important benefits that the EPA could not monetize. Due to current 

data and modeling limitations, our estimates of the benefits from reducing CO2 emissions do not 

include important impacts like ocean acidification or potential tipping points in natural or 

managed ecosystems. Unquantified benefits also include climate benefits from reducing 

emissions of non-CO2 greenhouse gases and co-benefits from reducing exposure to SO2, NOX, 

and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effects 
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and visibility impairment. Upon considering these limitations and uncertainties, it remains clear 

that the benefits of this final rule are substantial and far outweigh the costs.
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Table ES-7. Monetized Benefits, Compliance Costs, and Net Benefits Under the Ratebased Illustrative Plan Approach (billions of 2011$) a

Rate-Based Approach 

2020 2025 2030

Climate Benefits b

5% discount rate $0.80 $3.1 $6.4 

3% discount rate $2.8 $10 $20 

2.5% discount rate $4.1 $15 $29 

95th percentile at 3% 

discount rate $8.2 $31 $61

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7%

Air Quality Health 

Co-benefits c $0.7 to $1.8 $0.6 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31

Compliance Costs d

$2.4 $1.1 $8.4

Net Benefits e

$1.1 to $2.2 $1.0 to $2.1 $17 to $27 $16 to $25 $26 to $46 $25 to $43

Non-Monetized 

Benefits

Non-monetized climate benefits

Reductions in exposure to ambient NO2 and SO2

Reductions in mercury deposition

Ecosystem benefits associated with reductions in emissions of NOX, SO2, PM, and mercury

and HCl

Visibility impairment

a All are rounded to two significant figures, so figures may not sum.

b The climate benefit estimate in this summary table reflects global impacts from CO2 emission changes and does 

not account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SC-CO2 than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The benefit 

estimates in this table are based on the average SC-CO2 estimated for a 3% discount rate, however we emphasize the 

importance and value of considering the full range of SC-CO2 values. As shown in the RIA, climate benefits are also 

estimated using the other three SC-CO2 estimates (model average at 2.5 percent discount rate, 3 percent, and 5 

percent; 95th percentile at 3 percent). The SC-CO2 estimates are year-specific and increase over time. 

c The air quality health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission reductions 

of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions from 

different epidemiology studies. The reduction in premature fatalities each year accounts for over 98 percent of total 

monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. Estimates in the table are presented for three 

analytical years with air quality co-benefits calculated using two discount rates. The estimates of co-benefits are 

annual estimates in each of the analytical years, reflecting discounting of mortality benefits over the cessation lag 

between changes in PM2.5 concentrations and changes in risks of premature death (see RIA Chapter 4 for more 

details), and discounting of morbidity benefits due to the multiple years of costs associated with some illnesses. The 

estimates are not the present value of the benefits of the rule over the full compliance period.

d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for 

the proposed guidelines and a discount rate of approximately 5 percent. This estimate also includes monitoring, 

recordkeeping, and reporting costs and demand-side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calculated using the global SC-CO2 at a 3 percent discount 

rate (model average). The RIA includes combined climate and health estimates based on additional discount rates.
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Table ES-8. Monetized Benefits, Compliance Costs, and Net Benefits under the Massbased Illustrative plan approach (billions of 2011$) a

Mass-Based Approach 

2020 2025 2030

Climate Benefits b

5% discount rate $[X] $[X] $[X]

3% discount rate $[X] $[X] $[X]

2.5% discount rate $[X] $[X] $[X]

95th percentile at 3% 

discount rate $[X] $[X] $[X]

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7%

Air Quality Health 

Co-benefits c $[X] to $[X] $[X] to $[X] $[X] to 

$[X] $[X] to $[X] $[X] to $[X] $[X] to $[X]

Compliance Costs d

$[X] $[X] $[X]

Net Benefits e

$[X] to $[X] $[X] to $[X] $[X] to 

$[X] $[X] to $[X] $[X] to $[X] $[X] to $[X]

Non-Monetized 

Benefits

Non-monetized climate benefits

Reductions in exposure to ambient NO2 and SO2

Reductions in mercury deposition

Ecosystem benefits associated with reductions in emissions of NOX, SO2, PM, and mercury

and HCl

Visibility improvement

a All are rounded to two significant figures, so figures may not sum.

b The climate benefit estimate in this summary table reflects global impacts from CO2 emission changes and does 

not account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SC-CO2 than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The benefit 

estimates in this table are based on the average SC-CO2 estimated for a 3% discount rate, however we emphasize the 

importance and value of considering the full range of SC-CO2 values. As shown in the RIA, climate benefits are also 

estimated using the other three SC-CO2 estimates (model average at 2.5 percent discount rate, 3 percent, and 5 

percent; 95th percentile at 3 percent). The SC-CO2 estimates are year-specific and increase over time. 

c The air quality health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission reductions 

of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions from 

different epidemiology studies. The reduction in premature fatalities each year accounts for over 98 percent of total 

monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. Estimates in the table are presented for three 

analytical years with air quality co-benefits calculated using two discount rates. The estimates of co-benefits are 

annual estimates in each of the analytical years, reflecting discounting of mortality benefits over the cessation lag 

between changes in PM2.5 concentrations and changes in risks of premature death (see RIA Chapter 4 for more 

details), and discounting of morbidity benefits due to the multiple years of costs associated with some illnesses. The 

estimates are not the present value of the benefits of the rule over the full compliance period.

d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for 

the proposed guidelines and a discount rate of approximately 5 percent. This estimate also includes monitoring, 

recordkeeping, and reporting costs and demand-side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calculated using the global SC-CO2 at a 3 percent discount 

rate (model average). The RIA includes combined climate and health estimates based on additional discount rates.
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ES.8 Economic Impacts

The final emission guidelines have important energy market implications. Average 

nationwide retail electricity prices are projected to increase/decrease roughly [X] percent in 

2020, increase/decrease roughly [X] percent in 2025, and increase/decrease roughly [X] percent 

in 2030 (contiguous U.S.), compared to base case price estimates modeled for these same years. 

Average monthly electricity bills are anticipated to increase/decrease by about [X]

percent in 2020, increase/decrease by about [X] percent in 2025, and increase/decrease by about 

[X] percent by 2030, compared to base case price estimates modeled for these same years.

The average delivered coal price to the power sector is projected to decrease by about [X] 

percent in 2020, [X] percent in 2025, and roughly [X] percent in 2030, relative to the base case. 

The EPA projects coal production for use by the power sector, a large component of total 

coal production, will decline by about [X] percent in 2025 and by about [X] percent in 2030, 

relative to base case levels. 

The EPA also projects that the electric power sector-delivered natural gas prices will

increase by about [X] percent in 2020, increase by about [X] percent in 2025, and increase by 

about [X] percent in 2030, relative to base case levels. 

Natural gas use for electricity generation will increase by as much as [X] trillion cubic 

feet (TCF) in 2025 relative to the base case, declining over time. 

Renewable energy capacity is anticipated to increase by roughly [X] GW in 2025 and by

[X] GW in 2030 under the final guidelines. Energy market impacts from the guidelines are 

discussed more extensively in Chapter 3 of this RIA. 

Changes in supply or demand for electricity, natural gas, and coal can impact markets for 

goods and services produced by sectors that use these energy inputs in the production process or 

that supply those sectors. Changes in cost of production may result in changes in price and/or 

quantity produced by these sectors and these market changes may affect the profitability of firms 

and the economic welfare of their consumers. The EPA recognizes that these final emission 

guidelines provide flexibility, and states implementing the guidelines may choose to mitigate 
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impacts to some markets outside the EGU sector. Similarly, demand for new generation or 

energy efficiency, for example, can result in changes in production and profitability for firms that 

supply those goods and services. 

ES.9 Employment Impacts

Executive Order 13563 directs federal agencies to consider the effect of regulations on 

job creation and employment. According to the Executive Order, “our regulatory system must 

protect public health, welfare, safety, and our environment while promoting economic growth, 

innovation, competitiveness, and job creation. It must be based on the best available science” 

(Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically 

included a separate analysis of regulation-induced employment impacts, we typically conduct 

employment analyses. During the current economic recovery, employment impacts are of 

particular concern and questions may arise about their existence and magnitude.

Given the wide range of approaches that may be used to meet the requirements of the 

Clean Power Plan Final Rule, quantifying the associated employment impacts is difficult. The 

EPA’s illustrative employment analysis includes an estimate of projected employment impacts 

associated with these guidelines for the utility power sector, coal and natural gas production, and 

demand-side energy efficiency activities. These projections are derived, in part, from a detailed 

model of the utility power sector used for this regulatory analysis, and U.S government data on 

employment and labor productivity. 

In the electricity, coal, and natural gas sectors, the EPA estimates that these guidelines 

could result in an increase of approximately [X] to [X] job-years in 2025 for the final guidelines

under the rate-based illustrative plan approach and [X] to [X] job-years in 2025 under the massbased approach. The Agency is also offering an illustrative calculation of potential employment 

effects due to demand-side energy efficiency programs. Employment impacts in 2025 could be 

an increase of approximately [X] jobs for the final guidelines. More detail about these analyses 

can be found in Chapter 6 of this RIA.
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ES.10 References

Bell, M.L., A. McDermott, S.L. Zeger, J.M. Sarnet, and F. Dominici. 2004. “Ozone and ShortTerm Mortality in 95 U.S. Urban Communities, 1987-2000.” Journal of the American 

Medical Association. 292(19):2372-8.

Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working 

Group on Social Cost of Carbon, with participation by the Council of Economic Advisers, 

Council on Environmental Quality, Department of Agriculture, Department of Commerce, 

Department of Energy, Department of Transportation, Environmental Protection Agency, 

National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February

2010). Available at: <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf>.

Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the 

Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, 

Interagency Working Group on Social Cost of Carbon, with Participation by Council of 

Economic Advisers, Council on Environmental Quality, Department of Agriculture, 

Department of Commerce, Department of Energy, Department of Transportation, Domestic 

Policy Council, Environmental Protection Agency, National Economic Council, Office of 

Management and Budget, Office of Science and Technology Policy, and Department of 

Treasury (May 2013, Revised November 2013). Also available at: 

<http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf>.

Fann, N., K.R. Baker, and C.M. Fulcher. 2012. “Characterizing the PM2.5-Related Health 

Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across 

the U.S.” Environment International. 49:41–151. 

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.

Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis 

Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the 

IPCC. Available at: 

<http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_synt

hesis_report.htm>. Accessed June 6, 2015.
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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.

National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee on 

the Toxicological Effects of Methylmercury.” Board on Environmental Studies and 

Toxicology. National Academies Press. Washington, DC. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. National Academies Press. Washington, DC.

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for 

Sulfur Oxides—Health Criteria (Final Report). National Center for Environmental 

Assessment – RTP Division, Research Triangle Park, NC. September. Available at: 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>. Accessed June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Final Ozone NAAQS Regulatory 

Impact Analysis. EPA-452/R-08-003. Office of Air Quality Planning and Standards Health 

and Environmental Impacts Division, Air Benefit and Cost Group Research Triangle Park, 

NC. March. Available at: < http://www.epa.gov/ttnecas1/regdata/RIAs/6-

ozoneriachapter6.pdf>. Accessed June 4, 2015.

U.S. EPA. 2008c. Integrated Science Assessment for Oxides of Nitrogen: Health Criteria (Final 

Report). Research Triangle Park, NC: National Center for Environmental Assessment. July. 

Available at < http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for 

Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. July. Available at: 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>. Accessed June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2009b. 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>. Accessed June 4, 2015.
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U.S. Environmental Protection Agency (U.S. EPA). 2010d. Section 3: Re-analysis of the Benefits 

of Attaining Alternative Ozone Standards to Incorporate Current Methods. Available at: 

<http://www.epa.gov/ttnecas1/regdata/RIAs/s3-supplemental_analysis-updated_benefits11-

5.09.pdf >. Accessed June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the 

Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Quality Planning and Standards, Health and Environmental 

Impacts Division, Research Triangle Park, NC. December. Available at: <

http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf>. Accessed June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2013b. 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>. Accessed June 4, 

2015.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The relationship between selected causes 

of postneonatal infant mortality and particulate air pollution in the United States.” 

Environmental Health Perspectives. 105(6): 608-612.
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CHAPTER 1: INTRODUCTION AND BACKGROUND FOR THE CLEAN POWER PLAN

1.1 Introduction

This document presents estimates of potential benefits, costs, and economic impacts of 

illustrative approaches states may implement to comply with the Final Carbon Pollution 

Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units (herein 

referred to as “final emission guidelines” or the “Clean Power Plan Final Rule”). This chapter 

contains background information on these rules and an outline of the chapters in the report.

1.2 Legal, Scientific and Economic Basis for this Rulemaking

1.2.1 Statutory Requirement

Clean Air Act section 111, which Congress enacted as part of the 1970 Clean Air Act 

Amendments, establishes mechanisms for controlling emissions of air pollutants from stationary

sources. This provision requires the EPA to promulgate a list of categories of stationary sources 

that the Administrator, in his or her judgment, finds “causes, or contributes significantly to, air 

pollution which may reasonably be anticipated to endanger public health or welfare.”12 The EPA 

has listed more than 60 stationary source categories under this provision.13 Once the EPA lists a 

source category, the EPA must, under CAA section 111(b)(1)(B), establish “standards of 

performance” for emissions of air pollutants from new sources in the source categories.14 These 

standards are known as new source performance standards (NSPS), and they are national 

requirements that apply directly to the sources subject to them. 

When the EPA establishes NSPS for new sources in a particular source category, the 

EPA is also required, under CAA section 111(d)(1), to prescribe regulations for states to submit 

plans regulating existing sources in that source category for any air pollutant that, in general, is 

not regulated under the CAA section 109 requirements for the NAAQS or regulated under the 

CAA section 112 requirements for hazardous air pollutants (HAP). CAA section 111(d)’s 

 

12 CAA §111(b)(1)(A).

13 See 40 CFR 60 subparts Cb – OOOO.

14 CAA §111(b)(1)(B), 111(a)(1).
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mechanism for regulating existing sources differs from the one that CAA section 111(b) provides 

for new sources because CAA section 111(d) contemplates states submitting plans that establish 

“standards of performance” for the affected sources and that contain other measures to 

implement and enforce those standards. 

“Standards of performance” are defined under CAA section 111(a)(1) as standards for 

emissions that reflect the emission limitation achievable from the “best system of emission 

reduction,” considering costs and other factors, that “the Administrator determines has been 

adequately demonstrated.” CAA section 111(d)(1) grants states the authority, in applying a 

standard of performance to a particular source, to take into account the source’s remaining useful 

life or other factors. 

Under CAA section 111(d), a state must submit its plan to the EPA for approval, and the 

EPA must approve the state plan if it is “satisfactory.”15 If a state does not submit a plan, or if the 

EPA does not approve a state’s plan, then the EPA must establish a plan for that state.16 Once a 

state receives the EPA’s approval of its plan, the provisions in the plan become federally 

enforceable against the entity responsible for noncompliance, in the same manner as the 

provisions of an approved State Implementation Plan (SIP) under the Act.

1.2.2 Health and Welfare Impacts from Climate Change

According to the National Research Council, “Emissions of CO2 from the burning of 

fossil fuels have ushered in a new epoch where human activities will largely determine the 

evolution of Earth’s climate. Because CO2 in the atmosphere is long lived, it can effectively lock 

Earth and future generations into a range of impacts, some of which could become very severe. 

Therefore, emission reduction choices made today matter in determining impacts experienced 

not just over the next few decades, but in the coming centuries and millennia.”17

 

15 CAA section 111(d)(2)(A).

16 CAA section 111(d)(2)(A).

17 National Research Council, Climate Stabilization Targets, p.3.
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In 2009, based on a large body of robust and compelling scientific evidence, the EPA 

Administrator issued the Endangerment Finding under CAA section 202(a)(1).18 In the 

Endangerment Finding, the Administrator found that the current, elevated concentrations of 

GHGs in the atmosphere—already at levels unprecedented in human history—may reasonably

be anticipated to endanger public health and welfare of current and future generations in the 

United States. 

Since the administrative record concerning the Endangerment Finding closed following 

the EPA’s 2010 Reconsideration Denial, the climate has continued to change, with new records 

being set for a number of climate indicators such as global average surface temperatures, Arctic 

sea ice retreat, CO2 concentrations, and sea level rise. Additionally, a number of major scientific 

assessments have been released that improve understanding of the climate system and strengthen 

the case that GHGs endanger public health and welfare both for current and future generations. 

These assessments are from the Intergovernmental Panel on Climate Change (IPCC), the U.S. 

Global Change Research Program (USGCRP), and the National Research Council (NRC). These 

and other assessments are discussed in more detail in the preamble and in Chapter 4 of this 

Regulatory Impact Assessment (RIA).

1.2.3 Market Failure

Many regulations are promulgated to correct market failures, which lead to a suboptimal 

allocation of resources within the free market. Air quality and pollution control regulations 

address “negative externalities” whereby the market does not internalize the full opportunity cost 

of production borne by society as public goods such as air quality are unpriced. 

GHG emissions impose costs on society, such as negative health and welfare impacts, 

that are not reflected in the market price of the goods produced through the polluting process. 

For this regulatory action the good produced is electricity. These social costs associated with the 

health and welfare impacts are referred to as negative externalities. If a fossil fuel-fired 

electricity producer pollutes the atmosphere when it generates electricity, this cost will be borne 

not by the polluting firm but by society as a whole. The equilibrium market price of electricity 

 

18 “Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air 

Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”).
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may fail to incorporate the full opportunity cost to society of generating electricity. All else 

equal, given this externality, the composition of EGUs used to generate electricity in a free 

market will not be socially optimal, and the quantity of electricity generated may not be at the 

socially optimal level. More electricity may be produced from fossil fuel-fired EGUs than would 

occur if they had to account for the full opportunity cost of production including the negative 

externality. Consequently, absent a regulation on emissions, the composition of the fleet of 

EGUs used to generate electricity may not be socially optimal, and the marginal social cost of 

the last unit of electricity produced will exceed its marginal social benefit. This regulation will 

address this market failure by beginning to internalize the negative externality by reducing CO2

emissions from existing EGUs which increases social welfare. 

1.3 Summary of Regulatory Analysis

In accordance with Executive Order 12866, Executive Order 13563, OMB Circular A-4, 

and the EPA’s “Guidelines for Preparing Economic Analyses,” the EPA prepared this RIA for 

this “significant regulatory action.” This action is an economically significant regulatory action 

because it is expected to have an annual effect on the economy of $100 million or more or 

adversely affect in a material way the economy, a sector of the economy, productivity, 

competition, jobs, the environment, public health or safety, or state, local, or tribal governments 

or communities. 

This RIA addresses the potential costs, emission reductions, and benefits of the final

emission guidelines that are the focus of this action. Additionally, this RIA includes information 

about potential impacts on electricity markets, employment, and markets outside the electricity 

sector. 

In evaluating the impacts of the final guidelines, we analyzed a number of uncertainties. 

For example, the analysis includes an evaluation of two approaches to formulate state emission 

performance goals, a rate-based and a mass-based approach. The RIA also examines key

uncertainties in the estimated benefits of reducing carbon dioxide and other air pollutants. For a 

further discussion of key evaluations of uncertainty in the regulatory analyses for this 

rulemaking, see Chapter 8 of this RIA.
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1.4 Background for the Final Emission Guidelines

1.4.1 Base Case and Years of Analysis

The rule analyzed in this RIA finalizes emission guidelines for states to limit CO2

emissions from certain existing EGUs. The base case for this analysis, which uses the Integrated 

Planning Model (IPM), includes state rules that have been finalized and/or approved by a state’s 

legislature or environmental agencies, as well as final federal rules. The IPM Base Case v.5.15 

includes the Cross-State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Rule 

(MATS), the proposed Carbon Pollution Standards for New Power Plants, the Cooling Water 

Intakes (316(b)) Rule, the Combustion Residuals from Electric Utilities (CCR), and other state 

and Federal regulations to the extent that they contain measures, permits, or other air-related 

limitations or requirements. Additional legally binding and enforceable commitments for GHG 

reductions considered in the base case are discussed in the documentation for IPM.19

Costs and benefits are presented for illustrative plan approaches for the analysis years of 

2020, 2025, and 2030. These years were selected because they represent initial build up, interim, 

and full implementation years for the two illustrative approaches analyzed. Analyses of energy, 

economic, and employment impacts are presented for illustrative plan approaches in 2020, 2025, 

and 2030. All dollar estimates are presented in 2011 dollars. 

1.4.2 Definition of Affected Sources

For the emission guidelines, an affected EGU is any steam generating unit, integrated 

gasification combined cycle (IGCC), or stationary combustion turbine that was in operation or 

had commenced construction as of January 8, 2014,20 and that meets the following criteria, which 

differ depending on the type of unit. To be an affected source, such a unit, if it is a steam 

generating unit or IGCC, must serve a generator capable of selling greater than 25 MW to a 

utility power distribution system and have a base load rating greater than 260 GJ/h (250 

MMBtu/h) heat input of fossil fuel (either alone or in combination with any other fuel). If such a 

 

19 Detailed documentation for IPM v.5.15 is available at: http://www.epa.gov/powersectormodeling

20 Under Section 111(a) of the CAA, determination of affected sources is based on the date that the EPA proposes 

action on such sources. January 8, 2014 is the date the proposed GHG standards of performance for new fossil fuelfired EGUs were published in the Federal Register (79 FR 1430).
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unit is a stationary combustion turbine, the unit must meet the definition of a combined cycle or 

combined heat and power combustion turbine, serve a generator capable of selling greater than 

25 MW to a utility power distribution system, and have a base load rating of greater than 260 

GJ/h (250 MMBtu/h). Certain EGUs are exempt from inclusion in a state plan. For specifics on 

these criteria see section IV of the preamble. 

When considering and understanding applicability, the following definitions may be 

helpful. Simple cycle combustion turbine means any stationary combustion turbine which does 

not recover heat from the combustion turbine engine exhaust gases for purposes other than 

enhancing the performance of the stationary combustion turbine itself. Combined cycle 

combustion turbine means any stationary combustion turbine which recovers heat from the 

combustion turbine engine exhaust gases to generate steam that is used to create additional 

electric power output in a steam turbine. Combined heat and power (CHP) combustion turbine 

means any stationary combustion turbine which recovers heat from the combustion turbine 

engine exhaust gases to heat water or another medium, generate steam for useful purposes other 

than exclusively for additional electric generation, or directly uses the heat in the exhaust gases 

for a useful purpose.

1.4.3 Regulated Pollutant

The purpose of this CAA section 111(d) rule is to address CO2 emissions from fossil 

fuel-fired power plants in the U.S. because they are the largest domestic stationary source of 

emissions of carbon dioxide (CO2), the most prevalent of the greenhouse gases (GHG), which 

are air pollutants that the EPA has determined endangers public health and welfare through their

contribution to climate change. This rule establishes for the first time federal emission guidelines 

for existing power plants that will lead to significant reductions in CO2 emissions.

1.4.4 Emission Guidelines

In this action, the Environmental Protection Agency (EPA) is establishing final emission 

guidelines for states, U.S. territories, and tribes to follow in developing plans to reduce 

greenhouse gas emissions from existing fossil fuel-fired electric generating units (EGUs). 

Specifically, the EPA is establishing: 1) carbon dioxide (CO2) emission performance rates for 

two source categories of existing fossil fuel-fired EGUs, fossil fuel-fired electric utility steam 

generating units and stationary combustion turbines, and 2) guidelines for the development, 
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submittal and implementation of state plans that implement the CO2 emission performance rates. 

This final rule will continue progress already underway in the U.S. to reduce CO2 emissions 

from the utility power sector.

1.5 Organization of the Regulatory Impact Analysis

This report presents the EPA’s analysis of the potential benefits, costs, and other 

economic effects of the final emission guidelines to fulfill the requirements of an RIA. This RIA 

includes the following chapters:

 Chapter 2, Electric Power Sector Profile

 Chapter 3, Control Strategies, Cost, Economic, and Energy Impacts

 Chapter 4, Estimated Climate Benefits and Health Co-benefits

 Chapter 5, Economic Impacts – Markets Outside the Electricity Sector

 Chapter 6, Employment

 Chapter 7, Statutory and Executive Order Analyses

 Chapter 8, Summary of Benefits and Cost of the Final Regulation

1.6 References

40 CFR Chapter I [EPA–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, 

“Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) 

of the Clean Air Act,” Federal Register / Vol. 74, No. 239 / Tuesday, December 15, 2009 / 

Rules and Regulations.

75 FR 49556. August 13, 2010. “EPA’s Denial of the Petitions to Reconsider the Endangerment 

and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean 

Air Act.”

Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds., 2014: Climate Change Impacts in the 

United States: The Third National Climate Assessment. U.S. Global Change Research 

Program. Available at <http://nca2014.globalchange.gov>. Accessed June 4, 2015.

National Research Council. Climate Stabilization Targets: Emissions, Concentrations, and 

Impacts over Decades to Millennia. Washington, DC: The National Academies Press, 2011.
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U.S. Environmental Protection Agency. EPA’s Power Sector Modeling Platform v.5.14. March 

25, 2015. Available online at <http://www.epa.gov/powersectormodeling/psmodel514.html>. 

Accessed June 4, 2015.
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CHAPTER 2: INDUSTRY PROFILE

2.1 Introduction

This chapter discusses important aspects of the power sector that relate to the Final Carbon 

Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units, 

including the types of power-sector sources affected by the regulation, and provides background 

on the power sector and EGUs. In addition, this chapter provides some historical background on 

trends in the past decade in the power sector, as well as about existing EPA regulation of the 

power sector

In the past decade there have been significant structural changes in the 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 

US economy, growth and regional changes in the US 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 in 

the future. The evolving economics of the power sector, in particular the increased natural gas 

supply and subsequent relatively low natural gas prices, have resulted in more gas being utilized 

as base load energy in addition to supplying electricity during peak load. This chapter presents 

data on the evolution of the power sector from 2002 through 2012. Projections of new capacity 

and the impact of this rule on these new sources 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. 

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 
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Capacity refers to the EGUs that are available for use in a given year, typically measured in 

megawatts (MW) or gigawatts (1 GW = 1000 MW). Electricity Generation refers to the amount 

of electricity actually produced by EGUs, measured in kilowatt-hours (kWh) or gigawatt-hours 

(GWh = 1 million kWh).

Individual EGUs are not used to generate electricity 100 percent of the time. Not only are 

units unavailable during routine and unanticipated outages for maintenance, but individual EGUs 

are also 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. 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 for generating electricity does so by creating heat to create 

high pressure steam that is released to rotate turbines which, in turn, create electricity. Other 

units generate electricity by using water or wind to rotate turbines, and a variety of other 

methods also make up a small part 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 in 2002 and 

2012.

In 2012 the power sector consists of over 19,000 generating units with a total capacity21 of 

1,168 GW, an increase of 19 percent from the capacity in 2002 (989 GW). The 188 GW increase 

in capacity consisted primarily of natural gas fired EGUs (134 GW) and wind generators (55 

GW), with substantially smaller net increases and decreases in other types of generating units. 

 

21 As with all data presented in this section, this includes generating capacity not only at EGUs primarily operated to 

supply electricity to the grid, but also generating capacity at commercial and industrial facilities that produce both 

electricity used onsite as well as dispatched to the grid.
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Table 2-1. Existing Electricity Generating Capacity by Energy Source, 2002 and 2012

2002 2012 Change Between '02 and '12

Energy Source

Generator 

Nameplate 

Capacity 

(MW)

% Total 

Capacity

Generator 

Nameplate 

Capacity 

(MW)

% Total 

Capacity

% 

Increase

Nameplate 

Capacity 

Change 

(MW)

% of 

Total 

Capacity 

Increase

Coal 338,199 35% 336,341 29% -1% -1,858 -1%

Natural Gas1352,128 36% 485,957 42% 38% 133,829 71%

Nuclear 104,933 11% 107,938 9% 3% 3,005 2%

Hydro 96,344 10% 99,099 8% 3% 2,755 1%

Petroleum 66,219 7% 53,789 5% -19% -12,430 -7%

Wind 4,531 0.5% 59,629 5.1% 1216% 55,098 29%

Other Renewable 14,208 1.5% 20,986 1.8% 47.7% 6,778 3.6%

Misc 3,023 0.3% 4,257 0.4% 40.8% 1,234 0.7%

Total 979,585 100% 1,167,995 100% 19% 188,410 100%

Note: This table presents generation capacity. Actual net generation is presented in Table 2-2.

Source: U.S. EIA Electric Power Annual, 2012. Downloaded from EIA Electricity Data Browser, Electric Power 

Plants Generating Capacity By Source, 2000 – 2012. Available at 

<http://www.eia.gov/electricity/data.cfm#gencapacity.> Accessed 12/19/2014

1 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 (31 percent of 2012 

capacity), Gas Turbine (30 percent), Combined Cycle Steam (19 percent), Steam Turbine (17 percent), and 

miscellaneous (< 1 percent).

The 19 percent increase in generating capacity increase is the net impact of newly built 

generating units, retirements of generating units, and a variety of increases and decreases to the 

rated capacity of individual existing units due to unit modifications, changes in emission control 

equipment, etc. During the period 2002 to 2012, a total of 315,752 MW of new generating 

capacity was built and brought online, and 64,763 MW existing units were retired. The net effect 

of the re-rating of existing units reduced the total capacity by 62,579 MW. The overall net 

change in capacity was 188,410 MW, as shown in Table 2-1.

The newly built generating capacity was primarily natural gas (226,605 MW), which was 

partially offset by gas retirements (29,859 MW). Wind capacity was the second largest type of 

new builds (55,583 MW), augmented by 2,807 MW of solar.22 The overall mix of newly built 

and retired capacity, along with the net effect, is shown on Figure 2-1.

 

22 Partially offset by 87 MW retired wind or solar capacity (87 MW). 
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Figure 2-1. New Build and Retired Capacity (MW) by Fuel Type, 2002-2012

Source: EIA Form 860

Not displayed: wind and solar retirements = 87 MW, net change in coal capacity = -56 MW

In 2012, electric generating sources produced a net 4,058 billion kWh to meet electricity

demand, a 5 percent increase from 2002 (3,858 billion kWh). As presented in Table 2-2, almost 

70 percent of electricity in 2012 was produced through the combustion of fossil fuels, primarily

coal and natural gas, with coal accounting for the largest single share. Although the share of the 

total generation from fossil fuels in 2012 (67 percent) was only modestly smaller than the total 

fossil share in 2002 (71 percent), the mix of fossil fuel generation changed substantially during 

that period. Coal generation declined by 18 percent and petroleum generation by 63 percent, 

while natural gas generation increased by 61 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. Wind generation also grew from a very small portion of the overall 

total in 2002 to 4.1 percent of the 2012 total.

-50,000

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

New Build Retirement Net Change

Change in Capacity (M

W)

Coal Nat Gas Wind & Solar Oil & Other
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Table 2-2. Net Generation in 2002 and 2012 (Trillion kWh = TWh)

2002 2012 Change Between '02 and '12

Net 

Generation 

(TWh)

Fuel 

Source 

Share

Net 

Generation 

(TWh)

Fuel 

Source 

Share

Net 

Generation 

Change 

(TWh)

% Change in 

Net 

Generation

Coal 1,933.1 50% 1,586.0 39% -347.1 -18.0%

Natural Gas 702.5 18% 1,125.9 28% 423.5 60.3%

Nuclear 780.1 20% 789.0 19% 9.0 1.1%

Hydro 255.6 7% 264.7 7% 9.1 3.6%

Petroleum 94.6 2.5% 26.9 0.7% -67.7 -71.6%

Wind 10.4 0.3% 167.7 4.1% 157.3 1519.3%

Other Renewable 68.8 1.8% 85.7 2.1% 16.9 24.6%

Misc 13.5 0.4% 12.4 0.3% -1.2 -8.7%

Total 3,858 100% 4,058 100% 200 5%

Source: U.S. EIA Monthly Energy Review, December 2014. Table 7.2a Electricity Net Generation: Total (All 

Sectors). Available at <http://www.eia.gov/totalenergy/data/monthly/. Accessed 12/19/2014

Coal-fired and nuclear generating units have historically supplied “base-load” electricity, 

the portion of electricity loads which are continually present, and typically operate throughout all 

hours of the year. The coal units meet the part of demand that is relatively constant. Although 

much of the coal fleet operates 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 

compose 37 percent of the total number of coal-fired units, but only 6 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. 

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 
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and newer. While 55 percent of the coal EGU fleet is over 500 MW per unit, 77 percent of the 

gas fleet is between 50 and 500 MW per unit. 

Table 2-3. Coal and Natural Gas Generating Units, by Size, Age, Capacity, and 

Thermal Efficiency (Heat Rate)

Unit Size 

Grouping 

(MW)

No. 

Units

% of All 

Units

Avg. 

Age

Avg. Net 

Summer 

Capacity 

(MW)

Total Net 

Summer 

Capacity 

(MW)

% Total 

Capacity

Avg. Heat 

Rate 

(Btu/kWh)

COAL

0 – 24 223 18% 40.7 11.4 2,538 1% 11,733

25 – 49 108 9% 44.2 36.7 3,963 1% 11,990

50 – 99 157 12% 49.0 74.1 11,627 4% 11,883

100 - 149 128 10% 50.6 122.7 15,710 5% 10,971

150 - 249 181 14% 48.7 190.4 34,454 11% 10,620

250 - 499 205 16% 38.4 356.2 73,030 23% 10,502

500 - 749 187 15% 35.4 604.6 113,056 36% 10,231

750 - 999 57 5% 31.4 823.9 46,963 15% 9,942

1000 - 1500 11 1% 35.7 1259.1 13,850 4% 9,732

Total Coal 1257 100% 42.6 250.7 315,191 100% 11,013

NATURAL GAS

0 – 24 1992 37% 37.6 7.0 13,863 3% 13,531

25 – 49 410 8% 21.8 125.0 51,247 12% 9,690

50 – 99 962 18% 15.6 174.2 167,536 39% 8,489

100 - 149 802 15% 23.4 39.9 31,982 8% 11,765

150 - 249 167 3% 28.7 342.4 57,179 13% 9,311

250 - 499 982 18% 24.6 71.1 69,788 16% 12,083

500 - 749 37 1% 40.0 588.8 21,785 5% 11,569

750 - 1000 14 0.3% 35.9 820.9 11,492 3% 10,478

Total Gas 5366 100% 27.7 79.2 424,872 100% 11,652

Source: National Electric Energy Data System (NEEDS) v.5.13

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. Table is limited to coal-steam units in operation in 2013 or earlier, and excludes those units in NEEDS 

with planned retirements in 2014-2015.

In terms of the age of the generating units, 50 percent of the total coal generating capacity

has been in service for more than 38 years, while 50 percent of the natural gas capacity has been 

in service less than 15 years. Figure 2-2 presents the cumulative age distributions of the coal and 
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2-7

gas fleets, highlighting the pronounced differences in the ages of the fleets of these two types of 

fossil-fuel generating capacity. Figure 2-2 also includes the distribution of generation.

Figure 2-2. Cumulative Distribution in 2010 of Coal and Natural Gas Electricity 

Capacity and Generation, by Age

Source: National Electric Energy Data System (NEEDS) v.5.13

Not displayed: coal units (376 MW total, 1 percent of total) and gas units (62 MW, < .01 percent of total)) over 70 

years old for clarity. Figure is limited to coal-steam units in NEEDS in operation in 2013 or earlier (excludes ~2,100 

MW of coal-fired IGCC and fossil waste capacity), and excludes those units in NEEDS with planned retirements in 

2014-2015.

The locations of existing fossil units in EPA”s National Electric Energy Data System 

(NEEDS) v.5.13 are shown in Figure 2-3.
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Figure 2-3. Fossil Fuel-Fired Electricity Generating Facilities, by Size

Source: National Electric Energy Data System (NEEDS) v.5.13

Note: This map displays fossil capacity at facilities in the NEEDS v.5.13 IPM frame. NEEDS reflects available 

fossil capacity on-line by the end of 2015. 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 movement 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,23 each operating synchronously. Within each of these transmission 

networks, there are multiple areas where the operation of power plants is monitored and 

controlled to ensure that electricity generation and load are kept in balance. In some areas, the 

 

23 These three network interconnections are the western US and Canada, corresponding approximately to the area 

west of the Rocky Mountains; eastern US and Canada, not including most of Texas; and a third network operating in 

most of Texas. These are commonly referred to as the Western Interconnect Region, Eastern Interconnect Region, 

and ERCOT, respectively.
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2-9

operation of the transmission system is under the control of a single regional operator; in others, 

individual utilities coordinate the operations of their generation, transmission, and distribution 

systems to balance their common generation and load needs.

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.

Transmission has generally been developed by the larger vertically integrated utilities that 

typically operate generation and distribution networks. Often distribution is handled by a large 

number of utilities that purchase and sell electricity, but do not generate it. Over the last couple 

of decades, several jurisdictions in the United States began restructuring the power industry to 

separate transmission and distribution from generation, ownership, and operation. 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 commercial, industrial and 

residential ultimate customers. Each of the three major ultimate categories consume roughly a 

quarter to a third of the total electricity produced24 (see Table 2-4). Some of these uses are highly 

variable, such as heating and air conditioning in residential and commercial buildings, while 

 

24 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 2002 and 2012.

Table 2-4. Total U.S. Electric Power Industry Retail Sales in 2012 (billion kWh)

Sales/Direct Use 

(Billion kWh)

Share of

Total End 

Use

Sales/Direct 

Use (Billion 

kWh)

Share of Total 

End Use

Residential 1,265 35% 1,375 35.9%

Commercial 1,104 30% 1,327 34.6%

Industrial 990 27% 986 25.7%

Transportation NA 7 0.2%

Other 106 3% NA

Total 3,465 95% 3,695 96%

166 5% 138 4%

3,632 100% 3,832 100%

Sales

Direct Use

Total End Use

2002 2012

Source: Table 2.2, EIA Electric Power Annual, 2013

Notes: Retail sales are not equal to net generation (Table 2-2) because net generation includes net exported 

electricity and loss of electricity that occurs through transmission and distribution.

Direct Use represents commercial and industrial facility use of onsite net electricity generation; and electricity sales 

or transfers to adjacent or co-located facilities for which revenue information is not available.

2.3.1 Electricity Prices

Electricity prices vary substantially across the United States, differing both between the 

ultimate customer categories and also 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 high distribution 

costs are the result both of the necessary distribution network reaching to virtually every part of 

the country, and also that generating stations are increasingly located relatively far from 

population centers. 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 
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2-11

customer prices are usually considerable closer to the wholesale marginal cost of generating 

electricity than residential and commercial prices.

On a state-by-state basis, all retail electricity prices vary considerably. The Northeast, 

California and Alaska have average retail prices that can be as much as double those of other 

states (see Figure 2-4), and Hawaii has electricity.

Figure 2-4. Average Retail Electricity Price by State (cents/kWh), 2011

Average national overall retail electricity prices increased between 2002 and 2012 by 36.7 

percent in nominal (current year $) terms. The amount of increase differed for the three major 

end use categories (residential, commercial and industrial). National average residential prices 

increased the most (40.8 percent), and commercial prices increased the least (27.9 percent). The 

nominal year prices for 2002 through 2012 are shown in Figure 2-5. 
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2-12

Figure 2-5. Nominal National Average Electricity Prices for Three Major End-Use 

Categories

Source: EIA AEO 2012, Table 2.4

Electricity prices for all three end-use categories increased more than overall inflation 

through this period, measured by either the GDP implicit price deflator (23.5 percent) or the 

consumer price index (CPI-U, which increased by 27.7 percent). Most of these electricity price 

increases occurred between 2002 and 2008; since 2008 nominal electricity prices have been 

relatively stable while overall inflation continued to increase. The increase in nominal electricity 

prices for the major end use categories, as well as increases in the GDP price and CPI-U indices 

for comparison, are shown in Figure 2-6.

.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

2002 2004 2006 2008 2010 2012

Electricity Price (Cents/kWh, nominal $

)

Residential Commercial Industrial Total
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2-13

Figure 2-6. Relative Increases in Nominal National Average Electricity Prices for Major 

End-Use Categories, With Inflation Indices

The real (inflation-adjusted) change in average national electricity prices can be calculated 

using the GDP implicit price deflator. Figure 2-7 shows real25 (2011$) electricity prices for the 

three major customer categories from 1960 to 2012, and Figure 2-8 shows the relative change in 

real electricity prices relative to the prices in 1960. As can be seen in the figures, the price for 

industrial customers has always been lower than for either residential or commercial customers, 

but the industrial price has been more volatile. While the industrial real price of electricity in 

2012 was relatively unchanged from 1960, residential and commercial real prices are 23% and 

28% lower respectively than in 1960.

 

25 All prices in this section are estimated as real 2011 prices adjusted using the GDP implicit price deflator unless 

otherwise indicated.
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2-14

Figure 2-7. Real National Average Electricity Prices (2011$) for Three Major End-Use 

Categories

Source: EIA Monthly Energy Review, April 2015, Table 9.8

0

2

4

6

8

10

12

14

16

18

1960 1970 1980 1990 2000 2010

Real Electricity Prices (2011$, ¢ /kWh)

Real Electricity Prices, 1960-2014 (including taxes)

Residential Commercial Industrial Total

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

1960 1970 1980 1990 2000 2010

Change in Real Electricity Prices

Relative Change in Electricity Prices,

1960-2014 (including taxes)

Residential Commercial Industrial Total
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2-15

Figure 2-8. Relative Change in Real National Average Electricity Prices (2011$) for 

Three Major End-Use Categories

Source: EIA Monthly Energy Review, April 2015, Table 9.8

2.3.2 Prices of Fossil Fuels Used for Generating Electricity

Another important factor in the changes in electricity prices are the changes in fuel prices 

for the three major fossil fuels used in electricity generation; coal, natural gas and oil. Relative to 

real prices in 2002, the national average real price (in 2011$) of coal delivered to EGUs in 2012 

had increased by 54 percent, while the real price of natural gas decreased by 22 percent. The real 

price of oil increased by 203 percent, but with oil declining as an EGU fuel (in 2012 oil 

generated only 1 percent of electricity) the doubling of oil prices had little overall impact in the 

electricity market. The combined real delivered price of all fossil fuels in 2012 increased by 23 

percent over 2002 prices. Figure 2-9 shows the relative changes in real price of all 3 fossil fuels 

between 2002 and 2012.

Figure 2-9. Relative Real Prices of Fossil Fuels for Electricity Generation; Change in 

National Average Real Price per MBtu Delivered to EGU

Source: EIA AEO 2012, Table 9.9
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2.3.3 Changes in Electricity Intensity of the U.S. Economy Between 2002 to 2012

An important aspect of the changes in electricity generation (i.e., electricity demand) 

between 2002 and 2012 is that while total net generation increased by 4.9 percent over that 

period, the growth of generation is lower than both the population growth (9.2 percent) and real 

GDP growth (19.8 percent). Figure 2-10 shows the growth of electricity generation, population 

and real GDP during this period.

Figure 2-10. Relative Growth of Electricity Generation, Population and Real GDP Since 

2002

Sources: U.S. EIA Monthly Energy Review, December 2014. Table 7.2a Electricity Net Generation: Total (All 

Sectors). U.S. Census.

Because electricity generation grew slower 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 2002 to 2012. On a per capita basis, real GDP per capita grew by 10.9 

percent, increasing from $44,900 (in 2011$) per person in 2002 to $49,800/person in 2012. At 

the same time electricity generation per capita decreased by 3.9 percent, declining from 13.4 

MWh/person in 2002 to 12.8 MWh/person in 2012. The combined effect of these two changes 

improved the overall electricity efficiency of the U.S. market economy. Electricity generation 

per dollar of real GDP decreased 12.5 percent, declining from 299 MWh per $1 million of GDP 

to 261 MWh/$1 million GDP). These relative changes are shown in Figure 2-11. Figures 2-10 
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2-17

and 2-11 clearly show the effects of the 2007 – 2009 recession on both GDP and electricity 

generation, as well as the effects of the subsequent economic recovery.

Figure 2-11. Relative Change of Real GDP, Population and Electricity Generation 

Intensity Since 2002

Sources: U.S. EIA Monthly Energy Review, December 2014. Table 7.2a Electricity Net Generation: Total (All 

Sectors). U.S. Census

2.4 Deregulation and Restructuring

The process of restructuring and deregulation of wholesale and retail electric markets has 

changed the structure of the electric power industry. In addition to reorganizing asset 

management between companies, restructuring sought a functional unbundling of the generation, 

transmission, distribution, and ancillary services the power sector has historically provided, with 

the aim of enhancing competition in the generation segment of the industry.

Beginning in the 1970s, government policy shifted against traditional regulatory

approaches and in favor of deregulation for many important industries, including transportation 

(notably commercial airlines), communications, and energy, which were all thought to be natural 

monopolies (prior to 1970) that warranted governmental control of pricing. However, 

deregulation efforts in the power sector were most active during the 1990s. Some of the primary

drivers for deregulation of electric power included the desire for more efficient investment 
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2-18

choices, the economic incentive to provide least-cost electric rates through market competition, 

reduced costs of combustion turbine technology that opened the door for more companies to sell 

power with smaller investments, and complexity of monitoring utilities’ cost of service and 

establishing cost-based rates for various customer classes. Deregulation and market restructuring 

in the power sector involved the divestiture of generation from utilities, the formation of 

organized wholesale spot energy markets with economic mechanisms for the rationing of scarce 

transmission resources during periods of peak demand, the introduction of retail choice 

programs, and the establishment of new forms of market oversight and coordination.

The pace of restructuring in the electric power industry slowed significantly in response to 

market volatility in California and financial turmoil associated with bankruptcy filings of key

energy companies. By the end of 2001, restructuring had either been delayed or suspended in 

eight states that previously enacted legislation or issued regulatory orders for its implementation 

(shown as “Suspended” in Figure 2-12). Eighteen other states that had seriously explored the 

possibility of deregulation in 2000 reported no legislative or regulatory activity in 2001 (EIA, 

2003) (“Not Active” in Figure 2-12). Currently, there are 15 states plus the District of Columbia 

where price deregulation of generation (restructuring) has occurred (“Active” in Figure 2-12). 

Power sector restructuring is more or less at a standstill; by 2010 there were no active proposals 

under review by the Federal Energy Regulatory Commission (FERC) for actions aimed at wider 

restructuring, and no additional states have begun retail deregulation activity since that time.
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2-19

Figure 2-12. Status of State Electricity Industry Restructuring Activities

Source: EIA 2010. “Status of Electricity Restructuring by State.” Available at 

<http://www.eia.gov/cneaf/electricity/page/restructuring/restructure_elect.html.

One major effect of the restructuring and deregulation of the power sector was a significant 

change in type of ownership of electricity generating units in the states that deregulated prices.

Throughout most of the 20th century electricity was supplied by vertically integrated regulated 

utilities. The traditional integrated utilities generation, transmission and distribution in their 

designated areas, and prices were set by cost of service regulations set by state government 

agencies (e.g., Public Utility Commissions). Deregulation and restructuring resulted in 

unbundling of the vertical integration structure. Transmission and distribution continued to 

operate as monopolies with cost of service regulation, while generation shifted to a mix of 

ownership affiliates of traditional utility ownership and some generation owned and operated by 

competitive companies known as Independent Power Producers (IPP). The resulting generating 

sector differed by state or region, as the power sector adapted to the restructuring and 

deregulation requirements in each state. 

By 2002 the major impacts of adapting to changes brought about by deregulation and 

restructuring during the 1990s were largely in place. The resulting ownership mix of generating 
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2-20

capacity (MW) in 2002 was 62 percent of the generating capacity owned by traditional utilities, 

35 percent owned by IPPs26, and 3 percent owned by commercial and industrial producers. The 

mix of electricity generated (MWh) was more heavily weighted towards the utilities, with a 

distribution in 2002 of 66 percent, 30 percent and 4 percent for utilities, IPPs and 

commercial/industrial, respectively.

Since 2002 IPPs have expanded faster than traditional utilities, substantially increasing 

their share by 2012 of both capacity (58 percent utility, 39 percent IPPs, and 3 percent 

commercial/industrial) and generation (58 percent, 38 percent and 4 percent). 

The mix of capacity and generation for each of the ownership types is shown in Figures 2-

13 (capacity) and 2-14 (generation). The capacity and generation data for commercial and 

industrial owners are not shown on these figures due to the small magnitude of those ownership 

types. Figures 2-13 and 2-14 present the mixes in 2002 and 2012. A portion of the shift of 

capacity and generation is due to sales and transfers of generation assets from traditional utilities 

to IPPs, rather than strictly the result of newly built units.

 

26 IPP data presented in this section include both combined and non-combined heat and power plants.
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2-21

Figures 2-13 and 2-14. Capacity and Generation Mix by Ownership Type, 2002 & 

2012

The mix of capacity by fuel types that have been built and retired between 2002 and 2012 

also varies significantly by type of ownership. Figure 2-15 presents the new capacity built during 

that period, showing that IPPs built the majority of both new wind and solar generating capacity, 

as well as somewhat more natural gas capacity than the traditional utilities built. Figure 2-16 

presents comparable data for the retired capacity, showing that utilities retired more coal and 

“other” capacity (mostly oil-fired) than IPPs retired, while the IPPs retired more natural gas 

capacity than the utilities retired. The retired gas capacity was primary (60%) steam and 

combustion turbines.

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

1 2 3 4 5

Capacity (M

W)

Capacity Mix, 2002 & 2012

Series1 Series2 Series3

Series4 Series5 Series6

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

1 2 3 4 5

Generation (G

W)

Generation Mix, 2002 & 2012

Series1 Series2 Series3

Series4 Series5 Series6
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2-22

Figures 2-15 and 2-16. Generation Capacity Built and Retired between 2002 and 2012 

by Ownership Type

2.5 Emissions of Greenhouse Gases from Electric Utilities

The burning of fossil fuels, which generates about 69 percent of our electricity nationwide, 

results in emissions of greenhouse gases. The power sector is a major contributor of CO2 in 

particular, but also contributes to emissions of sulfur hexafluoride (SF6), CH4, and N2O. In 2013, 

the electricity generation accounted for 38 percent of national CO2 emissions. Including both 

generation and transmission (a source of SF6) and generation, the power sector accounted for 31 

percent of total nationwide greenhouse gas emissions (measured in CO2 equivalent). Table 2-5 

and Figure 2-17 show the CO2 emissions from the power sector relative to other major economic 

sectors. Table 2-6 shows the contributions of CO2 and other GHGs from the power and other 

major emitting economic sectors. 

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

1 2

New

Capacity (M

W)

Capacity Built 2002-2012

by Ownership Type

Series1 Series2 Series3 Series4

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

1 2

Retired Capacity (M

W) Capacity Retirements 2002-2012

by Ownership Type

Series1 Series2 Series3 Series4
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2-23

Table 2-5. Domestic Emissions of Greenhouse Gases, by Economic Sector (million metric 

tonnes of CO2 equivalent)

2002 2013 Change Between '02 and '13

Sector/Source

GHG 

Emissions

% Total 

GHG 

Emissions

GHG 

Emissions

% Total 

GHG 

Emissions

Change in 

Emissions

% Change 

in 

Emissions

% of 

Total 

Change in 

Emissions

Electric Power Industry 2,313 33% 2,077 31% -236 -10% 64%

Transportation 1,958 28% 1,806 27% -151 -8% 41%

Industry 1,419 20% 1,392 21% -27 -2% 7%

Agriculture 561 8% 587 9% 26 5% -7%

Commercial 365 5% 401 6% 37 10% -10%

Residential 374 5% 375 6% 1 0% 0%

US Territories 52 < 1% 35 < 1% -17 -33% 5%

Total Emissions 7,041 100% 6,673 100% -368 -5% 100%

Sinks and Reductions -885 -882 4 0%

Net Emissions 6,156 5,791 -368 -6%

Source: EPA, 2015 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012”, Table ES-6. Includes 

CO2, CH4, N2O and SF6 emissions.

Figure 2-17. Domestic Emissions of Greenhouse Gases from Major Sectors, 2002 and 2013

(million metric tonnes of CO2 equivalent)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2002 2013

G

H

G Emissions (Million Tonnes)

Electric Power Industry Transportation Industry

Agriculture Commercial Residential
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

2-24

Source: EPA, 2015 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013”, Table ES-6

Table 2-6. Greenhouse Gas Emissions from the Electricity Sector (Generation, 

Transmission and Distribution), 2002 and 2012 (million metric tonnes of CO2

equivalent)

2002 2013

Change Between '02 and 

'13

Gas/Fuel Type or Source GHG 

Emissions

% of 

Total 

GHG 

Emissions 

from

Power 

Sector

GHG 

Emissions

% of Total 

GHG 

Emissions 

from Power 

Sector

Change in 

Emissions

% Change 

in 

Emissions

CO2 2,287 98.9% 2,052 98.8% -235 -10%

Fossil Fuel 

Combustion

2,273 98.2% 2,040 98.2% -233 -10%

Coal 1,890 81.7% 1,575 75.8% -315 -17%

Natural Gas 306 13.22% 442 21.28% 136 45%

Petroleum 76.8 3.32% 22.4 1.08% -54.4 -71%

Geothermal 0.4 0.02% 0.4 0.02% 0.0 0%

Incineration of Waste 11.8 0.51% 10.1 0.49% -1.7 -14%

Other Process Uses 

of Carbonates

2.6 0.11% 2.2 0.11% -0.4 -15%

CH4 0.4 0.02% 0.4 0.02% 0.0 0%

Stationary 

Combustion*

0.4 0.02% 0.4 0.02% 0.0 0%

Incineration of Waste + + 

N2O 12.4 0.54% 19.4 0.93% 7.0 56%

Stationary 

Combustion*

12.0 0.52% 19.1 0.92% 7.1 59%

Incineration of Waste 0.4 0.02% 0.3 0.01% -0.1 -25%

SF6 13.3 0.57% 5.1 0.25% -8.2 -62%

Electrical 

Transmission and 

Distribution

13.3 0.57% 5.1 0.25% -8.2 -62%

Total GHG Emissions 2,313 2,064 -236 -10%

Source: EPA, 2015 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013”, Table 2-11

* Includes only stationary combustion emissions related to the generation of electricity.

** SF6 is not covered by this rule, which specifically regulates GHG emissions from combustion.

+ Does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.

The amount of CO2 emitted during the combustion of fossil fuels varies according to the 

carbon content and heating value of the fuel used. The CO2 emission factors used in IPM v5.14 
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(same as used in v5.13) are shown in Table 2-7. Coal has higher carbon content than oil or 

natural gas, and thus releases more CO2 during combustion. Coal emits around 1.7 times as much 

carbon per unit of energy when burned as natural gas (EPA 2013).

Table 2-7. Fossil Fuel Emission Factors in EPA Base Case 5.14 IPM Power Sector 

Modeling Application

Fuel Type Carbon Dioxide (lbs/MMBtu)

Coal 

Bituminous 202.8 – 209.6

Subbituminous 209.2 – 215.8

Lignite 212.6 – 219.

Natural Gas 117.1

Fuel Oil 

Distillate 161.4

Residual 161.4 – 173.9

Biomass 195

Waste Fuels 

Waste Coal 204.7

Petroleum Coke 225.1

Fossil Waste 321.1

Non-Fossil Waste 0

Tires 189.5

Municipal Solid Waste 91.9

Source: Documentation for IPM Base Case v.5.13, Table 11-5. The emission factors used in Base Case 5.14 are 

identical to the emission factors in IPM Base Case 5.13.

Note: CO2 emissions presented here for biomass account for combustion only and do not reflect lifecycle 

emissions from initial photosynthesis (carbon sink) or harvesting activities and transportation (carbon 

source).

2.6 Carbon Dioxide Control Technologies

In the power sector, current approaches available for significantly reducing the CO2

emissions of new fossil fuel combustion sources intended for intermediate and baseload 

operations include the use of: carbon capture and storage (CCS), higher-efficiency designs (e.g., 

supercritical or ultrasupercritical steam units, integrated gasification combined cycle (IGCC), or 

combined-cycle combustion-turbine/steam-turbine units), and/or lower-emitting fuels (e.g. 

natural gas rather than coal).
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Daily peak electricity demands, involving operation for relatively few hours per year, are 

often most economically met by simple-cycle combustion turbines (CT). Stationary CTs used for 

power generation can be installed quickly, at relatively low capital cost. They can be remotely 

started and loaded quickly, and can follow rapid demand changes. Full-load efficiencies of large 

current technology CTs are 30-33 percent (high heating value basis), as compared to efficiencies 

of 50 percent or more for new combined-cycle units that recover and use the exhaust heat 

otherwise wasted from a CT . A simple-cycle CT’s lower efficiency causes it to burn much more 

fuel to produce a MWh of electricity than a combined-cycle unit. Thus, when burning natural gas 

its CO2 emission rate per MWh could be 40-60 percent higher than a more efficient NGCC unit. 

Baseload electricity demand can be met with NGCC generation, coal and other fossil-fired 

steam generation, and IGCC technology, as well as generation from sources that do not emit 

CO2, such as nuclear and hydro. IGCC employs the use of a gasifier to transform fossil fuels into 

synthesis gas (“syngas”) and heat. The syngas is used to fuel a combined cycle generator, and the 

heat from the syngas conversion can produce steam for the steam turbine portion of the 

combined cycle generator. Electricity can be generated through this IGCC process somewhat 

more efficiently than through conventional boiler-steam generators. Additionally, with 

gasification, some of the syngas can be converted into other marketable products such as 

fertilizer, and CO2 can be captured for use in EOR.

2.6.1 Carbon Capture and Storage

CCS can be achieved through either pre-combustion or post-combustion capture of CO2

from a gas stream associated with the fuel combusted. Furthermore, CCS can be designed and 

operated for full capture of the CO2 in the gas stream (i.e., above 90 percent) or for partial 

capture (below 90 percent). For post-combustion capture, CO2 is stripped from the flue gas by 

passing the flue gas through a liquid absorbent which selectively reacts with the gaseous carbon 

dioxide to remove it from the combustion gas stream. The absorbent, upon saturation, transfers 

to a downstream operation which regenerates the absorbent by desorbing the CO2 back to 

gaseous form. The absorbent recycles back into the process to repeat the capture cycle while the 

removed carbon dioxide is compressed, sent to storage and sequestered. This process is 

illustrated for a pulverized coal power plant in Figure 2-18. For post-combustion, a station's net 

generating output could be 20-30 percent lower due to the energy needs of the capture process.
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Figure 2-18. Post-Combustion CO2 Capture for a Pulverized Coal Power Plant

Source: Interagency Task Force on Carbon Capture and Storage 2010

Pre-combustion capture is mainly applicable to IGCC facilities, where the fuel is converted 

into gaseous components (“syngas”) under heat and pressure and some percentage of the carbon 

contained in the syngas is captured before combustion. For pre-combustion technology, a 

significant amount of energy is needed to gasify the fuel(s). This process is illustrated in Figure

2-18. Application of post-combustion CCS with IGCC can be designed to use no water-gas shift, 

or single- or two-stage shift processes, to obtain varying percentages of CO2 removal – from a 

“partial capture” percentage to 90 percent “full capture.” Pre-combustion CCS typically has a 

lesser impact on net energy output than does post-combustion CCS. For more detail on CCS 
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technology, see the “Report of the Interagency Task Force on Carbon Capture and Storage” 

(2010).27

 

27 For more information on the cost and performance of CCS, see http://www.netl.doe.gov/energyanalyses/baseline_studies.html.
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Figure 2-18. Pre-Combustion CO2 Capture for an IGCC Power Plant

Source: Interagency Task Force on Carbon Capture and Storage 2010

Carbon capture technology has been successfully applied since 1930 on several smaller 

scale industrial facilities and more recently in a number of demonstration phase projects 

worldwide for power sector applications. In October 2014 the first industrial-scale coal capture 

and storage demonstration project for electricity generation began operation at the Boundary

Dam Power Station in Saskatchewan, Canada. The Boundary Dam CCS project is the first 

industrial-scale electric power project in the world to begin operation. Boundary Dam Station is 

owned by the Province of Saskatchewan, and operated by SaskPower, a provincially owned 

corporation that is the primary electric utility in the Province. The demonstration project retrofit 

Unit 3 (a 130 MW, coal fired built in 1970, and rebuilt in 2013) at a total cost of approximately 

$1.5 billion (Canadian, or about $1.2 billion US), including a partial subsidy of $240 million 

(Canadian) by the Canadian federal government. The carbon capture system is a postcombustion process designed to capture 90 percent of the CO2 emitted by Unit #3. Retrofitting 

the carbon capture system reduced the capacity of the unit to 110 MW. The majority of the 
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captured CO2 is used for an EOR project in southern Saskatchewan. The portion of the CO2 is 

being stored in a nearby research and monitoring geological storage facility, where the captured 

CO2 will be injected 3.4 kilometers underground into a sandstone formation located below the 

major coal field supplying lignite to Unit # 3. The remaining captured CO2 will be injected into 

deep saline formations.

In the United States the Kemper County Carbon Dioxide Capture and Storage Project in 

Mississippi. Construction began in 2010, and the startup is currently scheduled for May, 2016. 

The Kemper County CCS project is constructing a new 524 MW lignite unit as well as a 58 MW 

natural gas unit. Mississippi Power (a division of Southern Power) is building and will operate 

the Kemper County project. The control system is designed to capture 65 percent of the CO2

generated by the plant, and is projected to capture 3.5 million tons of CO2 per year. The resulting

CO2 emission rate is expected to ~800 pounds per MWh produced. The current total cost 

estimate is $5.6 billion, a substantial increase from the original $2.4 billion estimate. The 

construction has received a $270 million grant from the US Department of Energy, and $133 

million in investment tax credits from the Internal Revenue Service. The captured CO2 will be 

transported via a 60 mile pipeline and used for EOR projects in mature Mississippi oil fields.28

The only other industrial-scale electricity power sector CCS project currently under 

construction is the W.A. Parish Petra Nova CCS Project near Houston, Texas. The Parish Petra 

project is a 50/50 partnership between NRG Energy (an integrated electricity company 

generating and supplying electricity to 1.6 million customers in Texas) and the Nippon Oil and 

Gas Exploration Company. The Parish project will retrofit a post-combustion CCS system on a 

portion of the flue gas from the existing 610 MW coal fired Unit # 8. The CCS system will treat 

a 240 MW slipstream of the flue gas, and is designed to capture 90 percent of the CO2 in the 

treated flue gas. The capacity rating of Unit # 8 will not be reduced due to the CCS project 

because an 85 MW custom-built natural gas fired combustion turbine co-generation unit is being 

built on-site to provide both electricity and steam to the CCS unit. The total cost of the CCS 

project is estimated to be $1 billion (including a $167 million grant from the US Department of 

 

28 Carbon Capture and Sequestration Technologies Program at MIT. Accessed 1/23/2015. 

https://sequestration.mit.edu/tools/projects/kemper.html
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Energy), and is expected to extract 1.4 – 1.6 million tons of CO2 per year. The construction 

contract was awarded in July, 2014, and operation is expected to begin in early 2016. The CO2

will be piped 85 miles to a reservoir for EOR in the West Ranch Oil Field.29

2.6.2 Geologic and Geographic Considerations for Geologic Sequestration

Geologic sequestration (GS) (i.e., long-term containment of a CO2 stream in subsurface 

geologic formations) is technically feasible and available throughout most of the United States. 

GS is feasible in different types of geologic formations including deep saline formations 

(formations with high salinity formation fluids) or in oil and gas formations, such as where 

injected CO2 increases oil production efficiency through a process referred to as enhanced oil 

recovery (EOR). CO2 may also be used for other types of enhanced recovery, such as for natural 

gas production. Reservoirs, such as un-mineable coal seams, also offer the potential for geologic 

storage. The geographic availability of deep saline formations, EOR, and un-mineable coal 

seams is shown in Figure 2-14. Estimates of CO2 storage resources by state compiled by the 

DOE’s National Carbon Sequestration Database and Geographic Information System 

(NATCARB) and published in a Carbon Utilization and Storage Atlas (discussed below) are 

provided in Table 2-8.

 

29 US DOE (2010) “Recovery Act: W.A. Parish Post-Combustion CO2 Capture and Sequestration Project”. 

http://www.netl.doe.gov/research/proj?k=FE0003311 Accessed 1/23/2015
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Figure 2-19. Geologic Sequestration in the Continental United States 

Source: EPA 2013: Data sources: EPA’s Greenhouse Gas Reporting Program; Department of Energy, NATCARB; 

Department of Transportation, National Pipeline Management System.
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Table 2-8. Total CO2 Storage Resource30

Million Metric Tons*

State Low Estimate High Estimate

Alabama 122,490 694,380

Alaska 8,640 19,750

Arizona 130 1,170

Arkansas 6,180 63,670

California 33,890 420,630

Colorado 37,610 357,190

Connecticut not assessed not assessed

Delaware 40 40

District of Columbia not assessed not assessed

Florida 102,740 555,010

Georgia 145,340 159,050

Hawaii not assessed not assessed

Idaho 40 390

Illinois 10,020 116,820

Indiana 32,020 68,210

Iowa 10 50

Kansas 10,880 86,340

Kentucky 2,920 7,650

Louisiana 169,500 2,103,980

Maine not assessed not assessed

Maryland 1,860 1,930

Massachusetts not assessed not assessed

 

30 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S Department of Energy, Office 

of Fossil Energy, National Energy Technology Laboratory (NETL).
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Table 2-8. Total CO2 Storage Resource, continued

Million Metric Tons*

State Low Estimate High Estimate

Michigan 19,050 47,210

Minnesota not assessed not assessed

Mississippi 145,010 1,185,030

Missouri 10 170

Montana 84,580 912,720

Nebraska 23,770 113,240

Nevada not assessed not assessed

New Hampshire not assessed not assessed

New Jersey 0 0

New Mexico 42,760 359,090

New York 4,640 4,640

North Carolina 1,340 18,390

North Dakota 67,090 147,480

Offshore Federal Only 489,840 6,440,090

Ohio 13,460 13,460

Oklahoma 56,950 244,550

Oregon 6,810 93,700

Pennsylvania 22,100 22,100

Rhode Island not assessed not assessed

South Carolina 30,100 34,180

South Dakota 8,760 24,030

Tennessee 430 3,860

Texas 443,800 4,329,930

Utah 25,470 240,910

Vermont not assessed not assessed

Virginia 440 2,910

Washington 36,620 496,730

West Virginia 16,650 16,650

Wisconsin 0 0

Wyoming 72,690 684,850

U.S. Total 2,296,680 20,092,180

* States with a “zero” value represent estimates of minimal CO2 storage resource. States that have not yet been 

assessed by the RCSPs have been identified.
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2.7.2 Availability of geologic sequestration in deep saline formations

DOE and the United States Geological Survey (USGS) have independently conducted 

preliminary analyses of the availability and potential CO2 sequestration capacity of deep saline 

formations in the United States. DOE estimates are compiled by the DOE’s National Carbon 

Sequestration Database and Geographic Information System (NATCARB) using volumetric 

models and published in a Carbon Utilization and Storage Atlas.31 DOE estimates that areas of 

the United States with appropriate geology have a sequestration potential of at least 2,035 billion 

metric tons of CO2 in deep saline formations. According to DOE and at least 39 states have 

geologic characteristics that are amenable to deep saline GS in either onshore or offshore 

locations. In 2013, the USGS completed its evaluation of the technically accessible GS resources 

for CO2 in U.S. onshore areas and state waters using probabilistic assessment.32 The USGS 

estimates a mean of 3,000 billion metric tons of subsurface CO2 sequestration potential, 

including saline and oil and gas reservoirs, across the basins studied in the United States. As 

shown in Figure 2-14, there are 39 states for which onshore and offshore deep saline formation 

storage capacity has been identified.33

2.7.3 Availability of CO2 storage via enhanced oil recovery (EOR)

Although the regulatory impact analysis for this rule relies on GS in deep saline 

formations, the EPA also recognizes the potential for securely sequestering CO2 via EOR. EOR 

has been successfully used at numerous production fields throughout the United States to 

increase oil recovery. The oil industry in the United States has over 40 years of experience with 

EOR. An oil industry study in 2014 identified more than 125 EOR projects in 98 fields in the 

United States.34 More than half of the projects evaluated in the study have been in operation for 

 

31 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S. Department of Energy, Office 

of Fossil Energy, National Energy Technology Laboratory (NETL).

32 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources—Results: U.S. Geological Survey Circular 1386, p. 41, 

http://pubs.usgs.gov/circ/1386/.

33 Alaska is not shown in the figure; it has deep saline formation storage capacity, geology amenable to EOR 

operations, and potential GS capacity in un-mineable coal.

34 Koottungal, Leena, 2014, 2014 Worldwide EOR Survey, Oil & Gas Journal, Volume 112, Issue 4, April 7, 2014 

(corrected tables appear in Volume 112, Issue 5, May 5, 2014).
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more than 10 years, and many have been in operation for more than 30 years. This experience 

provides a strong foundation for demonstrating successful CO2 injection and monitoring 

technologies, which are needed for safe and secure GS that can be used for deployment of CCS 

across geographically diverse areas.

Currently, 12 states have active EOR operations and most have developed an extensive 

CO2 infrastructure, including pipelines, to support the continued operation and growth of EOR. 

An additional 23 states are within 150 miles of current EOR operations. See Figure 2-14. The 

vast majority of EOR is conducted in oil reservoirs in the Permian Basin, which extends through 

southwest Texas and southeast New Mexico. States where EOR is utilized include Alabama, 

Colorado, Louisiana, Michigan, Mississippi, New Mexico, Oklahoma, Texas, Utah, and 

Wyoming. 

At the project level, the volume of CO2 already injected for EOR and the duration of 

operations are of similar magnitude to the duration and volume of CO2 expected to be captured 

from fossil fuel-fired EGUs. The volume of CO2 used in EOR operations can be large (e.g., 55 

million tons of CO2 were stored in the SACROC unit in the Permian Basin over 35 years), and 

operations at a single oil field may last for decades, injecting into multiple parts of the field.35

According to data reported to the EPA’s Greenhouse Gas Reporting Program (GHGRP), 

approximately 60 million metric tons of CO2 were supplied to EOR in the United States in 

2013.36 Approximately 70 percent of this total CO2 supplied was produced from natural 

(geologic) CO2 sources, and approximately 30 percent was captured from anthropogenic 

sources.37

A DOE-sponsored study has analyzed the geographic availability of applying EOR in 11 

major oil producing regions of the United States and found that there is an opportunity to 

 

35 Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. “Evaluation of CO2 trapping mechanisms at the 

SACROC northern platform, Permian basin, Texas, site of 35 years of CO2 injection.” American Journal of Science 

310. (2010): 282-324.

36 Greenhouse Gas Reporting Program, data reported as of August 18, 2013.

37 Greenhouse Gas Reporting Program, data reported as of August 18, 2013.
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significantly increase the application of EOR to areas outside of current operations.38 DOEsponsored geologic and engineering analyses show that expanding EOR operations into areas 

additional to the capacity already identified and applying new methods and techniques over the 

next 20 years could utilize 18 billion metric tons of anthropogenic CO2 and increase total oil 

production by 67 billion barrels. The availability of anthropogenic CO2 in areas outside of 

current sources could drive new EOR projects by making more CO2 locally available.

2.8 GHG and Clean Energy Regulation in the Power Sector

2.8.1 State Policies

Several states have also established emission performance standards or other measures to 

limit emissions of GHGs from new EGUs that are comparable to this rulemaking. 

In 2003, then-Governor George Pataki sent a letter to his counterparts in the Northeast and 

Mid-Atlantic inviting them to participate in the development of a regional cap-and-trade program 

addressing power plant CO2 emissions. This program, known as the Regional Greenhouse Gas 

Initiative (RGGI), began in 2009 and sets a regional CO2 cap for participating states. The 

currently participating states39 include: Connecticut, Delaware, Maine, Maryland, Massachusetts, 

New Hampshire, New York, Rhode Island, and Vermont. The cap covers CO2 emissions from all 

fossil-fired EGUs greater than 25 MW in participating states, and limits total emissions to 91 

million short tons in 2014. The 2014 emissions cap is a 51 percent reduction below the initial cap 

in 2009 to 2011 of 188 million tons. This emissions budget is reduced 2.5 percent annually from 

2015 to 2020. RGGI CO2 allowances are sold in a quarterly auction. RGGI conducted their 27th

quarterly allowance auction in March, 2015 the market clearing price was $5.41 per ton of CO2

for current allowances, which was a record high price (the February ’15 price of $5.21 was the 

previous record). A total of allowances for 15.3 million tons were sold in the March ’15 auction, 

 

38 “Improving Domestic Energy Security and Lowering CO2 Emissions with “Next Generation” CO2-Enhanced Oil 

Recovery”, Advanced Resources International, Inc. (ARI), 2011. Available at: 

http://www.netl.doe.gov/research/energy-analysis/publications/details?pub=df02ffba-6b4b-4721-a7b4-

04a505a19185.

39 New Jersey was originally a part of RGGI, but opted to leave the program in 2011, reducing the number of 

participating states to 9.
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well below the record of 38.7 million tons sold in June ’13 for $3.21. The RGGI CO2 emissions 

cap was reduced from 188 million tons (in a 10 state region) in 2009-2011, and reduced again to 

165 million tons (in a 9 state region) in 2012-2014. The cap is further reduced by 2.5 percent 

each year from 2014 through 2020.

In September 2006, California Governor Schwarzenegger signed into law Senate Bill 

1368. The law limits long-term investments in baseload generation by the state's utilities to 

power plants that meet an emissions performance standard jointly established by the California 

Energy Commission and the California Public Utilities Commission. The Energy Commission 

has designed regulations that establish a standard for new and existing baseload generation 

owned by, or under long-term contract to publicly owned utilities, of 1,100 lb CO2/MWh.

In 2006 Governor Schwarzenegger also signed into law Assembly Bill 32, the Global 

Warming Solutions Act of 2006. This act includes a multi-sector GHG cap-and-trade program 

which covers approximately 85 percent of the state GHG emissions. EGUs are includes in phase 

I of the program, which began in 2013. Phase II begins in 2020 and includes upstream sources.

The cap is based on a 2 percent reduction from total 2012 expected emissions, and declines 2 

percent annually through 2014, then 3 percent each year until 2020. The AB32 cap and trade 

program began functioning in 2011, and functioning market is now operating on the NYMEX 

futures commodity market. The final 2014 market price for 2014 carbon allowances was 

$12.38/tonne of carbon. On April 17, 2015 the 2015 allowance futures price was $12.65/tonne, 

and the spot price was $12.46/tonne.

In May 2007, Washington Governor Gregoire signed Substitute Senate Bill 6001, which 

established statewide GHG emissions reduction goals, and imposed an emission standard that 

applies to any baseload electric generation that commenced operation after June 1, 2008 and is 

located in Washington, whether or not that generation serves load located within the state. 

Baseload generation facilities must initially comply with an emission limit of 1,100 lb 

CO2/MWh.

In July 2009, Oregon Governor Kulongoski signed Senate Bill 101, which mandated that 

facilities generating baseload electricity, whether gas- or coal-fired, must have emissions equal to 

or less than 1,100 lb CO2/MWh, and prohibited utilities from entering into long-term purchase 
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agreements for baseload electricity with out-of-state facilities that do not meet that standard. 

Natural gas- and petroleum distillate-fired facilities that are primarily used to serve peak demand 

or to integrate energy from renewable resources are specifically exempted from the performance 

standard.

In August 2011, New York Governor Cuomo signed the Power NY Act of 2011. This 

regulation establishes CO2 emission standards for new and modified electric generators greater 

than 25 MW. The standards vary based on the type of facility: baseload facilities must meet a 

CO2 standard of 925 lb/MWh or 120 lb/MMBtu, and peaking facilities must meet a CO2 standard 

of 1,450 lbs/MWh or 160 lbs/MMBtu.

Additionally, most states have implemented Renewable Portfolio Standards (RPS), or 

Renewable Electricity Standards (RES). These programs are designed to increase the renewable 

share of a state’s total electricity generation. Currently 30 states and the District of Columbia 

have enforceable RPS or other mandatory renewable capacity policies, and 7 states have 

voluntary goals.40 These programs vary widely in structure, enforcement, and scope.

2.8.2 Federal Policies

In April 2007, the Supreme Court concluded that GHGs met the CAA definition of an air 

pollutant, giving the EPA the authority to regulate GHGs under the CAA contingent upon an 

agency determination that GHG emissions from new motor vehicles cause or contribute to air 

pollution that may reasonably be anticipated to endanger public health or welfare. This decision 

to regulate GHG emissions for motor vehicles set the stage for the determination of whether 

other sources of GHG emissions, including stationary sources, would need to be regulated as 

well.

In response to the FY2008 Consolidated Appropriations Act (H.R. 2764; Public Law 110–

161), the EPA issued the Mandatory Reporting of Greenhouse Gases Rule (74 FR 5620) which 

required reporting of GHG data and other relevant information from fossil fuel suppliers and 

industrial gas suppliers, direct greenhouse gas emitters, and manufacturers of heavy-duty and 

 

40 EIA 2012a
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off-road vehicles and engines. The purpose of the rule was to collect accurate and timely GHG 

data to inform future policy decisions. As such, it did not require that sources control greenhouse 

gases, but sources above certain threshold levels must monitor and report emissions.

In August 2007, the EPA issued a prevention of significant deterioration (PSD) permit to 

Deseret Power Electric Cooperative, authorizing it to construct a new waste-coal-fired EGU near 

its existing Bonanza Power Plant, in Bonanza, Utah. The permit did not include emissions 

control requirements for CO2. The EPA acknowledged the Supreme Court decision, but found 

that decision alone did not require PSD permits to include limits on CO2 emissions. Sierra Club 

challenged the Deseret permit. In November 2008, the Environmental Appeals Board (EAB) 

remanded the permit to the EPA to reconsider “whether or not to impose a CO2 BACT (best 

available control technology) limit in light of the ‘subject to regulation’ definition under the 

CAA.” The remand was based in part on EAB’s finding that there was not an established EPA 

interpretation of the regulatory phrase “subject to regulation.” 

In December 2008, the Administrator issued a memo indicating that the PSD Permitting 

Program would apply to pollutants that are subject to either a provision in the CAA or a

regulation adopted by the EPA under the CAA that requires actual control of emissions of that 

pollutant. The memo further explained that pollutants for which the EPA regulations only require 

monitoring or reporting, such as the provisions for CO2 in the Acid Rain Program, are not subject 

to PSD permitting. Fifteen organizations petitioned the EPA for reconsideration, prompting the 

agency to issue a revised finding in March 2009. After reviewing comments, the EPA affirmed 

the position that PSD permitting is not triggered for a pollutant such as GHGs until a final 

nationwide rule requires actual control of emissions of the pollutant. For GHGs, this meant 

January 2011 when the first national rule limiting GHG emissions for cars and light trucks was 

scheduled to take effect. Therefore, a permit issued after January 2, 2011, would have to address 

GHG emissions.

The Administrator signed two distinct findings in December 2009 regarding greenhouse 

gases under section 202(a) of the Clean Air Act. The endangerment finding indicated that current 

and projected concentrations of the six key well-mixed greenhouse gases —CO2, CH4, N2O, 

hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and SF6 — in the atmosphere threaten the 
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public health and welfare of current and future generations. These greenhouse gases have long 

lifetimes and, as a result, become homogeneously distributed through the lower level of the 

Earth’s atmosphere (IPCC, 2001). This differentiates them from other greenhouse gases that are 

not homogeneously distributed in the atmosphere. The cause and contribute finding indicated 

that the combined emissions of these well-mixed greenhouse gases from new motor vehicles and 

new motor vehicle engines contribute to the greenhouse gas pollution which threatens public 

health and welfare. Both findings were published in the Federal Register on December 15, 2009 

(Docket ID EPA-HQ-OAR-2009-0171). These findings did not themselves impose any

requirements on any industry or other entities, but allowed the EPA to regulate greenhouse gases 

under the CAA (see preamble section II.E for regulatory background). This action was a 

prerequisite to implementing the EPA's proposed greenhouse gas emission standards for lightduty vehicles, which was finalized in January 2010. Once a pollutant is regulated under the 

CAA, it is subject to permitting requirements under the PSD and Title V programs. The 2009 

Endangerment Finding and a denial of reconsideration were challenged in a lawsuit; on June 26, 

2012, the DC Circuit Court upheld the Endangerment Finding and the Reconsideration Denial, 

ruling that the Finding was neither arbitrary nor capricious, was consistent with Massachusetts v. 

EPA, and was adequately supported by the administrative record. The Court found that the EPA 

had based its decision on “substantial scientific evidence,” noted that the EPA’s reliance on 

assessments was consistent with the methods decision-makers often use to make a science-based 

judgment, and stated that “EPA’s interpretation of the governing CAA provisions is 

unambiguously correct.”

In May 2010, the EPA issued the final Tailoring Rule which set thresholds for GHG 

emissions that define when permits under the New Source Review and Title V Operating Permit 

programs are required for new and existing industrial facilities. Facilities responsible for nearly

70 percent of the national GHG emissions from stationary sources, including EGUs, were subject 

to permitting requirements under the rule. This rule was upheld by the D.C. Circuit in 2012.

The EPA entered into a proposed settlement agreement in December 2010 to issue rules 

that will address greenhouse gas emissions from fossil fuel-fired power plants. On March 27, 

2012, EPA proposed NSPS for CO2 emissions from new source natural gas, coal, and other solid 

fossil-fired EGUs. After consideration of information provided in more than 2.7 million 
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comments on the March 27, 2012 proposal, as well as consideration of continuing changes in the 

electricity sector, the EPA determined that revisions in its initially proposed approach were 

warranted. EPA replaced that prior proposal with a new proposal for NSPS for CO2 emissions 

from EGUs on September 20, 2013; this action finalizes that rulemaking. Existing source 

standards are not addressed in this action. Details of the settlement agreement can be found on 

the EPA website.41

2.9 Revenues and Expenses

Due to lower retail electricity sales, total utility operating revenues declined in 2012 to 

$271 billion from a peak of almost $300 billion in 2008. Despite revenues not returning to 2008 

levels in 2012, operating expenses were appreciably lower and as a result, net income also rose 

in comparison to 2008 (see Table 2-9). Recent economic events have put downward pressure on 

electricity demand, thus dampening electricity prices and consumption (utility revenues), but 

have also reduced the price and cost of fossil fuels and other expenses. In 2012 electricity 

generation was 1.28 percent below the generation in 2011, and has declined in 4 of the past 5 

years.

Table 2-9 shows that investor-owned utilities (IOUs) earned income of about 13.0 percent

compared to total revenues in 2012. The 2012 return on revenue was the third highest year for 

the period 2002 to 2012 (average: 11.9 percent range: 10.6 percent to 13.32 percent).

 

41 http://www.epa.gov/airquality/ghgsettlement.html
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Table 2-9. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric Utilities 

for 2010 (nominal $millions) 

2002 2008 2012

Utility Operating Revenues 219,609 298,962 270,912

Electric Utility 200,360 266,124 249,166

Other Utility 19,250 32,838 21,745

Utility Operating Expenses 189,062 267,263 235,694

Electric Utility 171,604 236,572 220,722

Operation 116,660 175,887 152,379

Production 90,715 140,974 111,714

Cost of Fuel 24,149 47,337 38,998

Purchased Power 58,810 84,724 54,570

Other 7,776 8,937 18,146

Transmission 3,560 6,950 7,183

Distribution 3,117 3,997 4,181

Customer Accounts 4,168 5,286 5,086

Customer Service 1,820 3,567 5,640

Sales 264 225 221

Admin. and 

General

13,018 14,718 18,353

Maintenance 10,861 14,192 15,489

Depreciation 16,199 19,049 23,677

Taxes and Other 26,716 26,202 29,177

 Other Utility 17,457 30,692 14,972

Net Utility Operating Income 30,548 31,699 35,218

Source: Table 8.3, EIA Electric Power Annual, 2012

Note: This data does not include information for public utilities, nor for Independent Power Producers (IPPs).

2.10 Natural Gas Market

The natural gas market in the United States has historically experienced significant price 

volatility from year to year, between seasons within a year, and can undergo major price swings 

during short-lived weather events (such as cold snaps leading to short-run spikes in heating 

demand). Over the period last decade, the annual average nominal price of gas delivered to the 

power sector have ranged from $3.42/MMBtu (in 2012) to $9.02/MMBtu (in 2008). During that 
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time, the daily price42 of natural gas reached as high as $18.48/MMBtu and as low as $2.03. 

Adjusting for inflation using the GDP implicit price deflator, in $2011 the annual average price 

of natural gas delivered to the power sector ranged from $9.38 (in 2008) to $3.36 (in 2012). The 

annual natural gas prices in both nominal and real (2011$) terms are in Figure 2-20. A 

comparison of the trends in the real price of natural gas with the real price of delivered coal and 

oil are shown in Figure 2-21. Figure 16 shows that while the real price of coal and oil increased 

from 2002 to 2012 (+54 percent and +203 percent respectively), the real price of natural gas 

declined by 22 percent in the same period. Most of the decline in real natural gas prices occurred 

between 2008 (the peak price year) and 2012, during which real gas prices declined by 64 

percent while coal and oil prices both increased by 9 percent. The sharp decline in natural gas 

prices from 2008 to 2012 was primarily caused by the rapid increase in natural gas production 

from shale formations.

Figure 2-20. Relative Change Nominal and Real (2011$) Prices of Natural Gas Delivered 

to the Power Sector ($/MMBtu)

Source: http://www.eia.gov/totalenergy/data/monthly/#prices. Downloaded 2/15/2015.

 

42 Henry Hub daily prices. Henry Hub is a major gas distribution hub in Louisiana; Henry Hub prices are generally 

seen as the primary metric for national gas prices for all end uses. 

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

$10.00

2002 2004 2006 2008 2010 2012

Natural Gas Prices ($/m

mBT

U)

Nominal Price Real Price
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Figure 2-21. Relative Change in Real (2011$) Prices of Fossil Fuels Delivered to the Power 

Sector ($/MMBtu)

Source: http://www.eia.gov/totalenergy/data/monthly/#prices. Downloaded 2/15/2015.

Current and projected natural gas prices are considerably lower than the prices observed 

over the past decade, largely due to advances in hydraulic fracturing and horizontal drilling 

techniques that have opened up new shale gas resources and substantially increased the supply of 

economically recoverable natural gas. According to AEO 2012 (EIA 2012):

Shale gas refers to natural gas that is trapped within shale formations. Shales are finegrained sedimentary rocks that can be rich sources of petroleum and natural gas. Over the 

past decade, the combination of horizontal drilling and hydraulic fracturing has allowed 

access to large volumes of shale gas that were previously uneconomical to produce. The 

production of natural gas from shale formations has rejuvenated the natural gas industry

in the United States.

The U.S. Energy Information Administration's Annual Energy Outlook 2014 estimates that 

the United States possessed 2,266 trillion cubic feet (Tcf) of technically recoverable dry natural 

gas resources as of January 1, 2012. Proven reserves make up 15 percent of the technically 

-50%

0%

50%

100%

150%

200%

250%

2002 2004 2006 2008 2010 2012

Coal Oil Gas Average
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recoverable total estimate, with the remaining 85 percent from unproven reserves. Natural gas 

from proven and unproven shale resources accounts for 611 Tcf of this resource estimate. 

Many shale formations, especially the Marcellus43, are so large that only small portions of 

the entire formations have been intensively production-tested. Furthermore, estimates from the 

Marcellus and other emerging fields with few wells already drilled are likely to shift significantly 

over time as new geological and production information becomes available. Consequently, the 

estimate of technically recoverable resources is highly uncertain, and is regularly updated as 

more information is gained through drilling and production. 

At the 2012 rate of U.S. consumption (about 25.6 Tcf per year), 2,266 Tcf of natural gas is 

enough to supply nearly 90 years of use. The AEO 2014 estimate of the shale gas resource base 

is modestly higher than the AEO 2012 estimate (2,214 Tcf) shale gas production estimates, 

driven by lower drilling costs and continued drilling in shale plays with high concentrations of 

natural gas liquids and crude oil, which have a higher value in energy equivalent terms than dry

natural gas.44

EIA’s projections of natural gas conditions did not change substantially in AEO 2014 from 

either the AEO 2012 or 2013, and EIA is still forecasting abundant reserves consistent with the 

above findings. Recent historical data reported to EIA is also consistent with these trends, with 

2014 being the highest year on record45 for domestic natural gas production.46

 

43 The Marcellus formation, underlying most of Pennsylvania and West Virginia, along with portions of New York 

and Ohio, in 2014 produced 36% of the U.S. total natural gas extracted from shale formations.

44 For more information, see: http://www.eia.gov/forecasts/archive/aeo11/IF_all.cfm#prospectshale; 

http://www.eia.gov/energy_in_brief/about_shale_gas.cfm

45 The total dry gas production in 2012 from the lower 48 states, including both onshore and offshore production, 

was 23.97 Tcf, a 1.5% increase from 2013 and a 7.9% total increase from 2011

46 http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2014&subject=8-AEO2014&table=72-

AEO2014®ion=0-0&cases=ref2014-d102413a
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2.11 References

Advanced Resources International. 2011. Improving Domestic Energy Security and Lowering 

CO2 Emissions with “Next Generation” CO2-Enhanced Oil Recovery (CO2-EOR). Available 

at <http://www.netl.doe.gov/research/energy-analysis/publications/details?pub=df02ffba6b4b-4721-a7b4-04a505a19185>. Accessed June 9, 2015.

Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. 2010. Evaluation of CO2

trapping mechanisms at the SACROC northern platform, Permian basin, Texas, site of 35 

years of CO2 injection. American Journal of Science 310: 282-324.

Interagency Task Force on Carbon Capture and Storage. Report of the Interagency Task Force 

on Carbon Capture and Storage. August 2010. Available at 

http://www.epa.gov/climatechange/downloads/CCS-Task-Force-Report-2010.pdf>. Accessed 

June 9, 2015. 

Intergovernmental Panel on Climate Change. 2001. Climate Change 2001: The Scientific Basis. 

Available at 

<http://www.grida.no/publications/other/ipcc_tar/?src=/climate/ipcc_tar/wg1/218.htm>. 

Accessed June 9, 2015. 

International Energy Agency (IEA). 2013. Tracking Clean Energy Progress 2013. Input to the 

Clean Energy Ministerial. Available at <http://www.iea.org/etp/tracking/>. Accessed June 9, 

2015.

Koottungal, Leena. 2014. 2014 Worldwide EOR Survey, Oil & Gas Journal, Volume 112, Issue 

4, April 7, 2014 (corrected tables appear in Volume 112, Issue 5, May 5, 2014).

National Energy Technology Laboratory (NETL). 2008. Reducing CO2 Emissions by Improving 

the Efficiency of Existing Coal-fired Power Plant Fleet. Available at 

<http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>. Accessed 

June 9, 2015.

National Energy Technology Laboratory (NETL). 2012. The United States 2012 Carbon 

Utilization and Storage Atlas, Fourth Edition. Available at 

<http://www.netl.doe.gov/technologies/carbon_seq/refshelf/atlasIV/>. Accessed June 9, 2015.

National Energy Technology Laboratory (NETL). 2013. Energy Analyses: Cost and 

Performance Baselines for Fossil Energy Plants. Available at 

<http://www.netl.doe.gov/energy-analyses/baseline_studies.html>. Accessed June 9, 2015.
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2-48

Pacific Northwest National Laboratory (PNNL). 2009. An Assessment of the Commercial 

Availability of Carbon Dioxide Capture and Storage Technologies as of June 2009. Available 

at <http://www.pnl.gov/science/pdf/PNNL-18520_Status_of_CCS_062009.pdf>. Accessed 

June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2000. Carbon Dioxide Emissions from the 

Generation of Electric Power in the United States. Available at 

<ftp://ftp.eia.doe.gov/environment/co2emiss00.pdf>. Accessed June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2003. Electric Power Annual 2003. 

Available at <http://www.eia.gov/electricity/annual/archive/03482003.pdf>. Accessed June 9, 

2015. 

U.S. Energy Information Administration (U.S. EIA). 2009. Electric Power Annual 2009. 

Available at <http://www.eia.gov/electricity/annual/archive/03482009.pdf>. Accessed June 9, 

2015.

U.S. Energy Information Administration (U.S. EIA). 2013. Electric Power Annual 2011. 

Available at <http://www.eia.gov/electricity/annual/>. Accessed June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2010a. “Status of Electricity Restructuring 

by State.” Available at 

<http://www.eia.gov/cneaf/electricity/page/restructuring/restructure_elect.html>. Accessed 

June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2010b. AEO 2010 Retrospective Review. 

Available at <http://www.eia.gov/forecasts/aeo/retrospective/>. Accessed June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2010c. Annual Energy Outlook 2010. 

Available at <http://www.eia.gov/oiaf/archive/aeo10/index.html>. Accessed June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2010d. Annual Energy Review 2010. 

Available at <http://www.eia.gov/totalenergy/data/annual/pdf/aer.pdf>. Accessed June 9, 

2015.

U.S. Energy Information Administration (U.S. EIA). 2011. Annual Energy Outlook 2011. 

Available at <http://www.eia.gov/forecasts/archive/aeo11/>. Accessed June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2012. Annual Energy Outlook 2012 (Early 

Release). Available at <http://www.eia.gov/forecasts/aeo/>. Accessed June 9, 2015.
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2-49

U.S. Energy Information Administration (U.S. EIA). Today in Energy: Most states have 

Renewable Portfolio Standards. 2012a. Available at 

<http://www.eia.gov/todayinenergy/detail.cfm?id=4850>. Accessed June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2013. Annual Energy Outlook 2013. 

Available at <http://www.eia.gov/forecasts/aeo/>. Accessed June 9, 2015.

U.S. Energy Information Administration (U.S. EIA). 2015. Monthly Energy Review, April 2015. 

Available at <http://www.eia.gov/totalenergy/data/monthly/>. Accessed June 9, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2013. Inventory of U.S. Greenhouse Gas 

Emissions and Sinks: 1990–2011. Available at 

<http://www.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2013-

Main-Text.pdf>. Accessed June 9, 2015.

U.S. Geological Survey (USGS) Carbon Dioxide Storage Resources Assessment Team. 2013. 

National assessment of geologic carbon dioxide storage resources – Results: U.S. Geological 

Survey Circular 1386. Available at <http://pubs.usgs.gov/circ/1386/>. Accessed June 9, 2015.
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3-1

CHAPTER 3: COST, EMISSIONS, ECONOMIC, AND ENERGY IMPACTS

3.1 Introduction

This chapter reports the compliance cost, emissions, economic, and energy impact 

analysis performed for the Clean Power Plan Final Rule. EPA used the Integrated Planning 

Model (IPM), developed by ICF International, to conduct most of the analysis discussed in this 

Chapter. IPM is a dynamic linear programming model that can be used to examine air pollution 

control policies for CO2, SO2, NOX, Hg, HCl, and other air pollutants throughout the contiguous 

United States for the entire power system. The IPM electricity demand projections are based on 

projections from the Energy Information Administration (EIA), adjusted for demand-side energy

efficiency measures that can be reasonably anticipated to occur under the Clean Power Plan. To 

estimate compliance costs, emissions, and other impacts for states and territories with power 

markets not represented in IPM, EPA performed supplementary analysis to account for the 

potential benefits and costs of the Clean Power Plan Final Rule for Alaska, Hawaii, and U.S. 

territories with affected EGUs. This chapter combines IPM analysis of the continental U.S. with 

the supplementary analysis performed for Alaska, Hawaii, and U.S. Territories. Supplementary

estimates of monitoring, recordkeeping, and reporting costs are reported in this chapter as well. 

The sum of emission reductions across these regions informs the benefits analyses presented 

elsewhere in this RIA.

3.2 Overview

This chapter of the RIA presents illustrative analyses of the final rule by making 

assumptions about the possible approaches that States might pursue as they develop their state 

plans. Over the last decade, EPA has conducted extensive analyses of regulatory actions 

affecting the power sector. These efforts support the Agency’s understanding of key variables 

that influence the effects of a policy and provide the framework for how the Agency estimates 

the costs and benefits associated with its actions.

3.3 Power Sector Modelling Framework

The Integrated Planning Model (IPM), developed by ICF Consulting, 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 examine prospective air pollution control 
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3-2

policies throughout the contiguous United States for the entire electric power system. EPA used 

IPM to project likely future electricity market conditions with and without the rule. Additional 

demand-side energy efficiency measures that may be adopted in response to the regulation, and 

the resulting changes to future demand projections, are also accounted for in the illustrative plan 

scenarios. The level of energy efficiency-driven reductions in electricity demand, and their

associated costs, are reported in section 3.6.

IPM is a multi-regional, dynamic, deterministic linear programming model of the 

contiguous U.S. electric power sector. It provides forecasts of least cost capacity expansion, 

electricity dispatch, and emission control strategies while meeting energy demand and 

environmental, transmission, dispatch, and reliability constraints. EPA has used IPM for over 

two decades to better understand power sector behavior under future business-as-usual 

conditions and to evaluate the economic and emission impacts of prospective environmental 

policies. The model is designed to reflect electricity markets as accurately as possible. EPA uses 

the best available information from utilities, industry experts, gas and coal market experts, 

financial institutions, and government statistics as the basis for the detailed power sector 

modeling in IPM. The model documentation provides additional information on the assumptions 

discussed here as well as all other model assumptions and inputs.47

Although the Agency typically focuses on broad system effects when assessing the 

economic impacts of a particular policy, EPA’s application of IPM includes a detailed and 

sophisticated regional representation of key variables affecting power sector behavior.

The model incorporates a detailed representation of the fossil-fuel supply system that is 

used to forecast equilibrium fuel prices. The model includes an endogenous representation of the 

North American natural gas supply system through a natural gas module that reflects a partial 

supply/demand equilibrium of the North American gas market accounting for varying levels of 

potential power sector and non-power sector gas demand and corresponding gas production and 

 

47 Detailed information and documentation of EPA’s Base Case using IPM (v5.15), including all the underlying 

assumptions, data sources, and architecture parameters can be found on EPA’s website at: 

http://www.epa.gov/powersectormodeling
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price levels.48 This module consists of 118 supply, demand, and storage nodes and 15 liquefied 

natural gas re-gasification facility locations that are tied together by a series of linkages (i.e., 

pipelines) that represent the North American natural gas transmission and distribution network.

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.49

EPA has used IPM extensively over the past two decades to analyze options for reducing 

power sector emissions. Previously, the model has been used to forecast the costs, emission 

changes, and power sector impacts for the Clean Air Interstate Rule, Cross-State Air Pollution 

Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), and the proposed Carbon 

Pollution Standards for New Power Plants. Recently IPM has also been used to estimate the air 

pollution reductions and power sector impacts of water and waste regulations affecting EGUs, 

including Cooling Water Intakes (316(b)) Rule, Disposal of Coal Combustion Residuals from 

Electric Utilities (CCR) and Steam Electric Effluent Limitation Guidelines (ELG).

The model and EPA's input assumptions undergo periodic formal peer review. The 

rulemaking process also provides opportunity for expert review and comment by a variety of 

stakeholders, including owners and operators of capacity in the electricity sector that is 

represented by the model, public interest groups, and other developers of U.S. electricity sector 

models. The feedback that the Agency receives provides a highly-detailed review of key input 

assumptions, model representation, and modeling results. IPM has received extensive review by

 

48 See Chapter 10 of EPA’s Base Case using IPM (v5.154) documentation, available at: 

http://www.epa.gov/powersectormodeling

49 See Chapter 9 of EPA’s Base Case using IPM (v5.15) documentation, available at: 

http://www.epa.gov/powersectormodeling
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energy and environmental modeling experts in a variety of contexts. For example, in the late 

1990s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 

prospective studies that are periodically conducted. The model has also undergone considerable 

interagency scrutiny when it was used to conduct over a dozen legislative analyses (performed at 

Congressional request) over the past decade. 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 15 years.

IPM has also been employed by states (e.g., for RGGI, the Western Regional Air 

Partnership, Ozone Transport Assessment Group), other Federal and state agencies, 

environmental groups, and industry, all of whom subject the model to their own review 

procedures.

3.3.1 Recent Updates to EPA’s Base Case using IPM (v.5.15)

The “Base Case” for this analysis is a business-as-usual scenario that would be expected 

under market and regulatory conditions in the absence of this rule. EPA frequently updates the 

IPM base case to reflect the latest available electricity demand forecasts, as well as, expected 

costs and availability of new and existing generating resources, fuels, and emissions control 

technologies. 

EPA’s IPM modeling platform used to analyze this final rule (v.5.15) incorporates 

updates to the version of the model used to analyze the impacts of the proposed rule (v.5.13). 

These updates are primarily routine calibrations with the Energy Information Agency's (EIA) 

Annual Energy Outlook (AEO), including updating the electric demand forecast consistent with 

the AEO 2015 and an update to natural gas supply. Additional updates, based on the most up-todate information and/or public comments received by EPA, include unit-level specifications 

(e.g., control configuration), planned new power plant construction and closures, and updated 

cost and performance for onshore wind and utility-scale solar technologies. This IPM modeling 

platform incorporates federal and most state laws and regulations whose provisions were either 

in effect or enacted and clearly delineated in March 2015. This update also includes two non-air 

federal rules affecting EGUs: Cooling Water Intakes (316(b)) Rule and Combustion Residuals 

from Electric Utilities (CCR). Additionally, all new capacity projected by the model is compliant 
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with Clean Air Act 111(b) standards. For a detailed account of all updates made to the v.5.15 

modeling platform, see the Incremental Documentation for EPA Base Case v.5.15 Using IPM.50

EPA also updated the National Electric Energy Data System (NEEDS). This database 

contains the unit-level data that is used to construct the "model" plants that represent existing and 

committed units in EPA modeling applications of IPM. NEEDS includes detailed information on 

each individual EGU, including geographic, operating, air emissions, and other data on every

generating units in the contiguous U.S.51

To estimate compliance costs, emissions and other impacts for states and territories with 

power markets not represented in IPM, EPA performed supplementary analysis to account for 

the potential benefits and costs of the Clean Power Plan Final Rule for Alaska, Hawaii, and U.S. 

territories with affected EGUs.

3.4 State Goals in this Final Rule

In this final rule, the EPA is establishing CO2 emission performance rates for each of two 

source categories of fossil fuel-fired EGUs -- fossil fuel-fired electric steam generating units and 

stationary combustion turbines. The EPA has translated the source category-specific CO2

emission performance rates into equivalent state-level rate-based and mass-based CO2 goals to 

afford states a range of choices, including application of statewide measures, for developing their 

plans. For modeling purposes, the illustrative rule impacts shown in the RIA reflect an 

application of both the state-wide rate-based and mass-based goals in each state.

 

50 Available at: http://www.epa.gov/powersectormodeling/

51 The NEEDS database can be found on the EPA’s website for the Base Case using IPM (v5.15), 

<http://www.epa.gov/powersectormodeling >.
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Table 3-1. Rate-Based and Mass-Based State Goals

Statewide Rate-Based CO2 Emission 

Performance Goals (Adjusted Output-WeightedAverage Pounds of CO2 Per Net MWh From All 

Affected Fossil Fuel-Fired EGUs)

State Interim Goal Final Goal Interim Goal Final Goal

Alaska

Alabama 1,174 1,018

Arkansas 1,319 1,130

Arizona 1,190 1,031

California 925 828

Colorado 1,377 1,174

Connecticut 870 786

Delaware 1,040 916

Florida 1,044 919

Fort Mojave

Georgia 1,214 1,049

Hawaii

Iowa 1,519 1,283

Idaho 851 771

Illinois 1,470 1,245

Indiana 1,466 1,242

Kansas 1,532 1,293

Kentucky 1,523 1,286

Louisiana 1,308 1,121

Massachusetts 920 824

Maryland 1,524 1,287

Maine 861 778

Michigan 1,370 1,169

Minnesota 1,428 1,213

Missouri 1,504 1,272

Mississippi 1,078 945

Montana 1,548 1,305

Navajo

North Carolina 1,327 1,135

North Dakota 1,548 1,305

Nebraska 1,536 1,296

New Hampshire 964 858

New Jersey 904 811

New Mexico 1,341 1,146

Nevada 960 854

New York 1,042 918

Ohio 1,398 1,190
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3-7

Statewide Rate-Based CO2 Emission 

Performance Goals (Adjusted Output-WeightedAverage Pounds of CO2 Per Net MWh From All 

Affected Fossil Fuel-Fired EGUs)

State Interim Goal Final Goal Interim Goal Final Goal

Oklahoma 1,239 1,068

Oregon 982 871

Pennsylvania 1,274 1,095

Rhode Island 851 771

South Carolina 1,353 1,156

South Dakota 1,367 1,166

Tennessee 1,425 1,211

Texas 1,204 1,041

Ute

Utah 1,383 1,179

Virginia 1,064 934

Washington 1,128 983

Wisconsin 1,379 1,176

West Virginia 1,548 1,305

Wyoming 1,541 1,300

3.5 Illustrative Plan Approaches Analyzed

To estimate the costs, benefits, and impacts of implementing the CPP guidelines, the EPA 

modeled two illustrative plan scenarios, each at the state level, based on a rate-based approach 

and a mass-based approach. The rate-based scenario requires affected sources in each state to 

achieve a single average emissions rate in each period as represented by the state-wide goals. 

The mass-based scenario requires affected sources in each state to limit their aggregate emissions 

not to exceed the mass goal for that state. 

In each of these scenarios, affected EGUs include:

 Existing fossil steam boilers with nameplate capacity greater than 25 MW

 Existing NGCC units with nameplate capacity greater than 25 MW 

In the rate-based scenario, generation (or avoided generation) from these additional sources 

represented in the model is counted toward meeting state goals:

 All renewable capacity (hydro, solar PV, wind, geothermal) that comes online 

after 2012
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 Under-construction nuclear

52

 Demand-side energy efficiency in addition to levels implicit in base case 

electricity demand. 

In the illustrative rate-based plan scenario analyzed in this RIA, the affected EGUs within 

each state are required to achieve an average emissions rate that is less than or equal to the state 

goals for each state. In order meet the goal for each state, the affected sources in this scenario 

have the ability to do one or both of the following:

1) generate in amounts within that state such that the average emissions rate is achieved, 

and/or

2) include in the average emissions rate calculation new renewable generation or 

demand-side energy efficiency located outside of the state but within each of the 

illustrative Interconnection-based regions shown in Figure 3-3 below.53

 

52 Includes three nuclear facilities at which construction has already commenced: Watts Barr (TN), Vogtle (GA), and 

Summer (SC)

53 In this scenario, EE/RE procurement is limited to within one of the three illustrative regions, as a conservative 

modeling approach. Since the interconnects do not always follow state borders, certain states that fall into more than 

one region were grouped in regions where there was a majority of geographic territory (area) or generation. In 

practice, sources in states that have adopted rate-based plans may be able to procure EE/RE from states outside of 

these illustrative regions. 
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Figure 3-3. Illustrative Regions for EE/RE Procurement Used in this Analysis

This rate-based implementation approach enables some sources to emit at rates higher 

than their applicable state goal, as long as there is corresponding generation coming from 

affected sources in that state (or new renewables or demand-side EE located in any state within 

the illustrative region for EE/RE procurement) that emits at a lower rate, such that each state’s 

goal (in lbs/MWh) is met across all affected sources located within the state including generation 

(or avoided generation) from EE/RE procurement across the illustrative regions. In this ratebased scenario, specific generation (or avoided generation) from EE/RE procurement may only 

be used once for compliance toward a state goal; in other words, while emitting sources in all 

states may avail themselves of qualifying EE/RE across the illustrative region, no particular 

EE/RE MWh can be claimed by more than one emitter as part of reaching a state goal. 

While the final rule enables states to achieve their mass goals with the flexibility of 

interstate trading, this RIA presents an illustrative scenario that conservatively assumes that each 

state achieves its goal independently. Cooperation between the states that allows for trading 

across states would provide EGUs with additional low cost abatement opportunities and would 

therefore lower the overall cost of compliance across the affected states. 
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Each illustrative plan scenario assumes identical levels of DS-EE megawatt-hour (MWh) 

savings and associated costs, which are specified exogenously and consistent with the EE plan 

scenario performance levels described in section 3.6. EPA has specified and imposed EE-related 

costs and changes in future electricity demand when modeling the illustrative plan scenarios 

presented for this rule. Details of the implementation of the demand reduction are reported in the 

following section.

The full array of estimates of the benefits, costs, and economic impacts of this proposed 

action are presented for both the illustrative rate- and mass-based scenarios. These illustrative 

plan scenarios are designed to reflect, to the extent possible, the scope and nature of the CPP 

guidelines. However, there is considerable uncertainty with regard to the regulatory form and 

precise measures that states will adopt to meet the requirements, since there are considerable 

flexibilities afforded to the states in developing state plans. Nonetheless, the analysis of the 

benefits, costs, and relevant impacts of the rule attempts to encapsulate some of those flexibilities 

in order to inform states and stakeholders of the potential overall impacts of the CPP. The 

relevant impacts, costs, and benefits are provided for 2020 (prior to implementation of the rule), 

2025, and 2030.

It is also important to note that the analysis does not specify any particular CO2 reduction 

measure to occur, with the exception of the level of demand-side energy efficiency (EE) assumed 

to be adopted in response to the CPP. In other words, aside from investments in EE, the analysis 

allows the power system the flexibility to respond to average emissions rate or mass constraints 

on affected sources in the illustrative scenarios to achieve the goals in the most cost-effective 

manner determined by IPM, as specified below. 

While IPM produces a cost-minimizing solution to achieve the state goals imposed in the 

illustrative scenarios, there may be yet lower-cost approaches that the states may adopt to 

achieve their state goals inasmuch as states and sources take advantage of emission reduction 

opportunities in practice, and flexibilities afforded under the final rule, that are not represented in 

this analysis and would yield different cost and emissions outcomes.
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3.6 Demand-Side Energy Efficiency

3.6.1 Demand-Side Energy Efficiency Improvements (Electricity Demand Reductions)54

The final CPP provides states the flexibility to use demand reductions resulting from 

demand-side energy efficiency measures as a component of their compliance strategy, either 

directly recognized towards compliance with a rate-based goal or as a complementary approach 

for achieving a mass-based goal. The EPA has included in our illustrative plan scenarios (both 

rate- and mass-based) a level of demand reduction that could be achieved, and the associated 

costs incurred, through implementation of demand-side energy efficiency measures. This 

“demand-side energy efficiency plan scenario” represents a level of performance that has already

been demonstrated or is required by policies (e.g., energy efficiency resource standards) of 

leading energy efficiency implementing states, and is consistent with a demonstrated or required 

annual pace of performance improvement over time. The resulting levels of demand reduction 

are consistent with recent studies of achievable demand reduction potential conducted 

throughout the U.S. For these reasons the demand-side energy efficiency plan scenario represents 

a reasonable assumption about the level of demand-side energy efficiency investments that may

be encouraged in response to the final CPP. 

For the illustrative demand-side energy efficiency plan scenario, electricity demand 

reductions for each state for each year are developed by ramping up from an historical basis55 to 

a target annual incremental demand reduction rate of 1.0 percent of electricity demand over a 

period of years starting in 2020, and maintaining that rate throughout the modeling horizon. 

Twenty leading states have either achieved, or have established requirements that will lead them 

to achieve, this rate of incremental electricity demand reduction on an annual basis. Based on 

historic performance and existing state requirements, for each state the pace of improvement 

from the state’s historical incremental demand reduction rate is set at 0.2 percent per year, 

beginning in 2020, until the target rate of 1.0 percent is achieved. States already at or above the 

 

54 For a more detailed discussion of the demand-side energy efficiency demand reductions and their associated costs, 

refer to the Greenhouse Gas Abatement Measures TSD.

55 The historical basis of the percentage of reduced electricity consumption differs for each state and is drawn from 

the data reported in Energy Information Administration (EIA) Form 861, 2013, available at 

http://www.eia.gov/electricity/data/eia861/.
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1.0 percent target rate are assumed to achieve a 1.0 percent rate beginning in 2020 and sustain 

that rate thereafter. The incremental demand reduction rate for each state, for each year, is used 

to derive cumulative annual electricity demand reductions based upon information about the 

average life of energy efficiency measures and the distribution of measure lives across energy

efficiency programs. The cumulative annual electricity demand reduction derived using this 

methodology is used to adjust base case electricity demand levels in the power sector compliance 

modeling.

To reflect the implementation of the illustrative energy efficiency plan scenario in 

modeling, the IPM base case electricity demand was adjusted exogenously to reflect the 

estimated future-year demand reductions calculated as described above. State-level demand 

reductions were scaled up to account for transmission losses and applied to base case generation 

demand in each model year to derive adjusted demand for each state, reflecting the energy

efficiency plan scenario energy reductions. The demand adjustments were applied proportionally 

across all segments (peak and non-peak) of the load duration curve. To reflect the adjusted statelevel demand within IPM model regions that cross state borders, energy reductions from a 

bisected state were distributed between the applicable IPM model regions using a distribution 

approach based on reported sales in 2013 as a proxy for the distribution of energy efficiency

investment opportunities.

Table 3-3 summarizes the results of the demand-side energy efficiency illustrative plan 

scenario at the national level.

Table 3-3. Demand-Side Energy Efficiency Plan Scenario: Net Cumulative Demand 

Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales)

2020 2025 2030

Net Cumulative Demand Reduction (GWh) 23,150 194,126 327,092

Net Cumulative Demand Reduction as Percent of BAU Sales 0.59% 4.81% 7.83%

Source: Greenhouse Gas Abatement Measures TSD
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3.6.2 Demand-Side Energy Efficiency Costs56

Total costs of achieving the demand-side energy efficiency scenario for each year were 

calculated exogenous to the power sector modeling. The power system cost impacts resulting 

from the illustrative plan scenario were captured within IPM and include the effects of reduced 

demand levels driven by the energy efficiency scenario discussed above. The integration of the 

exogenously calculated demand-side energy efficiency scenario costs with the power system cost 

impacts of the illustrative plan scenario are discussed in section 3.7.2. In addition to the demand 

reduction results, the demand-side energy efficiency costs were based upon an estimate of the 

total first-year cost of saved energy (i.e., reduced demand), the average life of the demand-side 

energy efficiency measures, the distribution of those measure lives, and cost factors as greater 

levels of demand reductions are achieved. The total first-year cost of saved energy accounts for 

both the costs of the demand-side energy efficiency programs, known as the program costs, and 

the additional cost to electricity consumers participating in the program (e.g., purchasing a more 

energy efficient technology), known as the participant costs.

To calculate total annualized demand-side energy efficiency costs, first-year costs for 

each year for each state were levelized (at 3 percent and 7 percent discount rates) over the 

estimated distribution of measure lives and the results summed for each year for each state. For 

example, the 2025 estimate of annualized EE cost includes levelized value of first-year costs for 

energy efficiency investments made in 2020 through 2025. The annualized costs rise in each 

analysis year as additional first-year costs are incurred. The annualized cost results are 

summarized below in Table 3-4. The total levelized cost of saved energy was calculated based 

upon the same inputs and using a 3 percent discount rate resulted in average values of 9.2 cents 

per kWh in 2020, 8.6 cents per kWh in 2025, and 8.1 cents per kWh in 2030.

 

56 For a more detailed discussion of the demand-side energy efficiency cost analysis, refer to the Greenhouse Gas 

Abatement Measures TSD.
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Table 3-4. Annualized Cost of Demand-Side Energy Efficiency Plan Scenario [Contiguous 

U.S.] (at discount rates of 3 percent and 7 percent, billions 2011$)

Discount Rate 2018 2020 2025 2030

at 3 percent 0.0 2.1 16.7 26.3

at 7 percent 0.0 2.6 20.6 32.5

Source: Greenhouse Gas Abatement Measures TSD

The funding for demand-side energy efficiency programs (to cover program costs) is 

typically collected through a standard per kWh surcharge to the ratepayer; the regional retail 

price impacts analyzed from this RIA’s illustrative plan scenarios assumes the recovery of these 

program costs through the following procedure.

57 For each state, the first-year energy efficiency

program costs are calculated for each year. These costs were distributed between the applicable 

IPM regions using an approach based on reported sales in 2013 as a proxy for the distribution of 

energy efficiency investment opportunities. These regionalized energy efficiency program costs 

were then incorporated into the regional retail price calculation as discussed in section 3.7.9.

58

Chapter 5 of the GHG Abatement Measures TSD provides complete details on the 

calculations of annualized costs and first-year costs as well as comprehensive results (by state, 

by year) for the illustrative demand-side energy efficiency plan scenario.

3.7 Projected Power Sector Impacts

The following sections present projected impacts from the two illustrative scenarios 

described above. The tables present impacts from 2020 (prior to the initial compliance year), 

2025 (representative of the interim compliance period), and 2030 (representative of the final 

compliance period). The narrative focuses on results during the initial and final compliance 

periods.

3.7.1 Projected Emissions (in the Contiguous U.S.)

Under the rate-based scenario, EPA projects annual CO2 reductions of 3 percent below 

the base case in 2020, 11 percent below the base case in 2025, and 19 percent below base case 

projections in 2030 (reaching 28 percent to 32 percent below 2005 emissions59 in 2025 and 2030, 

respectively). For the mass-based scenario, EPA projects annual CO2 reductions of [X] percent 

 

57 The full retail price analysis method is discussed in section 3.7.9 of this chapter.

58 The effect on equilibrium supply and demand of electricity due to changing retail rates to fund energy efficiency 

programs is not captured in the IPM modeling.

59 For purposes of these calculations, EPA has used historical CO2 emissions from eGRID for 2005, which reports 

EGU emissions as 2,434 million metric tonnes in the contiguous US.
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below the base case in 2020, [X] percent below the base case in 2025 and [X] percent below base 

case projections in 2030 (reaching [X] percent to [X] percent below 2005 emissions60 in 2025 

and 2030, respectively). 

Table 3-5. Projected CO2 Emission Impacts, Relative to Base Case

CO2 Emissions (MM Tonnes) CO2 Emissions Reductions 

from Base Case (MM Tonnes)

CO2 Emissions Reductions: 

Percent Change from Base 

Case

2020 2025 2030 2020 2025 2030 2020 2025 2030

Base Case 1,955 1,964 2,021

Rate 1,892 1,754 1,644 63 210 377 3% 11% 19%

Mass [X] [X] [X] [X] [X] [X] [X] [X] [X]

Source: Integrated Planning Model run by EPA, 2015

Table 3-6. Projected CO2 Emission Impacts, Relative to 2005

CO2 Emissions (MM Tonnes) CO2 Emissions Reductions 

from 2005 (MM Tonnes)

CO2 Emissions Reductions: 

Percent Change from 2005

2020 2025 2030 2020 2025 2030 2020 2025 2030

Base Case 1,955 1,964 2,021

Rate 1,892 1,754 1,644 542 680 790 22% 28% 32%

Mass [X] [X] [X] [X] [X] [X] [X] [X] [X]

Source: Integrated Planning Model run by EPA, 2015

Under the rate-based illustrative scenario, EPA projects a 14 percent reduction of SO2, 13 

percent reduction of NOX, and a 11 percent reduction of mercury in 2025, and a 24 percent 

reduction of SO2, 22 percent reduction of NOX, and a 27 percent reduction of mercury in 2030. 

Under the mass-based illustrative scenario, EPA projects a [X] percent reduction of SO2, [X] 

percent reduction of NOX, and a [X] percent reduction of mercury in 2025, and a [X] percent 

reduction of SO2, [X] percent reduction of NOX, and a [X] percent reduction of mercury in 2030. 

The projected non-CO2 reductions are summarized below in Table 3-7.

 

60 For purposes of these calculations, EPA has used historical CO2 emissions from eGRID for 2005, which reports 

EGU emissions as 2,434 million metric tonnes in the contiguous US.
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Table 3-7. Projected Non-CO2 Emission Impacts, 2020-2030

Base Case Rate Mass Rate Mass

2020

SO2

(thousand tons) 1,311 1,298 [X] -1.0% [X]

NOX

(thousand tons) 1,333 1,282 [X] -3.8% [X]

Hg

(tons) 6.6 6.4 [X] -2.8% [X]

PM2.5

(thousand tons) [X] [X] [X] [X] [X]

2025

SO2

(thousand tons) 1,275 1,097 [X] -13.9% [X]

NOX

(thousand tons) 1,302 1,138 [X] -12.6% [X]

Hg

(tons) 6.6 5.9 [X] -10.8% [X]

PM2.5

(thousand tons) [X] [X] [X] [X] [X]

2030

SO2

(thousand tons) 1,314 996 [X] -24.2% [X]

NOX

(thousand tons) 1,293 1,010 [X] -21.9% [X]

Hg

(tons) 6.8 5.6 [X] -17.3% [X]

PM2.5

(thousand tons) [X] [X] [X] [X] [X]

Source: Integrated Planning Model run by EPA, 2015

While the EPA has not quantified the climate impacts of non-CO2 emissions changes or 

CO2 emissions changes outside the electricity sector for the final emissions guidelines, the 

Agency has analyzed the potential changes in upstream methane emissions from the natural gas 

and coal production sectors that may result from the illustrative approaches examined in this 

RIA. The EPA assessed whether the net change in upstream methane emissions from natural gas 

and coal production is likely to be positive or negative and also assessed the potential magnitude 

of changes relative to CO2 emissions reductions anticipated at power plants. This assessment 

included CO2 emissions from the flaring of methane, but did not evaluate potential changes in 

other combustion-related CO2 emissions, such as emissions associated with drilling, mining, 
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processing, and transportation in the natural gas and coal production sectors. This analysis found 

that the net upstream methane emissions from natural gas systems and coal mines and CO2

emissions from flaring of methane will likely decrease under the final emissions guidelines. 

Furthermore, the changes in upstream methane emissions are small relative to the changes in 

direct CO2 emissions from power plants. The projections include voluntary and regulatory

activities to reduce emissions from coal mining and natural gas and oil systems, including the 

2012 Oil and Natural Gas NSPS. In addition, the EPA plans to issue a proposed rule later this 

summer that would build on its 2012 Oil and Gas NSPS. When these standards are finalized and 

implemented, they would further reduce projected emissions from natural gas and oil systems.

The technical details supporting this analysis can be found in the Appendix to this chapter.

3.7.2 Projected Compliance Costs (in the Contiguous U.S.)

The power industry’s “compliance costs” are represented in this analysis as the change in 

electric power generation costs between the base case and illustrative CPP scenarios, inclusive of 

the cost of demand-side EE measures. The system costs reflect the least cost power system 

outcome in which the sector employs all the flexibilities assumed in the modeling, as discussed 

above, and pursues the most cost-effective emission reduction opportunities in order to meet the 

rate- and mass-based goals, as represented in the illustrative plan scenarios. In simple terms, 

these costs are an estimate of the increased power industry expenditures required to meet demand 

projections while complying with state goals, including the total demand-side energy efficiency

costs. 61 The compliance costs for the final emissions guidelines for EGUs in the contiguous U.S. 

states is forecast using IPM. The cost of demand-side EE programs assumed in the IPM analysis 

are reported in section 3.6.2. The compliance costs for non-contiguous states and territories are 

reported in section 3.X.

EPA projects that the annual compliance cost of the rate-based illustrative plan scenario 

are $2.4 billion in 2020, $1.1 billion in 2025, and $8.4 billion in 2030. The annual compliance 

cost of the mass-based illustrative plan scenario are estimated to be [X] billion in 2020, $[X]

billion in 2025, and $[X] billion in 2030. The annual compliance cost is the projected additional 

 

61 The compliance costs also capture the effect of changes in equilibrium fuel prices on the expenditures of the 

electricity sector to serve demand. 
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cost of complying with the rule in the year analyzed and includes the net change in the 

annualized cost of capital investment in new generating sources and heat rate improvements at 

coal steam facilities,62 the change in the ongoing costs of operating pollution controls, the change 

in expenditures on various fuels (inclusive of changes in the price of these fuels), demand-side 

energy efficiency measures, and other actions associated with compliance. 

Table 3-8. Annualized Compliance Costs (billions of 2011$)

2020 2025 2030

Rate $2.4 $1.1 $8.4

Mass [X] [X] [X]

* These costs do not include monitoring, reporting, and recordkeeping (MM&R) costs. For more information on 

MM&R costs, see section 3.11.

Source: Integrated Planning Model run by EPA, 2015 and with post-processing to account for exogenous demand 

side management energy efficiency costs.

In order to contextualize EPA’s projection of the additional costs in 2030 across the two 

illustrative plan scenarios evaluated in this RIA, it is useful to compare these incremental cost 

estimates to total projected power sector expenditures. The power sector is expected in the base 

case to expend over $201 billion in 2030 to generate, transmit, and distribute electricity to enduse consumers. In 2014, according to EIA, the power sector generated $389 billion in revenue 

from retail sales of electricity. For context, the projected costs of compliance with the proposed 

rule amount to a 4 percent increase in the cost of meeting electricity demand, while securing 

public health and welfare benefits that are several times greater (as described in Chapters 4 and 

8).

The following example uses projected results for the year 2030 to illustrate how different 

components of estimated expenditures are combined to form the full compliance costs presented 

in Table 3-8. In Table 3-9 we present the IPM modeling results for the two illustrative plan 

scenarios in 2030. The results show that annualized expenditures required to supply enough 

electricity to meet demand decline by $15.7 billion (rate) and $[X] billion (mass) from the base 

case in 2030. This incremental decline is a net outcome of two simultaneous effects that move in 

opposite directions. First, imposing the CO2 constraints represented by each illustrative plan 

scenario on electric generators would, other things equal, result in an incremental increase in 

 

62 See Chapter 2 of the Greenhouse Gas Abatement Measures TSD and EPA’s Base Case using IPM (v5.15) 

documentation, available at: http://www.epa.gov/powersectormodeling
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expenditures to supply any given level of electricity. However, once electricity demand is 

reduced to reflect demand-side energy efficiency improvements, there is a substantial reduction 

in the expenditures needed to supply a correspondingly lower amount of electricity demand. 

Table 3-9. Total Power Sector Generating Costs (IPM) (billions 2011$)

2020 2025 2030

Base Case $166.5 $178.3 $201.3

Rate $166.8 $162.7 $183.4

Mass

Source: Integrated Planning Model run by EPA, 2015

In order to reflect the full compliance cost attributable to the CPP scenarios, it is 

necessary to include the annualized expenditures needed to secure the demand-side energy

efficiency improvements. As described in section 3.6.2, EPA has estimated these energy

efficiency-related expenditures to be $26.3 billion in 2030 (using a 3 percent discount rate). The 

EE-related expenditures include costs incurred by parties administering EE programs and costs 

incurred by participants in those programs. As a result, this analysis finds the cost of the ratebased and mass-based illustrative plan scenarios in 2030 to be $8.4 billion and $[X] billion, 

respectively.

3.7.3 Projected Compliance Actions for Emissions Reductions

Heat Rate Improvements (HRI)

EPA analysis assumes that the existing coal steam electric generating fleet has, on 

average, the ability to improve operating efficiency (i.e., reduce the average net heat rate, or the 

Btu of fuel energy needed to produce one kWh of net electricity output). All else held constant, 

an HRI allows the EGU to generate the same amount of electricity using less fuel. The decrease 

in required fossil fuel results in a lower output-based CO2 emissions rate (lbs/MWh), as well as a 

lower variable cost of electricity generation. In the modeling conducted for these illustrative plan 

scenarios, coal boilers have the choice to improve heat rates by 4.3 percent in the eastern 

illustrative compliance region, 2.1 percent in the western illustrative compliance region, and 2.3 
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percent in Texas, all at a capital cost of $100 per kW.63 The option for heat rate improvement is 

only made available in the illustrative plan scenarios, in response to the final rule.

The vast majority of existing coal boilers are projected to adopt the aforementioned heat 

rate improvements. EPA projects that 99 GW of existing coal steam capacity (greater than 25 

MW) will improve operating efficiency (i.e., reduce the average net heat rate) under the ratebased scenario by 2030. Under the mass-based scenario, EPA projects that [X] GW of existing 

coal steam capacity with improve operating efficiency by 2030.

Generation Shifting

Another approach for reducing the average emission rate from existing units is to shift 

some generation from more CO2-intensive generation to less CO2-intensive generation. 

Compared to the base case, existing coal steam capacity is, on average, projected to operate at a 

lower capacity factor for both illustrative plan scenarios. Under the illustrative rate-based plan 

scenario, the average 2030 capacity factor is 69 percent, and under the mass-based scenario, the 

average capacity factor for existing coal steam is [X] percent. Existing natural gas combined 

cycle units, which are less carbon-intensive than coal steam capacity on an output basis, operate 

at noticeably higher capacity factor under both illustrative plan scenarios, on average. See Table 

3-10. 

The utilization of existing natural gas combined cycle capacity is lower than the BSER 

level of 75 percent on an annual average basis in these illustrative plan scenarios, reflecting the 

fact that, in practice, the most cost-effective CO2 reduction strategies to meet each state’s goal 

may not require that each building block be achieved in entirety. For example, building block 2 

(generation shifting) is not deployed to the maximum extent in the illustrative plan scenarios.

 

63 The option for heat rate improvement is only made available in the illustrative plan scenarios, and is not available 

in the base case. For an explanation of the regional differences in average ability to improve heat rates, see GHG 

Abatement Measures TSD.
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Table 3-10. Projected Capacity Factor of Existing Coal Steam and Natural Gas 

Combined Cycle Capacity

Existing Coal Steam Existing Natural Gas Combined Cycle

2020 2025 2030 2020 2025 2030

Base Case 77% 76% 79% 54% 56% 51%

Rate 78% 75% 69% 56% 59% 61%

Mass [X] [X] [X] [X] [X] [X]

Source: Integrated Planning Model run by EPA, 2015

Demand-Side Energy Efficiency

Another approach for reducing emissions from affected EGUs is to consider reductions in 

demand attributable to demand-side energy efficiency measures as discussed in section 3.6. In

the illustrative plan scenarios presented in this RIA, each state is credited for total demand-side 

energy efficiency consistent with the state-by-state demand reductions that are represented by the 

demand-side energy efficiency scenario discussed in section 3.6.1. 

Deployment of Cleaner Generating Technologies

Another key opportunity to reduce emissions from existing sources is to build more 

lower- or zero-emitting generating resources, in particular renewable energy. These sources of 

electricity, including wind and solar, can displace higher emitting existing sources and are also 

directly credited toward the state goals in the rate-based illustrative scenario. Increased 

deployment results in CO2 reductions in both rate-based and mass-based scenarios. 

3.7.4 Projected Generation Mix

Table 3-3 and Figure 3-4 show the generation mix in the base case and under the two 

illustrative plan scenarios. In both scenarios, total generation declines relative to the base case as 

a result of the reduction in total demand attributable to the demand-side energy efficiency

applied in the illustrative scenarios, by 5 percent in 2025 and 8 percent in 2030.

Under the rate-based scenario, coal-fired generation is projected to decline 12 percent in 

2025, and natural-gas-fired generation from existing combined cycle capacity is projected to 

increase 5 percent relative to the base case. The coal-fired fleet in 2030 generates 23 percent less 

than in the base case, while natural-gas-fired generation from existing combined cycles increases 

18 percent relative to the base case. Gas-fired generation from new combined cycle capacity
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decreases in 2025 and 2030, consistent with the decrease in new capacity (see section 3.7.6). 

Relative to the base case, generation from non-hydro renewables decreases 1 percent in 2025 and 

increases 9 percent in 2030.

Similarly, under the mass-based scenario, coal-fired generation is projected to decline [X] 

percent in 2025, and natural-gas-fired generation from existing combined cycle capacity is 

projected to increase [X] percent relative to the base case. The coal-fired fleet in 2030 generates 

[X] percent less than in the base case, while natural-gas-fired generation from existing combined 

cycles increases [X] percent relative to the base case. Gas-fired generation from new combined 

cycle capacity changes [X] percent relative to the base case. Relative to the base case, generation 

from non-hydro renewables increases [X] percent in 2025 and [X] percent in 2030. 
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Table 3-11. Generation Mix (thousand GWh) 

Base Case Rate Mass Rate Mass

2020

Coal 1,462 1,391 -5%

NG Combined Cycle (existing) 1,111 1,125 1%

NG Combined Cycle (new) 33 54 63%

Combustion Turbine 15 20 39%

Oil/Gas Steam 51 50 0%

Non-Hydro Renewables 393 399 2%

Hydro 310 311 0%

Nuclear 798 792 -1%

Other 18 18 0%

Total 4,190 4,160 -1%

2025

Coal 1,428 1,256 -12%

NG Combined Cycle (existing) 1,152 1,206 5%

NG Combined Cycle (new) 113 54 -53%

Combustion Turbine 23 30 31%

Oil/Gas Steam 39 21 -46%

Non-Hydro Renewables 417 414 -1%

Hydro 340 340 0%

Nuclear 799 791 -1%

Other 17 17 0%

Total 4,328 4,129 -5%

2030

Coal 1,466 1,131 -23%

NG Combined Cycle (existing) 1,042 1,231 18%

NG Combined Cycle (new) 324 101 -69%

Combustion Turbine 22 26 21%

Oil/Gas Steam 22 11 -52%

Non-Hydro Renewables 450 489 9%

Hydro 340 341 0%

Nuclear 783 777 -1%

Other 17 17 0%

Total 4,467 4,124 -8%

Note: “Other” mostly includes generation from MSW and fuel cells. Source: Integrated Planning Model run by 

EPA, 2015
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Figure 3-4. Generation Mix with the Base Case and 111(d) Options, 2020-2030 (thousand GWh)

Source: Integrated Planning Model run by EPA, 2015
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3.7.5 Projected Incremental Retirements

Relative to the base case, about 23 GW of additional coal-fired capacity is projected to be 

uneconomic to maintain (about 11 percent of all coal-fired capacity projected to be in service in 

the base case) by 2025 under the rate-based illustrative scenario. Under the mass-based scenario, 

about [X] GW of additional coal-fired capacity is projected to be uneconomic to maintain (about 

[X] percent of all coal-fired capacity projected to be in service in the base case) by 2025. 

Capacity changes from the base case are shown in Table 3-12.
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Table 3-12. Total Generation Capacity by 2020-2030 (GW)

Base Case Rate Mass Rate Mass

2020

Coal 208 195 -6%

NG Combined Cycle (existing) 233 231 -1%

NG Combined Cycle (new) 4 7 64%

Combustion Turbine 141 137 -3%

Oil/Gas Steam 88 81 -8%

Non-Hydro Renewables 130 132 1%

Hydro 106 106 0%

Nuclear 100 100 -1%

Other 5 5 0%

Total 1,016 994 -2%

2025

Coal 208 187 -10%

NG Combined Cycle (existing) 233 231 -1%

NG Combined Cycle (new) 15 7 -52%

Combustion Turbine 143 138 -4%

Oil/Gas Steam 82 71 -14%

Non-Hydro Renewables 139 137 -1%

Hydro 112 112 0%

Nuclear 100 99 -1%

Other 5 5 0%

Total 1,037 988 -5%

2030

Coal 207 183 -11%

NG Combined Cycle (existing) 233 231 -1%

NG Combined Cycle (new) 44 14 -68%

Combustion Turbine 147 138 -6%

Oil/Gas Steam 82 70 -16%

Non-Hydro Renewables 154 174 13%

Hydro 112 112 0%

Nuclear 99 98 -1%

Other 5 5 0%

Total 1,082 1,025 -5%

Source: Integrated Planning Model run by EPA, 2015

3.7.6 Projected Capacity Additions

Due largely to the electricity demand reduction attributable to the demand-side energy

efficiency improvements applied in the illustrative scenarios, the EPA projects less new natural 

gas combined cycle capacity built under the rate-based scenario than is built in the base case over 
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the period covered by the rule. While this new NGCC capacity cannot be directly counted 

towards the average emissions rate used for compliance in this scenario, it can displace some 

generation from covered sources and thus indirectly lower the average emissions rate from 

covered sources. Conversely, the EPA projects an overall increase in new renewable capacity. 

New non-hydro renewables are able to contribute their generation to the average emissions rate 

in each state or region.

Under the rate-based illustrative scenario, new natural gas combined cycle capacity is 

projected to decrease by 8 GW in 2025 and 30 GW in 2030 (52 percent and 68 percent decrease 

relative to the base case). New renewable capacity is projected to decrease by about 2 GW (3 

percent decrease) below the base case in 2025, and increase by 21 GW (28 percent increase) by 

2030.

Under the mass-based illustrative scenario, new natural gas combined cycle capacity is 

projected to [X] in 2025 and [X] in 2030 ([X] percent and [X] percent relative to the base case). 

New renewable capacity is projected to [X] (X percent) relative to the base case in 2025, and [X] 

([X] percent increase) by 2030.

Table 3-13. Projected Capacity Additions, Gas (GW)

Cumulative Capacity Additions: Gas 

Combined Cycle

Incremental Cumulative Capacity Additions: 

Gas Combined Cycle

2020 2025 2030 2020 2025 2030

Base Case 4.4 14.9 44.0

Rate 7.1 7.2 14.0 2.8 -7.7 -30.0

Mass

Source: Integrated Planning Model run by EPA, 2015

Table 3-14. Projected Capacity Additions, Renewable (GW)

Cumulative Capacity Additions: 

Renewables

Incremental Cumulative Capacity Additions: 

Renewables

2020 2025 2030 2020 2025 2030

Base Case 39.1 59.1 74.1

Rate 40.5 57.4 94.6 1.4 -1.8 20.5

Mass

Source: Integrated Planning Model run by EPA, 2015
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3.7.7 Projected Coal Production and Natural Gas Use for the Electric Power Sector

Coal production is projected to decrease in 2025 and beyond in the illustrative scenarios 

due to (1) improved heat rates (generating efficiency) at existing coal units, (2) electricity

demand reduction attributable to demand-side energy efficiency improvements, and (3) a shift in 

generation from coal to less-carbon intensive generation. As shown in Table 3-15, the largest 

decrease in coal production is projected to occur in the western region.

Table 3-15. Coal Production for the Electric Power Sector, 2025

Coal Production (MM Tons) Percent Change from Base Case

Base Case Rate Mass Rate Mass

Appalachia 92 71 -23%

Interior 250 242 -3%

West 379 306 -19%

Waste Coal 6 6 0%

Imports 1 1 -37%

Total 729 626 -14%

Source: Integrated Planning Model run by EPA, 2015

Power sector natural gas use is projected to decrease by about 1 percent in 2025 and 2030 

under the rate-based illustrative plan scenario. In the mass-based scenario, [X]. These trends are 

consistent with the change in generation mix described above in Section 3.7.4.

Table 3-16. Power Sector Gas Use

Power Sector Gas Use (TCF) Percent Change in Power Sector Gas Use

2020 2025 2030 2020 2025 2030

Base Case 8.62 9.38 9.72

Rate 8.91 9.28 9.60 3.4% -1.0% -1.2%

Mass

Source: Integrated Planning Model run by EPA, 2015

3.7.8 Projected Fuel Price, Market, and Infrastructure Impacts

The impacts of the two illustrative plan scenarios on coal and natural gas prices before 

shipment are shown below in Table 3-17, Table 3-18, Table 3-19, and Table 3-20, and are 

attributable to the changes in overall power sector demand for each fuel due to the CPP. 
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Coal demand decreases by 2030, resulting in a decrease in the price of coal delivered to 

the electric power sector. In 2030, gas demand and price decrease below the base case 

projections, due to the cumulative impact of demand-side energy efficiency improvements and 

the consequent reduced overall electricity demand.

IPM modeling of natural gas prices uses both short- and long-term price signals to 

balance supply and demand for the fuel across the modeled time horizon. As such, it should be 

understood that the pattern of IPM natural gas price projections over time is not a forecast of 

natural gas prices incurred by end-use consumers at any particular point in time. The natural gas 

market in the United States has historically experienced some degree of price volatility from year 

to year, between seasons within a year, and during short-lived weather events (such as cold snaps 

leading to short-run spikes in heating demand). These short-term price signals are fundamental 

for allowing the market to successfully align immediate supply and demand needs. However, 

end-use consumers are typically shielded from experiencing these rapid fluctuations in natural 

gas prices by retail rate regulation and by hedging through longer-term fuel supply contracts by

the power sector. IPM assumes these longer-term price arrangements take place “outside of the 

model” and on top of the “real-time” shorter-term price variation necessary to align supply and 

demand. Therefore, the model’s natural gas price projections should not be mistaken for 

traditionally experienced consumer price impacts related to natural gas, but a reflection of 

expected average price changes over the period of time represented by the modeling horizon.

There are very small changes to natural gas pipeline infrastructure needs over time, in 

response to the illustrative plan scenarios. These changes, compared to historical deployment of 

new infrastructure, are very modest. In the rate-based scenario, pipeline capacity construction 

through 2020 is projected to increase 1 percent beyond base case projections. By 2025 and 2030, 

however, the total cumulative pipeline capacity construction built is projected to decrease by up 

to 5 percent compared to the base case, consistent with the projected decrease in total demand 

and natural gas use. The projected increase in pipeline capacity in the near term is largely the 

result of building pipeline capacity a few years earlier than projected in the base case. 
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Table 3-17. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu)

Minemouth Delivered - Electric Power Sector

2020 2025 2030 2020 2025 2030

Base Case 1.55 1.67 1.79 2.38 2.50 2.68

Rate 1.54 1.58 1.73 2.34 2.35 2.46

Mass

Rate -0.8% -5.0% -3.8% -1.7% -6.2% -8.0%

Mass

Source: Integrated Planning Model run by EPA, 2015

Table 3-19. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices 

(2011$/MMBtu)

Source: Integrated Planning Model run by EPA, 2015

3.8 Projected Primary PM Emissions from Power Plants

IPM is not configured to endogenously model primary PM emissions from power plants. 

These emissions are calculated as a function of IPM outputs, emission factors and control 

configuration. IPM-projected fuel use (heat input) is multiplied by PM emission factors (based in 

part on the presence of PM-relevant pollution control devices) to determine PM emissions. 

Primary PM emissions are calculated by adding the filterable PM and condensable PM 

emissions.

Filterable PM emissions for each unit are based on historical information regarding 

existing emissions controls and types of fuel burned and ash content of the fuel burned, as well 

as the projected emission controls (e.g., scrubbers and fabric filters).

Condensable PM emissions are based on plant type, sulfur content of the fuel, and 

SO2/HCl and PM control configurations. Although EPA’s analysis is based on the best available 

emission factors, these emission factors do not account for the potential changes in condensable 

PM emissions due to the installation and operation of SCRs. The formation of additional 

condensable PM (in the form of SO3 and H2SO4) in units with SCRs depends on a number of 
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factors, including coal sulfur content, combustion conditions and characteristics of the catalyst 

used in the SCR, and is likely to vary widely from unit to unit. SCRs are generally designed and 

operated to minimize increases in condensable PM. This limitation means that IPM postprocessing is potentially underestimating condensable PM emissions for units with SCRs. In 

contrast, it is possible that IPM post-processing overestimates condensable PM emissions in a 

case where the unit is combusting a low-sulfur coal in the presence of a scrubber.

For a more complete description of the methodologies used to post-process PM emissions 

from IPM, see “IPM ORL File Generation Methodology” (March, 2011), available in the docket.

3.9 Limitations of Analysis

EPA’s modeling is based on expert judgment of various input assumptions for variables 

whose outcomes are in fact uncertain. As a general matter, the Agency reviews the best available 

information from engineering studies of air pollution controls, the ability to improve operating 

efficiency, and new capacity construction costs to support a reasonable modeling framework for 

analyzing the cost, emission changes, and other impacts of regulatory actions.

The costs presented in this RIA include both the IPM-projected annualized estimates of 

private compliance costs as well as the estimated costs incurred by utilities and program 

participants to achieve demand-side energy efficiency improvements. The IPM-projected 

annualized 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 final rule. To 

estimate these annualized costs, 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 cost of capital 

(private discount rate), the amount of insurance coverage required, local property taxes, and the 

life of capital.64

The demand-side energy efficiency costs are developed based on a review of energy

efficiency data and studies, and expert judgment. The EPA recognizes that significant variation 

exists in these analyses reflecting data and methodological limitations. The method used for 

 

64 See Chapter 8 of EPA’s Base Case using IPM (v5.15) documentation, available at: 

http://www.epa.gov/powersectormodeling.
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estimating the demand-side energy efficiency costs is discussed in more detail in the Greenhouse 

Gas Abatement Measures TSD. The evaluation, measurement and verification (EM&V) of 

demand-side energy efficiency is addressed in the section VIII, State Plans, of the preamble for 

the final rule.

The base case electricity demand in IPM v.5.15 is calibrated to reference case demand in 

AEO 2015. AEO 2015 demand may reflect, to some extent, a continuation of the impacts of state 

demand-side energy efficiency policies but does not explicitly represent the most significant 

existing state policies in this area (e.g., energy efficiency resource standards). To some degree 

the implicit representation of state policies in the EPA’s base case alters the impacts assessment, 

but the direction and magnitude of change is not known with certainty. This issue is discussed in 

the Greenhouse Gas Abatement Measures TSD.

The modeling of these policy scenarios does not account for an additional compliance 

flexibility allowed in the final rule whereby “overcompliance” with state goals (more reductions 

than required) during the interim period can be “banked” for compliance with state goals in the 

final period.

Cost estimates for the proposed rule are based on rigorous power sector modeling using

ICF’s Integrated Planning Model.65 IPM assumes “perfect foresight” of market conditions over 

the time horizon modeled; to the extent that utilities and/or energy regulators misjudge future 

conditions affecting the economics of pollution control, costs may be understated as well.

Furthermore, IPM does not represent electricity markets in Alaska, Hawaii, and U.S. territories 

outside the contiguous U.S. and therefore the costs (and benefits) that may be expected from the 

proposed rule in this states and territories are not accounted for in the compliance cost modeling. 

3.10 Significant Energy Impacts

The CPP, under both illustrative plan scenarios analyzed, would have a significant impact 

according to E.O. 13211: Actions that Significantly Affect Energy Supply, Distribution, or Use. 

Under the rate-based illustrative scenario, EPA projects that approximately 23 GW of additional 

 

65 Full documentation for IPM can be found at <http:// http://www.epa.gov/powersectormodeling>.
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coal-fired generation (about 11 percent of all coal-fired capacity and 2.2 percent of total 

generation capacity in 2025) may be removed from operation by 2025. 

EPA also projects the average delivered coal price decreases by 6.2 percent with 

decreased production of 103 million tons (14 percent of US production) in 2025 and that electric 

power sector delivered natural gas prices will decrease by about 7.7 percent with decreased 

power sector consumption of 95 billion cubic feet (BCF) in 2025. Average retail electricity

prices are projected to increase in the contiguous U.S. by [X] percent in 2025.

3.11 Monitoring, Reporting, and Recordkeeping Costs

EPA projected monitoring, reporting and recordkeeping costs for both state entities and 

affected EGUs for the compliance years 2020, 2025 and 2030. In calculating the costs for state 

entities, EPA estimated personnel costs to oversee compliance, and review and report annually to 

EPA on program progress relative to meeting the state’s reduction goal. To calculate the national 

costs, EPA estimated that 49 states, 2 territories and 1,052 facilities would be affected.

The EPA estimated that the majority of the cost to EGUs would be in calculating net 

energy output, which is needed whether the state plan utilizes a rate-based or a mass-based 

limit. Since the majority of EGUs do have some energy usage meters or other equipment 

available to them, EPA believes a new system for calculating net energy output is not needed. 

Under the CPP, states are required to use monitoring and reporting requirements for their 

affected EGUs to ensure that the sources are meeting the appropriate CO2 emission performance 

rates or emission goals. In general, the EPA has made it a priority to streamline reporting and 

monitoring requirements. In this rule, the EPA is making implementation as efficient as possible 

for both the states and the affected EGUs by allowing state plans to utilize the current monitoring 

and recordkeeping requirements and pathways that have already been well established in other 

EPA rulemakings. For example, under the Acid Rain Program’s continuous emissions 

monitoring, 40 CFR Part 75, the EPA has established requirements for the majority of the EGUs 

that would be affected by a 111(d) state plan to monitor CO2 emissions and report that data 

using the Emissions Collection and Monitoring Plan System (ECMPS). Additionally since the 

CO2 hourly data is already reported to the EPA’s ECMPS there is no additional burden 

associated with the reporting of that data. Since the ECMPS pathway is already in place, the EPA 
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will allow for states to utilize the ECMPS system to facilitate the data reporting of the additional 

net energy output data required under the emission guidelines. However, because the Acid Rain 

Program does not require net energy output to be reported, there is some additional burden 

(Shown in Table 3-25) in updating an affected EGUs monitoring system to be able to report the 

associated net energy output of an affected EGU.

The EPA estimates that it would take 3 working months for a technician to retrofit any 

existing energy meters to meet the requirements set in the state plan. Additionally EPA believes 

that 50 hours will be needed for each EGU operator to read the rule and understand how the 

facility will comply with the rule, based on an average reading rate of 100 words per minute and 

a projected rule word count of 300,000 words.

66 Also, after all modifications are made at a 

facility to measure net energy output, each EGU’s Data Acquisition System (DAS) would need 

to be upgraded to supply the rate-based emissions value to either the state or EPA’s Emissions 

Collection and Monitoring Plan System (ECMPS). Note the costs to develop net energy output 

monitoring and to upgrade each facility’s DAS system are one-time costs incurred in 2020. 

Recordkeeping and reporting costs substantially decrease for the period 2021-2030. The 

projected costs for 2020, 2025, and 2030 are summarized below.

In calculating the cost for states to comply, EPA estimates that each state will rely on the 

equivalent of 2 full time staff (1 each from the energy and air offices) to oversee program 

implementation, assess progress, develop possible contingency measures, perform state plan 

revisions and host the subsequent public meetings if revisions are indeed needed, download data 

from the ECMPS for their annual reporting and develop their annual EPA report. The burden 

estimate was based on an analysis of similar tasks performed under the Regional Haze Program, 

whereby states were required to develop their list of eligible sources, draft implementation plans, 

revise initial drafts, identify baseline controls, identify data gaps, identify initial strategies, 

conduct various reviews, and manage their programs. A total estimate of 78,000 hours of labor 

performed by 7 states over a 3 year period resulted in 3,714 hours per year, per entity. Due to the 

 

66 According to one source, the average person can proofread at about 200 words per minute on paper and 180 words 

per minute on a monitor. (Reference: Ziefle, M. (December 1988). “Effects of Display Resolution on Visual 

Performance.” Human Factors 40 (4): 554-68). Due to the highly technical nature of the rule requirements in subpart 

UUUU, a more conservative estimate of 100 words per minute was used to determine the burden estimate for 

reading and understanding rule requirements.
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nature of this final rule whereby we believe the air office and the energy office will both be 

involved in performing the above-mentioned tasks, we rounded up to the equivalent of 2, full 

time staff, which totaled 4,160 hours per year.67 In addition, in the absence of specific 

information, EPA assumed that 27 states with strong, state-driven EE programs as documented 

in the Energy and Environment Guide to Action68 would submit a plan under the state measures 

approach, which adds to the state respondent burden. Table 3-25 shows the annual state 

respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030. Table 3-26

shows the annual industry respondent burden and costs of reporting and recordkeeping for 2020,

2025 and 2030. Table 3-27 shows the annual territories respondent burden and costs of reporting 

and recordkeeping for 2020, 2025 and 2030.

Table 3-25. Years 2020, 2025 and 2030: Summary of State Annual Respondent Burden 

and Cost of Reporting and Recordkeeping Requirements (2011$)

Nationwide

Totals

Total Annual 

Labor Burden 

(Hours)

Total

Annual

Labor Costs

Total 

Annualized

Capital Costs

Total

Annual

O&M 

Costs

Total 

Annualized 

Costs

Total 

Annual

Respondent 

Costs

Year 2020 203,840 14,427,298 0 36,015 36,01511 14,463,313

Year 2025 225,920 15,990,066 0 24,500 24,500 16,014,566

Year 2030 225,920 15,990,066 0 24,500 24,500 16,014,566

Table 3-26. Years 2020, 2025 and 2030: Summary of Industry Annual Respondent 

Burden and Cost of Reporting and Recordkeeping Requirements (2011$)

Nationwide 

Totals

Total Annual 

Labor Burden 

(Hours)

Total

Annual

Labor Costs

Total 

Annualized

Capital Costs

Total

Annual

O&M 

Costs

Total 

Annualized 

Costs

Total 

Annual

Respondent 

Costs

Year 2020 595,432 51,125,815 0 1,556,500 1,556,500 52,682,315

Year 2025 0 0 0 0 0 0

Year 2030 0 0 0 0 0 0

 

67 Renewal of the ICR for the Regional Haze Rule, Section 6(a) Tables 1 through 4 based on 7 states’ burden. EPAHQ-OAR-2003-0162-0001.

68 U.S. Environmental Protection Agency. Energy and Environment Guide to Action: State Policies and Best 

Practices for Advancing Energy Efficiency, Renewable Energy, and Combined Heat and Power, 2015 Edition. 

Available online at: http://epa.gov/statelocalclimate/resources/action-guide.html.
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Table 3-27. Years 2020, 2025 and 2030: Summary of Territories Annual Respondent 

Burden and Cost of Reporting and Recordkeeping Requirements (2011$)

Nationwide 

Totals

Total Annual 

Labor Burden 

(Hours)

Total

Annual

Labor Costs

Total 

Annualized

Capital Costs

Total

Annual

O&M 

Costs

Total 

Annualized 

Costs

Total 

Annual

Respondent

Costs

Year 2020 8,320 588,869 0 1,470 1,470 590,339

Year 2025 8,960 634,167 0 1,000 1,000 635,167

Year 2030 8,960 634,167 0 1,000 1,000 635,167

The annual costs of the rate-based and mass-based illustrative plan scenarios, including 

monitoring reporting and recordkeeping costs, are shown in Table 3-28 below.

Table 3-28. Annualized Compliance Costs Including Monitoring, Reporting and 

Recordkeeping Costs Requirements (billions of 2011$)

2020 2025 2030

Rate 2.5 1.1 8.5

Mass [X] [X] [X]

Source: Integrated Planning Model run by EPA, 2015 and GHG Abatement Measures TSD. Monitoring, reporting 

and recordkeeping costs calculated outside IPM.

3.12 Social Costs

As discussed in the EPA Guidelines for Preparing Economic Analyses, social costs are 

the total economic burden of a regulatory action. 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 as a result of reallocating some 

resources towards pollution mitigation. Estimates of social costs may be compared to the social 

benefits expected as a result of a regulation to assess its net impact on society. The social costs of 

a regulatory action will not necessarily be equivalent to the expenditures associated with 

compliance. Nonetheless, here we use compliance costs as a proxy for social costs. This section 

provides a qualitative discussion of the relationship between social costs and compliance cost 

estimates presented in this chapter. 

The cost estimates for the illustrative plan scenarios presented in this chapter are the sum 

of expenditures on demand-side energy efficiency and the change in expenditures required by the

electricity sector to comply with the proposed guidelines. These two components are estimated 

separately. The expenditures required to achieve the assumed demand reductions through 

demand-side energy efficiency programs are estimated using historical data, analysis, and expert 
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judgment. The change in the expenditures required by the electricity sector to meet demand and 

maintain compliance are estimated by IPM and reflect both the reduction in electricity 

production costs due to the reduction in demand caused by the demand-side energy efficiency

measures and the increase in electricity production costs required to achieve the additional 

emission reductions necessary to comply with the state goals.

As described in section 3.6.1, the illustrative plan scenarios assume that, in achieving 

their goals, demand-side energy efficiency measures are adopted which lead to demand 

reductions in each year represented by the illustrative energy efficiency plan scenario. The 

estimated expenditures required to achieve those demand reductions through demand-side energy

efficiency are presented in this chapter and detailed in Chapter 5 of the GHG Abatement 

Measures TSD. The social cost of achieving these energy savings comes in the form of increased 

expenditures on technologies and/or services that are required to lower electricity consumption 

beyond the business as usual. Under the assumption of complete and well-functioning markets 

the expenditures required to reduce electricity consumption on the margin will represent

society’s opportunity cost of the resources required to produce the energy savings. 

Due to the flexibility held by states in implementing their compliance with the proposed 

standards these energy efficiency expenditures may be borne by end-users through direct 

participant expenditures or electricity rate increases, or by producers through reductions in their 

profits. While the allocation of these expenditures between consumers and producers is 

important for understanding the distributional impact of potential compliance strategies, it does 

not necessarily affect the opportunity cost required for the production of the energy savings from 

a social perspective. However, specific design elements of demand-side energy efficiency

measures included to address distributional outcomes may have an effect on the economic 

efficiency of the programs and therefore the social cost.

Another reason the expenditures associated with demand-side energy efficiency may

differ from social costs is due to differences in the services provided by more energy efficient 

technologies and services adopted under the program relative to the baseline. For example, if 

under the program end-users adopted more energy efficient products which were associated with 

quality or service attributes deemed less desirable, then there would be an additional welfare loss 

that should be accounted for in social costs but is not necessarily captured in the measure of 
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expenditures. However, there is an analogous possibility that in some cases the quality of 

services, outside of the energy savings, provided by the more energy efficient products and 

practices are deemed more desirable by some end-users. For example, weatherization of 

buildings to reduced electricity demand associated with cooling will likely have a significant 

impact on natural gas use associated with heating. In either case these real welfare impacts are 

not fully captured by end-use energy efficiency expenditure estimates.

The fact that such quality and service differences may exist in reality but may not be 

reflected in the price difference between more and less energy efficient products is one potential 

hypothesis for the energy paradox. The energy paradox is the observation that end-users do not 

always purchase products that are more energy efficient when the additional cost is less than the 

reduction in the net present value of expected electricity expenditures achieved by those 

products.69 Such circumstances are present in the analysis presented in this chapter, whereby in 

some regions the base case and illustrative scenarios suggest that cost of reducing demand 

through energy efficiency programs is less than the retail electricity price. In addition to 

heterogeneity in product services and consumer preferences, there are other explanations for the 

energy paradox, falling both within and outside the neoclassical rational expectations paradigm 

that is used in benefit/cost analysis. Chapter 5 of the GHG Abatement Measures TSD discusses 

the energy paradox and provides additional hypothesis for why consumers may not make energy

efficiency investments that ostensibly seem to be in their own interest. The TSD discussion also 

provides details on how the presence of additional market failures can lead to levels of energy 

efficiency investment that may be too low from society’s perspective even if that is not the case 

for the end-user. In such cases there is the potential for properly designed energy efficiency

programs to address the source of under-investment, such as principal-agent problems where 

there is a disconnect between those making the purchase decision regarding energy efficient 

investments and energy use and those that would receive the benefits associated with reduced 

energy use through lower electricity bills.

The other component of compliance cost reported in this chapter is the change in resource 

cost (i.e., expenditures) required by the electricity sector to fulfill the remaining demand while 

 

69 An analogous situation is present when some EGUs have assumed to have the ability to make heat rate 

improvements at a capital cost that is less than the anticipated fuel expenditure savings. 
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making additional CO2 emissions reductions necessary to comply with the state goals. Included 

in the estimate of these compliance costs, developed using IPM, are the cost savings associated 

with the reduction in required electricity generation due to the demand reductions from demandside energy efficiency measures and improvements in heat rate. By shifting the demand curve for 

electricity, demand-side energy efficiency reduces the production cost in the sector. The resource 

cost estimates from IPM therefore account for the increased cost of providing electricity while 

EGUs comply with their obligations net of the reduction in production costs due to lower 

demand resulting from demand-side energy efficiency measures.

3.13 References

Just, R.J., D.L. Hueth and A. Schmitz, (2004) The Welfare Economics of Public Policy: A 

Practical Guide to Policy and Project Evaluation, Edwin Elgar Press, Cheltenham UK.

US EPA (2010) EPA Guidelines for Preparing Economic Analyses, Chapters 8 and Appendix A.

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.html
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CHAPTER 4: ESTIMATED CLIMATE BENEFITS AND HUMAN HEALTH CO-BENEFITS

4.1 Introduction

Implementing the Final Carbon Pollution Emission Guidelines for Existing Stationary

Sources: Electric Utility Generating Units (hereafter referred to as the “final emission 

guidelines” or “Clean Power Plan Final Rule”) is expected to reduce emissions of carbon dioxide 

(CO2) and have ancillary human health benefits (i.e., co-benefits) associated with lower ambient 

concentrations of criteria air pollutants. This chapter describes the methods used to estimate the 

monetized climate benefits and the monetized air quality health co-benefits associated with 

reducing exposure to ambient fine particulate matter (PM2.5) and ozone by reducing emissions of 

precursor pollutants (i.e., sulfur dioxide (SO2), nitrogen dioxide (NO2), and directly emitted 

PM2.5). Data, resource, and methodological limitations prevent the EPA from monetizing the 

benefits from several important co-benefit categories, including reducing direct exposure to SO2, 

NO2, and hazardous air pollutants (HAP), as well as ecosystem effects and visibility impairment. 

We qualitatively discuss these unquantified benefits in this chapter.

This chapter provides estimates of the monetized climate benefits and air quality health cobenefits associated with emission reductions for the illustrative rate-based and mass-based

scenarios across several analysis years and discount rates. The estimated benefits associated with 

these emission reductions are beyond those achieved by previous EPA rulemakings, including 

the Mercury and Air Toxics Standards (MATS).

4.2 Estimated Climate Benefits from CO2

The primary goal of the final emission guidelines is to reduce emissions of CO2. In this 

section, we provide a brief overview of the 2009 Endangerment Finding and climate science 

assessments released since then. We also provide information regarding the economic valuation 

of CO2 using the Social Cost of Carbon (SC-CO2), a metric that estimates the monetary value of 

impacts associated with marginal changes in CO2 emissions in a given year. Table 4-1 

summarizes the quantified and unquantified climate benefits in this analysis. 
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Table 4-1. Climate Effects

Benefits 

Category

Specific Effect Effect Has Been 

Quantified

Effect Has Been 

Monetized

More 

Information

Improved Environment

Reduced 

climate effects

Global climate impacts from CO2 —1  SCC TSD

Climate impacts from ozone and black 

carbon (directly emitted PM)

— — Ozone ISA, PM 

ISA2

Other climate impacts (e.g., other GHGs 

such as methane, aerosols, other impacts)

— — IPCC2

1 The global climate and related impacts of CO2 emissions changes, such as sea level rise, are estimated within each 

integrated assessment model as part of the calculation of the SC-CO2. The resulting monetized damages, which 

are relevant for conducting the benefit-cost analysis, are used in this RIA to estimate the welfare effects of 

quantified changes in CO2 emissions.

2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods.

4.2.1 Climate Change Impacts

Through the implementation of CAA regulations, the EPA addresses the negative 

externalities caused by air pollution. In 2009, the EPA Administrator found that elevated 

concentrations of greenhouse gases in the atmosphere may reasonably be anticipated both to 

endanger public health and to endanger public welfare. It is these adverse impacts that make it 

necessary for the EPA to regulate GHGs from EGU sources. The preamble summarizes the 

public health and public welfare impacts that were detailed in the 2009 Endangerment Finding. 

For health, these include the increased likelihood of heat waves, negative impacts on air quality, 

more intense hurricanes, more frequent and intense storms and heavy precipitation, and impacts 

on infectious and waterborne diseases. For welfare, these include reduced water supplies in some 

regions, increased water pollution, increased occurrences of floods and droughts, rising sea 

levels and damage to coastal infrastructure, increased peak electricity demand, changes in 

ecosystems, and impacts on indigenous communities. 

The preamble also summarizes new scientific assessments and recent climatic 

observations. Major scientific assessments released since the 2009 Endangerment Finding have 

improved scientific understanding of the climate, and provide even more evidence that GHG 

emissions endanger public health and welfare for current and future generations. The National 

Climate Assessment (NCA3), in particular, assessed the impacts of climate change on human 

health in the United States, finding that, Americans will be impacted by “increased extreme 
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weather events, wildfire, decreased air quality, threats to mental health, and illnesses transmitted 

by food, water, and disease-carriers such as mosquitoes and ticks.” These assessments also detail 

the risks to vulnerable groups such as children, the elderly and low income households. 

Furthermore, the assessments present an improved understanding of the impacts of climate 

change on public welfare, higher projections of future sea level rise than had been previously 

estimated, a better understanding of how the warmth in the next century may reach levels that 

would be unprecedented relative to the preceding millions of years of history, and new 

assessments of the impacts of climate change on permafrost and ocean acidification. The impacts 

of GHG emissions will be realized worldwide, independent upon their location of origin, and 

impacts outside of the United States will produce consequences relevant to the United States.

4.2.2 Social Cost of Carbon

We estimate the global social benefits of CO2 emission reductions expected from the final 

emission guidelines using the SC-CO2 estimates presented in the Technical Support Document: 

Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive 

Order 12866 (May 2013, Revised June 2015) (“current TSD”).

70 We refer to these estimates, 

which were developed by the U.S. government, as “SC-CO2 estimates.” The SC-CO2 is a metric 

that estimates the monetary value of impacts associated with marginal changes in CO2 emissions 

in a given year. It includes a wide range of anticipated climate impacts, such as net changes in 

agricultural productivity and human health, property damage from increased flood risk, and 

changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is typically used to assess the avoided damages as a result of regulatory actions 

(i.e., benefits of rulemakings that lead to an incremental reduction in cumulative global CO2

emissions).

 

70 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost 

of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and 

Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised June 2015). 

Available at:
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The SC-CO2 estimates used in this analysis were developed over many years, using the 

best science available, and with input from the public. Specifically, an interagency working 

group (IWG) that included the EPA and other executive branch agencies and offices used three 

integrated assessment models (IAMs) to develop the SC-CO2 estimates and recommended four 

global values for use in regulatory analyses. The SC-CO2 estimates were first released in 

February 2010 and updated in 2013 using new versions of each IAM. As discussed further 

below, the IWG published two minor corrections to the SC-CO2 estimates in June 2015. 

The SC-CO2 estimates were developed using an ensemble of the three most widely cited 

integrated assessment models in the economics literature with the ability to estimate the SC-CO2. 

A key objective of the IWG was to draw from the insights of the three models while respecting 

the different approaches to linking GHG emissions and monetized damages taken by modelers in 

the published literature. After conducting an extensive literature review, the interagency group 

selected three sets of input parameters (climate sensitivity, socioeconomic and emissions 

trajectories, and discount rates) to use consistently in each model. All other model features were 

left unchanged, relying on the model developers’ best estimates and judgments, as informed by 

the literature. Specifically, a common probability distribution for the equilibrium climate 

sensitivity parameter, which informs the strength of climate’s response to atmospheric GHG 

concentrations, was used across all three models. In addition, a common range of scenarios for 

the socioeconomic parameters and emissions forecasts were used in all three models. Finally, the 

marginal damage estimates from the three models were estimated using a consistent range of

discount rates, 2.5, 3.0, and 5.0 percent. See the 2010 TSD for a complete discussion of the 

methods used to develop the estimates and the key uncertainties, and the current TSD for the 

latest estimates.

The SC-CO2 estimates represent global measures because of the distinctive nature of the 

climate change, which is highly unusual in at least three respects. First, emissions of most GHGs 

contribute to damages around the world independent of the country in which they are emitted. 

The SC-CO2 must therefore incorporate the full (global) damages caused by GHG emissions to 

address the global nature of the problem. Second, the U.S. operates in a global and highly 

interconnected economy, such that impacts on the other side of the world can affect our 

economy. This means that the true costs of climate change to the U.S. are larger than the direct 
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impacts that simply occur within the U.S. Third, climate change represents a classic public goods 

problem because each country’s reductions benefit everyone else and no country can be excluded 

from enjoying the benefits of other countries’ reductions, even if it provides no reductions itself. 

In this situation, the only way to achieve an economically efficient level of emissions reductions 

is for countries to cooperate in providing mutually beneficial reductions beyond the level that 

would be justified only by their own domestic benefits. In reference to the public good nature of 

mitigation and its role in foreign relations, thirteen prominent academics noted that these “are 

compelling reasons to focus on a global SCC” in a recent article on the SCC (Pizer et al., 2014).

In addition, as noted in OMB’s Response to Comments on the SCC, there is no bright line 

between domestic and global damages. Adverse impacts on other countries can have spillover 

effects on the United States, particularly in the areas of national security, international trade, 

public health and humanitarian concerns.71

The 2010 TSD noted a number of limitations to the SC-CO2 analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. Currently integrated assessment models do not assign value to all of the important 

physical, ecological, and economic impacts of climate change recognized in the climate change 

literature due to a lack of precise information on the nature of damages and because the science 

incorporated into these models understandably lags behind the most recent research. The limited 

amount of research linking climate impacts to economic damages makes the modeling exercise 

even more difficult. These individual limitations do not all work in the same direction in terms of 

their influence on the SC-CO2 estimates, though taken together they suggest that the SC-CO2

estimates are likely conservative. In particular, the IPCC Fourth Assessment Report (2007), 

which was the most current IPCC assessment available at the time of the IWG’s 2009-2010 

review, concluded that “It is very likely that [SC-CO2 estimates] underestimate the damage costs 

because they cannot include many non-quantifiable impacts.” Since then, the peer-reviewed 

literature has continued to support this conclusion. For example, the IPCC Fifth Assessment 

 

71 See Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean 

Air Act, 74 Fed. Reg. 66,496, 66,535 (Dec. 15, 2009) and National Research Council 2013a.
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report observed that SC-CO2 estimates continue to omit various impacts that would likely

increase damages. The 95th percentile estimate was included in the recommended range for 

regulatory impact analysis to address these concerns.

The EPA and other agencies have continued to consider feedback on the SC-CO2 estimates 

from stakeholders through a range of channels, including public comments on this rulemaking 

and others that use the SC-CO2 in supporting analyses and through regular interactions with 

stakeholders and research analysts implementing the SC-CO2 methodology used by the 

interagency working group. The SC-CO2 comments received on this rulemaking covered a wide 

range of topics including the technical details of the modeling conducted to develop the SC-CO2

estimates, the aggregation and presentation of the SC-CO2 estimates, and the process by which 

the SC-CO2 estimates were derived. Many but not all commenters were supportive of the SCCO2 and its application to this rulemaking. Commenters also provided constructive 

recommendations for potential opportunities to improve the SC-CO2 estimates in future updates.

The EPA Response to Comments document provides a summary and response to the SC-CO2

comments submitted to this rulemaking.

Many of the comments EPA received were similar to those that OMB’s Office of 

Information and Regulatory Affairs received in response to a separate request for public 

comment on the approach used to develop the estimates. After careful evaluation of the full 

range of comments submitted to OMB’s Office of Information and Regulatory Affairs, the IWG 

continues to recommend the use of these SC-CO2 estimates in regulatory impact analysis. With 

the release of the response to comments, the IWG announced plans to obtain expert independent 

advice from the National Academy of Sciences’ National Research Council (NRC) to ensure that 

the SC-CO2 estimates continue to reflect the best available scientific and economic information 

on climate change. The NRC process will be informed by the public comments received and 

focus on the technical merits and challenges of potential approaches to improving the SC-CO2

estimates in future updates. 

Concurrent with OMB’s publication of the response to comments on SC-CO2 and 

announcement of the NRC process, OMB posted a revised TSD that includes two minor 

technical corrections to the current estimates. One technical correction addressed an inadvertent 
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4-7

omission of climate change damages in the last year of analysis (2300) in one model and the 

second addressed a minor indexing error in another model. On average the revised SC-CO2

estimates are one dollar less than the mean SC-CO2 estimates reported in the November 2013 

revision to the May 2013 TSD. The change in the estimates associated with the 95th percentile 

estimates when using a 3% discount rate is slightly larger, as those estimates are heavily 

influenced by the results from the model that was affected by the indexing error. 

The four SC-CO2 estimates are as follows: $13, $45, $66, and $130 per metric ton of CO2

emissions in the year 2020 (2011$).72 The first three values are based on the average SC-CO2

from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SC-CO2 estimates 

for several discount rates are included because the literature shows that the SC-CO2 is quite 

sensitive to assumptions about the discount rate, and because no consensus exists on the 

appropriate rate to use in an intergenerational context (where costs and benefits are incurred by 

different generations). The fourth value is the 95th percentile of the SC-CO2 from all three 

models at a 3 percent discount rate. It is included to represent higher-than-expected impacts from 

temperature change further out in the tails of the SC-CO2 distribution (representing less likely, 

but potentially catastrophic, outcomes).

Table 4-2 presents the global SC-CO2 estimates for the years 2015 to 2050. In order to 

calculate the dollar value for emission reductions, the SC-CO2 estimate for each emissions year 

would be applied to changes in CO2 emissions for that year, and then discounted back to the 

analysis year using the same discount rate used to estimate the SC-CO2.

73 The SC-CO2 increases 

over time because future emissions are expected to produce larger incremental damages as 

physical and economic systems become more stressed in response to greater climate change. 

Note that the interagency group estimated the growth rate of the SC-CO2 directly using the three 

 

72 The current version of the TSD is available at: [X]. The 2010 and 2013 TSDs present SCC in 2007$. The 

estimates were adjusted to 2011$ using GDP Implicit Price Deflator, http://www.gpo.gov/fdsys/pkg/ECONI-2013-

02/pdf/ECONI-2013-02-Pg3.pdf.

73 This analysis considered the climate impacts of only CO2 emission change. As discussed below, the climate 

impacts of other pollutants were not calculated for the proposed guidelines. While CO2 is the dominant GHG 

emitted by the sector, we recognize the representative facilities within these comparisons may also have different 

emission rates for other climate forcers that will serve a minor role in determining the overall social cost of 

generation.
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integrated assessment models rather than assuming a constant annual growth rate. This helps to 

ensure that the estimates are internally consistent with other modeling assumptions. Tables 4-3 

through 4-5 report the incremental climate benefits estimated in three analysis years (2020, 2025, 

and 2030) for the rate-based and mass-based scenarios. 

Table 4-2. Social Cost of CO2, 2015-2050 (in 2011$)*

Year

Discount Rate and Statistic

5% Average 3% Average 2.5% Average 3% (95th percentile)

2015 $12 $38 $59 $110

2020 $13 $45 $66 $130

2025 $15 $49 $72 $150

2030 $17 $53 $77 $160

2035 $19 $58 $83 $180

2040 $22 $64 $89 $190

2045 $24 $68 $94 $210

2050 $28 $73 $100 $230

* The SC-CO2 values vary depending on the year of CO2 emissions and are defined in real terms, i.e., adjusted for 

inflation using the GDP implicit price deflator. These SC-CO2 values are stated in $/metric ton and rounded to 

two significant figures.

Table 4-3. Estimated Global Climate Benefits of CO2 Reductions for the Final Emission 

Guidelines in 2020 (billions of 2011$)*

Discount rate and statistic Rate-Based Scenario Mass-Based Scenario

Million metric tonnes of CO2 reduced 63

5% (average) $0.80

3% (average) $2.8

2.5% (average) $4.1

3% (95th percentile) $8.2

* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting 

of climate impacts.

Table 4-4. Estimated Global Climate Benefits of CO2 Reductions for the Final Emission 

Guidelines in 2025 (billions of 2011$)*

Discount rate and statistic Rate-Based Scenario Mass-Based Scenario

Million metric tonnes of CO2 reduced 210

5% (average) $3.1

3% (average) $10

2.5% (average) $15

3% (95th percentile) $31

* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting 

of climate impacts.
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Table 4-5. Estimated Global Climate Benefits of CO2 Reductions for the Final Emission 

Guidelines in 2030 (billions of 2011$)*

Discount rate and statistic Rate-Based Scenario Mass-Based Scenario

Million metric tonnes of CO2 reduced 377

5% (average) $6.4

3% (average) $20

2.5% (average) $29

3% (95th percentile) $61

* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting 

of climate impacts.

It is important to note that the climate benefits presented above are associated with changes 

in CO2 emissions only. Implementing these final emission guidelines, however, will have an 

impact on the emissions of other pollutants that would affect the climate. Both predicting 

reductions in emissions and estimating the climate impacts of these other pollutants, however, is 

complex. The climate impacts of these other pollutants have not been calculated for the final 

emission guidelines.74

The other emissions potentially reduced as a result of the final emission guidelines include 

other greenhouse gases (such as methane), aerosols and aerosol precursors such as black carbon, 

organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as nitrogen oxides 

and volatile organic carbon compounds. Changes in emissions of these pollutants (both increases 

and decreases) could directly result from changes in electricity generation, upstream fossil fuel 

extraction and transport, and/or downstream secondary market impacts. Reductions in black 

carbon or ozone precursors are projected to lead to further cooling, but reductions in the other 

aerosol species and precursors are projected to lead to warming. Therefore, changes in non-CO2

pollutants could potentially augment or offset the climate benefits calculated here. These 

pollutants can act in different ways and on different timescales than carbon dioxide. For 

example, aerosols reflect (and in the case of black carbon, absorb) incoming radiation, whereas 

greenhouse gases absorb outgoing infrared radiation. In addition, these aerosols are thought to 

affect climate indirectly by altering properties of clouds. Black carbon can also deposit on snow 

and ice, darkening these surfaces and accelerating melting. In terms of lifetime, while carbon 

dioxide emissions can increase concentrations in the atmosphere for hundreds or thousands of 

 

74 The SC-CO2 estimates used in this analysis are designed to assess the climate benefits associated with changes in 

CO2 emissions only.
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years, many of these other pollutants are short lived and remain in the atmosphere for short 

periods of time ranging from days to weeks and can therefore exhibit large spatial and temporal 

variability. 

While the EPA has not quantified the climate impacts of these other pollutants for the final 

emission guidelines, the Agency has analyzed the potential changes in upstream methane 

emissions from the natural gas and coal production sectors that may result from the illustrative 

plan scenarios examined in this RIA in the appendix to Chapter 3. The EPA assessed whether the 

net change in upstream methane emissions from natural gas and coal production is likely to be 

positive or negative and also assessed the potential magnitude of changes relative to CO2

emissions reductions anticipated at power plants. This assessment included CO2 emissions from 

the flaring of methane, but did not evaluate potential changes in other combustion-related CO2

emissions, such as emissions associated with drilling, mining, processing, and transportation in 

the natural gas and coal production sectors. This analysis found that the net upstream CH4

emissions from natural gas systems and coal mines and CO2 emissions from flaring of methane 

will likely decrease under the final emission guidelines. Furthermore, the analysis suggests that 

the changes in upstream methane emissions are small relative to the changes in direct emissions 

from power plants.

4.3 Estimated Human Health Co-Benefits

In addition to reducing emissions of CO2, implementing these final emission guidelines is 

expected to reduce emissions of SO2 and NOX, which are precursors to formation of ambient 

PM2.5, as well as directly emitted fine particles.75 Therefore, reducing these emissions would also 

reduce human exposure to ambient PM2.5 and the incidence of PM2.5-related health effects. In 

addition, in the presence of sunlight, NOX and VOCs can undergo a chemical reaction in the 

atmosphere to form ozone. Depending on localized concentrations of volatile organic compounds

(VOCs), reducing NOX emissions would also reduce human exposure to ozone and the incidence 

 

75 We estimate the health co-benefits associated with emission reductions of two categories of directly emitted 

particles: elemental carbon plus organic carbon (EC+OC) and crustal. Crustal emissions are composed of 

compounds associated with minerals and metals from the earth’s surface, including carbonates, silicates, iron, 

phosphates, copper, and zinc. Often, crustal material represents particles not classified as one of the other species 

(e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).
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of ozone-related health effects. Although we do not have sufficient data to quantify these impacts

in this analysis, reducing emissions of SO2 and NOx would also reduce ambient exposure to SO2

and NO2 and their associated health effects, respectively. In this section, we provide an overview 

of the monetized PM2.5 and ozone-related co-benefits estimated for the final emission guidelines. 

A full description of the underlying data, studies, and assumptions is provided in the PM 

NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d). The 

estimated co-benefits associated with these emission reductions are beyond those achieved by 

previous EPA rulemakings, including MATS.

There are several important considerations in assessing the air quality-related health cobenefits for a climate-focused rulemaking. First, these estimated health co-benefits do not 

account for any climate-related air quality changes (e.g., increased ambient ozone associated 

with higher temperatures) but rather changes in precursor emissions affected by this rulemaking. 

Excluding climate-related air quality changes may underestimate ozone-related health cobenefits. It is unclear how PM2.5-related health co-benefits would be impacted by excluding 

climate-related air quality changes since the science is unclear as to how climate change may

affect PM2.5 exposure. Second, the estimated health co-benefits also do not consider temperature 

modification of PM2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, 

the estimated climate benefits reported in this RIA reflect global benefits, while the estimated 

health co-benefits are calculated for the contiguous U.S. only. Excluding temperature 

modification of air pollution risks and international air quality-related health benefits likely leads 

to underestimation of quantified health co-benefits. Fourth, as noted earlier, we do not estimate 

the climate benefits associated with reductions in PM and O3 precursors.

Implementing the final emission guidelines may lead to reductions in ambient PM2.5 

concentrations below the National Ambient Air Quality Standards (NAAQS) for PM and ozone 

in some areas and assist other areas with attaining these NAAQS. Because the NAAQS RIAs 

(U.S. EPA, 2012a, 2008b, 2010d) also calculated PM and ozone benefits, there are important 

differences worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs 

illustrate the potential costs and benefits of attaining a revised air quality standard nationwide 

based on an array of emission reduction strategies for different sources reflecting the application 

of known and unknown controls, incremental to implementation of existing regulations and 
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controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not 

predict, the reduction strategies that States may choose to enact when implementing a revised 

NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, the 

EPA’s NAAQS RIAs are merely illustrative and the estimated costs and benefits are not intended 

to be added to the costs and benefits of other regulations that result in specific costs of control 

and emission reductions. Some of the emissions reductions estimated to result from 

implementation of the final emission guidelines may achieve some of the air quality

improvements that resulted from the hypothesized attainment strategies presented in the 

illustrative NAAQS RIAs. The emissions reductions from implementing the final emission 

guidelines will decrease the remaining amount of emissions reductions needed in non-attainment 

areas and reduce the costs and benefits attributable to meeting the NAAQS.

Similar to NAAQS RIAs, the emission reduction scenarios estimated for the final emission 

guidelines are also illustrative. In contrast to NAAQS RIAs, all of the emission reductions for the 

illustrative plan scenarios would occur in one well-characterized sector (i.e., the EGU sector). In 

general, the EPA is more confident in the magnitude and location of the emission reductions for 

implementation rules, which typically require specific emission reductions in a specific sector. 

As such, emission reductions achieved under promulgated implementation rules will ultimately

be reflected in the baseline of future NAAQS analyses, which would reduce the incremental 

costs and benefits associated with attaining revised future NAAQS. The EPA does not re-issue 

illustrative RIAs outside of the rulemaking process that retroactively update the baseline to 

account for implementation rules promulgated after an RIA was completed. For more 

information on the relationship between illustrative analyses, such as for the NAAQS and this 

final emission guidelines, and implementation rules, please see section 1.3 of the PM NAAQS 

RIA (U.S. EPA, 2012a).

4.3.1 Health Impact Assessment for PM2.5 and Ozone

The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b) 

identified the human health effects associated with ambient PM2.5 exposure, which include 

premature mortality and a variety of morbidity effects associated with acute and chronic 

exposures. Similarly, the Integrated Science Assessment for Ozone and Related Photochemical 

Oxidants (Ozone ISA) (U.S. EPA, 2013b) identified the human health effects associated with 
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ambient ozone exposure, which include premature mortality and a variety of morbidity effects 

associated with acute and chronic exposures. Table 4-6 identifies the quantified and unquantified 

co-benefit categories captured in the EPA’s health co-benefits estimates for reduced exposure to 

ambient PM2.5 and ozone. Although the table below does not list unquantified health effects such 

as those associated with exposure to SO2, NO2, and mercury nor welfare effects such as 

acidification and nutrient enrichment, these effects are described in detail in Chapters 5 and 6 of 

the PM NAAQS RIA (U.S. EPA, 2012a) and summarized later in this chapter. It is important to 

emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification 

of each effect complete.
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Table 4-6. Human Health Effects of Ambient PM2.5 and Ozone

Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More 

Information

Improved Human Health

Reduced incidence of 

premature mortality 

from exposure to 

PM2.5

Adult premature mortality based on cohort study 

estimates and expert elicitation estimates (age >25 or 

age >30)

  PM ISA

Infant mortality (age <1)   PM ISA

Reduced incidence of 

morbidity from 

exposure to PM2.5

Non-fatal heart attacks (age > 18)   PM ISA

Hospital admissions—respiratory (all ages)   PM ISA

Hospital admissions—cardiovascular (age >20)   PM ISA

Emergency room visits for asthma (all ages)   PM ISA

Acute bronchitis (age 8-12)   PM ISA

Lower respiratory symptoms (age 7-14)   PM ISA

Upper respiratory symptoms (asthmatics age 9-11)   PM ISA

Asthma exacerbation (asthmatics age 6-18)   PM ISA

Lost work days (age 18-65)   PM ISA

Minor restricted-activity days (age 18-65)   PM ISA

Chronic Bronchitis (age >26) — — PM ISA1

Emergency room visits for cardiovascular effects (all 

ages) — — PM ISA1

Strokes and cerebrovascular disease (age 50-79) — — PM ISA1

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

Reproductive and developmental effects (e.g., low 

birth weight, pre-term births, etc.) — — PM ISA2,3

Cancer, mutagenicity, and genotoxicity effects — — PM ISA2,3

Reduced incidence of 

mortality from 

exposure to ozone

Premature mortality based on short-term study 

estimates (all ages)   Ozone ISA

Premature mortality based on long-term study 

estimates (age 30–99) — — Ozone ISA1

Reduced incidence of 

morbidity from 

exposure to ozone

Hospital admissions—respiratory causes (age > 65)   Ozone ISA

Hospital admissions—respiratory causes (age <2)   Ozone ISA

Emergency department visits for asthma (all ages)   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 ISA1

Other respiratory effects (e.g., premature aging of 

lungs) — — Ozone ISA2

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3

1 We assess these co-benefits qualitatively due to data and resource limitations for this analysis, but we have quantified them in 

sensitivity analyses for other analyses.

2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.

3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other significant 

concerns over the strength of the association.
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We follow a “damage-function” approach in calculating benefits, which estimates changes 

in individual health endpoints (specific effects that can be associated with changes in air quality) 

and assigns values to those changes assuming independence of the values for those individual 

endpoints. Because the EPA rarely has the time or resources to perform new research to measure 

directly, either health outcomes or their values for regulatory analyses, our estimates are based 

on the best available methods of benefits transfer, which is the science and art of adapting 

primary research from similar contexts to estimate benefits for the environmental quality change 

under analysis. We use a “benefit-per-ton” approach to estimate the PM2.5 and ozone co-benefits

in this RIA. Benefit-per-ton approaches apply an average benefit per ton derived from modeling 

of benefits of specific air quality scenarios to estimates of emissions reductions for scenarios 

where no air quality modeling is available. This section describes the underlying basis for the 

health and economic valuation estimates that inform the benefit-per-ton estimates, and the 

subsequent section provides an overview of the benefit-per-ton estimates,76 which are described 

in detail in the appendix to this chapter.

The benefit-per-ton approach we use in this RIA relies on estimates of human health 

responses to exposure to PM and ozone obtained from the peer-reviewed scientific literature.

These estimates are used in conjunction with population data, baseline health information, air 

quality data and economic valuation information to conduct health impact and economic benefits 

assessments. These assessments form the key inputs to calculating benefit-per-ton estimates. The 

next sections provide an overview of the health impact assessment (HIA) methodology and 

additional details on several key elements.

The HIA quantifies the changes in the incidence of adverse health impacts resulting from 

changes in human exposure to PM2.5 and ozone. We use the environmental Benefits Mapping and 

Analysis Program – Community Edition (BenMAP-CE) (version 1.1) to systematize health 

impact analyses by applying a database of key input parameters, including population 

projections, health impact functions, and valuation functions (Abt Associates, 2012). For this 

 

76 We have updated the benefit-per-ton estimates since the proposal RIA. In this RIA, we apply benefit-per-ton 

estimates that were derived from air quality modeling of the proposed Clean Power Plan (Option 1 State).
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assessment, the HIA is limited to those health effects that are directly linked to ambient PM2.5 

and ozone concentrations. There may be other indirect health impacts associated with reducing 

emissions, such as occupational health exposures. Epidemiological studies generally provide 

estimates of the relative risks of a particular health effect for a given increment of air pollution 

(often per 10 μg/m3for PM2.5 or ppb for ozone). These relative risks can be used to develop risk 

coefficients that relate a unit reduction in PM2.5 to changes in the incidence of a health effect. We 

refer the reader to the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 

2008b, 2010d) for more information regarding the epidemiology studies and risk coefficients

applied in this analysis, and we briefly elaborate on adult premature mortality below. The size of 

the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, 

and the high monetary value ascribed to prolonging life make mortality risk reduction the most 

significant health endpoint quantified in this analysis.

4.3.1.1 Mortality Concentration-Response Functions for PM2.5

Considering a substantial body of published scientific literature and reflecting thousands 

of epidemiology, toxicology, and clinical studies, the PM ISA documents the association 

between elevated PM2.5 concentrations and adverse health effects, including increased premature 

mortality (U.S. EPA, 2009b). The PM ISA, which was twice reviewed by the Clean Air 

Scientific Advisory Committee of the EPA’s Science Advisory Board (SAB-CASAC) (U.S. 

EPA-SAB, 2009b, 2009c), 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 supports the use of a no-threshold loglinear model to portray the PM-mortality concentration-response relationship while recognizing 

potential uncertainty about the exact shape of the concentration-response function. In addition to 

adult mortality discussed in more detail below, we use effect coefficients from Woodruff et al.

(1997) to estimate PM-related infant mortality.

For adult PM-related mortality, we use the effect coefficients from the most recent 

epidemiology studies examining two large population cohorts: the American Cancer Society 

cohort (Krewski et al., 2009) and the Harvard Six Cities cohort (Lepeule et al, 2012). The PM 

ISA (U.S. EPA, 2009b) concluded that the ACS and Six Cities cohorts produce the strongest 

evidence of the association between long-term PM2.5 exposure and premature mortality with 
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support from a number of additional cohort studies. The SAB's Health Effects Subcommittee 

(SAB-HES) also supported using these two cohorts for analyses of the benefits of PM reductions 

(U.S. EPA-SAB, 2010a). As both the ACS and Six Cities cohort studies have inherent strengths 

and weaknesses, we present PM2.5 co-benefits estimates based on benefits-per-ton derived using 

relative risk estimates from both these cohorts.

As a characterization of uncertainty regarding the adult PM2.5-mortality relationship, the 

EPA graphically presents the PM2.5 co-benefits based on benefits-per-ton estimated using C-R 

functions derived from EPA’s expert elicitation study (Roman et al., 2008; IEc, 2006). The 

primary goal of the 2006 study was to elicit from a sample of health experts probabilistic 

distributions describing uncertainty in estimates of the reduction in mortality among the adult 

U.S. population resulting from reductions in ambient annual average PM2.5 concentrations. In 

that study, twelve experts provided independent opinions regarding the PM2.5-mortality

concentration-response function. Because the experts relied upon the ACS and Six Cities cohort 

studies to inform their concentration-response functions, the benefits estimates based on the 

expert responses generally fall between benefits estimates based on these studies (see Figure 4-

1). We do not combine the expert results in order to preserve the breadth and diversity of opinion 

on the expert panel. This presentation of the expert-derived results is generally consistent with 

SAB advice (U.S. EPA-SAB, 2008), which recommended that the EPA emphasize that 

“scientific differences existed only with respect to the magnitude of the effect of PM2.5 on 

mortality, not whether such an effect existed” and that the expert elicitation “supports the 

conclusion that the benefits of PM2.5 control are very likely to be substantial”. Although it is 

possible that newer scientific literature could revise the experts’ quantitative responses if elicited 

again, we believe that these general conclusions are unlikely to change.

4.3.1.2 Mortality Concentration-Response Functions for Ozone

In 2008, the National Academies of Science (NRC, 2008) issued a series of 

recommendations to the EPA regarding the quantification and valuation of ozone-related shortterm mortality. 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 NMMAPS 
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[National Morbidity, Mortality, and Air Pollution Study] studies without exclusion of the metaanalyses” (NRC, 2008). In view of the findings of the National Academies panel, we estimate the 

co-benefits of avoiding short-term ozone mortality using the Bell et al. (2004) NMMAPS 

analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as 

effect estimates from the three meta-analyses (Bell et al. (2005), Levy et al. (2005), and Ito et al.

(2005)). These studies are consistent with the studies used in the Ozone NAAQS RIA (U.S. 

EPA, 2008b, 2010d).77 For simplicity, we report the ozone mortality estimates in this RIA as a 

range reflecting application of dollar-per-ton estimates based on Bell et al. (2004) and Levy et al.

(2005) to represent the lowest and the highest co-benefits estimates based on these six ozone 

mortality studies. In addition, we graphically present in Figure 4-1 the estimated co-benefits

based on dollar-per-ton estimates derived from all six studies mentioned above as a 

characterization of uncertainty regarding the ozone -mortality relationship.

4.3.2 Economic Valuation for Health Co-benefits

After quantifying the change in adverse health impacts, we estimate the economic value of 

these avoided impacts. Reductions in ambient concentrations of air pollution generally lower the 

risk of future adverse health effects by a small amount for a large population. Therefore, the 

appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. 

For some health effects, such as hospital admissions, WTP estimates are generally not available, 

so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 

of a health effect. They tend to reflect the direct expenditures related to treatment but not the 

value of avoided pain and suffering from the health effect. The unit values applied in this 

analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 

2012a).

 

77 Since the EPA received NAS advice, the Agency published the Ozone ISA (U.S. EPA, 2013b) and the second 

draft Ozone Health Risk and Exposure Assessment (U.S. EPA, 2014a). Therefore, the ozone mortality studies 

applied in this analysis, while current at the time of the previous Ozone NAAQS RIAs, do not reflect the most 

updated literature available. The selection of ozone mortality studies used to estimate benefits in RIAs will be 

revisited in the forthcoming RIA accompanying the on-going review of the Ozone NAAQS.
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Avoided premature deaths account for 98 percent of monetized PM-related co-benefits and 

over 90 percent of monetized ozone-related co-benefits. The economics literature concerning the 

appropriate method for valuing reductions in premature mortality risk is still developing. The 

adoption of a 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 single estimate of an 

individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA-SAB, 

2000). The VSL approach is a summary measure for the value of small changes in mortality risk 

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, the EPA has decided to apply the VSL 

that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses

(U.S. EPA, 2014)

78 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 $6.3 million (2000$).79 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 VSLs applied in this 

analysis in 2011$ after adjusting for income growth are $9.9 million for 2020 and $10.1 million 

for 2025 and 2030.

80

 

78 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), the EPA retained the VSL 

endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be 

forthcoming.

79 In 1990$, this base VSL is $4.8 million.

80 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2030 

estimates use income growth only through 2024 and are therefore likely underestimates. 
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The Agency is committed to using scientifically sound, appropriately reviewed evidence in 

valuing mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the process, the Agency has identified a number of 

important issues to be considered in updating its mortality risk valuation estimates. These are 

detailed in a white paper, “Valuing Mortality Risk Reductions in Environmental Policy” (U.S. 

EPA, 2010c), which recently underwent review by the SAB-EEAC. A meeting with the SAB on 

this paper was held on March 14, 2011 and formal recommendations were transmitted on 

July 29, 2011 (U.S. EPA-SAB, 2011). The EPA is taking SAB’s recommendations under 

advisement.

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 (OMB, 2003). We assume that 

there is a “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 assume a segmented lag structure characterized by 30 percent of mortality

reductions 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, 2004c). Changes in the cessation lag assumptions 

do not change the total number of estimated deaths but rather the timing of those deaths. Because 

short-term ozone-related premature mortality occurs within the analysis year, the estimated 

ozone-related co-benefits are identical for all discount rates.

4.3.3 Benefit-per-ton Estimates for PM2.5

We used a “benefit-per-ton” approach to estimate the PM2.5 co-benefits in this RIA. The 

EPA has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011c, 2012b, 

2014a). These benefit-per-ton estimates provide the total monetized human health co-benefits 

(the sum of premature mortality and premature morbidity), of reducing one ton of PM2.5 (or 

PM2.5 precursor such as NOX or SO2) from a specified source. Specifically, in this analysis, we 

multiplied the benefit-per-ton estimates by the corresponding emission reductions that were 

generated from air quality modeling of the proposed Clean Power Plan. 

The method used to calculate the regional benefit-per-ton estimates is similar to the 

average EGU sector estimates used for the proposal (U.S. EPA, 2013a), but relies on air quality 
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modeling of the proposed Clean Power Plan. Similar to the proposal, we generated regional 

benefit-per-ton estimates by aggregating the impacts in BenMAP to the region (i.e., East, West, 

and California) rather than aggregating to the nation. The appendix to this chapter provides 

additional detail regarding these calculations.

As noted below in the characterization of uncertainty, all benefit-per-ton estimates have 

inherent limitations. Specifically, all benefit-per-ton estimates reflect the geographic distribution 

of the modeled proposal, which may not match the emission reductions anticipated by the final 

emission guidelines, and they may not reflect local variability in population density, 

meteorology, exposure, baseline health incidence rates, or other local factors for any specific 

location. The regional benefit-per-ton estimates, although less subject to these types of 

uncertainties than national estimates, still should be interpreted with caution. Even though we 

assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary

between precursors depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. 

4.3.4 Benefit-per-ton Estimates for Ozone

Similar to PM2.5, we used a “benefit-per-ton” approach in this RIA to estimate the ozone

co-benefits, which represent the total monetized human health co-benefits (the sum of premature 

mortality and premature morbidity) of reducing one ton of NOx (an ozone precursor). Also 

consistent with the PM2.5 estimates, we generated regional benefit-per-ton estimates for ozone

based on air quality modeling for the proposed Clean Power Plan. In contrast to the PM2.5

estimates, the ozone estimates are not based on changes to annual emissions. Instead, the 

regional estimates (i.e., East, West, and California) correspond to NOX emissions from U.S. 

EGUs during the ozone-season (May to September). Because we estimate ozone health impacts 

from May to September only, this approach underestimates ozone co-benefits in areas with a 

longer ozone season such as southern California and Texas. These estimates assume that EGUattributable ozone formation at the regional-level is due to NOx alone. Because EGUs emit little 

VOC relative to NOX emissions, it is unlikely that VOCs emitted by EGUs would contribute 

substantially to regional ozone formation. As noted above, all benefit-per-ton estimates have 

inherent limitations and should be interpreted with caution. We provide more detailed 

information regarding the generation of these estimates in the appendix to this chapter.
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4.3.5 Estimated Health Co-Benefits Results

Tables 4-7 through 4-9 provide the regional benefit-per-ton estimates for three analysis 

years: 2020, 2025, and 2030. Tables 4-10 through 4-12 and 4-13 through 4-15 provide the 

emission reductions estimated to occur in each analysis year for the rate-based and mass-based

illustrative plan scenarios, respectively, by region (i.e., East, West, and California).

81 Tables 4-16

through 4-18 and 4-19 through 4-21 summarize the national monetized PM and ozone-related 

health co-benefits estimated to occur in each analysis year for the illustrative rate-based and 

mass-based plan scenarios, respectively, by precursor pollutant using discount rates of 3 percent

and 7 percent. Tables 4-22 through 4-24 and 4-25 through 4-27- provide national summaries of 

the reductions in estimated health incidences associated with the illustrative rate-based and massbased plan scenarios, respectively, in each analysis year. Figure 4-1 provides a visual 

representation of the range of estimated PM2.5 and ozone-related co-benefits using benefit-perton estimates based on concentration-response functions from different studies and expert 

opinion for the illustrative rate-based and mass-based plan scenarios evaluated in 2025 as an 

illustrative analysis year. Figure 4-2 provides a breakdown of the monetized health co-benefits 

for the rate-based and mass-based plan scenarios evaluated in 2025 as an illustrative analysis 

year by precursor pollutant. 

 

81 See Chapter 3 of this RIA for more information regarding the expected emission reductions used to calculate the 

health co-benefits in this chapter. Chapter 3 also provides more information regarding the illustrative plan scenario.
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Table 4-7. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality 

Modeling from Proposed Clean Power Plan in 2020 (2011$)*

Pollutant Discount Rate Regional

East West California

SO2

3% $33,000 to $75,000 $6,200 to $14,000 $95,000 to $210,000

7% $30,000 to $68,000 $5,600 to $13,000 $85,000 to $190,000

Directly emitted PM2.5 

(EC+OC)

3% $140,000 to $320,000 $27,000 to $60,000 $370,000 to $830,000

7% $130,000 to $290,000 $24,000 to $54,000 $330,000 to $740,000

Directly emitted PM2.5 

(crustal)

3% $23,000 to $52,000 $11,000 to $25,000 $73,000 to $160,000

7% $21,000 to $47,000 $9,900 to $22,000 $66,000 to $150,000

NOX (as PM2.5)

3% $3,100 to $7,000 $0,670 to $1,500 $22,000 to $49,000

7% $2,800 to $6,300 $0,610 to $1,400 $19,000 to $44,000

NOX (as Ozone) N/A $6,500 to $28,000 $2,000 to $8,900 $14,000 to $59,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012).

Table 4-8. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality 

Modeling from Proposed Clean Power Plan in 2025 (2011$)*

Pollutant Discount Rate Regional

East West California

SO2

3% $37,000 to $83,000 $7,100 to $16,000 $110,000 to $240,000

7% $33,000 to $75,000 $6,400 to $14,000 $97,000 to $220,000

Directly emitted PM2.5 

(EC+OC)

3% $160,000 to $360,000 $30,000 to $68,000 $410,000 to $930,000

7% $140,000 to $320,000 $27,000 to $61,000 $370,000 to $830,000

Directly emitted PM2.5 

(crustal)

3% $25,000 to $58,000 $12,000 to $28,000 $82,000 to $180,000

7% $23,000 to $52,000 $11,000 to $25,000 $74,000 to $170,000

NOX (as PM2.5)

3% $3,300 to $7,500 $0,750 to $1,700 $24,000 to $54,000

7% $3,000 to $6,800 $0,670 to $1,500 $22,000 to $49,000

NOX (as Ozone) N/A $7,100 to $30,000 $2,300 to $10,000 $15,000 to $66,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012).
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Table 4-9. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality 

Modeling from Proposed Clean Power Plan in 2030 (2011$)*

Pollutant Discount 

Rate

Regional

East West California

SO2

3% $40,000 to $89,000 $7,800 to $18,000 $120,000 to $270,000

7% $36,000 to $81,000 $7,100 to $16,000 $110,000 to $240,000

Directly emitted PM2.5 

(EC+OC)

3% $170,000 to $380,000 $33,000 to $75,000 $450,000 to $1,000,000

7% $150,000 to $340,000 $30,000 to $68,000 $410,000 to $920,000

Directly emitted PM2.5 

(crustal)

3% $28,000 to $62,000 $14,000 to $31,000 $90,000 to $200,000

7% $25,000 to $56,000 $13,000 to $28,000 $81,000 to $180,000

NOX (as PM2.5)

3% $3,500 to $8,000 $0,820 to $1,900 $26,000 to $60,000

7% $3,200 to $7,200 $0,740 to $1,700 $24,000 to $54,000

NOX (as Ozone) N/A $7,600 to $33,000 $2,600 to $11,000 $17,000 to $73,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012).

Table 4-10. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Rate-based Scenario in 2020 (thousands of short tons)*

Region SO2 All-year NOx Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)

Directly emitted 

PM2.5 (crustal)

East 12 50 19

West 1 1 0

California 0 0 0

National Total 13 51 19

*All emissions shown in the table are rounded, so regional emission reductions may appear to 

not sum to national total.

Table 4-11. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Rate-based Scenario in 2025 (thousands of short tons)*

Region SO2 All-year NOx Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)

Directly emitted 

PM2.5 (crustal)

East 170 155 67

West 7 8 3

California 1 2 0

National Total 178 164 70

*All emissions shown in the table are rounded, so regional emission reductions may appear to 

not sum to national total.
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Table 4-12. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Rate-based Scenario in 2030 (thousands of short tons)*

Region SO2 All-year NOx Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)

Directly emitted 

PM2.5 (crustal)

East 307 264 109

West 11 15 9

California 1 4 0

National Total 319 283 118

*All emissions shown in the table are rounded, so regional emission reductions may appear to 

not sum to national total.

Table 4-13. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Mass-based Scenario in 2020 (thousands of short tons)*

Region SO2 All-year NOx Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)

Directly emitted 

PM2.5 (crustal)

East

West

California

National Total

*All emissions shown in the table are rounded, so regional emission reductions may appear to 

not sum to national total.

Table 4-14. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Mass-based Scenario in 2025 (thousands of short tons)*

Region SO2 All-year NOx Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)

Directly emitted 

PM2.5 (crustal)

East

West

California

National Total

*All emissions shown in the table are rounded, so regional emission reductions may appear to 

not sum to national total.

Table 4-15. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Mass-based Scenario in 2030 (thousands of short tons)*

Region SO2 All-year NOx Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)

Directly emitted 

PM2.5 (crustal)

East

West

California

National Total
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*All emissions shown in the table are rounded, so regional emission reductions may appear to 

not sum to national total.

Table 4-16. Summary of Estimated Monetized Health Co-Benefits for the Final Emission 

Guidelines Rate-based Scenario in 2020 (billions of 2011$) *

Pollutant 3% Discount Rate 7% Discount Rate

SO2 $0.41 to $0.92 $0.37 to $0.83

Directly emitted PM2.5 (EC+OC)

Directly emitted PM2.5 (crustal)

NOx (as PM2.5) $0.15 to $0.33 $0.13 to $0.30

NOx (as Ozone) $0.12 to $0.53 $0.12 to $0.53

Total $0.67 to $1.8 $0.62 to $1.7

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al.

(2012).

Table 4-17. Summary of Estimated Monetized Health Co-Benefits for the Final Emission 

Guidelines Rate-based Scenario in 2025 (billions of 2011$) *

Pollutant 3% Discount Rate 7% Discount Rate

SO2 $6.4 to $14 $5.7 to $13

Directly emitted PM2.5 (EC+OC)

Directly emitted PM2.5 (crustal)

NOx (as PM2.5) $0.56 to $1.3 $0.50 to $1.1

NOx (as Ozone) $0.48 to $2.1 $0.48 to $2.1

Total $7.4 to $18 $6.7 to $16

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al.

(2012).
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Table 4-18. Summary of Estimated Monetized Health Co-Benefits for the Final Emission 

Guidelines Rate-based Scenario in 2030 (billions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate

SO2 $12 to $28 $11 to $25

Directly emitted PM2.5 (EC+OC)

Directly emitted PM2.5 (crustal)

NOx (as PM2.5) $1.0 to $2.3 $0.94 to $2.1

NOx (as Ozone) $0.86 to $3.7 $0.86 to $3.7

Total $14 to $34 $13 to $31

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al.

(2012).

Table 4-19. Summary of Estimated Monetized Health Co-Benefits for the Final Emission 

Guidelines Mass-based Scenario in 2020 (billions of 2011$) *

Pollutant 3% Discount Rate 7% Discount Rate

SO2

Directly emitted PM2.5 (EC+OC)

Directly emitted PM2.5 (crustal)

NOx (as PM2.5)

NOx (as Ozone)

Total

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al.

(2012).
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Table 4-20. Summary of Estimated Monetized Health Co-Benefits for the Final Emission 

Guidelines Mass-based Scenario in 2025 (billions of 2011$) *

Pollutant 3% Discount Rate 7% Discount Rate

SO2

Directly emitted PM2.5 (EC+OC)

Directly emitted PM2.5 (crustal)

NOx (as PM2.5)

NOx (as Ozone)

Total

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al.

(2012).

Table 4-21. Summary of Estimated Monetized Health Co-Benefits for the Final Emission 

Guidelines Mass-based Scenario in 2030 (billions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate

SO2

Directly emitted PM2.5 (EC+OC)

Directly emitted PM2.5 (crustal)

NOx (as PM2.5)

NOx (as Ozone)

Total

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al.

(2012).
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Table 4-22. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for the Final Emission Guidelines Rate-based Scenario in 

2020*

PM2.5-related Health Effects

Avoided Premature Mortality

Krewski et al. (2009) (adult) 60

Lepeule et al. (2012) (adult) 140

Woodruff et al. (1997) (infant) 0

Avoided Morbidity

Emergency department visits for asthma (all ages) 32

Acute bronchitis (age 8–12) 89

Lower respiratory symptoms (age 7–14) 1,100

Upper respiratory symptoms (asthmatics age 9–11) 1,600

Minor restricted-activity days (age 18–65) 44,000

Lost work days (age 18–65) 7,500

Asthma exacerbation (age 6–18) 4,000

Hospital admissions—respiratory (all ages) 18

Hospital admissions—cardiovascular (age > 18) 22

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 69

Pooled estimate of 4 studies 7

Ozone-related Health Effects

Avoided Premature Mortality

Bell et al. (2004) (all ages) 11

Levy et al. (2005) (all ages) 52

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 67

Hospital admissions—respiratory causes (ages < 2) 34

Emergency room visits for asthma (all ages) 37

Minor restricted-activity days (ages 18-65) 68,000

School absence days 23,000

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).
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Table 4-23. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Final Emission Guidelines Rate-based Scenario in 2025*

PM2.5-related Health Effects

Avoided Premature Mortality

Krewski et al. (2009) (adult) 740

Lepeule et al. (2012) (adult) 1,700

Woodruff et al. (1997) (infant) 2

Avoided Morbidity

Emergency department visits for asthma (all ages) 370

Acute bronchitis (age 8–12) 1,100

Lower respiratory symptoms (age 7–14) 14,000

Upper respiratory symptoms (asthmatics age 9–11) 20,000

Minor restricted-activity days (age 18–65) 520,000

Lost work days (age 18–65) 89,000

Asthma exacerbation (age 6–18) 48,000

Hospital admissions—respiratory (all ages) 220

Hospital admissions—cardiovascular (age > 18) 270

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 860

Pooled estimate of 4 studies 93

Ozone-related Health Effects

Avoided Premature Mortality

Bell et al. (2004) (all ages) 44

Levy et al. (2005) (all ages) 200

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 280

Hospital admissions—respiratory causes (ages < 2) 130

Emergency room visits for asthma (all ages) 140

Minor restricted-activity days (ages 18-65) 250,000

School absence days 87,000

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).
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Table 4-24. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Final Emission Guidelines Rate-based Scenario in 2030*

PM2.5-related Health Effects

Avoided Premature Mortality

Krewski et al. (2009) (adult) 1,400

Lepeule et al. (2012) (adult) 3,200

Woodruff et al. (1997) (infant) 3

Avoided Morbidity

Emergency department visits for asthma (all ages) 540

Acute bronchitis (age 8–12) 2,000

Lower respiratory symptoms (age 7–14) 26,000

Upper respiratory symptoms (asthmatics age 9–11) 37,000

Minor restricted-activity days (age 18–65) 970,000

Lost work days (age 18–65) 160,000

Asthma exacerbation (age 6–18) 90,000

Hospital admissions—respiratory (all ages) 440

Hospital admissions—cardiovascular (age > 18) 540

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 1,700

Pooled estimate of 4 studies 180

Ozone-related Health Effects

Avoided Premature Mortality

Bell et al. (2004) (all ages) 73

Levy et al. (2005) (all ages) 330

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 500

Hospital admissions—respiratory causes (ages < 2) 200

Emergency room visits for asthma (all ages) 220

Minor restricted-activity days (ages 18-65) 400,000

School absence days 140,000

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).
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Table 4-25. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for the Final Emission Guidelines Mass-based Scenario in 

2020*

PM2.5-related Health Effects

Avoided Premature Mortality

Krewski et al. (2009) (adult)

Lepeule et al. (2012) (adult)

Woodruff et al. (1997) (infant)

Avoided Morbidity

Emergency department visits for asthma (all ages)

Acute bronchitis (age 8–12)

Lower respiratory symptoms (age 7–14)

Upper respiratory symptoms (asthmatics age 9–11)

Minor restricted-activity days (age 18–65)

Lost work days (age 18–65)

Asthma exacerbation (age 6–18)

Hospital admissions—respiratory (all ages)

Hospital admissions—cardiovascular (age > 18)

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001)

Pooled estimate of 4 studies

Ozone-related Health Effects

Avoided Premature Mortality

Bell et al. (2004) (all ages) 

Levy et al. (2005) (all ages) 

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 

Hospital admissions—respiratory causes (ages < 2) 

Emergency room visits for asthma (all ages) 

Minor restricted-activity days (ages 18-65) 

School absence days 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).
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Table 4-26. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Final Emission Guidelines Mass-based Scenario in 2025*

PM2.5-related Health Effects

Avoided Premature Mortality

Krewski et al. (2009) (adult)

Lepeule et al. (2012) (adult)

Woodruff et al. (1997) (infant)

Avoided Morbidity

Emergency department visits for asthma (all ages)

Acute bronchitis (age 8–12)

Lower respiratory symptoms (age 7–14)

Upper respiratory symptoms (asthmatics age 9–11)

Minor restricted-activity days (age 18–65)

Lost work days (age 18–65)

Asthma exacerbation (age 6–18)

Hospital admissions—respiratory (all ages)

Hospital admissions—cardiovascular (age > 18)

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001)

Pooled estimate of 4 studies

Ozone-related Health Effects

Avoided Premature Mortality

Bell et al. (2004) (all ages) 

Levy et al. (2005) (all ages) 

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 

Hospital admissions—respiratory causes (ages < 2) 

Emergency room visits for asthma (all ages) 

Minor restricted-activity days (ages 18-65) 

School absence days 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Table 4-27. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Final Emission Guidelines Mass-based Scenario in 

2030*

PM2.5-related Health Effects

Avoided Premature Mortality

Krewski et al. (2009) (adult)

Lepeule et al. (2012) (adult)

Woodruff et al. (1997) (infant)

Avoided Morbidity

Emergency department visits for asthma (all ages)

Acute bronchitis (age 8–12)

Lower respiratory symptoms (age 7–14)

Upper respiratory symptoms (asthmatics age 9–11)

Minor restricted-activity days (age 18–65)

Lost work days (age 18–65)

Asthma exacerbation (age 6–18)

Hospital admissions—respiratory (all ages)

Hospital admissions—cardiovascular (age > 18)

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001)

Pooled estimate of 4 studies

Ozone-related Health Effects

Avoided Premature Mortality

Bell et al. (2004) (all ages) 

Levy et al. (2005) (all ages) 

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 

Hospital admissions—respiratory causes (ages < 2) 

Emergency room visits for asthma (all ages) 

Minor restricted-activity days (ages 18-65) 

School absence days 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).
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Figure 4-1. Monetized Health Co-benefits of Rate-based and Mass-based Scenarios for 

the Final Emission Guidelines in 2025 *

*The PM2.5 graphs show the estimated PM2.5 co-benefits at discount rates of 3% and 7% using effect coefficients 

derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 12 effect coefficients 

derived from EPA’s expert elicitation on PM mortality (Roman et al., 2008). The results shown are not the direct 

results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response 

functions provided in those studies. The ozone graphs show the estimated ozone co-benefits derived from six ozone 

mortality studies (i.e., Bell et al. (2004), Schwartz (2005), Huang et al. (2005), Bell et al. (2005), Levy et al. (2005),

and Ito et al. (2005). Ozone co-benefits occur in the analysis year, so they are the same for all discount rates. These 

estimates do not include climate benefits. The monetized co-benefits do not include climate benefits or reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment.

PM2.5 Ozone 
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Low Health Co-benefits High Health Co-benefits

Figure 4-2. Breakdown of Monetized Health Co-benefits by Precursor Pollutant at a 3% 

Discount Rate for Rate-based Scenario for Final Emission Guidelines in 2025*

* “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) 

mortality study for ozone with the Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5.

4.3.6 Characterization of Uncertainty in the Estimated Health Co-benefits

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. This analysis 

includes many data sources as inputs, including emission inventories, air quality data from 

models (with their associated parameters and inputs), population data, population estimates, 

health effect estimates from epidemiology studies, economic data for monetizing co-benefits, 

and assumptions regarding the future state of the world (i.e., regulations, technology, and human 

behavior). Each of these inputs may be uncertain and would affect the estimate of co-benefits. 

When the uncertainties from each stage of the analysis are compounded, even small uncertainties 

can have large effects on the total quantified benefits. In addition, the use of the benefit-per-ton 

approach adds additional uncertainties beyond those for analyses based directly on air quality

modeling. Therefore, the estimates of co-benefits in each analysis year should be viewed as 

representative of the general magnitude of co-benefits of the illustrative plan scenario, rather 

than the actual co-benefits anticipated from implementing the final emission guidelines.

SO2 86%

Directly 

emitted 

PM2.5 

(EC+OC)

0%

Directly 

emitted 

PM2.5 

(Crustal) 0%

NOx (as 

PM2.5) 8%

NOx (as 

Ozone)

6%

Rate-based Scenario

SO2 81% Directly 

emitted 

PM2.5 

(EC+OC) 0%

Directly 

emitted 

PM2.5 

(Crustal) 0%

NOx (as 

PM2.5) 7%

NOx (as 

Ozone)

12%

Rate-based Scenario
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This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAAQS RIA (U.S. EPA, 2008b) because we lack 

the necessary air quality modeling input and/or monitoring data to run the benefits model. 

However, the results of the quantitative and qualitative uncertainty analyses presented in the PM 

NAAQS RIA and Ozone NAAQS RIA can provide some information regarding the uncertainty 

inherent in the estimated co-benefits results presented in this analysis. For example, sensitivity

analyses conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions 

could change the estimated PM2.5-related mortality co-benefits discounted at 3 percent by

between 10 percent and –27 percent and that alternative income growth adjustments could 

change the PM2.5-related mortality co-benefits by between 33 percent and −14 percent. Although 

we generally do not calculate confidence intervals for benefit-per-ton estimates and they can 

provide an incomplete picture about the overall uncertainty in the benefits estimates, the PM 

NAAQS RIA can provide an indication of the random sampling error in the health impact and 

economic valuation functions using Monte Carlo methods. In general, the 95th percentile 

confidence interval for monetized PM2.5 benefits ranges from approximately -90 percent to +180

percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The 

95th percentile confidence interval for the health impact function alone ranges from approximately 

±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012). 

Unlike RIAs for which the EPA conducts scenario-specific air quality modeling, we do not 

have information on the specific location of the air quality changes associated with the final 

emission guidelines. As such, it is not feasible to estimate the proportion of co-benefits occurring 

in different locations, such as designated nonattainment areas. Instead, we applied benefit-perton estimates, which reflect specific geographic patterns of emissions reductions and specific air 

quality and benefits modeling assumptions. For example, these estimates may not reflect local 

variability in population density, meteorology, exposure, baseline health incidence rates, or other 

local factors that might lead to an over-estimate or under-estimate of the actual co-benefits of 

controlling PM and ozone precursors. Use of these benefit-per-ton values to estimate co-benefits 

may lead to higher or lower benefit estimates than if co-benefits were calculated based on direct 

air quality modeling. Great care should be taken in applying these estimates to emission 

reductions occurring in any specific location, as these are all based on a broad emission reduction 
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scenario and therefore represent average benefits-per-ton over the entire region. The benefit-perton for emission reductions in specific locations may be very different than the estimates 

presented here. To the extent that the geographic distribution of the emissions reductions 

achieved by implementing the final emission guidelines is different than the emissions in the air 

quality modeling of the proposal, the co-benefits may be underestimated or overestimated. The 

appendix to this chapter provides additional uncertainty information regarding the benefit-perton estimates applied in this RIA, including an evaluation of the similarities and differences in 

the spatial distribution of emissions in the Clean Power Plan proposal modeling and the final 

illustrative plan scenarios in Chapter 3 of this RIA.

Our estimate of the total monetized co-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 

PM2.5-related premature mortality, which accounts for 98 percent of the monetized PM2.5 health 

co-benefits.

1. We assume that all fine particles, regardless of their chemical composition, are equally

potent in causing premature mortality. This is an important assumption, because PM2.5 

varies considerably in composition across sources, but the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. The PM ISA 

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, 2009b).

2. We assume that the health impact function for fine particles is log-linear without a 

threshold. Thus, the estimates include health co-benefits from reducing fine particles in 

areas with varied concentrations of PM2.5, including both areas that do not meet the fine 

particle standard and those areas that are in attainment, down to the lowest modeled 

concentrations. 

3. We assume that there is a “cessation” lag between the change in PM exposures and the 

total realization of changes in mortality effects. Specifically, we assume that some of the 

incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion 

over the 20 years following exposure based on the advice of the SAB-HES (U.S. EPASAB, 2004c), which affects the valuation of mortality co-benefits at different discount 

rates.
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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. Concentration benchmark analyses (e.g., lowest measured level [LML], one 

standard deviation below the mean of the air quality data in the study, etc.) allow readers to 

determine the portion of population exposed to annual mean PM2.5 levels at or above different 

concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and 

one standard deviation below the mean) that have been incorporated into recent RIAs and the 

EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties 

inherent in identifying any particular point at which our confidence in reported associations 

becomes appreciably less, and the scientific evidence provides no clear dividing line. However, 

the EPA does not view these concentration benchmarks as a concentration threshold below 

which we would not quantify health co-benefits of air quality improvements.82 Rather, the cobenefits estimates reported in this RIA are the best estimates because they reflect the full range 

of air quality concentrations associated with the emission reduction strategies. The PM ISA 

concluded that the scientific evidence collectively is sufficient to conclude that the relationship 

between long-term PM2.5 exposures and mortality is causal and that overall the studies support 

the use of a no-threshold log-linear model to estimate PM-related long-term mortality (U.S. EPA, 

2009b). 

For this analysis, policy-specific air quality data is not available, and the plan scenarios are 

illustrative of what states may choose to do. However, we believe that it is still important to 

characterize the distribution of exposure to baseline concentrations. As a surrogate measure of 

mortality impacts, we provide the percentage of the population exposed at each PM2.5

concentration in the baseline of the air quality modeling used to calculate the benefit-per-ton 

estimates for this final RIA using 12 km grid cells across the contiguous U.S. It is important to 

 

82 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b).
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4-40

note that baseline exposure is only one parameter in the health impact function, along with 

baseline incidence rates population and change in air quality. In other words, the percentage of 

the population exposed to air pollution below the LML is not the same as the percentage of the 

population experiencing health impacts as a result of a specific emission reduction policy. The 

most important aspect, which we are unable to quantify without rule-specific air quality

modeling, is the shift in exposure anticipated by implementing the final emission guidelines. 

Therefore, caution is warranted when interpreting the LML assessment in this RIA because these 

results are not consistent with results from RIAs that had air quality modeling.

Table 4-28 provides the percentage of the population exposed above and below two 

concentration benchmarks (i.e., LML and one standard deviation below the mean) in the Clean 

Power Plan proposal modeling. Figure 4-3 shows a bar chart of the percentage of the population 

exposed to various air quality levels in the proposal modeling, and Figure 4-4 shows a 

cumulative distribution function of the same data. Both figures identify the LML for each of the 

major cohort studies.

Table 4-28. Population Exposure in the Clean Power Plan Proposal Modeling (used to 

generate the benefit-per-ton estimates) Above and Below Various 

Concentrations Benchmarks in the Underlying Epidemiology Studies *

Epidemiology Study

Below 1 Standard

Deviation.

Below AQ Mean

At or Above 1 Standard

Deviation Below AQ 

Mean

Below LML At or Above LML

Krewski et al. (2009) 3% 97% 12% 88%

Lepeule et al. (2012) N/A N/A 54% 46%

*One standard deviation below the mean is equivalent to the middle of the range between the 10th and 25th

percentile. For Krewski, the LML is 5.8 μg/m3and one standard deviation below the mean is 11.0 μg/m3. For 

Lepeule et al., the LML is 8 μg/m3and we do not have the data for one standard deviation below the mean. It is 

important to emphasize that although we have lower levels of confidence in levels below the LML for each study, 

the scientific evidence does not support the existence of a level below which health effects from exposure to PM2.5

do not occur.
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Figure 4-3. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure 

in the Clean Power Plan Proposal Modeling (used to generate the benefit-per-ton 

estimates)

Among the populations exposed to PM2.5 in the baseline:

88% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

46% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

5%

10%

15%

20%

25%

30%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Percentage of Population Exposed

Baseline Annual Mean PM2.5 Level (μg/m3)

LML of Lepeule et al. 

(2012) study

LML of Krewski et 

al. (2009) study
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Figure 4-4. Cumulative Distribution of Adult Population (age 30+) by Annual Mean 

PM2.5 Exposure in the Clean Power Plan Proposal Modeling (used to generate 

the benefit-per-ton estimates)

4.4 Combined Climate Benefits and Health Co-benefits Estimates

In this analysis, we were able to monetize the estimated co-benefits associated with the 

decreased emissions of CO2 and reduced exposure to PM2.5 and ozone, but we were unable to 

monetize the co-benefits associated with reducing exposure to mercury, hydrogen chloride, 

carbon monoxide, SO2, and NO2, as well as ecosystem effects and visibility impairment. 

Specifically, we estimated the combined climate benefits at discount rates of 5 percent, 3 

percent, 2.5 percent, and 3 percent (95th percentile) (as recommended by the interagency working 

group), and health co-benefits at discount rates of 3 percent and 7 percent (as recommended by 

Among the populations exposed to PM2.5 in the baseline:

88% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

46% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Cumulative percentage of population exposed

Baseline Annual Mean PM2.5 Level (μg/m3)

LML of Krewski et 

al. (2009) study

LML of 

Lepeule et 

al. (2012) 

study
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4-43

the EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2014) and OMB’s Circular 

A-4 [OMB, 2003]). 

Different discount rates are applied to SC-CO2 than to the health co-benefit estimates 

because CO2 emissions are long-lived and subsequent damages occur over many years. 

Moreover, several rates are applied to SC-CO2 because the literature shows that it is sensitive to 

assumptions about discount rate and because no consensus exists on the appropriate rate to use in 

an intergenerational context. The SC-CO2 interagency group centered its attention on the 3 

percent discount rate but emphasized the importance of considering all four SC-CO2 estimates.83

The EPA has evaluated the range of potential impacts by combining all SCC values with health 

co-benefits values at the 3 percent and 7 percent discount rates. Combining the 3 percent SC-CO2

values with the 3 percent health benefit values assumes that there is no difference in discount 

rates between intragenerational and intergenerational impacts.

Tables 4-29 through 4-31 provide the combined climate and health benefits for the 

illustrative plan scenarios evaluated for each analysis year: 2020, 2025, and 2030. Figure 4-5

shows the breakdown of the monetized benefits by pollutant for the illustrative plan scenarios

evaluated in 2025 as an illustrative analysis year using a 3 percent discount rate for both climate 

and health benefits.

 

83 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-0472-114577 or 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf for 

details. 
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Table 4-29. Combined Climate Benefits and Health Co-Benefits for Final Emission 

Guidelines in 2020 (billions of 2011$)*

SCC Discount Rate Climate 

Benefits Only

Climate and Health Benefits (Discount Rate Applied to 

Health Co-Benefits)

3% 7%

Rate-based Scenario 63 million metric tons CO2

5% $0.80 $1.5 to $2.6 $1.4 to $2.5

3% $2.8 $3.5 to $4.6 $3.4 to $4.5

2.5% $4.1 $4.8 to $5.9 $4.8 to $5.8

3% (95th percentile) $8.2 $8.9 to $10 $8.8 to $9.9

Mass-based Scenario - million metric tons CO2

5%

3%

2.5%

3% (95th percentile)

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NO2, SO2, and HAP;

ecosystem effects; or visibility impairment. 

Table 4-30. Combined Climate Benefits and Health Co-Benefits for Final Emission 

Guidelines in 2025 (billions of 2011$)*

SCC Discount Rate Climate 

Benefits Only

Climate and Health Benefits (Discount Rate Applied to 

Health Co-Benefits)

3% 7%

Rate-based Scenario 210 million metric tons CO2

5% $3.1 $11 to $21 $9.9 to $19

3% $10 $18 to $28 $17 to $26

2.5% $15 $23 to $33 $22 to $31

3% (95th percentile) $31 $38 to $49 $38 to $47

Mass-based Scenario - million metric tons CO2

5%

3%

2.5%

3% (95th percentile)

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NO2, SO2, and HAP;

ecosystem effects; or visibility impairment. 
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4-45

Table 4-31. Combined Climate Benefits and Health Co-Benefits for Final Emission 

Guidelines in 2030 (billions of 2011$)* 

SCC Discount Rate Climate 

Benefits Only

Climate and Health Benefits (Discount Rate Applied to 

Health Co-Benefits)

3% 7%

Rate-based Scenario 377 million metric tons CO2

5% $6.4 $21 to $40 $19 to $37

3% $20 $34 to $54 $33 to $51

2.5% $29 $43 to $63 $42 to $60

3% (95th percentile) $61 $75 to $95 $74 to $92

Mass-based Scenario - million metric tons CO2

5%

3%

2.5%

3% (95th percentile)

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NO2, SO2, and HAP;

ecosystem effects; or visibility impairment. 

Low Health Co-benefits High Health Co-benefits

Figure 4-5. Breakdown of Combined Monetized Climate and Health Co-benefits of Final 

Emission Guidelines in 2025 for Rate-based and Mass-based Scenarios and 

Pollutant (3% discount rate)*

* “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) 

mortality study for ozone with the Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5.

CO2

58%

SO2

36% Directly 

emitted 

PM2.5 

(EC+OC)

0%

Directly 

emitted 

PM2.5 

(Crustal)

0%

NOx (as 

PM2.5)

3%

NOx (as 

Ozone)

3%

Rate-based Scenario

CO2

37%

SO2

51%

Directly 

emitted 

PM2.5 

(EC+OC)

0%

Directly 

emitted 

PM2.5 

(Crustal)

0%

NOx (as 

PM2.5)

5%

NOx (as 

Ozone)

7%

Rate-based Scenario
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4.5 Unquantified Co-benefits

The monetized co-benefits estimated in this RIA reflect a subset of co-benefits attributable 

to the health effect reductions associated with ambient fine particles and ozone. Data, time, and 

resource limitations prevented the EPA from quantifying the impacts to, or monetizing the cobenefits from several important benefit categories, including co-benefits associated with 

exposure to several HAP (including mercury and hydrogen chloride) SO2 and NO2, as well as 

ecosystem effects, and visibility impairment due to the absence of air quality modeling data for 

these pollutants in this analysis. This does not imply that there are no co-benefits associated with 

these emission reductions. In this section, we provide a qualitative description of these benefits, 

which are listed in Table 4-32. 

Table 4-32. Unquantified Health and Welfare Co-benefits Categories

Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information

Improved Human Health

Reduced incidence of 

morbidity from exposure 

to NO2

Asthma hospital admissions (all ages) — — NO2ISA1

Chronic lung disease hospital admissions (age > 

65) — — NO2

ISA1

Respiratory emergency department visits (all ages) — — NO2ISA1

Asthma exacerbation (asthmatics age 4–18) — — NO2ISA1

Acute respiratory symptoms (age 7–14) — — NO2ISA1

Premature mortality — — NO2ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — NO2ISA2,3

Reduced incidence of 

morbidity from exposure 

to SO2

Respiratory hospital admissions (age > 65) — — SO2ISA1

Asthma emergency department visits (all ages) — — SO2ISA1

Asthma exacerbation (asthmatics age 4–12) — — SO2ISA1

Acute respiratory symptoms (age 7–14) — — SO2ISA1

Premature mortality — — SO2ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — SO2ISA1,2

Reduced incidence of 

morbidity from exposure 

to CO

Cardiovascular effects — — CO ISA 1,2

Respiratory effects — — CO ISA 1,2,3

Central nervous system effects — — CO ISA 1,2,3

Premature mortality — — CO ISA 1,2,3

Reduced incidence of 

morbidity from exposure 

to methylmercury

Neurologic effects—IQ loss — — IRIS; NRC, 20001

Other neurologic effects (e.g., developmental 

delays, memory, behavior) — — IRIS; NRC, 20002

Cardiovascular effects — —

IRIS; NRC, 

20002,3
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Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information

Genotoxic, immunologic, and other toxic effects — —

IRIS; NRC, 

20002,3

Reduced incidence of 

morbidity from exposure 

to HAP

Effects associated with exposure to hydrogen 

chloride — — ATSDR, IRIS1,2

Improved Environment

Reduced visibility 

impairment

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1

Reduced effects on 

materials

Household soiling — — PM ISA1,2

Materials damage (e.g., corrosion, increased wear) — — PM ISA2

Reduced effects from PM 

deposition (metals and 

organics)

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation and 

ecosystem effects from 

exposure to ozone

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

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

Reduced effects from acid 

deposition

Recreational fishing — — NOx SOxISA1

Tree mortality and decline — — NOx SOxISA2

Commercial fishing and forestry effects — — NOx SOxISA2

Recreational demand in terrestrial and aquatic 

ecosystems — — NOx SOx

ISA2

Other non-use effects NOx SOxISA2

Ecosystem functions (e.g., biogeochemical cycles) — — NOx SOxISA2

Reduced effects from 

nutrient enrichment

Species composition and biodiversity in terrestrial 

and estuarine ecosystems — — NOx SOx

ISA2

Coastal eutrophication — — NOx SOxISA2

Recreational demand in terrestrial and estuarine 

ecosystems — — NOx SOx

ISA2

Other non-use effects NOx SOxISA2

Ecosystem functions (e.g., biogeochemical cycles, 

fire regulation) — — NOx SOx

ISA2

Reduced vegetation 

effects from ambient 

exposure to SO2 and NOx

Injury to vegetation from SO2 exposure — — NOx SOxISA2

Injury to vegetation from NOx exposure — — NOx SOxISA2

Reduced ecosystem 

effects from exposure to 

methylmercury

Effects on fish, birds, and mammals (e.g., 

reproductive effects) — —

Mercury Study 

RTC2

Commercial, subsistence and recreational fishing — —

Mercury Study 

RTC1

1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA.

2We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.

3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other significant 

concerns over the strength of the association.
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4.5.1 HAP Co-benefits

Due to methodology and resource limitations, we were unable to estimate the co-benefits 

associated with reducing emissions of the hazardous air pollutants in this analysis. The EPA’s 

SAB-HES concluded that “the challenges for assessing progress in health improvement as a 

result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack 

of exposure-response functions, uncertainties in emissions inventories and background levels, the 

difficulty of extrapolating risk estimates to low doses and the challenges of tracking health 

progress for diseases, such as cancer, that have long latency periods” (U.S. EPA-SAB, 2008). In 

2009, the EPA convened a workshop to address the inherent complexities, limitations, and 

uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations 

from this workshop included identifying research priorities, focusing on susceptible and 

vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011). 

Chapter 4 of the MATS RIA (U.S. EPA, 2011b) describes the health effects associated 

with HAP emitted by EGUs. Below we describe the health effects associated with the two HAP 

for which we were able to quantify emission reductions for the final emission guidelines:

mercury and hydrogen chloride. Using the IPM modeling described in Chapter 3 of this RIA, we 

estimate that the illustrative plan scenarios for the final emission guidelines would reduce 

mercury emissions by up to [X] tons and hydrogen chloride by up to [X] tons by 2030. These

HAP emission reductions are beyond those achieved by MATS.

4.5.1.1 Mercury

Mercury in the environment is transformed into a more toxic form, methylmercury

(MeHg). Because Hg is a persistent pollutant, MeHg accumulates in the food chain, especially

the tissue of fish. When people consume these fish, they consume MeHg. In 2000, the NAS 

Study was issued which provides a thorough review of the effects of MeHg on human health 

(NRC, 2000).

84 Many of the peer-reviewed articles cited in this section are publications 

 

84 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Washington, DC: National 

Academies Press.
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originally cited in the Mercury Study.

85 In addition, the EPA has conducted literature searches to 

obtain other related and more recent publications to complement the material summarized by the 

NRC in 2000.

In its review of the literature, the NAS found neurodevelopmental effects to be the most 

sensitive and best documented endpoints and appropriate for establishing a reference dose (RfD)

(NRC, 2000); in particular NAS supported the use of results from neurobehavioral or 

neuropsychological tests. The NAS report noted that studies on animals reported sensory effects 

as well as effects on brain development and memory functions and supported the conclusions 

based on epidemiology studies. The NAS noted that their recommended endpoints for a RfD are 

associated with the ability of children to learn and to succeed in school. They concluded the 

following: “The population at highest risk is the children of women who consumed large 

amounts of fish and seafood during pregnancy. The committee concludes that the risk to that 

population is likely to be sufficient to result in an increase in the number of children who have to 

struggle to keep up in school.”

The NAS summarized data on cardiovascular effects available up to 2000. Based on these 

and other studies, the NRC concluded that “Although the data base is not as extensive for 

cardiovascular effects as it is for other end points (i.e., neurologic effects), the cardiovascular 

system appears to be a target for MeHg toxicity in humans and animals.” The NRC also stated 

that “additional studies are needed to better characterize the effect of methylmercury exposure on 

blood pressure and cardiovascular function at various stages of life.”

Additional cardiovascular studies have been published since 2000. The EPA did not 

develop a quantitative dose-response assessment for cardiovascular effects associated with 

MeHg exposures, as there is no consensus among scientists on the dose-response functions for 

these effects. In addition, there is inconsistency among available studies as to the association 

between MeHg exposure and various cardiovascular system effects. The pharmacokinetics of 

some of the exposure measures (such as toenail Hg levels) are not well understood. The studies 

 

85 U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress, EPA–HQ–OAR–

2009–0234–3054. December. Available on the Internet at <http://www.epa.gov/hg/report.htm>.
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have not yet received the review and scrutiny of the more well-established neurotoxicity data 

base. 

The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing 

chromosomal damage in a number of experimental systems. The NAS concluded that evidence 

that human exposure to MeHg caused genetic damage is inconclusive; they note that some earlier 

studies showing chromosomal damage in lymphocytes may not have controlled sufficiently for 

potential confounders. One study of adults living in the Tapajós River region in Brazil (Amorim 

et al., 2000) reported a direct relationship between MeHg concentration in hair and DNA damage 

in lymphocytes, as well as effects on chromosomes.

86 Long-term MeHg exposures in this 

population were believed to occur through consumption of fish, suggesting that genotoxic effects 

(largely chromosomal aberrations) may result from dietary and chronic MeHg exposures similar 

to and above those seen in the Faroes and Seychelles populations.

Although exposure to some forms of Hg can result in a decrease in immune activity or an 

autoimmune response (ATSDR, 1999), evidence for immunotoxic effects of MeHg is limited 

(NRC, 2000).

87

Based on limited human and animal data, MeHg is classified as a “possible” human 

carcinogen by the International Agency for Research on Cancer (IARC, 1994)88 and in IRIS 

(U.S. EPA, 2002).89 The existing evidence supporting the possibility of carcinogenic effects in 

humans from low-dose chronic exposures is tenuous. Multiple human epidemiological studies 

 

86 Amorim, M.I.M., D. Mergler, M.O. Bahia, H. Dubeau, D. Miranda, J. Lebel, R.R. Burbano, and M. Lucotte. 

2000. Cytogenetic damage related to low levels of methyl mercury contamination in the Brazilian Amazon. An. 

Acad. Bras. Ciênc. 72(4): 497-507.

87 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological Profile for Mercury. U.S. 

Department of Health and Human Services, Public Health Service, Atlanta, GA.

88 International Agency for Research on Cancer (IARC). 1994. IARC Monographs on the Evaluation of 

Carcinogenic Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, and Exposures in the Glass 

Manufacturing Industry. Vol. 58. Jalili, H.A., and A.H. Abbasi. 1961. Poisoning by ethyl mercury toluene 

sulphonanilide. Br. J. Indust. Med. 18(Oct.):303-308 (as cited in NRC, 2000).

89 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on 

Methylmercury. National Center for Environmental Assessment. Office of Research and Development. Available at 

http://www.epa.gov/iris/subst/0073.htm.
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have found no significant association between Hg exposure and overall cancer incidence, 

although a few studies have shown an association between Hg exposure and specific types of 

cancer incidence (e.g., acute leukemia and liver cancer) (NRC, 2000).

There is also some evidence of reproductive and renal toxicity in humans from MeHg 

exposure. However, overall, human data regarding reproductive, renal, and hematological 

toxicity from MeHg are very limited and are based on either studies of the two high-dose 

poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of 

chronic exposures at the levels of interest in this analysis.

4.5.1.2 Hydrogen Chloride

Hydrogen chloride (HCl) is a corrosive gas that can cause irritation of the mucous 

membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes throat 

irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour.90 The greatest impact is on 

the upper respiratory tract; exposure to high concentrations can rapidly lead to swelling and 

spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of 

rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Exposure to HCl can 

lead to Reactive Airways Dysfunction Syndrome (RADS), a chemically, or irritant-induced type 

of asthma. Children may be more vulnerable to corrosive agents than adults because of the 

relatively smaller diameter of their airways. Children may also be more vulnerable to gas 

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly

when exposed. Hydrogen chloride has not been classified for carcinogenic effects.91

4.5.2 Additional NO2 Health Co-Benefits

In addition to being a precursor to PM2.5 and ozone, NOx emissions are also linked to a 

variety of adverse health effects associated with direct exposure. We were unable to estimate the 

 

90 Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen 

Chloride. Atlanta, GA: U.S. Department of Health and Human Services. Available at 

http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147#bookmark02.

91 U.S. Environmental Protection Agency (U.S. EPA). 1995. “Integrated Risk Information System File of Hydrogen 

Chloride.” Washington, DC: Research and Development, National Center for Environmental Assessment. This 

material is available at http://www.epa.gov/iris/subst/0396.htm.
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health co-benefits associated with reduced NO2 exposure in this analysis. Therefore, this analysis 

only quantified and monetized the PM2.5 and ozone co-benefits associated with the reductions in 

NO2 emissions.

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, 2008c) 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. 

4.5.3 Additional SO2 Health Co-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 cobenefits associated with reduced SO2 in this analysis because we do not have air quality 

modeling data available. Therefore, this analysis only quantifies and monetizes the PM2.5 cobenefits 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, 2008a). 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 concentrationresponse 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 
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identified three short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for pollutants. We did not quantify these co-benefits due to data 

constraints.

4.5.4 Additional NO2 and SO2 Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —

Ecological Criteria (NOx/SOx ISA) (U.S. EPA, 2008d), SO2 and NOx emissions also contribute 

to a variety of adverse welfare effects, including acidic deposition, visibility impairment, and

nutrient enrichment. Deposition of nitrogen 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 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, 2008d)

Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In 

estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of 
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4-54

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, 

2008d)

4.5.5 Ozone Welfare Co-Benefits

Exposure to ozone has been associated with a wide array of vegetation and ecosystem 

effects in the published literature (U.S. EPA, 2013b). 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 are considered adverse to the public welfare 

and 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.

4.5.6 Carbon Monoxide Co-Benefits

CO in ambient air is formed primarily by the incomplete combustion of carbon-containing 

fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these 

reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the combustion zone, 

such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses 

through the respiratory system to the blood, which can cause hypoxia (reduced oxygen 

availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ 

systems that depend on concentration and duration of exposure. The Integrated Science 

Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to CO 

is “likely to have a causal relationship” with cardiovascular morbidity, particularly in individuals 

with coronary heart disease. Epidemiologic studies associate short-term CO exposure with 

increased risk of emergency department visits and hospital admissions. Coronary heart disease 
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includes those who have angina pectoris (cardiac chest pain), as well as those who have 

experienced a heart attack. Other subpopulations potentially at risk include individuals with

diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and 

individuals in very early or late life stages, such as older adults or the developing young. The 

evidence is suggestive of a causal relationship between short-term exposure to CO and 

respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for 

birth outcomes and developmental effects following long-term exposure to CO, and for central 

nervous system effects linked to short- and long-term exposure to CO.

4.5.7 Visibility Impairment Co-Benefits

Reducing secondary formation of PM2.5 would improve levels visibility in the U.S. because 

suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 

2009b). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, 

organic carbon, elemental carbon, and soil (Sisler, 1996). 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 haze in the eastern 

U.S. and particulate nitrate is an important contributor to light extinction in California and the 

upper Midwestern U.S., particularly during winter (U.S. EPA, 2009b). Previous analyses (U.S. 

EPA, 2011a) show that visibility co-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 emission reductions associated with the final emission 

guidelines would be likely to have a significant impact on visibility in urban areas or Class I 

areas.

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ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>.

Accessed June 4, 2015.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009c. 

Review of Integrated Science Assessment for Particulate Matter (Second External Review 

Draft, July 2009). EPA-CASAC-10-001. November. Available at: 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/15

1B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>. Accessed 

June 4, 2015.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010a. 

Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the 

Clean Air Act. EPA-COUNCIL-10-001. June. Available at: < 

http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA

39E48CDB28525774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>. Accessed 

June 4, 2015.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011. 

Review of Valuing Mortality Risk Reductions for Environmental Policy: A White Paper 

(December 10, 2010). EPA-SAB-11-011 July. Available at: 

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/

EPA-SAB-11-011-unsigned.pdf>. Accessed June 4, 2015.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship between Selected of 

postneonatal infant mortality and particulate air pollution in the United States.” 

Environmental Health Perspectives. 105(6): 608-612.
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APPENDIX 4A: GENERATING REGIONAL BENEFIT-PER-TON ESTIMATES

The purpose of this appendix is to provide additional detail regarding the generation of 

the benefit-per-ton estimates applied in Chapter 4 of this Regulatory Impact Analysis (RIA). 

Specifically, this appendix describes the methods for generating benefit-per-ton estimates by

region for PM2.5 and ozone precursors emitted by the electrical generating unit (EGU) sector in 

the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility

Generating Units (hereafter referred to as the “final emission guidelines” or “Clean Power Plan 

Final Rule”).

4A.1 Overview of Benefit-per-Ton Estimates

As described in the Technical Support Document: Estimating the Benefit per Ton of 

Reducing PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013), the general procedure for 

calculating average benefit-per-ton coefficients generally follows three steps. As an example, in 

order to calculate regional average benefit-per-ton estimates for the key precursor pollutants 

emitted from EGU sources, we:

1. Use air quality modeling to predict changes in ambient concentrations of primary PM2.5, 

nitrate, sulfate, and ozone at a 12km2grid resolution across the contiguous U.S. that are 

attributable to the proposed Clean Power Plan.

2. For each grid cell, estimate the health impacts, and the economic value of these impacts, 

associated with the attributable ambient concentrations using the environmental Benefits 

Mapping and Analysis Program – Community Edition (BenMAP-CE v1.1).

92

 Aggregate 

those impacts and economic values to the three regions of East, West, and California.

3. Divide the regional health impacts attributable to each precursor, and the regional 

monetary value of these impacts, by the amount of associated regional precursor 

emissions. That is, directly emitted PM2.5 benefits are divided by directly emitted PM2.5 

emissions, sulfate benefits are divided by SO2 emissions, nitrate benefits are divided by

NOx emissions, and ozone benefits are divided by ozone-season NOx emissions.

 

92 When estimating these impacts we apply effect coefficients that relate changes in total PM

2.5

mass to the risk of adverse health outcomes; we do not apply effect coefficients 

that are differentiated by PM

2.5

species. 
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4A-2

4A.2 Air Quality Modeling for the Proposed Clean Power Plan

The EPA ran the Comprehensive Model with Extensions (CAMx) photochemical model 

(ENVIRON, 2014) to predict ozone and PM2.5 concentrations for the following emissions 

scenarios: a 2011 base year, a 2025 base case, and the 2025 proposed Clean Power Plan (Option 

1 State) scenario. Each of the CAMx model simulations was performed for a nationwide 

modeling domain93 using a full year of meteorological conditions for 2011. The modeling for 

2011 was used as the anchor point for projecting ozone and annual PM2.5 concentration values 

for the 2025 base case and for the 2025 Clean Power Plan proposal scenario using methodologies 

consistent with the EPA’s air quality modeling guidance (U.S. EPA, 2007). The air quality 

modeling results for the 2025 base case served as the baseline for gauging the future year 

impacts on ozone and annual PM2.5 of the Clean Power Plan proposal scenario. The 2025 base 

case reflects emissions reductions between 2011 and 2025 that are expected to result from 

regional and national rules including the Clean Air Interstate Rule (CAIR), the Mercury and Air 

Toxics Standards (MATS), mobile source rules up through Tier-3, and various state emissions 

control programs and consent decrees. The general methods for estimating the EGU emissions

for the proposal are described in Chapter 3 of the RIA for the Clean Power Plan proposal (U.S. 

EPA, 2014). State total annual EGU emissions for NOx and SO2 for each of the scenarios 

modeled are provided in Tables 4A-1 and 4A-2, respectively. The data indicate that, overall 

nationwide, EGU SO2 and NOx emissions with proposed Option 1 (state) would be about 28% 

lower than the 2025 base case. 

Table 4A-1. State Total Annual EGU Emissions for NOx for the 2011 Base Year, 2025 

Base Case, and 2025 Clean Power Plan Proposal (Option 1 State) (in thousands 

of tons)

State 2011 Base Year 2025 Base Case

2025 Clean Power Plan 

Proposal 

(Option 1 State)

Alabama 63 38 19

Arizona 35 17 4

Arkansas 38 43 9

California 6 33 28

Colorado 51 29 21

 

93 The modeling domain (i.e., region modeled) includes all of the lower 48 states plus adjacent portions of Canada 

and Mexico) at a spatial resolution of 12 km.
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4A-3

State 2011 Base Year 2025 Base Case

2025 Clean Power Plan 

Proposal 

(Option 1 State)

Connecticut 1 1 1

Delaware 4 1 1

Florida 61 52 15

Georgia 54 33 18

Idaho - 1 0

Illinois 73 38 32

Indiana 121 97 90

Iowa 40 24 24

Kansas 44 28 27

Kentucky 92 59 74

Louisiana 47 18 14

Maine 2 4 2

Maryland 19 11 11

Massachusetts 5 2 1

Michigan 75 73 51

Minnesota 32 27 13

Mississippi 26 15 3

Missouri 66 61 58

Montana 20 16 15

Nebraska 37 38 35

Nevada 7 5 3

New Hampshire 4 1 0

New Jersey 6 7 2

New Mexico 23 7 6

New York 22 11 7

North Carolina 46 35 23

North Dakota 51 51 48

Ohio 104 63 60

Oklahoma 82 52 26

Oregon 5 3 3

Pennsylvania 149 106 71

Rhode Island 0 0 1

South Carolina 25 13 8

South Dakota 11 13 8

Tennessee 27 16 13

Texas 146 144 64

Tribal Data 65 33 33

Utah 51 49 33

Vermont 0 0 0

Virginia 38 21 12

Washington 7 3 2

West Virginia 58 49 46

Wisconsin 32 19 11

Wyoming 53 50 38

National Total 2,024 1,508 1,084
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4A-5

Table 4A-2. State Total Annual EGU Emissions for SO2 for the 2011 Base Year, 2025 

Base Case, and 2025 Clean Power Plan Proposal (Option 1 State) (in thousands 

of tons)

State

2011 Base Year 2025 Base Case

2025 Clean Power Plan 

Proposal 

(Option 1 State)

Alabama 186 79 45

Arizona 28 18 4

Arkansas 74 30 5

California 1 4 4

Colorado 45 15 10

Connecticut 1 - -

Delaware 11 1 1

Florida 95 70 7

Georgia 187 37 12

Idaho - 0 0

Illinois 227 45 48

Indiana 382 126 121

Iowa 100 18 18

Kansas 39 15 15

Kentucky 246 109 119

Louisiana 93 14 11

Maine 1 1 1

Maryland 32 5 9

Massachusetts 23 1 0

Michigan 228 122 95

Minnesota 40 21 12

Mississippi 43 10 3

Missouri 205 80 76

Montana 19 18 17

Nebraska 73 25 24

Nevada 5 1 1

New Hampshire 24 0 0

New Jersey 5 7 1

New Mexico 6 4 4

New York 41 4 2

North Carolina 78 36 33

North Dakota 93 15 14

Ohio 594 105 102

Oklahoma 96 21 6

Oregon 13 1 1

Pennsylvania 338 67 47

Rhode Island 0 - -

South Carolina 68 19 12

South Dakota 11 11 7

Tennessee 120 38 31

Texas 426 149 48

Tribal Data 18 19 19
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4A-6

State

2011 Base Year 2025 Base Case

2025 Clean Power Plan 

Proposal 

(Option 1 State)

Utah 22 14 10

Vermont 0 0 0

Virginia 75 8 4

Washington 1 1 1

West Virginia 103 78 47

Wisconsin 92 17 11

Wyoming 55 23 17

National Total 4,665 1,504 1,077

As indicated above, the air quality modeling was used to project gridded ozone and 

annual PM2.5 concentrations at the 12km2resolution for the 2025 base case and the Clean Power 

Plan proposal scenario modeled for this analysis. The air quality modeling results were combined 

with monitored ozone and PM2.5 data to create projected spatial fields of annual PM2.5 and 

seasonal mean (May through September) 8-hour daily maximum ozone for the 2025 base case 

and for the proposal scenario. These spatial fields were then used as inputs to estimate the health 

co-benefits of the proposed Clean Power Plan as described below.

4A.3 Regional PM2.5 Benefit-per-Ton Estimates for EGUs Derived from Air Quality 

Modeling of the Proposed Clean Power Plan

After estimating the 12km2resolution PM2.5 benefits for each of the analysis years 

applied in this RIA (i.e., 2020, 2025, and 2030), we aggregated the benefits results regionally

(i.e., East, West, and California), as shown in Figure 4A-1.94 Due to the low emissions of SO2, 

NOX, and directly emitted particles from EGUs in California and the high population density, we 

separated out California in order not to bias the benefit-per-ton estimates for the rest of the 

Western U.S. In order to calculate the benefit-per-ton estimates, we divided the regional benefits 

estimates by the corresponding emissions, as shown in Table 4A-1. Lastly, we adjusted the 

benefit-per-ton estimates for a currency year of 2011$.95

This method provides estimates of the regional average benefit-per-ton for a subset of the 

major PM2.5 precursors emitted from EGU sources. For precursor emissions of NOx, there is 

generally a non-linear relationship between emissions and formation of PM2.5. This means that 

each ton of NOx reduced would have a different impact on ambient PM2.5 depending on the 

 

94 This aggregation is identified as the shapefile “Report Regions” in BenMAP’s grid definitions.

95 Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefit-per-ton estimates in 2010$. We 

then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer Price Index.
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4A-7

initial level of emissions and potentially on the levels of emissions of other pollutants. In

contrast, SO2 is generally linear in forming PM2.5. For precursors like NOx which form PM2.5

non-linearly, a marginal benefit-per-ton approach would better approximate the specific benefits 

associated with an emissions reduction scenario for a given set of base case emissions, because it 

would allow the benefit-per-ton to vary depending on the level of emissions reductions and the 

baseline emissions levels. However, we do not have sufficient air quality modeling data to 

calculate marginal benefit-per-ton estimates for the EGU sector. Therefore, using an average 

benefit-per-ton estimate for NOx adds uncertainty to the co-benefits estimated in this RIA. 

Because most of the estimated co-benefits for the proposed guidelines are attributable to 

reductions in SO2 emissions, the added uncertainty is likely to be small.

Figure 4A-1. Regional Breakdown

In this RIA, we estimate emission reductions from EGUs using IPM.96 IPM outputs 

provide endogenously projected unit level emissions of SO2, NOx, CO2, Hg, HCl from EGUs, 

 

96 See Chapter 3 of this RIA for additional information regarding the Integrated Planning Model (IPM).
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4A-8

but CO, VOC, NH3 and total directly emitted PM2.5 and PM10 emissions are post-calculated.97 In 

addition, directly emitted particle emissions calculated from IPM outputs do not include 

speciation, i.e. they are only the total emissions. In order to conduct air quality modeling, directly 

emitted PM2.5 from EGUs is speciated into components during the emissions modeling process

based on emission profiles for EGUs by source classification code. Even though these speciation 

profiles are not unit-specific, an emission profile based on the source classification code is highly

sophisticated and reflects the fuel and the unit configuration. Model-predicted concentrations of 

nitrate and sulfate include both the directly emitted nitrate and sulfate from speciated PM2.5 and 

secondarily formed nitrate and sulfate from emissions of NOx and SO2, respectively. 

In order to estimate the benefits associated with reduced emissions of directly emitted 

particles without performing air quality modeling, we must determine the fraction of total PM2.5 

emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions.98

Based on the work by Fann, Baker, and Fulcher (2012), the national average EC+OC fraction of 

emitted PM2.5 is 10% with a range of 5% to 63% in different states due to the different proportion 

of fuels. The national average is similar to the averages for the east and west regions at 10% and 

7%, respectively. Only five states had EC+OC fractions greater than 30%. For crustal emissions, 

the national average fraction of emitted PM2.5 from EGUs is 78% with a range of 26% to 83%. 

The national average is similar to the averages for the east and west regions at 78% and 81%, 

respectively. Only four states had crustal fractions less than 50%. In calculating the PM2.5 cobenefits in this RIA, we estimate the emission reductions of EC+OC and crustal emissions by

applying the national average fractions (i.e., 78% crustal and 10% EC+OC) to the emission 

reductions of all directly emitted particles from EGUs. Because the benefit-per-ton estimates for 

reducing emissions of EC+OC are larger than the benefit-per-ton estimate for crustal emissions, 

this assumption underestimates the monetized PM2.5 co-benefits in certain states with higher 

EC+OC fractions, such as California and North Dakota.

 

97 Detailed documentation of this post-processing is available at 

http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Methodology.pdf 

98 Crustal emissions are composed of compounds associated with minerals and metals from the earth’s surface, 

including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material represents particles not 

classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).
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4A-9

Although it is possible to calculate 95th percentile confidence intervals using the approach 

described in this appendix (e.g., U.S. EPA, 2011b), we generally do not calculate confidence 

intervals for benefit-per-ton estimates. Instead, we refer the reader to Chapter 5 of PM NAAQS 

RIA (U.S. EPA, 2012a) for an indication of the combined random sampling error in the health 

impact and economic valuation functions using Monte Carlo methods. In general, the 95th

percentile confidence interval for the total monetized PM2.5 benefits ranges from approximately -

90% to +180% of the central estimates based on concentration-response functions from Krewski 

et al. (2009) and Lepeule et al. (2012). The 95th percentile confidence interval for the health 

impact function alone ranges from approximately ±30% for mortality incidence based on 

Krewski et al. (2009) and ±46% based on Lepeule et al. (2012). These confidence intervals do 

not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence 

rates, populations exposed, and transferability of the effect estimate to diverse locations. As a 

result, the reported confidence intervals and range of estimates give an incomplete picture about 

the overall uncertainty in the benefits estimates.

Tables 4A-3 through 4A-5 provide the regional benefit-per-ton estimates for the EGU 

sector at discount rates of 3% and 7% in 2020, 2025, and 2030 respectively. The benefit-per-ton 

values for 2020 and 2030 are based on applying the air quality modeling from 2025 to population 

and health information from 2020 and 2030. Estimated benefit-per-ton for these years have 

additional uncertainty relative to 2025 because of potential differences in atmospheric responses 

to reductions in PM2.5 precursors in those years, however, these uncertainties are likely to be 

relatively small. Tables 4A-6 through 4A-8 provide the incidence per ton estimates for the EGU 

sector in 2020, 2025, and 2030 respectively.
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4A-10

Table 4A-3. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan 

in 2020 (2011$)*

Pollutant Discount 

Rate National Region

East West California

SO2 3% $35,000 to $78,000 $37,000 to $83,000 $7,100 to $16,000 $110,000 to $240,000

7% $31,000 to $70,000 $33,000 to $75,000 $6,400 to $14,000 $97,000 to $220,000

Directly emitted 

PM2.5 (EC+OC)

3% $150,000 to $340,000 $160,000 to $360,000 $30,000 to $68,000 $410,000 to $930,000

7% $130,000 to $290,000 $140,000 to $320,000 $27,000 to $61,000 $370,000 to $830,000

Directly emitted 

PM2.5 (Crustal)

3% $24,000 to $55,000 $25,000 to $58,000 $12,000 to $28,000 $82,000 to $180,000

7% $22,000 to $49,000 $23,000 to $52,000 $11,000 to $25,000 $74,000 to $170,000

NOx (as PM2.5) 3% $3,200 to $7,300 $3,300 to $7,500 $0,750 to $1,700 $24,000 to $54,000

7% $2,900 to $6,000 $3,000 to $6,800 $0,670 to $1,500 $22,000 to $49,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 

Table 4A-4. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan 

in 2025 (2011$)*

Pollutant Discount 

Rate National Region

East West California

SO2 3% $32,000 to $71,000 $33,000 to $75,000 $6,200 to $14,000 $95,000 to $210,000

7% $28,000 to $64,000 $30,000 to $68,000 $5,600 to $13,000 $85,000 to $190,000

Directly emitted 

PM2.5 (EC+OC)

3% $140,000 to $310,000 $140,000 to $320,000 $27,000 to $60,000 $370,000 to $830,000

7% $120,000 to $270,000 $130,000 to $290,000 $24,000 to $54,000 $330,000 to $740,000

Directly emitted 

PM2.5 (Crustal)

3% $22,000 to $49,000 $23,000 to $52,000 $11,000 to $25,000 $73,000 to $160,000

7% $20,000 to $44,000 $21,000 to $47,000 $9,900 to $22,000 $66,000 to $150,000

NOx (as PM2.5) 3% $3,000 to $6,800 $3,100 to $7,000 $0,670 to $1,500 $22,000 to $49,000

7% $2,700 to $5,600 $2,800 to $6,300 $0,610 to $1,400 $19,000 to $44,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 
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4A-11

Table 4A-5. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan 

in 2030 (2011$)*

Pollutant Discount 

Rate National Region

East West California

SO2 3% $37,000 to $85,000 $40,000 to $89,000 $7,800 to $18,000 $120,000 to $270,000

7% $34,000 to $76,000 $36,000 to $81,000 $7,100 to $16,000 $110,000 to $240,000

Directly emitted 

PM2.5 (EC+OC)

3% $160,000 to $360,000 $170,000 to $380,000 $33,000 to $75,000 $450,000 to $1,000,000

7% $150,000 to $320,000 $150,000 to $340,000 $30,000 to $68,000 $410,000 to $920,000

Directly emitted 

PM2.5 (Crustal)

3% $26,000 to $59,000 $28,000 to $62,000 $14,000 to $31,000 $90,000 to $200,000

7% $24,000 to $53,000 $25,000 to $56,000 $13,000 to $28,000 $81,000 to $180,000

NOx (as PM2.5) 3% $3,400 to $7,800 $3,500 to $8,000 $0,820 to $1,900 $26,000 to $60,000

7% $3,100 to $6,400 $3,200 to $7,200 $0,740 to $1,700 $24,000 to $54,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 
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4A-12

Table 4A-6. Summary of Regional PM2.5 Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power 

Plan in 2020*

Health Endpoint

East West California

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal

Premature Mortality

Krewski et al. (2009) – adult 0.003700 0.000340 0.016000 0.002500 0.000680 0.000073 0.002900 0.001200 0.010000 0.002400 0.040000 0.008000

Lepeule et al. (2012) – adult 0.008300 0.000770 0.036000 0.005700 0.001500 0.000170 0.006600 0.002700 0.023000 0.005400 0.091000 0.018000

Woodruff et al. (1997) – infants 0.000009 0.000001 0.000037 0.000006 0.000002 0.000000 0.000007 0.000003 0.000023 0.000007 0.000097 0.000019

Morbidity

Emergency department visits for asthma 0.001900 0.000190 0.007800 0.001300 0.000290 0.000031 0.001200 0.000470 0.005300 0.001400 0.022000 0.004200

Acute bronchitis 0.005400 0.000510 0.023000 0.003700 0.001300 0.000200 0.005200 0.002100 0.019000 0.005000 0.077000 0.015000

Lower respiratory symptoms 0.069000 0.006500 0.300000 0.047000 0.016000 0.002500 0.067000 0.026000 0.240000 0.064000 0.970000 0.190000

Upper respiratory symptoms 0.098000 0.009300 0.420000 0.068000 0.023000 0.003600 0.095000 0.038000 0.340000 0.092000 1.400000 0.270000

Minor restricted-activity days 2.700000 0.250000 11.000000 1.900000 0.580000 0.078000 2.400000 0.920000 9.400000 2.200000 35.000000 6.800000

Lost work days 0.450000 0.043000 1.900000 0.310000 0.098000 0.013000 0.410000 0.160000 1.600000 0.380000 6.000000 1.100000

Asthma exacerbation 0.240000 0.023000 1.000000 0.170000 0.056000 0.008800 0.230000 0.091000 0.840000 0.220000 3.400000 0.650000

Hospital Admissions, Respiratory 0.001100 0.000100 0.004500 0.000720 0.000150 0.000015 0.000640 0.000260 0.002500 0.000580 0.009400 0.001900

Hospital Admissions, Cardiovascular 0.001300 0.000120 0.005600 0.000910 0.000200 0.000019 0.000820 0.000330 0.003000 0.000680 0.011000 0.002200

Non-fatal Heart Attacks (Peters) 0.004100 0.000390 0.018000 0.002800 0.000650 0.000064 0.002800 0.001200 0.011000 0.002400 0.041000 0.007900

Non-fatal Heart Attacks (All others) 0.000450 0.000042 0.001900 0.000310 0.000070 0.000007 0.000300 0.000130 0.001100 0.000260 0.004400 0.000850

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from 

precursor emissions to ambient fine particles. 
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4A-13

Table 4A-7. Summary of Regional PM2.5 Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power 

Plan in 2025*

Health Endpoint

East West California

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal

Premature Mortality

Krewski et al. (2009) – adult 0.003900 0.000350 0.017000 0.002700 0.000750 0.000079 0.003200 0.001300 0.011000 0.002600 0.044000 0.008700

Lepeule et al. (2012) – adult 0.008900 0.000800 0.038000 0.006200 0.001700 0.000180 0.007300 0.003000 0.026000 0.005800 0.099000 0.020000

Woodruff et al. (1997) – infants 0.000008 0.000001 0.000035 0.000006 0.000002 0.000000 0.000007 0.000003 0.000022 0.000007 0.000093 0.000018

Morbidity

Emergency department visits for asthma 0.002000 0.000200 0.006300 0.001300 0.000320 0.000033 0.001000 0.000510 0.005500 0.001500 0.018000 0.004400

Acute bronchitis 0.005700 0.000520 0.024000 0.003900 0.001300 0.000210 0.005600 0.002200 0.020000 0.005300 0.080000 0.015000

Lower respiratory symptoms 0.072000 0.006700 0.310000 0.050000 0.017000 0.002700 0.071000 0.028000 0.250000 0.067000 1.000000 0.200000

Upper respiratory symptoms 0.100000 0.009600 0.440000 0.071000 0.024000 0.003800 0.100000 0.040000 0.360000 0.096000 1.500000 0.280000

Minor restricted-activity days 2.800000 0.250000 12.000000 1.900000 0.610000 0.083000 2.500000 0.970000 9.600000 2.300000 36.000000 6.900000

Lost work days 0.470000 0.043000 2.000000 0.320000 0.100000 0.014000 0.430000 0.160000 1.600000 0.390000 6.100000 1.200000

Asthma exacerbation 0.250000 0.023000 1.100000 0.170000 0.059000 0.009300 0.250000 0.097000 0.880000 0.230000 3.500000 0.680000

Hospital Admissions, Respiratory 0.001200 0.000110 0.005100 0.000810 0.000180 0.000017 0.000740 0.000300 0.002800 0.000650 0.011000 0.002100

Hospital Admissions, Cardiovascular 0.001400 0.000130 0.006200 0.001000 0.000220 0.000022 0.000930 0.000380 0.003300 0.000750 0.012000 0.002400

Non-fatal Heart Attacks (Peters) 0.004600 0.000430 0.020000 0.003100 0.000740 0.000071 0.003200 0.001300 0.012000 0.002700 0.046000 0.008900

Non-fatal Heart Attacks (All others) 0.000490 0.000046 0.002100 0.000340 0.000080 0.000008 0.000340 0.000140 0.001300 0.000290 0.004900 0.000950

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from 

precursor emissions to ambient fine particles. 
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Table 4A-8. Summary of Regional PM2.5 Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power 

Plan in 2030*

Health Endpoint

East West California

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal

Premature Mortality

Krewski et al. (2009) – adult 0.004200 0.000380 0.018000 0.002900 0.000840 0.000087 0.003600 0.001500 0.013000 0.002800 0.048000 0.009600

Lepeule et al. (2012) – adult 0.009600 0.000850 0.041000 0.006700 0.001900 0.000200 0.008100 0.003400 0.029000 0.006400 0.110000 0.022000

Woodruff et al. (1997) – infants 0.000008 0.000001 0.000033 0.000005 0.000002 0.000000 0.000007 0.000003 0.000021 0.000006 0.000088 0.000017

Morbidity

Emergency department visits for asthma 0.001600 0.000160 0.006600 0.001100 0.000260 0.000027 0.001100 0.000420 0.004500 0.001200 0.019000 0.003500

Acute bronchitis 0.005900 0.000540 0.025000 0.004100 0.001400 0.000220 0.005900 0.002300 0.021000 0.005400 0.083000 0.016000

Lower respiratory symptoms 0.075000 0.006800 0.320000 0.052000 0.018000 0.002800 0.075000 0.030000 0.260000 0.069000 1.100000 0.200000

Upper respiratory symptoms 0.110000 0.009800 0.460000 0.074000 0.026000 0.004000 0.110000 0.042000 0.370000 0.099000 1.500000 0.290000

Minor restricted-activity days 2.900000 0.260000 12.000000 2.000000 0.650000 0.088000 2.700000 1.000000 9.800000 2.300000 37.000000 7.100000

Lost work days 0.480000 0.043000 2.000000 0.330000 0.110000 0.015000 0.450000 0.170000 1.700000 0.400000 6.300000 1.200000

Asthma exacerbation 0.260000 0.024000 1.100000 0.180000 0.063000 0.009800 0.260000 0.100000 0.920000 0.240000 3.700000 0.710000

Hospital Admissions, Respiratory 0.001300 0.000120 0.005600 0.000900 0.000200 0.000019 0.000830 0.000340 0.003200 0.000740 0.012000 0.002400

Hospital Admissions, Cardiovascular 0.001600 0.000150 0.006800 0.001100 0.000250 0.000024 0.001000 0.000420 0.003800 0.000850 0.014000 0.002700

Non-fatal Heart Attacks (Peters) 0.005000 0.000460 0.021000 0.003500 0.000830 0.000079 0.003600 0.001500 0.014000 0.003100 0.052000 0.010000

Non-fatal Heart Attacks (All others) 0.000540 0.000049 0.002300 0.000370 0.000090 0.000009 0.000380 0.000160 0.001500 0.000330 0.005600 0.001100

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from 

precursor emissions to ambient fine particles. 
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4A.4 Regional Ozone Benefit-per-Ton Estimates

The process for generating the regional ozone benefit-per-ton estimates is consistent with 

the process for PM2.5. Ozone is not directly emitted, and is a non-linear function of NOx and 

VOC emissions. For the purpose of estimating benefit-per-ton for this RIA, we assume that all of 

the ozone impacts from EGUs are attributable to NOx emissions. VOC emissions, which are also 

a precursor to ambient ozone formation, are insignificant from the EGU sector relative to both 

NOx emissions from EGUs and the total VOC emissions inventory. Therefore, we believe that 

our assumption that EGU-attributable ozone formation at the regional-level is due to NOx alone 

is reasonable.

Similar to PM2.5, this method provides estimates of the regional average benefit-per-ton. 

Due to the non-linear chemistry between NOx emissions and ambient ozone, using an average 

benefit-per-ton estimate for NOx adds uncertainty to the ozone co-benefits estimated for the 

proposed guidelines. Because most of the estimated co-benefits for the proposed guidelines are 

attributable to changes in ambient PM2.5, the added uncertainty is likely to be small.

In the ozone co-benefits estimated in this RIA, we apply the benefit-per-ton estimates 

calculated using NOX emissions derived from modeling the Clean Power Plan proposal during 

the ozone-season only (May to September). As shown in Table 4A-1, ozone-season NOx 

emissions from EGUs are slightly less than half of all-year NOX emissions. Because we estimate 

ozone health impacts from May to September only, this approach underestimates ozone cobenefits in areas with longer ozone seasons such as southern California and Texas. When the 

underestimated benefit-per-ton estimate is multiplied by ozone-season only NOx emission 

reductions, this results in an underestimate of the monetized ozone co-benefits. For illustrative 

purposes, Tables 4A-9 through 4A-11 provide the ozone benefit-per-ton estimates using both allyear NOx emissions and ozone-season only NOx for 2020, 2025, and 2030, respectively. Tables 

4A-12 through 4A-14 provide the ozone season incidence-per-ton estimates for 2020, 2025, and 

2030, respectively. Similar to PM2.5, the ozone benefit-per-ton values for 2020 and 2030 are 

based on applying the air quality modeling from 2025 to population and health information from 

2020 and 2030. Estimated benefit-per-ton for these years have additional uncertainty relative to 

2025 because of potential differences in atmospheric responses to reductions in ozone precursors 
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4A-16

in those years. Uncertainties may be somewhat larger in the case of ozone due to high degree of 

dependence of ozone responses to baseline meteorology and emissions levels.

Table 4A-9. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)*

Ozone precursor 

Pollutant National Regional

East West California

Ozone season NOx $6,000 to $26,000 $6,500 to $28,000 $2,000 to $8,900 $14,000 to $59,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx

precursor emissions to ambient ozone. 

Table 4A-10. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)*

Ozone precursor 

Pollutant National Regional

East West California

Ozone season NOx $6,600 to $27,000 $7,100 to $30,000 $2,300 to $10,000 $15,000 to $66,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx

precursor emissions to ambient ozone. 

Table 4A-11. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2030 (2011$)*

Ozone precursor 

Pollutant National Regional

East West California

Ozone season NOx $7,100 to $29,000 $7,600 to $33,000 $2,600 to $11,000 $17,000 to $73,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx

precursor emissions to ambient ozone. 
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4A-17

Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2020*

Health Endpoint East West California

Premature Mortality – adult 

Bell et al. (2004) 0.000600 0.000190 0.001300

Levy et al. (2005) 0.002800 0.000880 0.005800

Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.003500 0.000900 0.006600

Hospital Admissions, Respiratory (ages < 2) 0.001800 0.000780 0.003300

Emergency Room Visits, Respiratory 0.002000 0.000500 0.003900

Acute Respiratory Symptoms 3.500000 1.300000 8.800000

School Loss Days 1.200000 0.490000 3.000000

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions. 

Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2025*

Health Endpoint East West California

Premature Mortality – adult 

Bell et al. (2004) 0.000640 0.000210 0.001400

Levy et al. (2005) 0.002900 0.000970 0.006400

Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.004100 0.001100 0.007800

Hospital Admissions, Respiratory (ages < 2) 0.001800 0.000820 0.003400

Emergency Room Visits, Respiratory 0.002000 0.000540 0.004100

Acute Respiratory Symptoms 3.600000 1.400000 8.900000

School Loss Days 1.300000 0.520000 3.200000

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions. 

Table 4A-14. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air 

Quality Modeling from Proposed Clean Power Plan in 2030*

Health Endpoint East West California

Premature Mortality – adult 

Bell et al. (2004) 0.000640 0.000230 0.001800

Levy et al. (2005) 0.002900 0.001100 0.008200

Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.004400 0.001300 0.011000

Hospital Admissions, Respiratory (ages < 2) 0.001800 0.000860 0.004100

Emergency Room Visits, Respiratory 0.002000 0.000580 0.005000

Acute Respiratory Symptoms 3.500000 1.500000 11.000000

School Loss Days 1.200000 0.550000 3.800000

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions. 
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4A.6 References

Abt Associates, Inc. 2010. “User’s Guide: Modeled Attainment Test Software.” Available at: 

<http://www.epa.gov/scram001/modelingapps_mats.htm>. Accessed June 6, 2015.

Abt Associates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research 

Triangle Park, NC: U. S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards. Available at: 

<http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. Accessed June 

6, 2015.

Bell, M.L., A. McDermott, S.L. Zeger, J.M. Sarnet, and F. Dominici. 2004. “Ozone and ShortTerm 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.

ENVIRON International Corporation. 2014. User’s Guide: Comprehensive Air Quality Model 

with Extensions, Version 6.1, Novato, CA. April. Available at <http://www.camx.com>.

Accessed June 6, 2015.

Fann, N., C.M. Fulcher, and B.J. Hubbell. 2009. “The Influence of Location, Source, and 

Emission Type in Estimates of the Human Health Benefits of Reducing a Ton of Air 

Pollution.” Air Quality and Atmospheric Health. 2:169–176.

Fann, N., K.R. Baker, and C.M. Fulcher. 2012. “Characterizing the PM2.5-Related Health 

Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across 

the U.S.” Environment International. 49:41–151. 

Fann, N., K.R. Baker, and C.M. Fulcher. 2013. “The Recent and Future Health Burden of Air 

Pollution Apportioned Across 23 U.S. Sectors.” Environmental Scientific Technology. 47(8): 

3580–3589. 

Huang Y., F. Dominici, and M. Bell. 2005. “Bayesian Hierarchical Distributed Lag Models for 

Summer Ozone Exposure and Cardio-Respiratory Mortality.” Environmetrics. 16:547-562.

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.
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4A-19

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.

Roman, H.A., K. D. Walker, T. L. Walsh, L. Conner, H. M. Richmond, B. J. Hubbell, and P. L. 

Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient 

Fine Particulate Matter in the U.S.” Environmental Scientific Technology. 42(7):2268-2274.

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.

U.S. Environmental Protection Agency (U.S. EPA). 2007. Guidance on the Use of Models and 

Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and 

Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

Available at <http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf>.

Accessed June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2011b Regulatory Impact Analysis for the 

Final Mercury and Air Toxics Standards. Research Triangle Park, NC: Office of Air Quality

Planning and Standards, Health and Environmental Impacts Division. (EPA document 

number EPA-452/R-11-011, December). Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>. Accessed June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the 

Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Quality Planning and Standards, Health and Environmental 

Impacts Division, Research Triangle Park, NC. December. Available at: <

http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf>. Accessed June 4, 2015.
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U.S. Environmental Protection Agency (U.S. EPA). 2013. Technical Support Document: 

Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. February. Available at: <

http://www2.epa.gov/sites/production/files/2014-

10/documents/sourceapportionmentbpttsd.pdf >. Accessed June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2014. Regulatory Impact Analysis for the 

Proposed Carbon Pollution Guidelines for Existing Power Plants and Emission Standards for 

Modified and Reconstructed Power Plants. EPA-542/R-14-002. Office of Air Quality

Planning and Standards, Research Triangle Park, NC. June. Available at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/111dproposalRIAfinal0602.pdf>. Accessed June 

4, 2015.
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5-1

CHAPTER 5: ECONOMIC IMPACTS – MARKETS OUTSIDE THE UTILITY POWER

SECTOR

5.1 Introduction 

The energy sector impacts presented in Chapter 3 of this RIA include potential changes in 

the prices for electricity, natural gas, and coal potentially resulting from the Clean Power Plan 

Final Rule. This chapter addresses the impact of these potential changes on other markets and 

discusses some of the determinants of the magnitude of these impacts. We refer to these changes 

as secondary market impacts.

Under the final emission guidelines, states are not required to use any of the measures that 

the EPA determines constitute BSER, or use those measures to the same degree of stringency

that the EPA determines is achievable at reasonable cost. Rather, CAA section 111(d) allows 

each state to determine the appropriate combination of, and the extent of its reliance on, 

measures for its state plan, by way of meeting its state-specific goal. Given the flexibilities 

afforded states in complying with the emission guidelines, the benefits, cost and economic 

impacts reported in this RIA are illustrative of actions that states may take. The implementation 

approaches adopted by the states, and the strategies adopted by affected EGUs, will ultimately

drive the magnitude and timing of secondary impacts from changes in the price of electricity, and 

the demand for inputs by the electricity sector, on other markets that use and produce these 

inputs.

The flexibility afforded to states in their state plans allows them to encourage compliance 

methods by affected EGUs, which include design elements that may mitigate or promote 

particular impacts based on their priorities. For example, states in the Regional Greenhouse Gas 

Initiative use the revenues from allowance auctions to support direct bill assistance for retail 

consumers, fund investments in clean energy and electricity demand reduction for business 

consumers, and support employment in the development of clean and renewable energy

technologies. In its recent regulations to limit greenhouse gas (GHG) emissions, California’s Air 

Resources Board designated a portion of allowances to be allocated to electric distribution 

companies in order to mitigate potential electricity rate increases and their associated impacts. 

Other states may encourage compliance methods by affected EGUs with particularly robust 
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5-2

deployment of renewables, energy efficiency, or natural gas to promote manufacturing demand 

or employment in those sectors. For example, energy efficiency investments may be targeted 

towards reducing both electricity consumption and natural gas or heating oil consumption, such 

as weatherization projects. The state plan approach and the composition of these programs will 

influence the effects of compliance with the final rulemaking.

To estimate the costs, benefits, and impacts of implementing the CPP guidelines, the EPA 

modeled two illustrative plan approaches: a rate-based approach and a mass-based approach. 

Chapter 3 provides a description of the illustrative plan approaches analyzed. This chapter 

provides a quantitative assessment of the energy price impacts for these illustrative approaches 

and a qualitative assessment of the factors that will, in part, determine the timing and magnitude 

of effects in other markets.

5.2 Methods

One potential quantitative approach to evaluating the secondary market impacts is to use a 

computable general equilibrium (CGE) model. CGE models are able to provide aggregated 

representations of the whole economy in equilibrium in the baseline and potentially with 

regulation in place. As such, CGE model may be able to capture interactions between economic 

sectors and provide information on changes outside of the directly regulated sector. In support of 

previous rulemakings, such as the 2008 Final Ozone NAAQS (U.S. EPA 2008) and the 2010 

Transport Rule proposal (U.S. EPA 2010), the EPA used the Economic Model for Policy

Analysis (EMPAX) CGE model to estimate the secondary market effects based on the cost 

impacts projected by the Integrated Planning Model (IPM) for the directly regulated sector. 

When considering the secondary market impacts of a regulation both the effects of the 

costs, the benefits of improved air quality, and their interaction may be relevant. Therefore, in 

the Second Prospective Analysis under Section 812 of the Clean Air Act Amendments the EPA 

incorporated a set of health benefits arising from air quality improvement into the EMPAX CGE 

model when studying the economy-wide impacts of the Clean Air Act (U.S. EPA 2011). While 

the external Council on Clean Air Compliance Analysis (Council) review of this study stated that 

inclusion of benefits in an economy-wide model “represent[ed] a significant step forward in 

benefit-cost analysis” (Hammitt 2010), the EPA recognizes that serious technical challenges 
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5-3

remain when attempting to evaluate the benefits and costs of potential regulatory actions using 

economy-wide models. 

In light of these challenges, the EPA has established a Science Advisory Board (SAB) 

panel on economy-wide modeling to consider the technical merits and challenges of using this 

analytical tool to evaluate costs, benefits, and economic impacts in regulatory development. The 

panel will also be asked to identify potential paths forward for improvements that could address 

the challenges posed when economy-wide models are used to evaluate the effects of regulations.

The final panel membership was announced in March 2015 and the first of multiple face-to-face 

meetings of the SAB panel has been scheduled for October 2015. The EPA will use the 

recommendations and advice of this panel as an input into its process for improving benefit-cost 

and economic impact analyses used to inform decision-making at the agency. 

The advice from the Science Advisory Board (SAB) panel formed specifically to address 

the subject of economy-wide modeling was not available in time for this final action. Given the 

ongoing SAB panel on economy-wide modeling, the uncertain nature of the ultimate energy

price impacts due to the state flexibility in choosing a plan and the compliance flexibility for 

affected EGUs, and the ongoing challenges of accurately representing costs, benefits, energy

efficiency savings in economy-wide modeling, this chapter considers the energy impacts 

associated with the illustrative plan approaches analyzed and a qualitative assessment of the 

factors that will, in part, determine the timing and magnitude of effects in other markets.

5.3 Summary of Secondary Market Impacts of Energy Price Changes

Electricity, natural gas, and coal are important inputs to the production of other goods and 

services. Therefore, changes in the price of these commodities will shift the production costs for 

sectors that use electricity, natural gas, and coal in the production of other goods and services. 

Such changes in production costs may lead to changes in the quantities and/or prices of the goods 

or services produced and changes in imports and exports. 

The EPA used IPM to estimate electricity, natural gas, and coal price changes based on the 

illustrative plan approaches modeled for this rule. IPM is a multi-regional, dynamic, 

deterministic linear programming model of the U.S. electric power sector that is described in 

more detail in Chapter 3. The Retail Price Model (RPM) uses forecast changes in wholesale 
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prices and the cost of demand-side energy efficiency programs to forecast changes in average 

retail prices. The prices are average prices over consumer classes and regions weighted by the 

amount used. Table 5-1 shows these estimated price changes. For other results generated by IPM 

and the RPM, please refer to Chapter 3.

There are many factors influencing the projected natural gas prices. IPM (and its integrated 

gas resource and supply module) models natural gas reserves appropriate natural gas supplies 

based on a multitude of factors. Since the model simulates perfect foresight, it anticipates future 

demand for natural gas and responds accordingly. In addition, IPM (and the natural gas module) 

are viewing a very long time horizon (through 2050), such that the impacts in certain years may

be responsive to other modeling assumptions or drivers. The modeling framework is 

simultaneously solving for all of these key market and policy parameters (both electric and 

natural gas), resulting in the impacts shown.

Table 5-1. Estimated Percentage Changes in Average Energy Prices by Energy Type for 

the Final Emission Guidelines, Rate-based and Mass-based Illustrative Plan 

Approaches

Rate-based Approach 2020 2025 2030

Electricity Price Change [X]% [X]% [X]%

Delivered Natural Gas Price Change 5.3% -7.7% 2.5%

Delivered Coal Price Change -0.8% -5.0% -3.8%

Mass-based Approach 2020 2025 2030

Electricity Price Change [X]% [X]% [X]%

Delivered Natural Gas Price Change [X]% [X]% [X]%

Delivered Coal Price Change [X]% [X]% [X]%

For years when the price of electricity, natural gas, or coal increased, one would expect 

decreases in production and increases in market prices in sectors for which these commodities 

are inputs, ceteris paribus. Conversely, for years when prices of these inputs decreased, one 

would expect increases in production and decreases in market prices within these sectors. 

Smaller changes in input price changes are assumed to lead to smaller impacts within secondary

markets. However, a number of factors in addition to the magnitude and sign of the energy price 

changes, influence the magnitude of the impact on production and market prices for sectors using 

electricity, natural gas, or coal as inputs to production. These factors are discussed below.
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5.3.1 Share of Total Production Costs

The impact of energy price changes in a particular sector depends, in part, on the share of 

total production costs attributable to those commodities. For sectors in which the directly 

affected inputs are only a small portion of production costs, the impact will be smaller than for 

sectors in which these inputs make up a greater portion of total production costs. Therefore, more 

energy-intensive sectors would potentially experience greater cost increases when electricity, 

natural gas, or coal prices increase, but would also experience greater reduced costs when these 

input prices decrease.99

5.3.2 Ability to Substitute between Inputs to the Production Process

The ease with which producers are able to substitute other inputs for electricity, natural 

gas, or coal, or even amongst those commodities, influences the impact of price changes for 

these inputs. Those sectors with a greater ability to substitute across energy inputs or to other 

inputs will be able to, at least partially, offset the increased cost of these inputs resulting in 

smaller market impacts. Similarly, when prices for electricity, natural gas, or coal decrease, some 

sectors may choose to use more of these inputs in place of other more costly substitutes.

5.3.3 Availability of Substitute Goods and Services

The ability of producers in sectors experiencing changes in their input prices to pass along 

the increased costs to their customers in the form of higher prices for their products depends, in 

part, on the availability of substitutes for the sectors’ products. Substitutes may be either other 

domestic products or foreign imports. If close substitutes exist, the demand for the product will 

in general be more elastic and the producers will be less able to pass on the added cost through a 

price increase.

Such substitution can also take place between foreign and domestic goods within the same 

sector. Changes in the price of electricity, natural gas, and coal can influence the quantities of 

goods imported or exported from sectors using these inputs. When the cost of domestic 

 

99 The net direct effect of this rulemaking on the production costs of a sector that is attributable to a change in the 

electricity price also depends on the expenditures the sector makes to reduce its demand for electricity under any 

energy efficiency program that was adopted to achieve a state goal. That said, those expenditures may lead to other 

reduced expenditures for the sector, such as reduced natural gas use from weatherization projects.
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production increases due to more expensive inputs, imports may increase as consumers substitute 

towards relatively less costly foreign-produced goods. If imports increase because of a regulation 

and those imports come from countries with higher emissions per unit of production, this can 

result in foreign emission increases that offset some portion of domestic decreases, an effect 

commonly referred to as “leakage.”100 Alternatively, if those imports are less emissions-intensive 

than the sectors that have contracted, emissions may fall even further. The potential for changes 

in global pollutants such as carbon dioxide (CO2) and other GHG emissions is noteworthy. 

Unlike most criteria pollutants and hazardous air pollutants, the impacts of CO2 emissions are not 

affected by the location from which those emissions originate. 

5.4 Effect of Changes in Input Demand from Electricity Sector

Section 5.2 focuses on the effects of changes in energy prices, and possible responses to 

those price changes, on sectors outside of the electricity sector. A change in demand for inputs in 

the electricity sector, as well as changes in demand for energy efficiency services and products, 

will also influence economic activity in other sectors of the economy. For example, there will be 

changes in the demand for new generation sources such as natural gas combined cycle units and 

renewables, and therefore sectors producing these technologies may expand. Therefore, while a 

sector that produces say, wind turbine blades, may face higher natural gas and electricity prices, 

production in that sector may ultimately increase due to higher demand from the electricity 

sector for wind turbines. 

5.5 Conclusions

Changes in the price of electricity, natural gas, and coal can affect markets for goods and 

services produced by sectors that use these energy inputs in the production process. The direction 

and magnitude of these impacts are influenced by a number of factors. Changes in cost of 

production may lead to changes in price, quantity produced, and profitability of firms within 

secondary markets. Furthermore, the demand for certain inputs in the electricity sector, as well as 

changes in the demand for energy efficiency services and products, will also affect secondary

 

100 EPA does not anticipate significant leakage in the utility power sector to occur from this regulation due to 

changes in the location of where domestically consumed electricity is produced because the nature of electricity 

transmission does not lend itself to significant imports or exports of electricity.
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markets. If regulation results in changes in domestic markets that lead to an increase in imports, 

increases in production in countries with more energy-intensive production may lead to changes 

in CO2 emissions elsewhere. 

5.6 References

Hammitt, J.K. 2010. Review of the Final Integrated Report for the Second Section 812 

Prospective Study of the Benefits and Costs of the Clean Air Act. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/1E6218D

E3BFF682E852577FB005D46F1/$File/EPA-COUNCIL-11-001-unsigned.pdf.> Accessed 

June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2008. Final Ozone NAAQS Regulatory

Impact Analysis. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf>. Accessed June 

4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis for the 

Proposed Federal Transport Rule. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. Available at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/proposaltrria_final.pdf>. Accessed June 4, 2015.

U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean 

Air Act from 1990 to 2020, Final Report – Rev A. Office of Air and Radiation, Washington, 

DC. Available at <http://www.epa.gov/cleanairactbenefits/feb11/fullreport_rev_a.pdf>. 

Accessed June 4, 2015.
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CHAPTER 6: EMPLOYMENT IMPACT ANALYSIS

6.1 Introduction 

Executive Order 13563 directs federal agencies to consider regulatory impacts on job 

creation and employment. According to the Executive Order, “our regulatory system must 

protect public health, welfare, safety, and our environment while promoting economic growth, 

innovation, competitiveness, and job creation. It must be based on the best available science” 

(Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically 

included a separate analysis of regulation-induced employment impacts,101 we typically conduct 

employment analyses for economically significant rules. During the current economic recovery, 

employment impacts are of particular concern and questions may arise about their existence and 

magnitude. This chapter discusses and projects potential employment impacts for the utility

power, coal and natural gas production, and demand-side energy efficiency sectors which may

result from the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: 

Electric Utility Generating Units (herein referred to as “final emission guidelines” or the “Clean 

Power Plan Final Rule”).

Section 6.2 describes the theoretical framework used to analyze regulation-induced 

employment impacts, discussing how economic theory alone cannot predict whether such 

impacts are positive or negative. Section 6.3 presents an overview of the peer-reviewed literature 

relevant to evaluating the effect of environmental regulation on employment. Section 6.4

provides background regarding recent employment trends in the electricity generation, coal and 

natural gas extraction, renewable energy, and demand-side energy efficiency-related sectors. 

Section 6.5 presents the EPA’s quantitative projections of potential employment impacts in these 

sectors. These projections are based in part on a detailed model of the electricity production 

sector used for this regulatory analysis. Additionally, this section discusses projected 

employment impacts due to demand-side energy efficiency activities. Section 6.6 offers several 

conclusions.

 

101 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses.
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6.2 Economic Theory and Employment

Regulatory employment impacts are difficult to disentangle from other economic changes 

affecting employment decisions over time and across regions and industries. Labor market 

responses to regulation are complex. They depend on labor demand and supply elasticities and 

possible labor market imperfections (e.g., wage stickiness, long-term unemployment, etc.). The 

unit of measurement (e.g., number of jobs, types of job hours worked, and earnings) may affect 

observability of that response. Net employment impacts are composed of a mix of potential 

declines and gains in different areas of the economy (e.g., the directly regulated sector, upstream 

and downstream sectors, etc.) over time. In light of these difficulties, economic theory provides a 

constructive framework for analysis.

Microeconomic theory describes how firms adjust their use of inputs in response to 

changes in economic conditions.102 Labor is one of many inputs to production, along with capital, 

energy, and materials. In competitive markets, firms choose inputs and outputs to maximize 

profit as a function of market prices and technological constraints.103,104 Berman and Bui (2001)

adapt this model to analyze how environmental regulations affect labor demand.105 They model 

environmental regulation as effectively requiring certain factors of production, such as pollution 

abatement capital, at levels that firms would not otherwise choose. Berman and Bui (2001) 

model two components that drive changes in firm-level labor demand: output effects and 

substitution effects.106 Regulation affects the profit-maximizing quantity of output by changing 

the marginal cost of production. If a regulation causes marginal cost to increase, it will place 

upward pressure on output prices, leading to a decrease in demand, and resulting in a decrease in 

production. The output effect describes how, holding labor intensity constant, a decrease in 

 

102 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 

103 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization. 

104 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs). 

105 Morgenstern, Pizer, and Shih (2002) develop a similar model.

106 The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect 

is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and 

Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a 

cost effect; and 3) a factor-shift effect.
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production causes a decrease in labor demand. As noted by Berman and Bui, although many

assume that regulations must increases marginal cost, it need not be the case. A regulation could 

induce a firm to upgrade to less polluting and more efficient equipment that lowers the marginal 

cost of production. In such a case, output could increase after firms comply with the regulation. 

For example, in the context of the current rule, improving the heat rate of utility boiler increases 

fuel efficiency, lowering marginal production costs, and thereby potentially increasing the utility

boiler’s generation. An unregulated profit-maximizing firm may not have chosen to install such 

an efficiency-improving technology if the return on investment were too low, but once the 

investment is required it lowers marginal production costs.

The substitution effect describes how, holding output constant, regulation affects the 

labor-intensity of production. Although increased environmental regulation may increase use of 

pollution control equipment and energy to operate that equipment, the impact on labor demand is 

ambiguous. For example, equipment inspection requirements, specialized waste handling, 

completing required paperwork, or pollution technologies that alter the production process may

affect the number of workers necessary to produce a unit of output. Berman and Bui (2001) 

model the substitution effect as the effect of regulation on pollution control equipment and 

expenditures required by the regulation and the corresponding change in the labor-intensity of 

production. 

In summary, as output and substitution effects may be positive or negative, economic 

theory alone cannot predict the direction of the net effect of regulation on labor demand at the 

level of the regulated firm. Operating within the bounds of standard economic theory, however, 

empirical estimation of net employment effects on regulated firms is possible when methods and 

data of sufficient detail and quality are available. The extant literature, however, illustrates 

difficulties with empirical estimation. For example, there is a paucity of publicly-available data 

on plant-level employment, thus most studies must rely on confidential plant-level employment 

data from the U.S. Census Bureau, typically combined with pollution abatement expenditure 

data, that are too dated to be reliably informative, or other measures of the stringency of 

regulation. In addition, the most commonly used empirical methods can only estimate the gross 

effects of regulation on employment (i.e. the impact of that more stringent regulation has on 

employment relative to less stringent or no regulation), not the net effects.
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The conceptual framework described thus far focused on regulatory effects on plant-level 

decisions within a regulated industry. Employment impacts at an individual plant do not 

necessarily represent impacts for the sector as a whole. The theoretical approach must be 

modified when applied at the industry level.

At the industry-level, labor demand is more responsive if: (1) the price elasticity of 

demand for the product is high, (2) other factors of production can be easily substituted for labor, 

(3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total 

production costs.107 For example, if all firms in an industry are faced with the same regulatory

compliance costs and product demand is inelastic, then industry output may not change much, 

and output of individual firms may change slightly.108 In this case, the output effect may be small, 

while the substitution effect depends on input substitutability. Suppose, for example, that new 

equipment for heat rate improvements requires labor to install and operate. In this case, the 

substitution effect may be positive, and with a small output effect, the total effect may be 

positive. As with potential effects for an individual firm, theory cannot determine the sign or 

magnitude of industry-level regulatory effects on labor demand. Determining these signs and 

magnitudes requires additional sector-specific empirical study. For environmental rules, much of 

the data needed for these empirical studies is not publicly available, would require significant 

time and resources in order to access confidential U.S. Census data for research, and also would 

not be necessary for other components of a typical Regulatory Impact Analysis (RIA). 

In addition to changes to labor demand in the regulated industry, net employment impacts 

encompass changes in other related sectors. For example, the final guidelines may increase 

demand for heat rate improving equipment and services. This increased demand may increase 

revenue and employment in the firms supporting this technology. At the same time, the regulated 

industry is purchasing the equipment, and these costs may impact labor demand at regulated 

firms. Therefore, it is important to consider the net effect of compliance actions on employment 

across multiple sectors or industries.

 

107 See Ehrenberg & Smith, p. 108. 

108 This discussion draws from Berman and Bui (2001), pp. 293. 
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If the U.S. economy is at full employment, even a large-scale environmental regulation is 

unlikely to have a noticeable impact on aggregate net employment.109 Instead, labor in affected 

sectors would primarily be reallocated from one productive use to another (e.g., from producing 

electricity or steel to producing high efficiency equipment), and net national employment effects 

from environmental regulation would be small and transitory (e.g., as workers move from one 

job to another).110 Some workers may retrain or relocate in anticipation of new requirements or 

require time to search for new jobs, while shortages in some sectors or regions could bid up 

wages to attract workers. These adjustment costs can lead to local labor disruptions. Although 

the net change in the national workforce is expected to be small, localized reductions in 

employment may adversely impact individuals and communities just as localized increases may

have positive impacts.

If, on the other hand, the economy is operating at less than full employment, economic 

theory does not clearly indicate the direction or magnitude of the net impact of environmental 

regulation on employment; it could cause either a short-run net increase or short-run net decrease 

(Schmalansee and Stavins, 2011). An important research question is how to accommodate 

unemployment as a structural feature in economic models. This feature may be important in 

assessing large-scale regulatory impacts on employment (Smith, 2012).

Environmental regulation may also affect labor supply and productivity. In particular, 

pollution and other environmental risks may impact labor productivity or employees’ ability to 

work.111 While the theoretical framework for analyzing labor supply effects is analogous to that 

for labor demand, it is more difficult to study empirically. There is a small emerging literature 

described in the next section that uses detailed labor and environmental data to assess these

impacts.

To summarize, economic theory provides a framework for analyzing the impacts of 

environmental regulation on employment. The net employment effect incorporates expected 

 

109 Full employment is a conceptual target for the economy where everyone who wants to work and is available to 

do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero. 

110 Arrow et al. 1996; see discussion on bottom of p. 8. In practice, distributional impacts on individual workers can 

be important, as discussed in later paragraphs of this section.

111 E.g. Graff Zivin and Neidell (2012).
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employment changes (both positive and negative) in the regulated sector and other related 

sectors. Labor demand impacts for regulated firms, and also for the regulated industry, can be 

decomposed into output and substitution effects which may be either negative or positive. 

Estimation of net employment effects for regulated sectors is possible when data of sufficient 

detail and quality are available. Finally, economic theory suggests that labor supply effects are 

also possible. In the next section, we discuss the empirical literature.

6.3 Current State of Knowledge Based on the Peer-Reviewed Literature

The labor economics literature contains an extensive body of peer-reviewed empirical 

work analyzing various aspects of labor demand, relying on the theoretical framework discussed 

in the preceding section.112 This work focuses primarily on effects of employment policies such 

as labor taxes and minimum wages.113 In contrast, the peer-reviewed empirical literature 

specifically estimating employment effects of environmental regulations is growing, but is more 

limited. In this section, we present an overview of the latter. As discussed in the preceding 

section on theory, determining the direction of employment effects in regulated industries is 

challenging because of the complexity of the output and substitution effects. Complying with a 

new or more stringent regulation may require additional inputs, including labor, and may alter 

the relative proportions of labor and capital used by regulated firms (and firms in other relevant 

industries) in their production processes.

Empirical studies, such as Berman and Bui (2001), suggest that net employment impacts

due to regulation were not statistically different from zero in the regulated sector. Other research 

suggests that more highly regulated counties may generate fewer jobs than less regulated ones, 

but since this study compares more regulated to less regulated counties it cannot address net 

employment effects at a national level (Greenstone, 2002). Moreover, environmental regulations 

may affect sectors that support pollution reduction earlier than the regulated industry. Rules are 

usually announced well in advance of their effective dates and then typically provide a period of 

time for firms to invest in technologies and process changes to meet the new requirements. When 

a regulation is promulgated, the initial response of firms is often to order pollution control 

 

112 Again, see Hamermesh (1993) for a detailed treatment. 

113 See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview. 
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equipment and services to enable compliance when the regulation becomes effective. Estimates 

of short-term increases in demand for specialized labor within the environmental protection 

sector have been prepared for several EPA regulations in the past, including the Mercury and Air 

Toxics Standards (MATS).114 Overall, the peer-reviewed literature does not contain evidence that 

environmental regulation has a large impact on net employment (either negative or positive) in 

the long run across the whole economy. 

6.3.1 Regulated Sector 

Several empirical studies, including Berman and Bui (2001) and Ferris, Shadbegian, and 

Wolverton (2014), suggest that regulation-induced net employment impacts may be zero or 

slightly positive, but small in the regulated sector. Gray et al (2014) find that pulp mills that had 

to comply with both the air and water regulations in EPA’s 1998 “Cluster Rule” experienced 

relatively small, and not always statistically significant, decreases in employment. Other research 

suggests that more highly regulated counties may generate fewer jobs than less regulated ones 

(Greenstone 2002, Walker 2011, 2013). However since these latter studies compare more 

regulated to less regulated counties they overstate the net national impact of regulation to the 

extent that regulation causes plants to locate in one area of the country rather than another. List et 

al. (2003) find some evidence that this type of geographic relocation may be occurring. Overall, 

the peer-reviewed literature does not contain evidence that environmental regulation has a large 

impact on net employment (either negative or positive) in the long run across the whole 

economy. 

A small literature examines impacts of environmental regulations on manufacturing 

employment. Greenstone (2002) and Walker (2011, 2013) study the impact of air quality 

regulations on manufacturing employment, estimating the effects in non-attainment areas relative 

to attainment areas. Kahn and Mansur (2013) study environmental regulatory impacts on 

geographic distribution of manufacturing employment, controlling for electricity prices and labor 

regulation (right to work laws). Their methodology identifies employment impacts by focusing 

on neighboring counties with different air quality regulations. They find limited evidence that 

environmental regulations may cause employment to be lower within “county-border-pairs.” 

 

114 U.S. EPA (2011b).
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This result suggests that regulation may cause an effective relocation of labor across a county 

border, but since one county’s loss is another’s gain, such shifts cannot be transformed into an 

estimate of a national net effect on employment. Moreover this result is sensitive to model 

specification choices.

The few studies in peer-reviewed journals evaluating employment impacts of policies 

that reduce CO2 emissions in the electric power generation sector are in the European context. In 

a sample of 419 German firms, 13 percent of which were in the electricity sector, Anger and 

Oberndorfer (2008) find that the initial allocation of emission permits did not significantly affect 

employment growth in the first year of the European Union (EU) Emissions Trading Scheme 

(ETS). Examining European firms from 1996-2007, Commins et al. (2011) find that a 1 percent 

increase in energy taxes is associated with a 0.01 percent decrease in employees in the electricity

and gas sector. Chan et al. (2013) estimate the impact of the EU ETS on a panel of almost 6,000 

firms in 10 European countries from 2005-2009. They find that firms in the power sector that 

participated in the ETS had 2-3 percent fewer employees relative to those that did not participate, 

but this effect is not statistically significant.

This literature suggests that the employment impacts of controlling CO2 emissions in the 

European power sector were small. The degree to which these studies’ results apply to the U.S. 

context is unclear. European policies analyzed in these studies effectively put a price on 

emissions either through taxes or tradable permits. A performance standard may not generate 

similar employment effects. Moreover, European firms face relative fuel prices and market 

regulatory structures different from their U.S. counterparts, further complicating attempts to 

transfer quantitative results from the EU experience to evaluate this rule.

6.3.2 Economy-Wide 

As noted above it is very difficult to estimate the net national employment impacts of 

environmental regulation. Given the difficulty with estimating national impacts of regulations, 

EPA has not generally estimated economy-wide employment impacts of its regulations in its 

benefit-cost analyses. However, in its continuing effort to advance the evaluation of costs, 

benefits, and economic impacts associated with environmental regulation, EPA has formed a 

panel of experts as part of EPA’s Science Advisory Board (SAB) to advise EPA on the technical 

merits and challenges of using economy-wide economic models to evaluate the impacts of its 
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regulations, including the impact on net national employment. Once EPA receives guidance from 

this panel it will carefully consider this input and then decide if and how to proceed on economywide modeling of employment impacts of its regulations. 

EPA received several comments regarding the potential net national employment impact 

of the proposed emission guidelines. Many of these comments referred to analyses developed 

using non-transparent, non-peer reviewed models. Additionally, some of the analysis pre-dated 

the Clean Power Plan proposal. However, one comment was based on an “economy-wide 

assessment of the employment impacts associated with the U.S. Environmental Protection 

Agency’s (EPA’s) proposed Clean Power Plan” using the Long-term Inter-industry Forecasting 

Tool (LIFT) model. The LIFT model, which is from the Interindustry Forecasting Project 

(Inforum) at the University of Maryland, has been used in the peer-reviewed academic 

literature115 and has also been used to examine the economic impacts of other national policies 

[Meade (2009); Werling (2011)].

116 The commenter noted that “While EPA’s analysis provides a 

reasonable first approximation of the proposed rule’s employment effects, its focus on direct 

employment impacts does not capture various indirect employment impacts that may be of 

interest to policymakers and the public.” [...] “These include the employment impact associated 

with changes in electricity and other energy prices (both positive and negative, depending on the 

year), the productivity impacts associated with heat rate improvements at power plants, 

households and businesses re-directing expenditures to other uses because of increased demandside energy efficiency, expenditures crowded out by energy efficiency expenditures, and changes 

in investments for air pollution control devices.” 

 

115 See, for example, Almon (1991) and Mccarthy (1991).

116 The commenter provides the following description of the LIFT model: “LIFT is a 97-sector dynamic 

representation of the U.S. national economy. The model combines an interindustry input / output (I-O) formulation 

with extensive use of regression analysis to employ a ’bottom-up’ approach to macroeconomic modeling. That is, 

the model works like the actual economy, building macroeconomic totals from details of industry activity, rather 

than distributing predetermined macroeconomic quantities among industries. The commenter also describes LIFT 

also captures interactions between industries across the economy, enabling the model to gauge how changes in 

prices, investment, or productivity in one industry cascade across the economy. In the context of the Clean Power 

Plan, this is an important feature for understanding how the rule’s direct impacts for the electric power sector affect 

other industries.”
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As mentioned previously, EPA is currently engaged in an SAB process on economy-wide 

modeling. 117 EPA will not make any determinations on whether modeling the economy-wide 

impacts of its regulations – including employment impacts - is feasible and, if so, how and when 

to do this until it receives guidance from the SAB panel. While the purpose of the SAB process 

is not to peer review any particular economy-wide model, it is worth noting that the class of 

models to which LIFT belongs are encompassed in one of the charge questions to the SAB: “Are 

there other economy-wide modeling approaches that EPA could consider in conjunction with 

CGE models to evaluate the short run implications of an air regulation (e.g., macro-economic, 

disequilibrium, input/output models)? What are the advantages or disadvantages of these 

approaches?”

6.3.3 Labor Supply Impacts

The empirical literature on environmental regulatory employment impacts focuses 

primarily on labor demand. However, there is a nascent literature focusing on regulation-induced 

effects on labor supply.118 Although this literature is limited by empirical challenges, researchers 

have found that air quality improvements lead to reductions in lost work days (e.g., Ostro, 1987). 

Limited evidence suggests worker productivity may also improve when pollution is reduced. 

Graff Zivin and Neidell (2012) used detailed worker-level productivity data from 2009 and 2010, 

paired with local ozone air quality monitoring data for one large California farm growing 

multiple crops, with a piece-rate payment structure. Their quasi-experimental structure identifies 

an effect of daily variation in monitored ozone levels on productivity. They find “ozone levels 

well below federal air quality standards have a significant impact on productivity: a 10 parts per 

billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent.” 

(Graff Zivin and Neidell, 2012, p. 3654).119

 

117 For further information see: 

http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument

118 For a recent review see Graff-Zivin and Neidell (2013).

119 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light 

of this recent research, however, the EPA is considering how best to incorporate possible productivity effects in the 

future.
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This section has outlined the challenges associated with estimating regulatory effects on 

both labor demand and supply for specific sectors. These challenges make it difficult to estimate 

net national employment estimates that would appropriately capture the way in which costs, 

compliance spending, and environmental benefits propagate through the macro-economy. 

Quantitative estimates are further complicated by the fact that macroeconomic models often have 

little sectoral detail and usually assume that the economy is at full employment. 

6.4 Recent Employment Trends 

The U.S. electricity system includes employees that support electric power generation, 

transmission and distribution; the extraction of fossil fuels; renewable energy generation; and 

supply-side and demand-side energy efficiency. This section describes recent employment trends 

in the electricity system.

6.4.1 Electric Power Generation

In 2014, the electric power generation, transmission and distribution sector (NAICS 

2211) employed about 390,000 workers in the U.S.120 Installation, maintenance, and repair 

occupations accounted for the largest share of workers (25 percent).121 These categories include 

inspection, testing, repairing and maintaining of electrical equipment and/or installation and 

repair of cables used in electrical power and distribution systems. Other major occupation 

categories include office and administrative support (18 percent), production occupations (16

percent), architecture and engineering (10 percent), business and financial operations (7 percent) 

and management (7 percent). As shown in Figure 6.1, employment in the Electric Power 

Industry averaged about 420,000 workers 2000 to 2005, declining to an average of about 

400,000 workers for the rest of the decade, and then declining to about 390,000 workers in 2014.

 

120 U.S. Bureau of Labor Statistics. “Current Employment Survey Seasonally Adjusted Employment for Electric 

Power Generation, Transmission, and Distribution (national employment).” Series ID: CES4422110001. Available 

at <http://www.bls.gov/data/>. Accessed June 9, 2015.

121 U.S. Bureau of Labor Statistics, Occupational Employment Statistics, May 2014 National Industry-Specific 

Occupational Employment and Wage Estimates, Electric Power Generation, Transmission, and Distribution (NAICS 

2211). Available at: <http://www.bls.gov/oes/current/naics4_221100.htm>. 
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Figure 6.1. Electric Power Industry Employment

6.4.2 Fossil Fuel Extraction

6.4.2.1 Coal Mining

The coal mining sector (NAICS 2121) is primarily engaged in coal mining and coal mine 

site development, excluding metal ore mining and nonmetallic mineral mining and quarrying. In 

2014, BLS reported about 74,000 coal mining employees (Figure 6.2). During the 2000 to 2015, 

period, coal mining employment peaked in 2011 at about 87,000 employees.

0

50

100

150

200

250

300

350

400

450

500

Power Generation and Supply Employment 

(NAICS = 2211, Annual Average, 1000s of Employees)
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Figure 6.2. Coal Production Employment

6.4.2.2 Oil and Gas Extraction

In 2014, there were close to 200,000 employees in the oil and gas extraction sector 

(NAICS 211).122 This sector includes production of crude petroleum, oil from oil shale and oil 

sands, production of natural gas, sulfur recovery from natural gas, and recovery of hydrocarbon 

liquids. Activities include the development of gas and oil fields, exploration activities for crude 

petroleum and natural gas, drilling, completing, and equipping wells, and other production 

activities.123 In contrast with coal, and looking at Figure 6.3, there has been a sharp increase in 

employment in this sector over the past decade.

 

122 BLS, Current Employment Statistics. Seasonally adjusted employment for oil and gas extraction (national 

employment), NAICS 211. Series ID: CES1021100001. Available at <http://www.bls.gov/data/>. Accessed June 9, 

2015.

123 U.S. Bureau of Labor Statistics. 20014. Available at: <http://www.bls.gov/iag/tgs/iag211.htm Accessed Feb. 19>. 
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Figure 6.3. Oil and Gas Production Employment

6.4.3 Clean Energy Employment Trends

Clean energy resources, such as energy efficiency and renewable energy, are used to 

meet energy demand, reduce peak electricity system loads, and reduce reliance on the most 

carbon-intensive sources of electricity. However, there is not a single clean energy sector in 

standard national accounts classifications. Renewable generation is not reported to the BLS 

separately from other electric power generation. Similarly, manufacturers of energy efficient 

appliances are not reported separately from conventional appliance manufacturers and green 

building design is not separate from the construction sector. Instead, clean energy technology

and services are supported by industries throughout the economy. 

Without a specific industrial classification, it is difficult to quantify the exact number of 

clean energy-related jobs or document the trends. Employees engaged in clean energy can span 

many job classifications, such as experts required to design and produce a renewable or energyefficient technology, workers that supply inputs and technicians who install service or operate 

equipment. As such, there are a variety of definitions of clean or green jobs used, some more 

expansive than others.

0

50

100

150

200

250

Oil and Gas Extraction Employment 

(NAICS = 211, Annual Average, 1000s of Employees)
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6.4.3.1 Defining Clean Energy Jobs

Two U.S. Government sources, the 2010 Department of Commerce (DOC) report, 

Measuring the Green Economy and the 2010 and 2011 BLS Green Goods and Services surveys 

have subdivided industrial classifications into “green” categories. In both cases the approach was 

to determine which product classifications, rather than industries, were green. They multiplied 

green production by product revenue and defined an industrial sector as green if it met a 

threshold of green revenue as a proportion of total revenue.

DOC broadly defined green jobs in 2010 as those “created and supported in businesses 

that produce green products and services.”124 They further classified green jobs into a broad and a 

narrow category. The narrow category includes only products deemed to be green without 

disagreement, while the broad category is more inclusive definition of green goods and services 

to over 22,000 product codes in the 2007 Economic Census to estimate their contribution to the 

U.S. economy. The report found that the number of green jobs in 2007 ranged from 1.8 million 

to 2.4 million jobs, accounting for between 1.5 and 2 percent of total private sector 

employment.125

BLS used an expansive definition of clean or green jobs in 2010 and 2011. It goes 

beyond direct clean energy-related investments and includes “those in businesses that produce 

goods and provide services that benefit the environment or conserve natural resources. These 

goods and services, which are sold to customers, include research and development, installation, 

and maintenance services for renewable energy and energy efficiency and education and training 

related to green technologies and practices” but also include recycling and natural resource 

conservation, such as forestry management.126 Based on surveys across the 325 industries it 

identified as potential producers of green goods and services, BLS counts approximately 2.3 

 

124 U.S. Department of Commerce Economics and Statistics Administration. 2010. “Measuring the Green 

Economy,” April. Available at: 

<http://www.esa.doc.gov/sites/default/files/reports/documents/greeneconomyreport_0.pdf>. 

125 U.S. Department of Commerce Economics and Statistics Administration. 2010. “Measuring the Green Economy, 

April. Available at: <http://www.esa.doc.gov/sites/default/files/reports/documents/greeneconomyreport_0.pdf>.

126 BLS has identified 325 detailed industries (6-digit NAICS) as potential producers of green goods and services. 

Available at: <http://www.bls.gov/ggs/ggsoverview.htm>. (Accessed on 1-14-14, last modified date: March 19, 

2013.
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million jobs in the green economy in 2010, rising 7.4 percent to 2.5 million in 2011,127 compared 

to increases of about one percent across all occupations in the entire economy over the same 

period.128 The table below, Table 6-1, presents BLS green job estimates nationally and for the 

utility sector. 

Table 6-1. U. S. Green Goods and Services (GGS) Employment (annual average)

Total GGS 

Employment

Utility GGS 

Employment

Total GGS Growth 2010-

11

Utility GGS Growth 2010-

11

2010 2,342,562 69,031 NA NA

2011 2,515,200 71,129 7.4% 3.0%

Source: Bureau of Labor Statistics

6.4.3.2 Renewable Electricity Generation Employment Trends

The DOC report does not separate renewable energy data and the BLS data include only

privately owned electricity generating facilities. As such, neither source isolates renewable 

electricity generation employment. For historical trends in this sector, we therefore, rely on a 

Brookings Institution study, Muro et al. (2011). This study built a national database of “clean 

economy” jobs from the bottom up, verifying each company individually.129 They include a list 

of categories similar but not identical to that of BLS, including agricultural and natural resources 

conservation, education and compliance, energy and resource efficiency, greenhouse gas 

reduction, environmental management and recycling, and renewable energy. This study found 

about 138,000 jobs in the renewable energy sector in 2010, with an overall average annual 

growth rate of 3.1 percent from 2003-2010. Table 6-2 details the national results by energy

source.

 

127 U.S. Department of Labor, U.S. Bureau of Labor Statistics. (n.d.). 2011. “Green Goods and Services 2010-2011.” 

(Retrieved on January 14, 2014). Available at :< http://www.bls.gov/ggs/ggsoverview.htm>.

128U.S. Department of Labor, U.S. Bureau of Labor Statistics. 2010. National Occupational Employment and Wage 

Estimates, United States. Available at: <http://www.bls.gov/oes/2010/may/oes_nat.htm>, May. National 

Occupational Employment and Wage Estimates, United States http://www.bls.gov/oes/2010/may/oes_nat.htm. 

129 <http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15.
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Table 6-2. Renewable Electricity Generation-Related Employment

Sector Jobs, 2010 2003-2010 Average Annual Growth Rate (%)

Biofuels/Biomass 20,680 8.9

Geothermal 2,720 6.7

Hydropower 55,467 -3.6

Renewable Energy Services 1,981 6.3

Solar Photovoltaic 24,152 10.7

Solar Thermal 5,379 18.4

Waste-to-Energy 3,320 3.7

Wave/Ocean Power 371 20.9

Wind 24,294 14.9

Total 138,364 3.1

Source: http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf, Appendix A.

More recent industry data, from 2014, indicate higher employment numbers and growth in the 

solar sector.130

6.4.3.3 Employment Trends in Demand-Side Energy Efficiency Activities

U.S. government data used for calculating the historical trends in the demand-side energy

efficiency sector come from the BLS green goods and services surveys. BLS reports an energy

efficiency category, finding 1.49 million private sector energy efficiency jobs in 2010 and 1.64 

million in 2011.

For a longer term trend the Brookings Institution study (Muro et al., 2011) built a 

national database of “clean economy” jobs from the bottom up, verifying each company

individually.

131 This study found about 428,000 jobs in the Energy and Resource Efficiency

sector in 2010, with an overall average annual growth rate of 2.6 percent from 2003-2010. Table 

6-3 details the results by energy sector.

 

130 The Solar Foundation, National Solar Jobs Census 2014. < http://www.thesolarfoundation.org/national-solarjobs-census-2014/>

131 <http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15
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Table 6-3. Energy and Resources Efficiency-Related Employment

Sector Jobs, 2010 2003-2010 Average Annual 

Growth Rate (%)

Appliances 36,608 -3.1

Energy-saving Building Materials 161,896 2.5

Energy-saving Consumer Products 19,210 -2.9

Green Architecture and Construction Services 56,190 6.4

HVAC and Building Control Systems 73,600 3.3

Lighting 14,298 -1.8

Professional Energy Services 49,863 6.9

Smart Grid 15,987 8.6

Total 427,652 2.6

Source: http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf, Appendix A

In addition, other research institutes and industry groups have clean economy or clean 

energy employment databases. While definitions and timeframes vary, all show positive 

employment trends of 1.9 percent or more growth in clean energy-related jobs annually. 

6.5 Projected Sectoral Employment Changes due to the Final Emission Guidelines

Affected EGUs may respond to these final CO2 emission performance rates by placing 

new orders for efficiency-related or renewable energy equipment and services to reduce GHG 

emissions. Installing and operating new equipment or improving heat rate efficiency could 

increase labor demand in the electricity generating sector itself, as well as associated equipment 

and services sectors. Specifically, the direct employment effects of supply-side initiatives include 

changes in labor demand for manufacturing, installing, and operating higher efficiency or 

renewable energy electricity generating assets supported by the initiative while reducing the 

demand for labor that would have been used by less efficient or higher emitting generating 

assets. Once implemented, increases in operating efficiency would impact the utility power 

sector’s demand for fuel and plans for EGU retirement and new construction.

In addition, EPA expects state compliance plans to also include demand-side energy

efficiency policies and programs that typically change energy consumption patterns of business 

and residential consumers by reducing the quantity of energy required for a given level of 

production or service. Demand-side initiatives generally aim to increase the use of cost-effective 

energy efficiency technologies (e.g., including more efficient appliances and air conditioning 

systems, more efficient lighting devices, more efficient design and construction of new homes 

and businesses), and advance efficiency improvements in motor systems and other industrial 

processes. Demand-side initiatives can also directly reduce energy consumption, such as through 
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programs encouraging changing the thermostat during the hours a building is unoccupied or 

motion-detecting room light switches. Such demand-side energy efficiency initiatives directly

affect employment by encouraging firms and consumers to shift to more efficient products and 

processes than would otherwise be the case. Employment in the sectors that provide these more 

efficient devices and services would be expected to increase, while employment in the sectors 

that produce less efficient devices would be expected to contract.

This analysis uses the cost projections from the engineering-based Integrated Planning 

Model (IPM) to project labor demand impacts of the final emission guidelines on affected

electric utility steam generating units (steam generating units) in the electricity power sector and 

the fuel production sector (coal and natural gas). These projections include effects attributable to 

heat rate improvements, construction of new EGUs, generation shifts, changes in fuel use, and 

reductions in electricity generation due to demand-side energy efficiency activities. To project 

labor requirements for demand-side energy efficiency activities, the analysis uses a different 

approach that combines data on historic changes in employment and expenditures in the energy

efficiency sector with projected changes in expenditures in the sector arising from state 

implementation of the emission guidelines. We project labor impacts for the rate-based and 

mass-based illustrative plan approach. Each scenario draws upon the building blocks described 

above, as well as assumes that demand-side energy efficiency plays an important role in 

compliance strategies

6.5.1 Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction 

The analytical approach used in this analysis is a bottom-up engineering method 

combining EPA’s cost analysis of the emission guidelines with data on labor productivity, 

engineering estimates of the amount and types of labor needed to manufacture, construct, and 

operate different types of generating units, and prevailing wage rates for skilled and general 

labor categories. This approach is different from the types of economic analyses discussed in 

section 6.2. Lacking robust peer-reviewed methods to estimate economy-wide impacts, the 

engineering-based analysis focuses on the direct impact on labor demand in industries closely

involved with electricity generation. The engineering approach projects labor changes measured 
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as the change in each analysis year in job-years132 employed in the utility power sector and 

directly related sectors (e.g., equipment manufacturing, fuel supply, EGU construction and 

generating efficiency services). For example, this approach projects the amounts and types of 

labor required to implement improvements in generating efficiency. It then uses the EPA’s 

estimated effect of efficiency improvements on fuel demand to project reductions in the amount 

of labor required to produce coal and gas.

This analysis relies on projections and the cost analysis from IPM, which uses industryspecific data and assumptions to estimate costs and energy impacts of the final guidelines (see 

Chapter 3). The EPA uses IPM to predict coal generating capacity that is likely to undertake 

improvements in heat rate efficiency (HRI).133 IPM also predicts the guidelines’ impacts on fuel 

use, retirement of existing units, and construction of new ones.

The methods EPA uses to estimate the labor impacts are based on the analytical methods 

used in many previous EPA regulatory analyses. The most relevant prior analysis was the 

Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS). While the 

methods used in this analysis to estimate the recurring labor impacts (e.g., labor associated with 

operating and maintaining generating units, as well as labor needed to mine coal and natural gas) 

are the same as we used in MATS (with updated data where available), the methods used to 

estimate the labor associated with installing new capacity and implementing heat rate 

improvements were developed for the purpose of the Clean Power Plan RIA. 

The bottom-up engineering-based labor analysis in the MATS RIA primarily was 

concerned with the labor needs of retrofitting pollution control equipment. A central feature of 

the labor analysis for this RIA, however, involves the quantity and timing of the labor needs of 

building new renewable and NGCC units and retiring coal units. The estimated response of the 

utility power sector involves changes in the amount and timing of retirements of existing coal 

and oil/gas units, as well as changes in the amount and timing of building new NGCC units and 

 

132 Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one full-time 

individual for one year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in 

that year.

133 Heat rate improvements (HRI) could include a range of activities in the power plant to lower the heat rate 

required to generate a net electrical output. Assuming all other things being equal, a lower heat rate is more efficient 

because more electricity is generated from each ton of coal.
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renewable generating capacity. In addition to the changes in retirements and building new units, 

there are also estimated changes in the utilization of existing generating units. 

For example, as presented in Chapter 3, the IPM analysis of the rate-based control 

scenario finds that in 2025 (part way through the 2022-2029 interim plan performance period)

less total generating capacity is needed than in the base case. The estimated reduction in capacity 

by 2025 with the rate-based scenario is 49.4 GW less than the estimated base case capacity (a 4.8 

percent net capacity reduction). This 49.4 GW net reduction includes more retirements of coal 

units (an additional 22.9 GW of coal-fired capacity retired) and oil/gas steam units (an additional 

9.3 GW of oil/gas retirement) compared to the base case, as well as a reduction in the amount of 

new natural gas units needed to be built by 2025 (a decrease of 10.9 GW in new capacity from 

the amount forecast in the base case) and non-hydro renewables (1.7 GW less renewable 

capacity built).

An important aspect of the labor analysis is that building new units, and all the associated 

construction-related labor, occurs before the new units become operational. While the financial 

costs of building the new units are amortized and recouped over the book life of the new 

equipment, the labor involved with manufacturing equipment and constructing the new units 

occurs, and is actually paid for, in a concentrated amount of time before the new capacity begins 

to generate electricity. IPM assumes134 that new NGCC units take 3 years to build, and both 

natural gas combustion turbines and wind-powered renewables take 2 years.

Avoiding some of the need for new capacity due to both demand and supply efficiency

improvements results in both a significant net cost savings to consumers and the power sector, as 

well as reduced emissions of both CO2 and precursor pollutants from fossil fuel-fired generation. 

The avoided new capacity, however, also has significant labor impacts. A portion of the labor

that would have been used to build the new capacity in the base case will not be employed in the 

power generation sector with the implementation of the GHG guidelines, though it likely will be 

employed in construction elsewhere. Similarly, less labor involved with operating and providing 

fuel for new units will be needed with the GHG guidelines than in the base case.

 

134 Table 4.13, IPM 5.13 Documentation.
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A critical component of the overall labor impacts of implementing the GHG guidelines is 

the impact of the labor associated with the demand-side energy efficiency activities. The 

demand-side labor impacts are presented in section 6.5.2. All of the labor impacts of the demandside energy efficiency activities are increases in labor needs, which more than offset the loss of 

supply-side jobs associated with the decreasing demand for electricity arising from the demandside programs. The IPM labor expenditure projections are distributed across different labor 

categories (e.g., general construction labor, boilermakers and engineering) using data from 

engineering analyses of labor’s overall share of total expenditures, and apportionment of total 

labor cost to various labor categories. Hourly labor expenditures (including wages, fringe 

benefits, and employer-paid costs including taxes, insurance and administrative costs) for each 

category are used to estimate the labor quantity (measured in full-time job-years) consistent with 

the compliance scenario projections. Projected labor impacts arising from changes in fuel 

demand are primarily derived from labor productivity data for coal mining (tons mined per 

employee hour) and natural gas extraction (MMBtu produced/job-year). Tables 6.4 and 6.5 

present projected changes relative to the baseline of four labor categories: 

1. manufacturing, engineering and construction for building, designing and implementing 

heat rate improvements;

2. manufacturing and construction for new generating capacity; 

3. operating and maintenance for existing generating capacity; and

4. extraction of coal and natural gas fuel.

All of the employment estimates presented in Tables 6-4 and 6-5 are estimates occurring 

in a single year. For the construction-related (one-time) labor impacts, including the installation 

of HRI, Tables 6-4 and 6-5 present the average annual impact occurring in each year of three

different intervals. The three intervals are from 2018 through 2020 (a three year interval), during 

which there are modest labor impacts from the early changes in the power utility sectors 

operations, from 2021 through 2025 (five years), and 2026 through 2029 (5 years). The 

construction-related labor analysis are based on the IPM estimates of the net change in capital 

investment that occurs during each multi-year interval to fund building new units completed 

during that interval. The new build labor analysis uses the net change in capital investment to 

estimate the amount and type of labor needed during the interval to build the new capacity. The 

analysis assumes that the new built labor within each interval is evenly distributed throughout the 
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interval. Tables 6-4 and 6-5 reflect this assumption by presenting the average labor utilization 

per year during each of the three intervals.

The HRI-related labor impacts are estimated based on the assumed capital cost of 

$100/kw (see section 3.7.3). The labor estimates for operating and maintaining generating units 

annually are based on IPMs estimates of Fixed Operating and Maintenance (FOM) Costs. IPM 

estimates FOM for each year individually, so the net changes in O&M-related labor estimates in 

Tables 6-4 and 6-5 are single year estimates for 2020, 2025 and 2030. These single year O&M 

labor estimates are not merely the average annual averages labor needs throughout each multiyear interval. There are O&M labor changes occurring in the all years throughout the entire 

period 2017-2030, but the labor impacts in each labor category change each year. The fuelrelated labor estimates are also single-year estimates, and not multi-year averages. The labor 

analysis of the impacts on the fuel extraction industries uses IPM’s estimates of the net changes 

in the amount of coal and natural gas in 2020, 2025 and 2030, which are inherently estimates of 

the fuel usage in a single year. As with the O&M labor impacts, the fuels-related labor impacts 

occur in every year throughout 2017-2030, and the labor impact changes every year.

It should be noted that the supply-side labor impact estimates in Tables 6-4 and 6-5 

reflect the supply-side changes that will potentially occur with each illustrative plan scenario. 

These labor impacts include not only the impacts of Building Blocks 1 through 3, but also the 

changes in total generation needed that result from the demand-side energy efficiency activities 

expected to be an important component of state compliance strategies. The additional labor 

impact estimates from demand-side energy efficiency activities are presented below in section 

6.5.2.

More details on methodology, assumptions, and data sources used to estimate the supplyside labor impacts discussed in this section can be found in Appendix 6A.
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Table 6-4. Engineering-Baseda Changes in Labor Utilization, Rate-based Scenario 

(Number of Job-Yearsbof Employment in a Single Year)

Construction-related (One-time) Changes*

2018-2020 2021-2025 2026-2030

Heat Rate Improvement: Total 0 15,400 2,200

Boilermakers and General Construction 0 11,000 1,600

Engineering and Management 0 2,800 400

Equipment-related 0 1,200 200

Material-related 0 400 0

New Capacity Construction: Total 500 -15,600 400

Renewables 700 -5,000 23,300

Natural Gas -200 -10,600 -22,900

Recurring Changes**

2020 2025 2030

Operation and Maintenance: Total -9,100 -17,000 -19,600

Changes in Renewables 600 -100 1,100

Changes in Gas 300 -1,100 -3,700

Retired Coal -8,000 -13,300 -14,700

Retired Oil and Gas -2,000 -2,500 -2,300

Fuel Extraction: Total 100 -7,800 -13,900

Coal -1,300 -7,300 -13,300

Natural Gas 1,400 -500 -600

Supply-Side Employment Impacts - Quantified -8,500 -25,000 -30,900

a

Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates 

(tons coals or MMBtu gas).

b All job-year estimates on this are full-time equivalent (FTE) jobs. Job estimates in the Demand-Side energy 

efficiency section (below) include both full-time and part-time jobs.

*Construction-related job-year changes are one-time impacts, occurring during each year of the 2 to 4 year period 

during which construction and HRI installation activities occur. Construction-related figures in table are average 

job-years during each of the years in each range. Negative construction job-year estimates occur when additional 

generating capacity must be built in the base case, but is avoided in the final rule due to HRI or Demand-side energy 

efficiency programs.

**Recurring 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 new built capacity, create a stream of negative job-years. In 

addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 

2020; the recurring jobs prior to 2020 are not estimated.
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Table 6-5. Engineering-Baseda Changes in Labor Utilization, Mass-Based Illustrative Plan 

Approach (Number of Job-Years of Employment in Year)

Construction-related (One-time) Changes*

20XX-20XX 20XX-20XX 20XX-20XX

Heat Rate Improvement: Total [X] [X] [X]

Boilermakers and General Construction [X] [X] [X]

Engineering and Management [X] [X] [X]

Equipment-related [X] [X] [X]

Material-related [X] [X] [X]

New Capacity Construction: Total [X] [X] [X]

Renewables [X] [X] [X]

Natural Gas [X] [X] [X]

Recurring Changes**

2020 2025 2030

Operation and Maintenance: Total [X] [X] [X]

Changes in Renewables [X] [X] [X]

Changes in Gas [X] [X] [X]

Retired Coal [X] [X] [X]

Retired Oil and Gas [X] [X] [X]

Fuel Extraction: Total [X] [X] [X]

Coal [X] [X] [X]

Natural Gas [X] [X] [X]

Supply-Side Employment Impacts - Quantified [X] [X] [X]

a

Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates 

(tons coals or MMBtu gas).

b All job-year estimates on this are full-time equivalent (FTE) jobs. Job estimates in the Demand-Side energy 

efficiency section (below) include both full-time and part-time jobs.

*Construction-related job-year changes are one-time impacts, occurring during each year of the 2 to 4 year period 

during which construction and HRI installation activities occur. Construction-related figures in table are average 

job-years during each of the years in each range. Negative construction job-year estimates occur when additional 

generating capacity must be built in the base case, but is avoided in the final rule due to HRI or Demand-side energy 

efficiency programs.

**Recurring 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 new built capacity, create a stream of negative job-years. In 

addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 

2020; the recurring jobs prior to 2020 are not estimated.

6.5.2 Projected Changes in Employment in Demand-Side Energy Efficiency Activities

While not a component of the best system of emission reduction (BSER), EPA 

anticipates that this rule may stimulate investment in clean energy technologies and services, 

resulting in considerable increases in energy efficiency. We expect these increases in energy
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efficiency, specifically, to support a significant number of jobs in related industries. For more 

information on EPA’s illustrative investment levels in demand-side energy efficiency activities, 

assumed to be adopted in response to the CPP, please see Section 3.6 “Demand-Side Energy

Efficiency” in Chapter 3 of this RIA.

In this section, we project employment impacts in demand-side energy efficiency

activities arising from these guidelines using illustrative calculations. The approach uses 

information from power sector modeling and projected impacts on energy efficiency investments 

analyzed (see Chapter 3), and U.S. government data on employment and expenditures in energy

efficiency.

135 This approach is limited by the fact that we do not know which options states will 

choose for demand-side energy efficiency activities and by uncertainties associated with 

methods. These illustrative employment projections are gross; thus they do not include impacts 

of any shift in resources from other sectors. Nor does this analysis attempt to quantify 

employment impacts arising from changes in consumer expenditures away from energy towards 

other sectors. In other words, these projections are not attempts at estimating net national job 

creation. Also, this approach attempts to calculate the number of employees (full-time and parttime) rather than job-years as discussed in section 6.5.1.

Investments in demand-side energy efficiency reduce energy required for a given activity

by encouraging more efficient technologies (e.g., ENERGY STAR appliances), implementing 

energy improvements for existing systems (e.g., weatherization of older homes), or encouraging 

changes in behavior (e.g., reducing air conditioning during periods of high electricity demand).

Employment impacts of demand-side energy efficiency programs have not been 

extensively studied in the peer-reviewed, published economics literature. Instead, most research 

has focused on consumer response to and amount of energy savings achieved by these programs 

(e.g., Allcott (2011a, 2011b), Arimura et al. (2012)). Results suggest that demand-side energy

efficiency programs reduce energy use and generate small increases in consumer welfare. These 

 

135 Investments in demand-side energy efficiency reduce energy required for a given activity by encouraging more 

efficient technologies (e.g., ENERGY STAR appliances), implementing energy improvements for existing 

systems (e.g., weatherization of older homes), or encouraging changes in behavior (e.g., reducing air 

conditioning during periods of high electricity demand).
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policy impacts are due to low investment in energy efficiency as described in “energy paradox” 

literature (Gillingham, Newell, and Palmer (2009), Gillingham and Palmer (2014)).136

Two recent articles discuss employment effects of demand-side energy efficiency

programs. Aldy (2013) describes clean energy investments funded by the American Recovery 

and Reinvestment Act of 2009, which “included more than $90 billion for strategic clean energy

investments intended to promote job creation and the deployment of low-carbon technologies” 

(p. 137), with nearly $20 billion for energy efficiency investments. The Council of Economic 

Advisors (CEA) (2011) estimated higher economic activity and employment than would have 

otherwise occurred without the American Recovery and Reinvestment Act. Using CEA’s 

methods to quantify job creation for the Recovery Act, Aldy uses the share of stimulus funds for 

clean energy investments to estimate job-years supported by the Recovery Act. The largest 

sources of job creation in clean energy are those that received the largest shares of stimulus 

funds: renewable energy, energy efficiency, and transit. Aldy’s estimates, while informative, are 

not directly applicable for employment analysis in this rulemaking as there are important 

differences in expected employment impacts from a historically large fiscal stimulus specifically

targeting job creation during a period of exceptionally high unemployment versus environmental 

regulations taking effect several years from now. 

Yi (2013) analyzes clean energy policies and employment for U.S. metropolitan areas in 

2006, prior to the Recovery Act, to evaluate impacts on clean energy job growth. Implementing 

an additional state clean energy policy tool (renewable energy policies, GHG emissions policies, 

and energy efficiency polices such as energy efficiency resource standards, appliance or 

equipment energy efficiency standards, tax incentives, and public building energy efficiency

standards) is associated with 1 percent more clean energy employment within that MSA. These 

estimates are not transferable to this rulemaking since states are likely to change intensity as well 

as number of clean energy programs.

Lacking a peer-reviewed methodology, we use the following approach to illustrate 

possible effects on labor demand in the energy efficiency sector due to demand-side management 

strategies. We use U.S. government data and divide energy efficiency employment by

 

136 For more information on this efficiency paradox see Chapter 3.
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expenditures on energy efficiency activities to calculate an estimate of jobs per million dollars. 

We then multiply this fraction by projected expenditure in energy efficiency activities

undertaken in response to these final guidelines.

Data used for calculating employment in energy efficiency sectors comes from the 

“energy efficiency” industry category of the BLS Green Goods and Services survey.

137 Using 

BLS Green Goods and Services information on 132 energy efficiency industries, as identified by

BLS,138 we adjusted the list to remove ten industries expected to not be directly affected by the 

rule, e.g. transportation.139 Next we used this detailed list of 122 industries to extract 2011 BLS 

data on green employment.140 Employment data at the most-detailed industry level (6-digit 

NAICS) is available only for a portion of these 122 industries. Therefore we use both the mostdetailed industry level (6-digit NAICS) and also a more aggregate level (4-digit NAICS) to 

estimate a range of energy efficiency employment with the 2011 BLS Green Goods and Services 

data.141

BLS does not collect data on energy efficiency expenditures directly, however. Instead, 

BLS collects data on the share of revenues associated with green goods and services, at the 

establishment level.

142 We multiply data on total revenues by NAICS by the share of green 

revenues reported by BLS to obtain a measure of green revenues by industry. The only U.S. 

 

137For more details on this survey, see section 6.5.3.1. 

138 See detailed listing available here: http://www.bls.gov/ggs/naics_2012.xlsx. Category 2 is “Energy Efficiency”. 

More information is available here: http://www.bls.gov/ggs/ggsfaq.htm#3. 

139 The ten industries we removed from the list were: NAICS 483114 Coastal and Great Lakes passenger transport, 

483212 Inland water passenger transportation, 485111 Mixed mode transit systems, 485112 Commuter rail systems, 

485113 Bus and other motor vehicle transit systems, 485119 Other urban transit systems, 485210 Interurban and 

rural bus transportation, 485410 School and employee bus transportation, 485999 All other ground passenger 

transportation, and 926120 Transportation program administration. 

140 BLS Green Goods and Services data is available here: 

http://download.bls.gov/pub/time.series/gg/gg.data.1.AllData. A BLS technical note indicates that the scope of the 

GGS survey changed between 2010 and 2011 (http://www.bls.gov/news.release/ggqcew.tn.htm). Some industries 

and establishments that were not previously included in the 2010 survey were included in 2011. Rather than using 

the change from 2010 to 2011, here we use only the 2011 data. 

141 At the 6-digit NAICS level, 27 of the 122 energy efficiency industries listed have employment data available. At 

the 4-digit NAICS level, 46 of the 122 energy efficiency industries listed have employment data available. 

142 More information is available here: http://www.bls.gov/ggs/ggsfaq.htm#5.
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Government data source containing this revenue information for all NAICS sectors is the U.S. 

Economic Census. This Census is conducted at 5-year intervals (the latest available year is 

2012), however, making it unsuitable for directly pairing with 2011 data from BLS. Instead, we 

use U.S. Census Bureau data on total value of shipments by industry, for 2011, from the Annual 

Survey of Manufacturers.143 The disadvantage of this data source is that the manufacturing sector 

makes up only 50 percent of the 132 NAICS codes belonging to the energy efficiency sector as 

defined by the BLS Green Goods and Services surveys, with the remainder in the construction or 

service sectors. Thus, this analysis implicitly projects that the same number of jobs per dollar are 

supported in construction and service sectors as in manufacturing. Also, the Annual Survey of 

Manufacturers contains data for some, but not all, detailed industry codes, e.g. 4-digit and 6-digit 

NAICS. We pair our BLS GGS data by industry, either by 4-digit or 6-digit NAICS, with data 

from the Annual Survey of Manufacturers. At the more detailed, 6-digit level, 17 industries have 

data available for both employment and total value of shipments.144 At the less detailed, 4-digit 

level, 15 industries have data available for both employment and total value of shipments.145

Using this approach we obtain estimates of 2.07 demand-side energy efficiency jobs per million 

2011 dollars of expenditure, using the less-detailed industry level (4-digit NAICS), and 3.29 

demand-side energy efficiency jobs per million 2011 dollars of expenditure, using the moredetailed industry level (6-digit NAICS). 

Having calculated estimates of jobs per million dollars of energy efficiency expenditure, 

we use EPA’s illustrative energy efficiency investment levels of the first-year costs expected for 

states to attain a target of 1 percent growth in demand-side efficiency improvements (see Chapter 

3.6 of this RIA for more information). If some states were to target rates of energy efficiency

savings greater than one percent, they may see increased energy efficiency employment impacts, 

relative to the one percent growth assumed in this analysis. The first year cost of saved energy 

 

143 Census data on total value of shipments, by industry, for 2011 is available here: 

http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_2011_31GS101&prodType

=table. 

144 The 17 industries are: NAICS 321219, 321991, 321992, 327993, 327999, 332913, 332996, 333414, 333415, 

334513, 334514, 334515, 335110, 335222, 335311, 335312, and 335999.

145 The 15 industries are: NAICS 314100, 321100, 324100, 326100, 327100, 327300, 327400, 332100, 333300, 

334200, 334300, 334400, 335200, 336300, and 337900. 
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6-30

accounts for both the costs to the utilities that are funding the demand-side energy efficiency

programs (known as the program costs), and the additional cost to the end-user purchasing a more 

energy efficient technology (known as the participant costs).146 Total costs were divided evenly, 50 

percent each, between program costs and participant costs. First-year costs are not annualized; they 

are the projected expenditures on demand-side energy efficiency activities in that year. As shown in 

the Greenhouse Gas Abatement Measures TSD, Table 5-29, first-year costs for a 1 percent growth 

target in energy efficiency activities are projected to be $18.1 billion (2011 $) in 2020. Multiplying 

this dollar expenditure by the jobs per dollar estimates results in projected employment impacts 

for demand-side energy efficiency activities ranging from 37,570 to 59,700 jobs in 2020

depending on the jobs per million dollars estimate used: low or high. Employment impacts for 

demand-side energy efficiency activities range from 52,590 to 83,590 jobs in 2025, and from 

52,440 to 83,360 jobs in 2030. These estimates are shown in Table 6-6 below. 

Table 6-6. Estimated Demand-Side Energy Efficiency Employment Impacts: Target 1 

percent Growth in Energy Efficiency

Source Factor

Employment impact (jobs)*

2020 2025 2030

EPA low estimate, using BLS and Census data, and 

power sector modeling projections 2.07 37,570 52,590 52,440

EPA high estimate, using BLS and Census data, and 

power sector modeling projections 3.29 59,700 83,590 83,360

*Since these figures represent number of employees (full- or part-time) they should not be added to the full-time 

equivalent job-years reported in Table 6-5. Energy efficiency costs are from 1 percent growth target projections for 

the continental U.S. (excludes Alaska, Hawaii, and U.S. Territories): $18.1 billion (2011 $) in 2020. First-year 

energy efficiency costs are the same for rate-based and mass-based scenarios. See chapter 3 of this RIA and 

Greenhouse Gas Abatement Measures TSD, Table 5-29, for more information.

Although this approach has the advantage of using a range of estimates, derived from 

U.S. government data, on energy efficiency employment per million dollars in industry

shipments, this approach is limited by its focus on manufacturing sectors and direction of bias 

(overestimation or underestimation) cannot be determined at this time.

Our estimates of 2.07 to 3.29 demand-side energy efficiency jobs per million dollars of 

2011 expenditure fit with other estimates in the literature, focused on government data sources, 

 

146 See Section 3.6.2, “Demand-Side Energy Efficiency Total Costs”, in this RIA, for more information. 
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and are on the smaller end of the range.147 Figure 6.6 shows the range of estimates, including 

EPA low (2.07) and EPA high (3.29). The 2010 Department of Commerce report estimates 

overall employment per million dollar values, ranging from 4.65 to 4.85 (Department of 

Commerce 2010a).148 The report also contains some case studies, and for those focused on 

energy efficiency, the Department of Commerce estimates 6.21 jobs per million dollars in green 

buildings activities and 7.53 jobs per million dollars for energy efficiency appliances.149

Lawrence Berkeley National Lab (Goldman et al. 2010) reports a wide range of estimates: from 

2.5 jobs per million dollars for energy service companies (ESCOs), to 8.9 jobs per million dollars 

for low income weatherization activities. The Pacific Northwest National Labs report (Anderson 

et al. 2014), in surveying the literature, estimates 8 jobs per million dollars of consumer 

electricity bill savings and 11 jobs per million dollars of initial energy efficiency investments. 

 

147 See Section 6.5.3 for more information on sources in the literature. 

148 Calculated values using data on employment and shipments reported in Department of Commerce (2010a), Table 

2, p. 12. 

149 Calculated values using data on employment and shipments reported in Department of Commerce (2010a), 

Appendix 2, Table 2B, p. 4. http://www.esa.doc.gov/sites/default/files/appendix2_0.pdf
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Figure 6.4. Demand-Side Energy Efficiency Employment: Jobs per One Million Dollars 

(2011$)

There is more uncertainty involved in this approach than the standard bottom-up 

engineering analysis used to estimate electricity generation and fuel production employment 

impacts of this rulemaking. For those, the EPA was able to identify a limited set of activities 

(e.g., constructing a new NGCC power plant), and study associated labor requirements. Demandside energy efficiency improvements, in contrast, encompass a wide array of activities (subsidies 

for efficient appliances, “smart meters,” etc.). In addition, there is considerable uncertainty

regarding which activities a state will choose. Thus, the validity of the jobs per dollar approach 

used here relies on the assumption that states will use a mix of activities similar to the 2011 

composition of energy efficiency sectors identified by BLS. 

In addition, the EPA does not have access to bottom-up information regarding labor 

requirements for these activities. Use of a constant job per dollar fraction is at best a crude 

approximation of these labor requirements. The EPA has identified several other limitations of 

this approach, outlined below.

Job Reclassification. Job numbers in this chapter represent gross changes in the affected 

sector. As such they may over-estimate impacts to the extent that jobs created displace 

11

8.9

8

7.53

6.5

6.21

6.2

4.85

4.65

3.29

2.5

2.07

0 5 10

Jobs per Million $

PNNL initial investments

LBNL low income weatherization

PNNL bill savings

DOC EE appliances

LBNL state and local gov't EER

DOC green buildings

LBNL ratepayer funded EE

DOC overall high

DOC overall low

EPA high

LBNL ESCOs

EPA low

Sources: EPA calculation, LBNL (2013), PNNL (2014), DOC (2010)

Demand-Side EE Employment
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workers employed elsewhere in the economy. For demand-side efficiency activities this 

potential over-statement is may be higher than in other sectors. If states encourage 

consumers to purchase ENERGY STAR appliances, for example, currently employed 

workers in factories and retail outlets may simply be given a different task. This 

approach, however, would count these workers as jobs created.

Imports. The job per dollar fraction used in the employment projection is calculated 

based on jobs per dollar of revenue for domestic firms only. To the extent that spending 

on demand-side energy efficiency activities goes toward the purchase of imported goods 

this projection will overstate the U.S. employment impact of those expenditures.

Fixed Coefficient. Implicit in this approach is the assumption that employment impacts 

can be projected decades into the future on the basis of a single calculation from 2011 

data. The labor intensity of demand-side energy efficiency will likely change with 

technological innovation in the sector. In addition, even absent technological change, 

labor intensity of expenditures will likely change over time as states alter their portfolio 

of efficiency activities (e.g., by moving to higher cost activities after exhausting 

opportunities for low cost efficiency gains).

Non-additional Activities. Here we assume that all activities financed by demand-side 

energy efficiency expenditures are additional to what would have been undertaken in the 

absence of these programs. If utilities finance some actions customers would have 

undertaken in the absence of these programs (e.g., if a customer receives a rebate for an 

energy efficient appliance that would have been purchased without the rebate), these 

numbers would overestimate employment impacts of the proposed guidelines.

6.6 Conclusion

This chapter presents qualitative and quantitative discussions of potential employment 

impacts of the final guidelines for electricity generation, fuel production, and demand-side 

energy efficiency sectors. The qualitative discussion identifies challenges associated with 

estimating net employment effects and discusses anticipated impacts related to the rule. It 

includes an in-depth discussion of economic theory underlying analysis of employment impacts. 

The employment impacts for regulated firms can be decomposed into output and substitution 

effects, both of which may be positive or negative. Consequently, economic theory alone cannot 

predict the direction or magnitude of a regulation’s employment impact. It is possible to combine 

theory with empirical studies specific to the regulated firms and other relevant sectors if data and 

methods of sufficient detail and quality are available. Finally, economic theory suggests that 

environmental regulations may have positive impacts on labor supply and productivity as well. 
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We examine the peer-reviewed economics literature analyzing various aspects of labor 

demand, relying on the above theoretical framework. Determining the direction of employment 

effects in regulated industries is challenging because of the complexity of the output and 

substitution effects. Complying with a new or more stringent regulation may require additional 

inputs, including labor, and may alter the relative proportions of labor and capital used by 

regulated firms (and firms in other relevant industries) in their production processes. The 

available literature illustrates some of the difficulties for empirical estimation: for example, there 

is a paucity of publicly data on plant-level employment, thus most studies must rely on 

confidential plant-level employment data from the U.S. Census Bureau, typically combined with 

pollution abatement expenditure data, that are too dated to be reliably informative, or other 

measures of the stringency of regulation. In addition, the most commonly used empirical 

methods can only estimate the gross effects of regulation on employment (i.e. the impact of that 

more stringent regulation has on employment relative to less stringent or no regulation), not the 

net effects. Empirical analysis at the industry level requires estimates of product demand 

elasticity; production factor substitutability; supply elasticity of production factors; and the share 

of total costs contributed by wages, by industry, and perhaps even by facility Econometric 

studies of environmental rules converge on the finding that employment effects, whether positive 

or negative, have been small in regulated sectors.

The illustrative quantitative analysis in this chapter projects a subset of potential 

employment impacts in the electricity generation, fuel production, and demand-side energy

efficiency sectors. States have the responsibility and flexibility to implement policies and 

practices for compliance with Final EGU GHG Existing Source Guidelines. As such, given the 

wide range of approaches that may be used, quantifying the associated employment impacts is 

difficult. EPA’s employment analysis includes projected employment impacts associated with 

these guidelines for the electric power industry, coal and natural gas production, and demandside energy efficiency activities. These projections are derived, in part, from a detailed model of 

the electricity production sector used for this regulatory analysis, and U.S. government data on 

employment and labor productivity. In the electricity, coal, and natural gas sectors, the EPA 

estimates that these guidelines could have an employment impact of roughly [X] job-years in 

20XX for the rate-based scenario and [X] job-years for the mass-based scenario of that same 

year (see Tables 6-4 and 6-5). 
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Employment impacts from demand-side energy efficiency activities are based on historic 

data on jobs supported per million dollars of expenditure on energy efficiency. Demand-side 

energy efficiency employment impacts would approximately range from 37,570 to 59,700 jobs in 

2020, 52,590 to 83,590 jobs in 2025, and from 52,440 to 83,360 jobs in 2030 for the rate-based 

approach and a 1 percent growth target for energy efficiency expenditures (see Table 6-6). 

The IPM-generated job-year numbers for the electricity, coal and natural gas sectors 

should not be added to the demand-side efficiency job impacts, since the former are reported in 

full-time equivalent jobs, whereas the latter do not distinguish between full- and part-time 

employment. Finally, note again that this is an illustrative analysis, and CAA section 111(d) 

allows each state to determine the appropriate combination of, and the extent of its reliance on, 

measures for its state plan, by way of meeting its state-specific goal. Given the flexibilities 

afforded states in complying with the emission guidelines, the impacts reported in this chapter 

are illustrative of actions states may take.

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6A-1

APPENDIX 6A: ESTIMATING SUPPLY SIDE EMPLOYMENT IMPACTS 

This appendix presents the methods used to estimate the supply-side employment impacts 

of the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric

Utility Generating Units (herein referred to as “final emission guidelines” or the “Clean Power 

Plan Final Rule”). The focus of the employment analysis is limited to the direct changes in the 

amount of labor needed in the power, fuels and generating equipment sectors directly influenced 

by the illustrative plan approaches analyzed for the final emission guidelines. It does not include 

the ripple effects of these impacts on the broader economy (i.e., the “multiplier” effect), nor does 

it include the wider economy-wide effects of the changes to the energy markets, such as changes 

in electricity prices.

The methods used to estimate the supply-side employments are based on methods 

previously developed for the Mercury and Air Toxics Standards (MATS) Regulatory Impact 

Analysis (RIA). The methods used in this analysis to estimate the recurring labor impacts (e.g., 

labor associated with operating and maintaining generating units, as well as labor needed to mine 

coal and natural gas) are the same as was used in MATS (with updated data where available). 

The labor analysis in the MATS RIA was primarily concerned with the labor needs of 

retrofitting pollution control equipment. The analysis for the Clean Power Plan Final Rule, 

however, involves the quantity and timing of the labor needs of building new renewable and 

natural gas, as well as making heat rate improvements (HRI) at existing coal fired EGUs. These 

construction-related compliance activities in the Clean Power Plan Final Rule required 

developing additional appropriate analytical methods that were not needed for the MATS 

analysis. The newly developed analytical methods for the construction-related activities are 

similar in structure and overall approach to the methods used in MATS, but required additional 

data and engineering information not needed in the MATS RIA.

6A.1 General Approach

The analytical approach used in this analysis is a bottom-up engineering method 

combining the EPA’s cost analysis of the final emission guidelines with data on labor 

productivity, engineering estimates of the amount and types of labor needed to manufacture, 
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6A-2

construct, and operate different types of generating units, and prevailing wage rates for skilled 

and general labor categories. The approach involved using utility power sector projections and 

various energy market implications under the final emission guidelines from modeling conducted 

with the EPA Base Case version 5.15, using the Integrated Planning Model (IPM®)150, along 

with data from secondary sources, to estimate the first order employment impacts for 2020, 

2025, and 2030. 

Throughout the supply-side labor analysis the engineering approach projects labor changes 

measured as the change in each analysis year in job-years151 employed in the power generation 

and directly related sectors (e.g., equipment manufacturing, fuel supply and generating efficiency 

services). Job-years are not individual jobs, nor are they necessarily permanent nor full time jobs.

Job-years the amount of work performed by one full time equivalent (FTE) employee in one 

year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in 

that year, or any combination of full- and part-time workers such that total 20 FTEs.

6A.1.1 Employment Effects Included In the Analysis

The estimates of the employment impacts (both positive and negative) are divided into 

five categories: 

 additional employment to make HRI152 at existing coal fired EGUs; 

 additional construction-related employment to manufacture and install additional new 

generating capacity (renewables, and natural gas combined cycle or combustion turbine 

units) when needed as part of early compliance actions;

 

150 Results for this analysis were developed using various outputs from EPA’s Base Case v.5.13 using ICF’s 

Integrated Planning Model (IPM®). This case includes all of the underlying modeling that was developed by EPA 

with technical support from ICF International, Inc. See 

http://www.epa.gov/powersectormodeling/BaseCasev513.html for more information.

151 Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one full-time 

individual for one year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in 

that year.

152 Heat rate improvements could include a range of activities in the power plant to lower the heat rate required to 

generate a net electrical output. Assuming all other things being equal, a lower heat rate is more efficient because 

less fuel is needed per unit of electric output.
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 lost construction-related employment opportunities due to reductions in the total amount 

of new generating capacity needed to be built in the later years because of reduced 

overall demand for electricity because of demand-side energy efficiency activities;

 lost operating and maintenance employment opportunities due to increased retirements of 

coal and small oil/gas units; 

 changes (both positive and negative) in coal mining and natural gas extraction 

employment due to the aggregate net changes in fuel demands arising from all the 

activities occurring due to compliance with the final emission guidelines.

Some of the changes are one-time labor effects which are associated with the building (or 

avoiding building) new generating capacity and installing HRI. This type of employment effects 

involves project-specific labor that is used for 2 to 4 years to complete a specific construction 

and installation type of project. There are other labor effects, however, which continue year after 

year. For example, bringing new generating capacity online creates an ongoing need for labor to 

operate and maintain the new generating capacity throughout the expected service life of the 

unit. New generating capacity also creates a need for additional employment to provide the fuel 

annually to run the new capacity. There are also continuing effects from the lost operations and 

maintenance (O&M) and fuel sector labor opportunities from decisions to retire existing 

capacity, as well as similar lost labor opportunities from decisions to reduce a portion of the 

amount of additional capacity needed in the base case.

6A.2 Employment Changes due to Heat Rate Improvements

The employment changes due to HRI were estimated based on the incremental MW 

capacity estimated to implement such improvements by 2020 as indicated by the analysis 

conducted by EPA. The heat rate improvement job impacts were assumed to have all occurred by 

2020 and thus this study assumes there will be no HRI related jobs after 2020 (i.e., no permanent 

O&M related jobs due to HRI for 2025 or 2030). EPA modeled the heat rate improvements 

exogenously in IPM using the assumption that all “relevant” units can improve their heat rate by

6 percent at a capital cost of $100/kW. This study assumes that these investments will occur over 

a four-year period culminating in 2020. Hence, the per-year cost of heat rate was calculated to be 

$25/kW, and this cost was used in the next step. 
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This cost was then allocated to four categories based on the estimates provided by Andover 

Technology Partners (ATP), which were adapted from proxy projects involving installation of 

combustion control retrofits, such as those installed under the Best Available Retrofit 

Technology (BART) submissions from coal-fired power plants located in Wyoming and 

Arizona. For more details, refer to the Staudt (2014) report.153 These proxies were chosen to 

ensure that the types of activities involved and their associated costs would be representative of 

those investments EPA expects power plants to undertake for efficiency upgrades.

Information on cost for these proxies were then extrapolated to approximate the labor 

requirements for four broad categories of labor – boilermakers and general construction, 

engineering and management support labor, labor required to produce the equipment in upstream 

sectors, and labor required to supply the materials (assumed to be primarily steel) in upstream 

sectors. More details about these estimates are provided in the Staudt (2014) report.

Based on the cost allocated in each categories and output per worker figures for respective 

industries in 2020, the employment gains for heat rate improvement were estimated154 for 2020 

using the assumptions summarized in Table 6A-1 below. Output per workers in future years 

were adjusted to account for growth in labor productivity, based on historical evidence of 

productivity growth rates for the relevant sectors.

Table 6A-1. Labor Productivity Growth Rate due to Heat Rate Improvement

Share of the 

Total Capital Cost

Output/Worker 

(2020)

Labor Productivity 

Growth Rate

Boilermaker and Gen. Const. 40% $78,500 0%

Management/Engineering 20% $141,000 1.3%

Equipment 30% $458,000 3.2%

 

153 Staudt, James, Andover Technology Partners, Inc. Estimating Labor Effects of Heat Rate Improvements. Report 

prepared for the Clean Power Plan Proposed Rule, March 6, 2014. 

154 Total value of shipments or receipts in 2007 and total employees were taken from 2007 Economic Census, 

Statistics by Industry for Mining and Manufacturing sectors. The average annual growth rate of labor productivity 

was taken from the Bureau of Labor Statistics. Average growth rate calculated for years 1992-2007, applied to 2007 

productivity to determine 2020 estimates of productivity. For the construction sector, BLS productivity growth rate 

data was unavailable. Because of this, and lack of reliable data on construction sector productivity growth, the 

output per worker for the construction sector was not forecasted to 2020, and the most recent available value from 

2007 was used. 
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Materials 10% $424,000 -1.2%

For these output per worker figures, a power sector construction industry (NAICS 237130) was 

used for general construction and boilermakers, Engineering Services (NAICS 54133) was used 

for the engineering and management component, Machinery Manufacturing (NAICS 333) was 

used for the equipment sector, and steel manufacturing (NAICS 33121) was used for materials.

Use of machinery manufacturing for equipment and steel for materials was based on an analysis 

of the types of materials and equipment needed for these projects, and what EPA determined to 

be the most appropriate industry sectors for those. For more details, refer to the Staudt (2014) 

report. 

6A.2.1 Employment Changes Due to Building (or Avoiding) New Generation Capacity

Employment changes due to new generation units were based on the incremental changes 

in capacity (MW), capital costs ($MM), and fixed operations and maintenance (FOM) costs 

($MM) between the policy scenario and the base case in a given year. 

New capacities were aggregated by generation type into the following categories:

• Combined Cycle, 

• Combustion Turbine, and 

• Renewables (which includes biomass, geothermal, landfill gas, onshore wind, and 

solar).

For each category, the analysis estimated the impacts due to both the construction and 

operating labor requirements for corresponding capacity changes. The construction labor was 

estimated using information on the capital costs, while the operating labor was estimated using 

the FOM costs. 

Because IPM outputs provide annualized capital costs ($MM), EPA first converted the 

annualized capital costs to changes in the total capital investment using the corresponding capital 

charge rates.155 These total capital investments were then converted to annual capital investments 

using assumptions about the estimated duration of the construction phase, in order to estimate the 

 

155 Capital charge rates obtained from EPA’s resource, EPA #450R13002: Documentation for EPA Base Case v.5.13 

using the Integrated Programming Model (IPM).
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annual impacts on construction phase labor. Duration estimates were based on assumptions for 

construction lengths used in EPA’s IPM modeling.156 Specific assumptions used for different 

generating technologies are shown in Table 6A-2 below.

Table 6A-2. Capital Charge Rate and Duration Assumptions

New Investment Technology Capital Charge Rate Duration (Years)

Advanced Combined Cycle 10.3% 3

Advanced Combustion Turbine 10.6% 2

Renewables

Biomass 9.5% 3

Wind (Onshore) 10.9% 3

Landfill Gas 10.9% 3

Solar 10.9% 3

Geothermal 10.9% 3

Annual capital costs for each generation type were then broken down into four categories: 

equipment, material (which is assumed to be primarily steel), installation labor, and support labor 

in engineering and management. The percentage breakdowns shown in Table 6A-3 were 

estimated using information provided by Staudt (2014), based primarily on published budgets for 

new unit assembled in a study for the National Energy Technology Laboratory (NETL). For 

more details, refer to the Staudt (2014) report. Annual capital costs for each generation type 

provided by the IPM output were allocated according to this breakdown.

Table 6A-3. Expenditure Breakdown due to New Generating Capacity

Equipment Material Labor Eng. and Const. Mgt

Renewables 54% 6% 31% 9%

Combined Cycle 65% 10% 18% 7%

Combustion Turbine 65% 10% 18% 7%

The short-term construction labor of the new generation units were based on output ($ per 

worker) figures for the respective sectors. The total direct workers per $1 million of output for 

the baseline year 2007 were forecasted to the years under analysis using the relevant labor 

productivity growth rate. Table 6A-4 shows the figures for each of the five productivities: 

general power plant construction; engineering and management; material use; equipment use; 

 

156 Ibid.
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and plant operators. The resulting values were multiplied by the capital costs to get the job 

impact.

Table 6A-4. Labor Productivity due to New Generating Capacity

Labor Productivity

Growth Rate 

Workers per

Million $ (2007)

General Power Plant Construction 0.0% 5.7

Engineering and Management 1.3% 5.2

Material Use (Steel) -1.2% 2.0

Equipment Use (Machinery) 3.2% 3.3

Plant Operators 2.8% 10.8

General installation labor, assumed to be mostly related to the general power plant 

construction phase, was matched with the power industry specific construction sector. 

Engineering/management was matched to the engineering services sector to determine their 

respective output per worker. For materials, EPA assumed steel to be the proxy and used the 

steel manufacturing sector for this productivity. Equipment was assumed to primarily come from 

machinery manufacturing sector (such as turbines, engines and fans).

The net labor impact for construction labor for a given year was adjusted to account for 

changes in capacity that has already taken place in the prior IPM run year. Because IPM reports 

cumulative changes for new generating capacity for any given run year, this adjustment ensured 

that the short-term construction phase job impacts in any given run year does not reflect the 

cumulative effects of prior construction changes for the given policy scenario. The estimated 

amount of the change in construction-related labor in a single IPM run year (e.g., 2025) 

represents the average labor impact that occurs in all years between that IPM run year and the 

previous run year (i.e., the labor estimates derived from the 2025 IPM run year are the average 

annual labor impacts in 2021 through 2025). The construction labor results for 2020 represent 

the average labor impacts in 2017 through 2020.

The plant operating employment estimates used a simpler methodology as the one 

described above. The operating employment estimates use the IPM estimated change in FOM 

costs for the IPM run year. Because the FOM costs are inherently estimates for a single year, the 

operating employment estimates are for a single year only. While there are obviously operating 

employment effects occurring in every year throughout the entire IPM estimation period (2017-

2030), the labor analysis only estimates the single year labor impacts in the IPM run years: 2020, 
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2025 and 2030. The total direct workers for $1 million and labor productivity growth rate 

provided for plant operators in Table 6A-4 were used to estimate the employment impact.

6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements

Employment changes due to plant retirements were calculated using the IPM projected 

changes in retirement capacities for coal and oil/gas units for the relevant year and the estimated 

changes in total FOM costs due to those retiring units. Thus, the basic assumption in this analysis 

was that increased retirements (over the base case) will lead to reduced FOM expenditures at 

those plants which were assumed to lead to direct job losses for plant workers.

In order to estimate the total FOM changes due to retirements, EPA first estimated the 

average FOM costs ($/kW) for existing coal-fired and oil/gas-fired units in the base case, as 

shown in Table 6A-5 below. It was assumed that the average FOM cost of existing units in the 

base case can be used as a proxy for the lost economic output due to fossil retirements. Thus, 

changes in the FOM costs for these retiring units were derived by taking the product of the 

incremental change in capacity and the average FOM costs. These values were converted to lost 

employment using data from the Economic Census and BLS on the output/worker estimates for 

the utility sector.157

Table 6A-5. Average FOM Cost Assumptions

2020 2025 2030

Coal 65 68 69

Oil and Gas 21 22 22

Note that the retirement related employment losses are assumed to include losses directly

affecting the utility sector, and do not include losses in upstream sectors that supply other inputs 

to the EGU sector (except fuel related job losses, which are estimated separately and discussed in 

the next section).

6A.2.3 Employment Changes due to Coal and Oil/Gas Retirements

Two types of employment impacts due to projected fuel use changes were estimated in this 

section. First, employment losses due to either reductions or shifts in coal demand were 

 

157 The same specific sources as cited before, however, used workers and total payroll.
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estimated using an approach similar to EPA’s coal employment analyses under Title IV of the 

Clean Air Act Amendments. Using this approach, changes in coal demand (in short tons) for 

various coal supplying regions were taken from EPA’s base and policy case runs for the 

proposed EGU GHG NSPS. These changes were converted to job-years using U.S. Energy

information Administration (EIA) data on regional coal mining productivity (in short tons per 

employee hour), using 2008 labor productivity estimates.158,159

Specifically, the incremental changes to coal demand were calculated based on the coal 

supply regions in IPM -- Appalachia, Interior, and West and Waste Coal (which was estimated 

using U.S. total productivity). Worker productivity values used for estimating coal related job 

impacts are shown in Table 6A-6 below.

Table 6A-6. Labor Productivity due to New Generating Capacity

Labor Productivity

Coal (Short tons/ employee hour)

Appalachia 2.91

Interior 4.81

West 19.91

Waste 5.96

Natural Gas (MMBtu/ employee hour) 126

Pipeline Construction (Workers per $Million) 5.1

For natural gas demand, labor productivity per unit of natural gas was unavailable, unlike 

coal labor productivities used above. Most secondary data sources (such as Census and EIA) 

provide estimates for the combined oil and gas extraction sector. This section thus used an 

adjusted labor productivity estimate for the combined oil and gas sector that accounts for the 

relative contributions of oil and natural gas in the total sector output (in terms of the value of 

energy output in MMBtu). This estimate of labor productivity was then used with the 

incremental natural gas demand for the respective IPM runs to estimate the job-years for the 

specific year (converting the TCF of gas used projected by IPM into MMBtu using the 

 

158 From EIA Annual Energy Review, Coal Mining Productivity Data. Used 2008. 

159 Unlike the labor productivity estimates for various equipment resources which were forecasted to 2020 using 

BLS average growth rates, this study uses the most recent historical productivity estimates for fuel sectors. In 

general, labor productivity for the fuel sectors (both coal and natural gas) showed a significantly higher degree of 

variability in recent years than the manufacturing sectors, which would have introduced a high degree of uncertainty 

in forecasting productivity growth rates for future years.
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appropriate conversion factors). In addition, the pipeline construction costs were estimated using 

endogenously determined gas market model parameters in IPM used by EPA for the MATS rule 

(using assumptions for EPA’s Base Case v4.10). This analysis assumed that the need for 

additional pipeline would be proportionate to those projected for the MATS rule and were hence 

extrapolated from those estimates.160 The job-years associated with the pipeline construction 

were included in the natural gas employment estimates.

 

160 See “Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015”. Technical 

Support Document, March 2011.
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CHAPTER 7: STATUTORY AND EXECUTIVE ORDER ANALYSIS

7.1 Executive Order 12866: Regulatory Planning and Review, and Executive Order 

13563: Improving Regulation and Regulatory Review

This final action is an economically significant regulatory action that was submitted to 

the OMB for review. Any changes made in response to OMB recommendations have been 

documented in the docket. The EPA prepared an analysis of the potential costs and benefits 

associated with this action.

Consistent with Executive Order 12866 and Executive Order 13563, the EPA estimated 

the costs and benefits for illustrative plan approaches of implementing the guidelines. The final 

rule establishes: 1) carbon dioxide (CO2) emission performance rates for two source categories 

of existing fossil fuel-fired EGUs, fossil fuel-fired electric utility steam generating units and 

stationary combustion turbines, and 2) guidelines for the development, submittal and 

implementation of state plans that implement the CO2 emission performance rates. Actions taken 

to comply with the guidelines will also reduce the emissions of directly-emitted PM2.5, SO2 and 

NOX. The benefits associated with these PM2.5, SO2 and NOX reductions are referred to as cobenefits, as these reductions are not the primary objective of this rule.

The EPA has used the social cost of carbon estimates presented in the Technical Support 

Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis 

Under Executive Order 12866 (May 2013, Revised June 2015) (“current TSD”) to analyze CO2

climate impacts of this rulemaking. We refer to these estimates, which were developed by the 

U.S. government, as “SC-CO2 estimates.” The SC-CO2 is an estimate of the monetary value of 

impacts associated with a marginal change in CO2 emissions in a given year. The four SC-CO2

estimates are associated with different discount rates (model average at 2.5 percent discount rate, 

3 percent, and 5 percent; 95th percentile at 3 percent), and each increases over time. In this 

summary, the EPA provides the estimate of climate benefits associated with the SC-CO2 value 

deemed to be central in the current TSD: the model average at 3 percent discount rate. 

The EPA estimates that, in 2020, the final guidelines will yield monetized climate 

benefits (in 2011$) of approximately $[X] billion (3 percent model average). The air pollution 

health co-benefits in 2020 are estimated to be $[X] billion to $[X] billion (2011$) for a 3 percent 
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discount rate and $[X] billion to $[X] billion (2011$) for a 7 percent discount rate. The annual, 

illustrative plan costs estimated by IPM and inclusive of demand-side energy efficiency program 

and participant costs and MRR costs, are approximately $[X] billion (2011$) in 2020. The 

quantified net benefits (the difference between monetized benefits and costs) in 2020 are 

estimated to range from $[X] billion to $[X] billion (2011$) using a 3 percent discount rate 

(model average).

The EPA estimates that, in 2025, the final guidelines will yield monetized climate 

benefits (in 2011$) of approximately $[X] billion (3 percent model average). The air pollution 

health co-benefits in 2025 are estimated to be $[X] billion to $[X] billion (2011$) for a 3 percent 

discount rate and $[X] billion to $[X] billion (2011$) for a 7 percent discount rate. The annual, 

illustrative state plan costs estimated by IPM and inclusive of demand side energy efficiency

program and participant costs and MRR costs, are approximately $[X] billion (2011$) in 2025. 

The quantified net benefits (the difference between monetized benefits and costs) in 2025 are 

estimated to range from $[X] billion to $[X] billion (2011$) using a 3 percent discount rate 

(model average).

The EPA estimates that, in 2030, the final guidelines will yield monetized climate 

benefits (in 2011$) of approximately $[X] billion (3 percent model average). The air pollution 

health co-benefits in 2030 are estimated to be $[X] billion to $[X] billion (2011$) for a 3 percent 

discount rate and $[X] billion to $[X] billion (2011$) for a 7 percent discount rate. The annual, 

illustrative state plan costs estimated by IPM and inclusive of demand side energy efficiency

program and participant costs and MRR costs, are approximately $[X] billion (2011$) in 2030. 

The quantified net benefits (the difference between monetized benefits and costs) in 2030 are 

estimated to range from $[X] billion to $[X] billion (2011$) using a 3 percent discount rate 

(model average).

There are additional important benefits that the EPA could not monetize. Due to current 

data and modeling limitations, our estimates of the benefits from reducing CO2 emissions do not 

include important impacts like ocean acidification or potential tipping points in natural or 

managed ecosystems. Unquantified benefits also include climate benefits from reducing 
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emissions of non-CO2 greenhouse gases (e.g., nitrous oxide and methane)161 and co-benefits 

from reducing direct exposure to SO2, NOx and hazardous air pollutants (e.g., mercury and 

hydrogen chloride), as well as from reducing ecosystem effects and visibility impairment. Based 

upon the foregoing discussion, it remains clear that the benefits of this proposal are substantial, 

and far exceed the costs. Additional benefit, cost and net benefit estimates are provided in 

Chapter 8 of this report.

7.2 Paperwork Reduction Act (PRA)

The information collection requirements in this rule have been submitted for approval to 

OMB under the PRA. The Information Collection Request (ICR) document prepared by the EPA 

has been assigned the EPA ICR number 2503.02. You can find a copy of the ICR in the docket 

for this rule, and it is briefly summarized here. The information collection requirements are not 

enforceable until OMB approves them.

This rule does not directly impose specific requirements on EGUs located in states, U.S. 

territories or areas of Indian country. The rule also does not impose specific requirements on 

tribal governments that have affected EGUs located in their area of Indian country. For areas of 

Indian country, the rule establishes CO2 emission performance goals that could be addressed 

through either tribal or federal plans. A tribe would have the opportunity under the Tribal 

Authority Rule (TAR), but not the obligation, to apply to the EPA for Treatment as State (TAS)

for purposes of a CAA section 111(d) plan and, if approved by the EPA, to establish a CAA 

section 111(d) plan for its area of Indian country. To date, no tribe has requested or obtained 

TAS eligibility for purposes of a CAA section 111(d) plan. For areas of Indian country with 

affected EGUs where a tribe has not applied for TAS and submitted any needed plan, if the EPA 

determines that a CAA section 111(d) plan is necessary or appropriate, the EPA would have the 

responsibility to establish the plans. Because tribes are not required to implement section 111(d) 

plans and because no tribe has yet sought TAS eligibility for this purpose, this action is not 

 

161 Although CO2 is the predominant greenhouse gas released by the power sector, electricity generating units also 

emit small amounts of nitrous oxide and methane. For more detail about power sector emissions, see RIA Chapter 2 

and the U.S. Greenhouse Gas Reporting Program’s power sector summary, 

http://www.epa.gov/ghgreporting/ghgdata/reported/powerplants.html.
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anticipated to impose any information collection burden on tribal governments over the 3-year 

period covered by this ICR.

This rule does impose specific requirements on state and U.S. territory governments with 

affected EGUs. The information collection requirements are based on the recordkeeping and 

reporting burden associated with developing, implementing, and enforcing a plan to limit CO2

emissions from existing sources in the utility power sector. These recordkeeping and reporting 

requirements are specifically authorized by CAA section 114 (42 U.S.C. 7414). All information 

submitted to the EPA pursuant to the recordkeeping and reporting requirements for which a 

claim of confidentiality is made is safeguarded according to agency policies set forth in 40 CFR 

part 2, subpart B.

The annual burden for this collection of information for the states (averaged over the first 

3 years following promulgation) is estimated to be a range of 529,000 to 859,000 hours at a total 

annual labor cost of $37.4 to $60.8 million. The lower bound estimate reflects the assumption 

that some states already have energy efficiency and renewable energy programs in place. The 

higher bound estimate reflects the overly-conservative assumption that no states have energy

efficiency and renewable energy programs in place. 

The annual burden for this collection of information for the two affected U.S. territories, 

i.e., Puerto Rico and Guam, (averaged over the first 3 years following promulgation of this 

action) is estimated to be 23,700 to 38,200 hours at a total annual labor cost of $1.68 to $2.71 

million.

The total annual burden for the federal government associated with the state collection of 

information (averaged over the first 3 years following promulgation) is estimated to be 54,901 

hours at a total annual labor cost of $3.04 million. The total annual burden for the federal 

government associated with the territorial collection of information (averaged over the first 3 

years following promulgation) is estimated to be 2,600 hours at a total annual labor cost of 

$145,000. Burden is defined at 5 CFR 1320.3(b). 

An agency may not conduct or sponsor, and a person is not required to respond to, a 

collection of information unless it displays a currently valid OMB control number. The OMB 

control numbers for the EPA’s regulations in 40 CFR are listed in 40 CFR part 9. When OMB 

approves this ICR, the agency will announce that approval in the Federal Register and publish a 
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technical amendment to 40 CFR part 9 to display the OMB control number for the approved 

information collection activities contained in this final rule.

7.3 Regulatory Flexibility Act (RFA)

The EPA certifies that this action will not have a significant economic impact on a 

substantial number of small entities under the RFA. This action will not impose any

requirements on small entities. Specifically, emission guidelines established under CAA section 

111(d) do not impose any requirements on regulated entities and, thus, will not have a significant 

economic impact upon a substantial number of small entities. After emission guidelines are 

promulgated, states and U.S. territories establish emission standards on existing sources, and it is 

those requirements that could potentially impact small entities.

Our analysis here is consistent with the analysis of the analogous situation arising when 

the EPA establishes NAAQS, which do not impose any requirements on regulated entities. As 

here, any impact of a NAAQS on small entities would only arise when states take subsequent 

action to maintain and/or achieve the NAAQS through their state implementation plans. See 

American Trucking Assoc. v. EPA, 175 F.3d 1029, 1043-45 (D.C. Cir. 1999) (NAAQS do not 

have significant impacts upon small entities because NAAQS themselves impose no regulations 

upon small entities). 

Nevertheless, the EPA is aware that there is substantial interest in the rule among small 

entities and, as detailed in section III.A of the preamble to the proposed carbon pollution 

emission guidelines for existing EGUs (79 FR 34845-34847; June 18, 2014) and in section II.D 

of the preamble to the proposed carbon pollution emission guidelines for existing EGUs in 

Indian Country and U.S. Territories (79 FR 65489; November 4, 2014), has conducted an 

unprecedented amount of stakeholder outreach. As part of that outreach, agency officials 

participated in many meetings with individual utilities and electric utility associations, as well as 

industry leaders and trade association representatives from various industries. While formulating 

the provisions of the rule, the EPA considered the input provided over the course of the 

stakeholder outreach as well as the input provided in the many public comments.
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7.4 Unfunded Mandates Reform Act (UMRA)

This action does not contain an unfunded mandate of $100 million or more as described 

in UMRA, 2 U.S.C. 1531–1538, and does not significantly or uniquely affect small governments.

The emission guidelines do not impose any direct compliance requirements on EGUs located in 

states, U.S. territories or areas of Indian country. As explained in section XII.B above, the rule

also does not impose specific requirements on tribal governments that have affected EGUs 

located in their area of Indian country. The rule does impose specific requirements on state and 

U.S. territory governments that have affected EGUs. Specifically, states and U.S. territories are 

required to develop plans to implement the guidelines under CAA section 111(d) for affected 

EGUs. The burden for states and U.S. territories to develop CAA section 111(d) plans in the 3-

year period following promulgation of the rule was estimated and is listed in section XII.B 

above, but this burden is estimated to be below $100 million in any one year. Thus, this rule is 

not subject to the requirements of section 202 or section 205 of the UMRA.

This rule is also not subject to the requirements of section 203 of UMRA because it 

contains no regulatory requirements that might significantly or uniquely affect small 

governments. Specifically, the state and U.S. territory governments to which rule requirements 

apply are not considered small governments.

In light of the interest among governmental entities, the EPA conducted outreach with 

national organizations representing state and local elected officials and U.S. territory and tribal 

governmental entities while formulating the provisions of this rule. Sections III.A and XI.F of 

the preamble to the proposed carbon pollution emission guidelines for existing EGUs (79 FR 

34845-34847; June 18, 2014) and sections II.D and VI.F of the preamble to the proposed carbon 

pollution emission guidelines for existing EGUs in areas of Indian Country and U.S. Territories

(79 FR 65489; November 4, 2014) describes the extensive stakeholder outreach the EPA has 

conducted on setting emission guidelines for existing EGUs. The EPA considered the input 

provided over the course of the stakeholder outreach as well as the input provided in the many

public comments when developing the provisions of these emission guidelines.

7.5 Executive Order 13132: Federalism

The EPA has concluded that this action may have federalism implications, pursuant to 

agency policy for implementing the Order, because it imposes substantial direct compliance 
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costs on state or local governments, and the federal government will not provide the funds 

necessary to pay those costs. As discussed in the Supporting Statement found in the docket for 

this rulemaking, the development of state plans will entail many hours of staff time to develop 

and coordinate programs for compliance with the rule, as well as time to work with state 

legislatures as appropriate, to develop a plan submittal. Consistent with this determination, the 

EPA provides the following federalism summary impact statement.

The EPA consulted with state and local officials early in the process of developing the 

proposed action to permit them to have meaningful and timely input into its development. As 

described in the Federalism discussion in the preamble to the proposed standards of performance 

for GHG emissions from new EGUs (79 FR 1501; January 8, 2014), the EPA consulted with 

state and local officials in the process of developing the proposed standards for newly 

constructed EGUs. This outreach addressed planned actions for new, reconstructed, modified and 

existing sources. The EPA invited the following 10 national organizations representing state and 

local elected officials to a meeting on April 12, 2011, in Washington DC: (1) National Governors 

Association; (2) National Conference of State Legislatures, (3) Council of State Governments, 

(4) National League of Cities, (5) U.S. Conference of Mayors, (6) National Association of 

Counties, (7) International City/County Management Association, (8) National Association of 

Towns and Townships, (9) County Executives of America, and (10) Environmental Council of 

States. The National Association of Clean Air Agencies also participated. On February 26, 2014, 

the EPA re-engaged with those governmental entities to provide a pre-proposal update on the 

emission guidelines for existing EGUs and emission standards for modified and reconstructed 

EGUs. In addition, as described in section III.A of the preamble to the proposed carbon pollution 

emission guidelines for existing EGUs (79 FR 34845-34847; June 18, 2014), extensive 

stakeholder outreach conducted by the EPA allowed state leaders, including governors, state 

attorneys general, environmental commissioners, energy officers, public utility commissioners, 

and air directors, opportunities to engage with EPA officials and provide input regarding 

reducing carbon pollution from power plants.

In the spirit of Executive Order 13132, and consistent with the EPA’s policy to promote 

communications between the EPA and state and local governments, the EPA specifically

solicited comment on the proposed action from state and local officials. The EPA received 

comments from over 400 entities representing state and local governments.
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Several themes emerged from state and local government comments. Commenters raised 

concerns with the building blocks that comprise the best system of emission reduction (BSER), 

including the stringency of the building blocks, and the timing of achieving interim CO2 levels. 

They also identified the potential for electric system reliability issues and stranded assets due to 

the proposed timeframe for plan submittals and CO2 emission reductions. In addition, states 

commented on state plan development and implementation topics, including state plan 

approaches, early actions, trading programs, interstate crediting for RE, and EPA guidance and 

outreach.

Commenters identified overarching concerns regarding the stringency of the CO2 goals 

and the timeframe for achieving reductions that encompassed the building blocks, the BSER, and 

associated timing for achievement of interim CO2 levels. State commenters, in particular, 

identified changes to the stringency of the building blocks, concerns with the timeframe over 

which reductions must be achieved, and concerns with the approaches and measures used for the 

BSER. For the final rule, in response to stakeholder comments, the EPA has made refinements to 

the building blocks, the period of time over which measures are deployed, and the stringency of 

emission limitations that those measures can achieve in a practical and reasonable cost way. The 

final BSER reflects those refinements. 

To many commenters, the proposal’s 2020 compliance date, together with the stringency

of the interim CO2 goal, bore significant reliability implications. In this final rule, the agency is 

addressing those concerns via adjustments to the compliance timeframe (an 8-year interim period 

that begins in 2022) and to the approach for meeting interim CO2 emission performance rates (a 

glide path separated into three steps, 2022-2024, 2025-2027, and 2028-2029). These adjustments 

provide more time for planning, consultation and decision making in the formulation of state 

plans and in EGUs’ choices of compliance strategies. The final rule also retains flexibilities 

presented in the proposal and offers additional opportunities, including opportunities for trading 

within and between states, and other multi-state plan approaches that will further support electric 

system reliability. The EPA is also requiring states to consult with relevant ISOs/RTOs and/or 

planning/reliability authorities during plan development, and to document recommendations in 

their plans – and is providing the time for states to do so. Even with this foundation of flexibility

in place, these final guidelines further provide states with the option of proposing amendments to 

approved plans in the event that unanticipated and significant reliability challenges arise.
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Commenters provided compelling information indicating that it will take longer than the 

agency initially anticipated to adjust investments and achieve interim CO2 reductions. 

Recognizing this, as well as the urgent need for actions to reduce GHG emissions, the EPA is 

requiring states to frame an initial plan by August 31, 2016, and is allowing states two additional 

years to submit a final plan, if justified (to be submitted by August 31, 2018).

States commented on state plan development and implementation topics that included 

state plan approaches, early actions being taken into account, trading programs being allowed,

interstate crediting for RE being allowed, and guidance and outreach being provided by the EPA.

For the state plan approaches, commenters expressed concerns with the proposed “portfolio 

approach” for state plans, including concerns with enforceability of requirements, and identified 

a “state commitment approach” with backstop measures as an option for state plans. In this final

rule, in response to stakeholder comments on the portfolio approach and alternative approaches, 

the EPA is finalizing a “state measures” approach that includes a requirement for the inclusion of 

backstop measures.

State commenters also expressed interest in the EPA ensuring that early actions to reduce 

CO2 are rewarded. In this final rule, states with currently existing programs and policies, and 

states that put in place new programs and policies early, will be better positioned to achieve the 

CO2 goals. Measures that a state takes prior to 2022, or programs already in place by 2022, that

result in CO2 emission reductions during the 2022-2029 period will contribute toward meeting 

both the interim and final CO2 emission performance rates. Thus, states with currently existing 

programs and policies, and states that put in place new programs and policies early, will be better 

positioned to achieve the goals. RE generating capacity and demand-side EE measures that are 

installed after 2012 will be eligible for use in adjusting a CO2 emission performance rate.

Many state commenters supported the use of mass-based and rate-based emission trading 

programs in state plans, including interstate emission trading programs. The EPA also received a 

number of comments from states and stakeholders about the value of EPA support in developing 

and/or administering tracking systems to support state administration of rate-based and massbased emission trading programs. In this final rule, states may use trading or averaging 

approaches and technologies or strategies that are not explicitly mentioned in any of the three 

building blocks as part of their overall plans, as long as they achieve the required emission 
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reductions from affected fossil-fuel-fired EUGs. In addition, in response to concerns from states 

and power companies that the need for up-front interstate cooperation in developing multi-state 

plans could inhibit the development of interstate programs that could lower cost, the final rule 

provides additional options to allow individual EGUs to use creditable out-of-state reductions to 

achieve required CO2 reductions, without the need for up-front interstate agreements. The EPA is 

committed to working with states to provide support for tracking of emissions and allowances or 

credits, to help implement multi-state trading or averaging approaches. 

In their comments, many states identified the need for the EPA to provide guidance, 

including guidance on RE and EE emission measurement and verification (EM&V), and to 

maintain regular contact/forums with states throughout the implementation process. To provide 

state and local governments and other stakeholders with an understanding of the rule 

requirements, and to provide efficiencies where possible and reduce the cost and administrative 

burden, the EPA will continue outreach throughout the plan development and submittal process. 

Outreach will include opportunities for states to participate in briefings, teleconferences, and 

meetings about the final rule. The EPA’s 10 regional offices will continue to be the entry point 

for states, tribes and territories to ask technical and policy questions. The agency will host (or 

partner with appropriate groups to co-host) a number of webinars about various components of 

the final rule during the first two months after the final rule is issued. The EPA will use 

information from this outreach process to inform the training and other tools that will be of most 

use to the states, tribes, and territories that are implementing the final rule. The EPA expects to 

issue guidance on specific topics, including evaluation, measurement and verification (EM&V) 

for RE and demand-side EE, state-community engagement, and resources and financial 

assistance for RE and demand-side EE. As guidance documents, tools, templates and other 

resources become available, the EPA, in consultation with the U.S. Department of Energy and 

other federal agencies, will continue to make these resources available via a dedicated website.

A list of the state and local government commenters has been provided to OMB and has 

been placed in the docket for this rulemaking. In addition, the detailed response to comments 

from these entities is contained in the EPA’s response to comments document on this final 

rulemaking, which has also been placed in the docket for this rulemaking. 
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As required by section 8(a) of Executive Order 13132, the EPA included a certification 

from its Federalism Official stating that the EPA had met the Executive Order’s requirements in 

a meaningful and timely manner when it sent the draft of this final action to OMB for review 

pursuant to Executive Order 12866. A copy of the certification is included in the public version 

of the official record for this final action.

7.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments

This action has tribal implications. However, it will neither impose substantial direct 

costs on federally recognized tribal governments, nor preempt tribal law. Tribes are not required 

to develop or adopt CAA programs, but they may apply to the EPA for treatment in a manner 

similar to states (TAS) and, if approved, do so. As a result, tribes are not required to develop 

plans to implement the guidelines under CAA section 111(d) for affected EGUs in their areas of 

Indian country. To the extent that a tribal government seeks and attains TAS status for that 

purpose, these emission guidelines would require that planning requirements be met and

emission management implementation plans be executed by the tribes. The EPA notes that this 

rule does not directly impose specific requirements on affected EGUs, including those located in 

areas of Indian country, but provides guidance to any tribe approved by the EPA to address CO2

emissions from EGUs subject to section 111(d) of the CAA. The EPA also notes that none of the 

affected EGUs are owned or operated by tribal governments. 

As described in sections III.A and XI.F of the preamble to the proposed carbon pollution 

emission guidelines for existing EGUs (79 FR 34845-34847; June 18, 2014) and sections II.D 

and VI.F of the preamble to the proposed carbon pollution emission guidelines for existing 

EGUs in Indian Country and U.S. Territories (79 FR 65489; November 4, 2014), the rule was

developed after extensive and vigorous outreach to tribal governments. These tribes expressed 

varied points of view. Some tribes raised concerns about the impacts of the regulations on EGUs 

located in their areas of Indian country and the subsequent impact on jobs and revenue for their 

tribes. Other tribes expressed concern about the impact the regulations would have on the cost of 

water covered under treaty to their communities as a result of increased costs to the EGU that 

provide energy to transport the water to the tribes. Other tribes raised concerns about the impacts 

of climate change on their communities, resources, ways of life and hunting and treaty rights. 

The tribes were also interested in the scope of the guidelines being considered by the agency
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(e.g., over what time period, relationship to state and multi-state plans) and how tribes will 

participate in these planning activities.

The EPA consulted with tribal officials under the EPA Policy on Consultation and 

Coordination with Indian Tribes early in the process of developing this action to permit them to 

have meaningful and timely input into its development. A summary of that consultation follows.

Prior to issuing the supplemental proposal on November 4, 2014, the EPA consulted with 

tribes as follows. The EPA held a consultation with the Ute Tribe, the Crow Nation, and the 

Mandan, Hidatsa, Arikara (MHA) Nation on July 18, 2014. On August 22, 2014, the EPA held a 

consultation with the Fort Mojave Tribe. On September 15, 2014, the EPA held a consultation 

with the Navajo Nation. The Navajo Nation sent a letter to the EPA on September 18, 2014, 

summarizing the information presented at the consultation and the Navajo Nation’s position on 

the supplemental proposal. One issue raised by tribal officials was the potential impacts of the 

June 18, 2014 proposal and the supplemental proposal on tribes with budgets that are dependent 

on revenue from coal mines and power plants, as well as employment at the mines and power 

plants. The tribes noted the high unemployment rates and lack of access to basic services on their 

lands. Tribal officials also asked whether the rules will have any impact on a tribe’s ability to 

seek TAS. Tribal officials also expressed interest in agency actions with regard to facilitating 

power plant compliance with regulatory requirements. The Navajo Nation made the following 

recommendations in their letter of September 18, 2014: the Navajo Nation supports a mass-based 

CO2 emission standard based on the highest historical CO2 emissions since 1996; the Navajo 

Nation requests that the EPA grant the Navajo Nation carbon credits and that the Navajo Nation 

retains ownership and control of such credits; building block 2 is not appropriate for the Navajo 

Nation because there are no NGCC plants located on the Navajo Nation; building block 3 is not 

appropriate for the Navajo Nation because the Navajo people already receive virtually all of their 

electricity from carbon-free sources (mostly hydroelectric power) and their use of electricity is 

negligible compared to the generation at the power plants; building block 4 is not appropriate for 

the Navajo Nation because of the inadequate access to electricity, and the goal should allow for 

an increase in energy consumption on the Navajo Nation; the supplemental proposal should 

consider the useful life of the power plants located on the Navajo Nation; and the supplemental 

proposal should clarify that RE projects located within the Navajo Nation that provide electricity
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outside the Navajo Nation should be counted toward meeting the relevant state’s RE goals under 

the Clean Power Plan.

After issuing the supplemental proposal, the EPA held additional consultation with tribes. 

On November 18, 2014, the EPA held consultations with the following tribes: Fort McDowell 

Yavapai Nation, Fort Mojave Tribe, Hopi Tribe, Navajo Nation, and Ak-Chin Indian 

Community. A consultation with the Ute Indian Tribe of the Uintah and Ouray Reservation was 

held on December 16, 2014 and with the Gila River Indian Community on January 15, 2015. The 

Navajo Nation reiterated the concerns raised during the previous consultation. Several tribes also 

again indicated that they wanted to ensure they would be included in the development of any

tribal or federal plans for areas of Indian country. The Fort Mojave Tribe and the Navajo Nation 

expressed concern with using data from 2012 as the basis for the goal for their areas of Indian 

country; in their view, that year was not representative for the affected EGU. On April 28, 2015, 

the EPA held an additional consultation with the Navajo Nation. The issues raised by the Navajo 

Nation during the consultation included whether the EPA has the authority to set less stringent 

standards on a case-by-case basis, and a suggested “parity glide path” that would account and 

adjust for the very low electricity usage by the Navajo Nation and promote Navajo Nation

economic growth and demand.

As required by section 7(a), the EPA’s Tribal Consultation Official has certified that the 

requirements of the executive order have been met in a meaningful and timely manner. A copy

of the certification is included in the docket for this action.

7.7 Executive Order 13045: Protection of Children from Environmental Health Risks 

and Safety Risks

This action is subject to Executive Order 13045 (62 FR 19885, April 23, 1997) because it 

is an economically significant regulatory action as defined by Executive Order 12866, and the 

EPA believes that the environmental health or safety risk addressed by this action has a 

disproportionate effect on children. Accordingly, the agency has evaluated the environmental 

health and welfare effects of climate change on children. 

CO2 is a potent greenhouse gas that contributes to climate change and is emitted in 

significant quantities by fossil fuel-fired power plants. The EPA believes that the CO2 emission 
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reductions resulting from implementation of these final guidelines, as well as substantial ozone 

and PM2.5 emission reductions as a co-benefit, will further improve children’s health. 

The assessment literature cited in the EPA’s 2009 Endangerment Finding concluded that 

certain populations and lifestages, including children, the elderly, and the poor, are most 

vulnerable to climate-related health effects. The assessment literature since 2009 strengthens 

these conclusions by providing more detailed findings regarding these groups’ vulnerabilities 

and the projected impacts they may experience.

These assessments describe how children’s unique physiological and developmental 

factors contribute to making them particularly vulnerable to climate change. Impacts to children 

are expected from heat waves, air pollution, infectious and waterborne illnesses, and mental 

health effects resulting from extreme weather events. In addition, children are among those 

especially susceptible to most allergic diseases, as well as health effects associated with heat 

waves, storms, and floods. Additional health concerns may arise in low income households, 

especially those with children, if climate change reduces food availability and increases prices, 

leading to food insecurity within households.

7.8 Executive Order 13211: Actions Concerning Regulations That Significantly Affect 

Energy Supply, Distribution, or Use

This action, which is a significant regulatory action under EO 12866, is likely to have a 

significant effect on the supply, distribution, or use of energy. The EPA has prepared a Statement 

of Energy Effects for this action as follows. We estimate a [X] to [X] percent change in retail 

electricity prices on average across the contiguous U.S. in [20XX], and a [X] to [X] percent 

reduction in coal-fired electricity generation as a result of this rule. The EPA projects that utility

power sector delivered natural gas prices will increase/decrease by about [X] to [X] percent in 

[20XX]. For more information on the estimated energy effects, please refer to the economic 

impact analysis for this proposal. The analysis is presented in more detail in Chapter 3 of this 

RIA.

7.9 National Technology Transfer and Advancement Act

This rulemaking does not involve technical standards.
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7.10 Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations

Executive Order 12898 (59 FR 7629; February 16, 1994) establishes federal executive 

policy on environmental justice. Its main provision directs federal agencies, to the greatest extent 

practicable and permitted by law, to make environmental justice part of their mission by

identifying and addressing, as appropriate, disproportionately high and adverse human health or 

environmental effects of their programs, policies and activities on minority populations and lowincome populations in the U.S. The EPA defines environmental justice as the fair treatment and 

meaningful involvement of all people regardless of race, color, national origin, or income with 

respect to the development, implementation, and enforcement of environmental laws, 

regulations, and policies. The EPA has this goal for all communities and persons across this 

Nation. It will be achieved when everyone enjoys the same degree of protection from 

environmental and health hazards and equal access to the decision-making process to have a 

healthy environment in which to live, learn, and work.

Leading up to this rulemaking the EPA summarized the public health and welfare effects 

of GHG emissions in its 2009 Endangerment Finding. See, section VIII.A of the preamble where 

the EPA summarizes the public health and welfare impacts from GHG emissions that were 

detailed in the 2009 Endangerment Finding under CAA section 202(a)(1).162 As part of the 

Endangerment Finding, the Administrator considered climate change risks to minority

populations and low-income populations, finding that certain parts of the population may be 

especially vulnerable based on their characteristics or circumstances. Populations that were 

found to be particularly vulnerable to climate change risks include the poor, the elderly, the very

young, those already in poor health, the disabled, those living alone, and/or indigenous 

populations dependent on one or a few resources. See sections XII.F and XII.G, above, where the 

EPA discusses Consultation and Coordination with Tribal Governments and Protection of 

Children. The Administrator placed weight on the fact that certain groups, including children, the 

elderly, and the poor, are most vulnerable to climate-related health effects.

 

162 “Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air 

Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”).
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The record for the 2009 Endangerment Finding summarizes the strong scientific evidence 

in the major assessment reports by the U.S. Global Change Research Program (USGCRP), the 

Intergovernmental Panel on Climate Change (IPCC), and the National Research Council (NRC) 

of the National Academies that the potential impacts of climate change raise environmental 

justice issues. These reports concluded that poor communities can be especially vulnerable to 

climate change impacts because they tend to have more limited adaptive capacities and are more 

dependent on climate-sensitive resources such as local water and food supplies. In addition, 

Native American tribal communities possess unique vulnerabilities to climate change, 

particularly those impacted by degradation of natural and cultural resources within established 

reservation boundaries and threats to traditional subsistence lifestyles. Tribal communities whose 

health, economic well-being, and cultural traditions that depend upon the natural environment 

will likely be affected by the degradation of ecosystem goods and services associated with 

climate change. The 2009 Endangerment Finding record also specifically noted that Southwest 

native cultures are especially vulnerable to water quality and availability impacts. Native 

Alaskan communities are already experiencing disruptive impacts, including coastal erosion and 

shifts in the range or abundance of wild species crucial to their livelihoods and well-being. 

The most recent assessments continue to strengthen scientific understanding of climate 

change risks to minority populations and low-income populations in the United States.163 The 

new assessment literature provides more detailed findings regarding these populations’ 

vulnerabilities and projected impacts they may experience. In addition, the most recent 

assessment reports provide new information on how some communities of color may be uniquely 

 

163 Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe, Eds., 2014: Climate Change Impacts in the 

United States: The Third National Climate Assessment. U.S. Global Change Research Program, 841 pp. 

IPCC, 2014: 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. Chatterjee, 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, 1132 pp.

IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution 

of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, 

V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, 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, 688 pp.
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vulnerable to climate change health impacts in the United States. These reports find that certain 

climate change related impacts—including heat waves, degraded air quality, and extreme 

weather events—have disproportionate effects on low-income populations and some 

communities of color, raising environmental justice concerns. Existing health disparities and 

other inequities in these communities increase their vulnerability to the health effects of climate 

change. In addition, assessment reports also find that climate change poses particular threats to 

health, well-being, and ways of life of indigenous peoples in the United States.

As the scientific literature presented above and as the 2009 Endangerment Finding 

illustrates, low income populations and some communities of color are especially vulnerable to 

the health and other adverse impacts of climate change. The EPA believes that communities will 

benefit from this final rulemaking because this action directly addresses the impacts of climate 

change by limiting GHG emissions through the establishment of CO2 emission guidelines for 

existing affected fossil fuel-fired EGUs.

In addition to reducing CO2 emissions, the guidelines finalized in this rulemaking would 

reduce other emissions from affected EGUs that reduce generation due to higher adoption of 

energy efficiency and renewable energy. These emission reductions will include SO2 and NOx, 

which form ambient PM2.5 and ozone in the atmosphere, and hazardous air pollutants (HAP), 

such as mercury and hydrochloric acid. In the final rule revising the annual PM2.5 NAAQS,164 the 

EPA identified low-income populations as being a vulnerable population for experiencing 

adverse health effects related to PM exposures. Low-income populations have been generally 

found to have a higher prevalence of pre-existing diseases, limited access to medical treatment, 

and increased nutritional deficiencies, which can increase this population’s risk to PM-related 

and ozone-related effects.165 Therefore, in areas where this rulemaking reduces exposure to 

PM2.5, ozone, and methylmercury, low-income populations will also benefit from such emissions 

reductions. The RIA for this rulemaking, included in the docket for this rulemaking, provides 

 

164 “National Ambient Air Quality Standards for Particulate Matter, Final Rule,” 78 FR 3086 (Jan. 15, 2013).

165 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. December. 

Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.
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additional information regarding the health and ecosystem effects associated with these emission 

reductions. 

Additionally, as outlined in the community considerations section IX of the preamble, the 

EPA has put a number of conditions in place to help ensure that this action will not have 

potential disproportionately high and adverse human health or environmental effects on all 

communities (low income, communities of color, indigenous populations). As described in the 

community considerations section of the preamble, the EPA also conducted a proximity analysis, 

which is available in the docket of this rulemaking.

7.11 Congressional Review Act (CRA)

This final action is subject to the CRA, and the EPA will submit a rule report to each 

House of the Congress and to the Comptroller General of the United States. This action is a 

“major rule” as defined by 5 U.S.C. 804(2).
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CHAPTER 8: COMPARISON OF BENEFITS AND COSTS

8.1 Comparison of Benefits and Costs

The benefits, costs, and net benefits of the illustrative plan scenarios are presented in this 

chapter of the Regulatory Impact Analysis (RIA) for the Final Carbon Pollution Emission 

Guidelines for Existing Stationary Sources: Electric Utility Generating Units. As discussed in 

Chapter 1, the EPA is establishing carbon dioxide (CO2) emission performance rates for two 

source categories of existing fossil fuel-fired EGUs, fossil fuel-fired electric utility steam 

generating units and stationary combustion turbines. Given the flexibilities afforded states in 

complying with the emission guidelines, the benefits, cost and economic impacts reported in this 

RIA are not definitive estimates, but are instead illustrative of plan approaches states may take.

The EPA has used the social cost of carbon estimates presented in the Technical Support 

Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis 

Under Executive Order 12866 (May 2013, Revised June 2015) (“current SC-CO2 TSD”) to 

analyze CO2 climate impacts of this rulemaking.

166 We refer to these estimates, which were 

developed by the U.S. government, as “SC-CO2 estimates.” The SC-CO2 is an estimate of the 

monetary value of impacts associated with a marginal change in CO2 emissions in a given year. 

The four SC-CO2 estimates are associated with different discount rates (model average at 2.5 

percent discount rate, 3 percent, and 5 percent; 95th percentile at 3 percent), and each increases 

over time. In this comparison of benefits and costs, the EPA provides the estimate of climate 

benefits associated with the SC-CO2 value deemed to be central in the current SC-CO2 TSD (the 

model average at 3% discount rate). In addition to reducing CO2 emissions, implementing these 

final emission guidelines is expected to reduce emissions of SO2 and NOX, which are precursors 

to formation of ambient PM2.5, as well as directly emitted fine particles. Therefore, reducing 

these emissions would also reduce human exposure to ambient PM2.5 and ozone precursors, and 

 

166 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost 

of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and 

Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised June 2015). 

Available at:
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the associated PM2.5 and ozone related health effects. Tables 8-1 and 8-2 provide a summary of 

the climate benefits, air quality co-benefits, and costs for the illustrative rate-based and massbased plan scenarios. 

The EPA could not monetize important categories of impacts. Due to current data and 

modeling limitations, our estimates of the benefits from reducing CO2 emissions do not include 

important impacts like ocean acidification or potential tipping points in natural or managed 

ecosystems. Unquantified impacts also include those associated with changes in emissions of 

other pollutants that affect the climate, such as methane. In addition, the analysis does not 

quantify co-benefits from reducing exposure to SO2, NOx, and hazardous air pollutants (e.g., 

mercury and hydrogen chloride), as well as ecosystem effects and visibility impairment.

Based upon the foregoing discussion, it remains clear that this final rule’s combined 

climate benefits and human health co-benefits associated with the reduction in other air 

pollutants substantially outweigh the illustrative plan scenario costs.
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Table 8-1. Monetized Benefits, Costs, and Net Benefits Under the Rate-based 

Illustrative Plan Approach (billions of 2011$)a

Rate-Based Scenario

2020 2025 2030

Climate Benefits b

5% discount rate $0.80 $3.1 $6.4 

3% discount rate $2.8 $10 $20 

2.5% discount rate $4.1 $15 $29 

95th percentile at 3% 

discount rate $8.2 $31 $61

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7%

Air Quality Health 

Co-benefits c $0.7 to $1.8 $0.6 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31

Compliance Costs d

$2.4 $1.16 $8.4

Net Benefits e

$1.1 to $2.2 $1.0 to $2.1 $17 to $27 $16 to $25 $26 to $46 $25 to $43

Non-Monetized 

Benefits

Non-monetized climate benefits

Reductions in exposure to ambient NO2 and SO2

Reductions in mercury deposition

Ecosystem benefits associated with reductions in emissions of NOX, SO2, PM, and mercury

Visibility improvement

a All are rounded to two significant figures, so figures may not sum.

b The climate benefit estimate in this summary table reflects global impacts from CO2 emission changes and does not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SC-CO2 than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The benefit 

estimates in this table are based on the average SC-CO2 estimated for a 3% discount rate, however we emphasize the 

importance and value of considering the full range of SC-CO2 values. As shown in the RIA, climate benefits are also 

estimated using the other three SC-CO2 estimates (model average at 2.5 percent discount rate, 3 percent, and 5 

percent; 95th percentile at 3 percent). The SC-CO2 estimates are year-specific and increase over time.

cThe air quality health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission reductions 

of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions from different 

epidemiology studies. The reduction in premature fatalities each year accounts for over 98 percent of total 

monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type.

d Total costs are approximated by the illustrative plan scenario costs estimated using the Integrated Planning Model 

for the proposed guidelines and a discount rate of approximately 5 percent. This estimate includes monitoring, 

recordkeeping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calculated using the global SC-CO2 at a 3 percent discount 

rate (model average). The RIA includes combined climate and health estimates based on additional discount rates.

f Estimates in the table are presented for three analytical years with air quality co-benefits calculated using two 

discount rates. The estimates of co-benefits are annual estimates in each of the analytical years, reflecting 

discounting of mortality benefits over the cessation lag between changes in PM2.5 concentrations and changes in 

risks of premature death (see RIA Chapter 4 for more details), and discounting of morbidity benefits due to the 

multiple years of costs associated with some illnesses. The estimates are not the present value of the benefits of the 

rule over the full compliance period.

Table 8-2. Monetized Benefits, Costs, and Net Benefits Under the Mass-based 

Illustrative Plan Approach Scenario (billions of 2011$) a
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8-4

Mass-Based Scenario

2020 2025 2030

Climate Benefits b

5% discount rate $[X] $[X] $[X]

3% discount rate $[X] $[X] $[X]

2.5% discount rate $[X] $[X] $[X]

95th percentile at 3% discount 

rate $[X] $[X] $[X]

Air Quality Co-benefits Discount Rate

3% 7% 3% 7% 3% 7%

Air Quality Health Cobenefits c

$[X] to 

$[X]

$[X] to 

$[X]

$[X] to 

$[X]

$[X] to 

$[X]

$[X] to 

$[X]

$[X] to 

$[X]

Costs d

$[X] $[X] $[X]

Net Benefits e $[X] to 

$[X]

$[X] to 

$[X]

$[X] to 

$[X]

$[X] to 

$[X]

$[X] to 

$[X]

$[X] to 

$[X]

Non-Monetized Benefits

Non-monetized climate benefits

Reductions in exposure to ambient NO2 and SO2

Reductions in mercury deposition

Ecosystem benefits associated with reductions in emissions of NOX, SO2, PM, and 

mercury

Visibility improvement

a All are rounded to two significant figures, so figures may not sum.

bThe climate benefit estimate in this summary table reflects global impacts from CO2 emission changes and does not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SC-CO2 than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The benefit 

estimates in this table are based on the average SC-CO2 estimated for a 3% discount rate, however we emphasize the 

importance and value of considering the full range of SC-CO2 values. As shown in the RIA, climate benefits are also 

estimated using the other three SC-CO2 estimates (model average at 2.5 percent discount rate, 3 percent, and 5 

percent; 95th percentile at 3 percent). The SC-CO2 estimates are year-specific and increase over time. 

c The air quality health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission reductions 

of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions from different 

epidemiology studies. The reduction in premature fatalities each year accounts for over 98 percent of total 

monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of their 

chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type.

dTotal costs are approximated by the illustrative plan scenario costs estimated using the Integrated Planning Model 

for the proposed guidelines and a discount rate of approximately 5 percent. This estimate includes monitoring, 

recordkeeping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calculated using the global SC-CO2 at a 3 percent discount 

rate (model average). The RIA includes combined climate and health estimates based on additional discount rates.

f Estimates in the table are presented for three analytical years with air quality co-benefits calculated using two 

discount rates. The estimates of co-benefits are annual estimates in each of the analytical years, reflecting 

discounting of mortality benefits over the cessation lag between changes in PM2.5 concentrations and changes in 

risks of premature death (see RIA Chapter 4 for more details), and discounting of morbidity benefits due to the 

multiple years of costs associated with some illnesses. The estimates are not the present value of the benefits of the 

rule over the full compliance period.
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8-5

8.2 Uncertainty Analysis

The Office of Management and Budget’s circular Regulatory Analysis (Circular A-4) 

provides guidance on the preparation of regulatory analyses required under E.O. 12866, and 

requires an uncertainty analysis for rules with annual benefits or costs of $1 billion or more.167

This final rulemaking surpasses that threshold for both benefits and costs. Throughout the RIA,

we considered a number of sources of uncertainty, both quantitatively and qualitatively, on 

benefits and costs. We summarize three key elements of our analysis of uncertainty here:

 Evaluating uncertainty in the illustrative plan approaches that states will implement, 

which influences both costs and benefits. 

 Assess uncertainty in the methods used to calculate the health co-benefits associated with 

the reduction in PM2.5 and ozone and the use of a benefits-per-ton approach in estimating 

these co-benefits. 

 Characterizing uncertainty in monetizing climate-related benefits. 

Some of these elements are evaluated using probabilistic techniques, whereas for others 

the underlying likelihoods of certain outcomes are unknown and we use scenario analysis to 

evaluate their potential effect on the benefits and costs of this rulemaking.

8.2.1 Uncertainty in Costs and Illustrative Plan Approaches

The calculation of the state goals is based on an evaluation of methods for reducing the 

carbon emissions intensity of electricity generation that may be achieved at reasonable cost. Our 

best estimates of the costs of these methods of intensity reduction are reported within the cost 

analysis of this rule and are included in the cost modeling in the RIA.

A significant source of uncertainty under this regulation is the ultimate approach states 

will take to comply with the guidelines, which will affect both the costs and benefits of this rule. 

For this reason we modeled two potential illustrative plan scenarios for each regulatory option: 

the rate-based illustrative plan scenario and the mass-based illustrative plan scenario. The cost...

 

167 Office of Management and Budget (OMB), 2003, Circular A-4, 

http://www.whitehouse.gov/omb/circulars_a004_a-4 and OMB, 2011. Regulatory Impact Analysis: A Primer.

http://www.whitehouse.gov/sites/default/files/omb/inforeg/regpol/circular-a-4_regulatory-impact-analysis-aprimer.pdf
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8-6

The reductions of CO2 and other pollutants from fossil fuel combustion....

However, from this analysis we see that the net benefits of the two scenarios, given consistent 

assumptions, 

8.2.2 Uncertainty Associated with Estimating the Social Cost of Carbon

The 2010 SC-CO2 TSD noted a number of limitations to the SC-CO2 analysis, including 

the incomplete way in which the integrated assessment models (IAM) capture catastrophic and 

non-catastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion.168 Currently integrated assessment models do not assign value to all of the important 

physical, ecological, and economic impacts of climate change recognized in the climate change 

literature due to a lack of precise information on the nature of damages and because the science 

incorporated into these models understandably lags behind the most recent research. These 

individual limitations do not all work in the same direction in terms of their influence on the SCCO2 estimates, though taken together they suggest that the SC-CO2 estimates are likely 

conservative. In particular, the IPCC Fourth Assessment Report (2007) concluded that “It is very

likely that [SC-CO2 estimates] underestimate the damage costs because they cannot include 

many non-quantifiable impacts” and the IPCC Fifth Assessment report observed that SC-CO2

estimates continue to omit various impacts that would likely increase damages. The 95th

percentile estimate was included in the recommended range for regulatory impact analysis, in 

part, to address these concerns.

The modeling underlying the development of the SC-CO2 estimates addressed 

uncertainty in several ways. An ensemble of three IAMs were used to generate the SC-CO2

estimates to capture differences in model structures that, in part, reflect uncertainty in the 

scientific literature about these relationships. Parametric uncertainty was explicitly addressed in 

 

168 Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 

12866, Interagency Working Group on Social Cost of Carbon, with participation by the Council of Economic 

Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of 

Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of 

Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and 

Department of Treasury (February 2010). Available at: 

<http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf>.
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8-7

each IAM, though to differing degrees, through Monte Carlo simulations in which explicit 

probability distributions for key parameters were specified, including the equilibrium climate 

sensitivity, which represents the long-run responsiveness of the climate to increasing GHG 

concentrations. Furthermore, the analysis considered five different socioeconomic and emissions 

forecasts to capture the sensitivity of the SC-CO2 estimates to key exogenous projections used in 

the modeling. Finally, the results were calculated for three discount rates, which were selected, in 

part, to reflect uncertainty about how interest rates may change over time and the possibility that 

climate damages are positively correlated with uncertain future economic activity. This analysis 

produced 45 different distributions of the SC-CO2 estimates for each emissions year. To produce 

a range of plausible estimates that are manageable in regulatory analysis but still reflects the 

uncertainty in the results four point estimates were recommended. The use of this range of point 

estimates in this rulemaking helps to reflect the uncertainty in the SC-CO2 estimates. Chapter 4 

of this RIA provides a comprehensive discussion about the methodology and application of the 

SC-CO2; see both the 2010 TSD and current SC-CO2 TSD for a full description.

In addition, OMB’s Office of Information and Regulatory Affairs received comments 

regarding uncertainty and the SC-CO2 estimates in response to a separate request for public 

comment on the approach used to develop the estimates. Commenters discussed the analyses and 

presentation of uncertainty in the TSD as well as the implications of uncertainty for use of the 

SC-CO2 estimates in regulatory impact analysis. In their response, the interagency working 

group (IWG) acknowledged uncertainty in the SC-CO2 estimates but disagreed with commenters 

that suggested the uncertainty undermines use of the SC-CO2 estimates in regulatory impact 

analysis. The IWG went on to note that the uncertainty in the SC-CO2 estimates is fully

acknowledged and comprehensively discussed in the TSDs and supporting academic literature, 

and that while all regulatory impact analysis involves uncertainty, these analyses can provide 

useful information to decision makers and the public. See the IWG Response to Comments for 

the complete response. 

8.2.3 Uncertainty Associated with PM2.5 and Ozone Health Co-Benefits Assessment 

Our estimate of the total monetized co-benefits is based on 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 
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8-8

PM2.5-related premature mortality, which accounts for 98 percent of the monetized PM2.5 health 

co-benefits:

 We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The PM ISA 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).

 We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health co-benefits from 

reducing fine particles in areas with varied concentrations of PM2.5, including both 

areas that do not meet the fine particle standard and those areas that are in 

attainment, down to the lowest modeled concentrations. 

 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 cobenefits at different discount rates. EPA quantitatively assessed uncertainty in the air 

quality health co-benefits, including probabilistic approaches. 

In addition, EPA provides the 95th percentile confidence interval for avoided PM-related 

premature deaths and the associated economic valuation using two key epidemiology studies. 

EPA provides the PM-related results using alternate concentration-response relationship 

provided by an expert elicitation and alternate ozone-related results using concentration-response 

relationships provided by alternate epidemiology studies. In addition, we include an assessment 

of the distribution of population exposure in the modeling underlying the benefit-per-ton 

estimates. For further discussion and characterization of those uncertainties influencing the 

benefit assessment, see Chapter 4 of this RIA. 
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8-9

As noted and described in Chapter 4 of this RIA, we use a benefit-per-ton approach to 

quantify health co-benefits. All benefit-per-ton have inherent limitations, including that the 

estimates reflect the geographic distribution of the modeled sector emissions, which may not 

match the emission reductions anticipated by the final emission guidelines, and they may not 

reflect local variability in population density, meteorology, exposure, baseline health incidence 

rates, or other local factors for any specific location. In addition, these estimates reflect the 

regional average benefit-per-ton for each ambient PM2.5 precursor emitted from EGUs, which 

assumes a linear atmospheric response to emission reductions. The regional benefit-per-ton 

estimates, although less subject to these types of uncertainties than national estimates, still 

should be interpreted with caution. Even though we assume that all fine particles have equivalent 

health effects, the benefit-per-ton estimates vary between precursors depending on the location 

and magnitude of their impact on PM2.5 levels, which drive population exposure. 

8.3 References

Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis 

Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the 

IPCC. Available at

<http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_synt

hesis_report.htm>. Accessed June 6, 2015.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. National Academies Press. Washington, DC.

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. EPACOUNCIL-LTR-05-001. December. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$File/co

uncil_ltr_05_001.pdf>. Accessed June 4, 2015.

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. Available at

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. Accessed June 4, 2015.
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\

United States

Environmental 

Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC

Publication No.

EPA-452/R-15-003

July 2015