From: Lamason, Bill 

Sent: Friday, July 17, 2015 9:41 PM 

To: nfrey@omb.eop.gov 

Cc: aszabo@omb.eop.gov; Vasu, Amy; Harvey, Reid; Culligan, Kevin 

Subject: EPA's draft updated RIA and revised goal computation document for interagency 

review 

Nathan, 

Please see attached the updated RIA and the latest version of the goal computation technical 

support document (TSD) for interagency review. The latest goal computation TSD contains a new 

section describing the methodology for producing mass goals. Both documents are accompanied 

by data spreadsheets. 

 

Please let us know if you have any questions about this. 

Thank you. 

<< EO12866_CPP_2060_AR33_RIA Final_20150717.docx >> 

<< EO12866_CPP_2060_AR33_RIA_Spreadsheet Final_20150717.xlsx >> 

<< 111(d) Technology Rate Quantification and State Goals_071715.docx >> 

<< Baseline, Category-specific rates, and State Goal Quantification (Draft OMB version 7-

16).xlsx >> 
EO12866_CPP 2060 AR33 RIA Final_20150717 

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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

iv 

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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 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

ACRONYMS ....................................................................................................................................xx

EXECUTIVE SUMMARY ..............................................................................................................ES-1

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

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

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

ES.4 Emissions Reductions .......................................................................................ES-6

ES.5 Costs .................................................................................................................ES-8

ES.6 Monetized Climate Benefits and Health Co-benefits .....................................ES-10

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

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

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

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

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

ES.9 Employment Impacts ......................................................................................ES-24

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

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

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

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

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

1.2.2 Health and Welfare Impacts from Climate Change ........................................ 1-2

1.2.3 Market Failure ................................................................................................ 1-3

1.3 Summary of Regulatory Analysis .............................................................................. 1-4

1.4 Background for the Final Emission Guidelines ......................................................... 1-4

1.4.1 Base Case and Years of Analysis ................................................................... 1-4

1.4.2 Definition of Affected Sources ....................................................................... 1-5

1.4.3 Regulated Pollutant......................................................................................... 1-6

1.4.4 Emission Guidelines ....................................................................................... 1-6

1.4.5 State Plans....................................................................................................... 1-6

1.5 Organization of the Regulatory Impact Analysis ....................................................... 1-8

1.6 References .................................................................................................................. 1-8

CHAPTER 2: ELECTRIC POWER SECTOR INDUSTRY PROFILE ................................................... 2-1

2.1 Introduction ................................................................................................................ 2-1

2.2 Power Sector Overview ............................................................................................. 2-1

2.2.1 Generation ...................................................................................................... 2-1

2.2.2 Transmission ................................................................................................... 2-9
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2.2.3 Distribution ................................................................................................... 2-10

2.3 Sales, Expenses and Prices ...................................................................................... 2-10

2.3.1 Electricity Prices ........................................................................................... 2-11

2.3.2 Prices of Fossil Fuels Used for Generating Electricity................................. 2-16

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

2012 17

2.4 Deregulation and Restructuring ............................................................................... 2-19

2.5 Emissions of Greenhouse Gases from Electric Utilities .......................................... 2-24

2.6 Carbon Dioxide Control Technologies .................................................................... 2-27

2.6.1 Carbon Capture and Storage ......................................................................... 2-29

2.6.2 Geologic and Geographic Considerations for Geologic Sequestration ........ 2-34

2.6.3 Availability of Geologic Sequestration in Deep Saline Formations ............. 2-38

2.6.4 Availability of CO2 Storage via Enhanced Oil Recovery (EOR) ................. 2-38

2.7 State Policies on GHG and Clean Energy Regulation in the Power Sector ............. 2-40

2.8 Revenues and Expenses ........................................................................................... 2-43

2.9 Natural Gas Market .................................................................................................. 2-44

2.10 References ................................................................................................................ 2-48

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

3.1 Introduction ................................................................................................................ 3-1

3.2 Overview .................................................................................................................... 3-1

3.3 Power Sector Modelling Framework ......................................................................... 3-1

3.4 Recent Updates to EPA’s Base Case using IPM (v.5.15) .......................................... 3-4

3.5 State Goals in this Final Rule.................................................................................... 3-5

3.6 Illustrative Plan Approaches Analyzed ...................................................................... 3-8

3.7 Demand-Side Energy Efficiency ............................................................................. 3-13

3.7.1 Demand-Side Energy Efficiency Improvements (Electricity Demand 

Reductions) .............................................................................................................. 3-13

3.7.2 Demand-Side Energy Efficiency Costs ........................................................ 3-15

3.8 Monitoring, Reporting, and Recordkeeping Costs .................................................. 3-16

3.9 Projected Power Sector Impacts .............................................................................. 3-19

3.9.1 Projected Emissions (in the Contiguous U.S.) ............................................. 3-19

3.9.2 Projected Compliance Costs (in the Contiguous U.S.) ................................. 3-22

3.9.3 Projected Compliance Actions for Emissions Reductions ........................... 3-24

3.9.4 Projected Generation Mix ............................................................................. 3-26

3.9.5 Projected Incremental Retirements ............................................................... 3-31

3.9.6 Projected Capacity Additions ....................................................................... 3-32

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

Sector 34

3.9.8 Projected Fuel Price, Market, and Infrastructure Impacts ............................ 3-34

3.9.9 Projected Retail Electricity Prices ................................................................ 3-36

3.9.10 Projected Electricity Bill Impacts ................................................................. 3-41

3.10 Projected Primary PM Emissions from Power Plants .............................................. 3-41

3.11 Limitations of Analysis ............................................................................................ 3-42

3.12 Social Costs .............................................................................................................. 3-44

3.13 References ................................................................................................................ 3-47
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APPENDIX 3A: ANALYSIS OF POTENTIAL UPSTREAM METHANE EMISSIONS CHANGES IN 

NATURAL GAS SYSTEMS AND COAL MINING ................................................................... 3A-1

3A.1 General Approach ............................................................................................ 3A-2

3A.1.1 Analytical Scope .......................................................................................... 3A-2

3A.1.2 Coal Mining Source Description ................................................................. 3A-3

3A.1.3 Natural Gas Systems Source Description .................................................... 3A-4

3A.1.4 Illustrative Plan Approaches Examined ...................................................... 3A-6

3A.1.5 Activity Drivers ........................................................................................... 3A-6

3A.2 Results .............................................................................................................. 3A-7

3A.3 Uncertainties and Limitations .......................................................................... 3A-8

3A.4 References ........................................................................................................ 3A-9

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

4.1 Introduction ................................................................................................................ 4-1

4.2 Estimated Climate Benefits from CO2 ....................................................................... 4-1

4.2.1 Climate Change Impacts ................................................................................. 4-2

4.2.2 Social Cost of Carbon ..................................................................................... 4-3

4.3 Estimated Human Health Co-Benefits ..................................................................... 4-10

4.3.1 Health Impact Assessment for PM2.5 and Ozone .......................................... 4-13

4.3.2 Economic Valuation for Health Co-benefits ................................................ 4-18

4.3.3 Benefit-per-ton Estimates for PM2.5 ............................................................. 4-20

4.3.4 Benefit-per-ton Estimates for Ozone ............................................................ 4-21

4.3.5 Estimated Health Co-Benefits Results ......................................................... 4-22

4.3.6 Characterization of Uncertainty in the Estimated Health Co-benefits ......... 4-36

4.4 Combined Climate Benefits and Health Co-Benefits Estimates.............................. 4-42

4.5 Unquantified Co-benefits ......................................................................................... 4-46

4.5.1 HAP Impacts................................................................................................. 4-48

4.5.2 Additional NO2 Health Co-Benefits ............................................................. 4-52

4.5.3 Additional SO2 Health Co-Benefits .............................................................. 4-53

4.5.4 Additional NO2 and SO2 Welfare Co-Benefits ............................................. 4-54

4.5.5 Ozone Welfare Co-Benefits.......................................................................... 4-55

4.5.6 Carbon Monoxide Co-Benefits ..................................................................... 4-55

4.5.7 Visibility Impairment Co-Benefits ............................................................... 4-56

4.6 References ................................................................................................................ 4-56

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

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

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

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

Quality Modeling of the Proposed Clean Power Plan ................................................... 4A-5

4A.4 Regional Ozone Benefit-per-Ton Estimates .................................................. 4A-14

4A.5 References ...................................................................................................... 4A-17

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

5.2 Methods...................................................................................................................... 5-2

5.3 Summary of Secondary Market Impacts of Energy Price Changes ........................... 5-3

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

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

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

5.4 Effect of Changes in Input Demand from Electricity Sector ..................................... 5-6

5.5 Conclusions ................................................................................................................ 5-6

5.6 References .................................................................................................................. 5-7

CHAPTER 6: EMPLOYMENT IMPACT ANALYSIS ......................................................................... 6-1

6.1 Introduction ................................................................................................................ 6-1

6.2 Economic Theory and Employment .......................................................................... 6-2

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

6.3.1 Regulated Sector ............................................................................................. 6-7

6.3.2 Economy-Wide ............................................................................................... 6-8

6.3.3 Labor Supply Impacts ................................................................................... 6-10

6.4 Recent Employment Trends ..................................................................................... 6-11

6.4.1 Electric Power Generation ............................................................................ 6-11

6.4.2 Fossil Fuel Extraction ................................................................................... 6-12

6.4.3 Clean Energy Employment Trends............................................................... 6-14

6.5 Projected Sectoral Employment Changes due to the Final Emission Guidelines .... 6-18

6.5.1 Projected Changes in Employment in Electricity Generation and Fossil 

Fuel Extraction ......................................................................................................... 6-19

6.5.2 Projected Changes in Employment in Demand-Side Energy Efficiency 

Activities .................................................................................................................. 6-26

6.6 Conclusion ............................................................................................................... 6-34

6.7 References ................................................................................................................ 6-36

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

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

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

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

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

Capacity .................................................................................................................. 6A-5

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

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

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

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

13563: Improving Regulation and Regulatory Review .................................................... 7-1

7.2 Paperwork Reduction Act (PRA) ............................................................................... 7-5

7.3 Regulatory Flexibility Act (RFA) .............................................................................. 7-6

7.4 Unfunded Mandates Reform Act (UMRA) ............................................................... 7-7
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7.5 Executive Order 13132: Federalism .......................................................................... 7-8

7.6 Executive Order 13175: Consultation and Coordination with Indian Tribal 

Governments ................................................................................................................... 7-12

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

and Safety Risks .............................................................................................................. 7-15

7.8 Executive Order 13211: Actions Concerning Regulations That Significantly 

Affect Energy Supply, Distribution, or Use .................................................................... 7-16

7.9 National Technology Transfer and Advancement Act ............................................ 7-16

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

Minority Populations and Low-Income Populations ...................................................... 7-16

7.11 Congressional Review Act (CRA) ........................................................................... 7-19

CHAPTER 8: COMPARISON OF BENEFITS AND COSTS ................................................................ 8-1

8.1 Comparison of Benefits and Costs ............................................................................. 8-1

8.2 Uncertainty Analysis .................................................................................................. 8-5

8.2.1 Uncertainty in Costs and Illustrative Plan Approaches .................................. 8-5

8.2.2 Uncertainty Associated with Estimating the Social Cost of Carbon .............. 8-6

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

Assessment ................................................................................................................. 8-8

8.3 References .................................................................................................................. 8-9

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

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LIST OF TABLES

Table ES-1. Emission Performance Rates (Adjusted Output-Weighted-Average Pounds 

of CO2 Per Net MWh from All Affected Fossil Fuel-Fired EGUs) ..............................ES-2

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

Illustrative Plan Approach .............................................................................................ES-6

Table ES-3. Climate and Air Pollutant Emission Reductions for the Mass-Based 

Illustrative Plan Appproach ...........................................................................................ES-7

Table ES-4. Projected CO2 Emission Reductions, Relative to 2005 ....................................ES-7

Table ES-5. Compliance Costs for the Illustrative Rate-Based and Mass-Based Plan 

Approaches ....................................................................................................................ES-9

Table ES-6. Quantified and Unquantified Benefits ............................................................ES-11

Table ES-7. Combined Estimates of Climate Benefits and Health Co-Benefits for RateBased Approach (billions of 2011$) ............................................................................ES-19

Table ES-8. Combined Estimates of Climate Benefits and Health Co-benefits for MassBased Approach (billions of 2011$) ............................................................................ES-20

Table ES-9. Monetized Benefits, Compliance Costs, and Net Benefits Under the Ratebased Illustrative Plan Approach (billions of 2011$) ..................................................ES-22

Table ES-10. Monetized Benefits, Compliance Costs, and Net Benefits under the Massbased Illustrative Plan Approach (billions of 2011$) ..................................................ES-23

Table ES-11. Summary Table of Important Energy Market Impacts (Percent Change from 

Base Case) ....................................................................................................................ES-24

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

Table 2-2. Net Generation in 2002 and 2012 (Trillion kWh = TWh).................................. 2-6

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

Thermal Efficiency (Heat Rate) ....................................................................................... 2-7

Table 2-4. Total U.S. Electric Power Industry Retail Sales in 2012 (billion kWh) ........... 2-11

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

metric tonnes of CO2 equivalent) ................................................................................... 2-25
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Table 2-6. Greenhouse Gas Emissions from the Electricity Sector (Generation, 

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

equivalent)...................................................................................................................... 2-26

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

Modeling Application .................................................................................................... 2-27

Table 2-8. Total CO2 Storage Resource (DOE-NETL) ..................................................... 2-36

Table 2-9. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric 

Utilities for 2010 (nominal $millions) ........................................................................... 2-44

Table 3-1. Statewide CO2 Emission Performance Goals, Rate-based and Mass-based ....... 3-7

Table 3-2. Demand-Side Energy Efficiency Plan Scenario: Net Cumulative Demand 

Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales) ............................ 3-14

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

[Contiguous U.S.] (at discount rates of 3 percent and 7 percent, billions 2011$) ......... 3-16

Table 3-4. Years 2020, 2025 and 2030: Summary of State and Industry Annual Respondent 

Burden and Cost of Reporting and Recordkeeping Requirements (2011$) .................. 3-19

Table 3-5. Projected CO2 Emission Impacts, Relative to Base Case ................................. 3-20

Table 3-6. Projected CO2 Emission Impacts, Relative to 2005 ......................................... 3-20

Table 3-7. Projected Non-CO2 Emission Impacts, 2020-2030 .......................................... 3-21

Table 3-8. Annualized Compliance Costs Including Monitoring, Reporting and 

Recordkeeping Costs Requirements (billions of 2011$) ............................................... 3-23

Table 3-9. Total Power Sector Generating Costs (IPM) (billions 2011$) ......................... 3-23

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

Combined Cycle Capacity ............................................................................................. 3-26

Table 3-11. Generation Mix (thousand GWh) ..................................................................... 3-28

Table 3-12. Total Generation Capacity by 2020-2030 (GW) .............................................. 3-32

Table 3-13. Projected Capacity Additions, Gas (GW) ......................................................... 3-33

Table 3-14. Projected Capacity Additions, Renewable (GW) ............................................. 3-33

Table 3-15. Coal Production for the Electric Power Sector, 2025 ....................................... 3-34

Table 3-16. Power Sector Gas Use ...................................................................................... 3-34
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Table 3-17. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu) .. 3-36

Table 3-18. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices 

(2011$/MMBtu) ............................................................................................................. 3-36

Table 3-19. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) .................................................................................................................... 3-38

Table 3-20. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) .................................................................................................................... 3-39

Table 3-21. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) .................................................................................................................... 3-40

Table 3-22. Projected Changes in Average Electricity Bills ................................................ 3-41

Table 3A-1. Base Year Upstream Methane-Related Emissions in the U.S. GHG Inventory 

 ......................................................................................................................... 3A-6

Table 3A-2. Projected Coal Production Impacts ................................................................. 3A-7

Table 3A-3. Projected Natural Gas Production Impacts ...................................................... 3A-7

Table 3A-4. Potential Upstream Emissions Changes .......................................................... 3A-8

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

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

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

Emission Guidelines in 2020 (billions of 2011$) ............................................................ 4-8

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

Emission Guidelines in 2025 (billions of 2011$) ............................................................ 4-9

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

Emission Guidelines in 2030 (billions of 2011$) ............................................................ 4-9

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

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$) .......................... 4-23

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$) .......................... 4-23

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$) .......................... 4-24
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Table 4-10. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines 

Rate-based Illustrative Plan Approach in 2020 (thousands of short tons) ..................... 4-24

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

Rate-based Illustrative Plan Approach in 2025 (thousands of short tons) ..................... 4-24

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

Rate-based Illustrative Plan Approach in 2030 (thousands of short tons) ..................... 4-25

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

Mass-based Illustrative Plan Approach in 2020 (thousands of short tons) .................... 4-25

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

Mass-based Illustrative Plan Approach in 2025 (thousands of short tons) .................... 4-25

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

Mass-based Illustrative Plan Approach in 2030 (thousands of short tons) .................... 4-25

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

Emission Guidelines Rate-based Illustrative Plan Approach in 2020 (billions of 

2011$) .......................................................................................................................... 4-26

Table 4-17. Summary of Estimated Monetized Health Co-Benefits for the Final 

Emission Guidelines Rate-based Illustrative Plan Approach in 2025 (billions of 

2011$) .......................................................................................................................... 4-26

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

Emission Guidelines Rate-based Illustrative Plan Approach in 2030 (billions of 

2011$) .......................................................................................................................... 4-27

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

Emission Guidelines Mass-based Illustrative Plan Approach in 2020 (billions of 

2011$) .......................................................................................................................... 4-27

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

Emission Guidelines Mass-based Illustrative Plan Approach in 2025 (billions o 

2011$) * ......................................................................................................................... 4-28

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

Emission Guidelines Mass-based Illustrative Plan Approach in 2030 (billions of 

2011$) .......................................................................................................................... 4-28

Table 4-22. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for the Final Emission Guidelines Rate-based Illustrative Plan 

Approach in 2020 .......................................................................................................... 4-29
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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 Illustrative Plan 

Approach in 2025 .......................................................................................................... 4-30

Table 4-24. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Final Emission Guidelines Rate-based Illustrative Plan 

Approach in 2030 .......................................................................................................... 4-31

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

Approach in 2020 .......................................................................................................... 4-32

Table 4-26. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Final Emission Guidelines Mass-based Illustrative Plan 

Approach in 2025 .......................................................................................................... 4-33

Table 4-27. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Final Emission Guidelines Mass-based Illustrative Plan 

Approach in 2030 .......................................................................................................... 4-34

Table 4-28. Population Exposure in the Clean Power Plan Proposal Option 1 State 

Scenario Modeling (used to generate the benefit-per-ton estimates) Above and 

Below Various Concentrations Benchmarks in the Underlying Epidemiology 

Studies ......................................................................................................................... 4-40 

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

Guidelines in 2020 (billions of 2011$) .......................................................................... 4-44

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

Guidelines in 2025 (billions of 2011$)* ........................................................................ 4-44

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

Guidelines in 2030 (billions of 2011$)* ........................................................................ 4-45

Table 4-32. Unquantified Health and Welfare Co-benefits Categories ............................... 4-47

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) ........................................................................................................................ 4A-2

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) ......................................................................................................................... 4A-4

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$) ......................... 4A-9
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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$) ......................... 4A-9

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$) ....................... 4A-10

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

Quality Modeling from Proposed Clean Power Plan in 2020..................................... 4A-11

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

Quality Modeling from Proposed Clean Power Plan in 2025..................................... 4A-12

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

Quality Modeling from Proposed Clean Power Plan in 2030..................................... 4A-13

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

Quality Modeling from Proposed Clean Power Plan in 2020 (2011$) ....................... 4A-15

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

Quality Modeling from Proposed Clean Power Plan in 2025 (2011$) ....................... 4A-15

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

Quality Modeling from Proposed Clean Power Plan in 2030 (2011$) ....................... 4A-15

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

Quality Modeling from Proposed Clean Power Plan in 2020..................................... 4A-16

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

Quality Modeling from Proposed Clean Power Plan in 2025..................................... 4A-16

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

Quality Modeling from Proposed Clean Power Plan in 2030..................................... 4A-16

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 ...................................................................................................................... 5-4

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

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

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

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

(Number of Job-Yearsb

 of Employment in a Single Year) ............................................ 6-25

Table 6-5. Engineering-Baseda Changes in Labor Utilization, Mass-Based Illustrative 

Plan Approach (Number of Job-Years of Employment in a Single Year) .................... 6-26
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xvi 

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

percent Growth in Energy Efficiency ............................................................................ 6-31

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

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

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

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

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

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

Table 7-1. Monetized Benefits, Compliance Costs, and Net Benefits Under the Ratebased Illustrative Plan Approach (billions of 2011$) ...................................................... 7-3

Table 7-2. Monetized Benefits, Compliance Costs, and Net Benefits under the Massbased Illustrative Plan Approach (billions of 2011$) ...................................................... 7-4

 
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xvii 

LIST OF FIGURES

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

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

Capacity and Generation, by Age .................................................................................... 2-8

Figure 2-3. Fossil Fuel-Fired Electricity Generating Facilities, by Size ............................... 2-9

Figure 2-4. Average Retail Electricity Price by State (cents/kWh), 2011 ........................... 2-12

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

Categories ...................................................................................................................... 2-13

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

End-Use Categories, With Inflation Indices .................................................................. 2-14

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

Categories ...................................................................................................................... 2-15

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

Three Major End-Use Categories .................................................................................. 2-16

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

National Average Real Price per MBtu Delivered to EGU ........................................... 2-17

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

Since 2002 ...................................................................................................................... 2-18

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

Intensity Since 2002 ....................................................................................................... 2-19

Figure 2-12. Status of State Electricity Industry Restructuring Activities ............................ 2-20

Figures 2-13 and 2-14. Capacity and Generation Mix by Ownership Type, 2002 & 2012 .... 2-23

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

Ownership Type ........................................................................................................... 2-24

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

2012 (million metric tonnes of CO2 equivalent) ............................................................ 2-25

Figure 2-18. Marketable products from Syngas Generation ................................................ 2-29

Figure 2-19. Post-Combustion CO2 Capture for a Pulverized Coal Power Plant ................. 2-31

Figure 2-20. Pre-Combustion CO2 Capture for an IGCC Power Plant ................................. 2-32
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xviii 

Figure 2-19. Geologic Sequestration in the Continental United States ................................. 2-35

Figure 2-20. Relative Change Nominal and Real (2011$) Prices of Natural Gas Delivered 

to the Power Sector ($/MMBtu) .................................................................................... 2-45

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

Power Sector ($/mmBtu) ............................................................................................... 2-46

Figure 3-1. Illustrative Regions for Demand-Side Energy Efficiency/Renewable Energy 

Procurement Used in this Analysis ................................................................................ 3-10

Figure 3-2 Generation Mix with the Base Case and 111(d) Options, 2020-2030 

(thousand GWh) ............................................................................................................. 3-29

Figure 3-3. Nationwide Generation: Historical (1990-2014) and Rate-Based Illustrative 

Plan Approach Projections (2020, 2025, 2030) ............................................................. 3-30

Figure 3-4. Nationwide Generation: Historical (1990-2014) and Mass-Based Illustrative 

Plan Approach Projections (2020, 2025, 2030) ............................................................. 3-30

Figure 3-5. Electricity Market Module Regions .................................................................. 3-41

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

Plan Approaches for the Final Emission Guidelines in 2025 ........................................ 4-35

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

Discount Rate for Rate-based and Mass-based Illustrative Plan Approaches for the 

Final Emission Guidelines in 2025 ................................................................................ 4-36

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

in the Option 1 State Scenario Clean Power Plan Proposal Modeling (used to 

generate the benefit-per-ton estimates) .......................................................................... 4-41

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

PM2.5 Exposure in the Option 1 State Scenario Clean Power Plan Proposal Modeling 

(used to generate the benefit-per-ton estimates) ............................................................ 4-42

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

Final Emission Guidelines in 2025 for Rate-based and Mass-based Illustrative Plan 

Approaches and Pollutants (3% discount rate) .............................................................. 4-46

Figure 4A-1. Regional Breakdown ....................................................................................... 4A-6

Figure 6.1. Electric Power Industry Employment ............................................................... 6-12

Figure 6.2. Coal Production Employment ........................................................................... 6-13

Figure 6.3. Oil and Gas Production Employment ............................................................... 6-14
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xix 

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

(2011$) .......................................................................................................................... 6-33

 
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xx 

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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xxi 

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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xxii 

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 

SF6 

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 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 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. 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 

Administrator determines has been adequately demonstrated.1

 To determine the BSER for 

 

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 
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ES-2 

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 zero-emitting generating units 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. The rates are intended to represent CO2 emission rates 

achievable by 2030 after a 2022-2029 interim period on an output-weighted-average basis 

collectively by all affected EGUs. The interim and final emission performance rates are 

presented in the following table: 

Table ES-1. Emission Performance Rates (Adjusted Output-Weighted-Average Pounds of 

CO2 Per Net MWh from All Affected Fossil Fuel-Fired EGUs) 

Subcategory Interim Rate Final Rate 

Fossil Fuel-Fired Electric Steam Generating Units 1,548 1,305 

Stationary Combustion Turbines 850 771 

Also, states, 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 

 

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 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 stationary combustion turbine) 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 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 stationary combustion turbine generation. Chapter 3 

presents finalized equivalent state rate-based CO2 emissions performance goals. 

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

for each state, which are also 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. For more detail on the translation methodology, 

please refer to the U.S. EPA’s CO2 Emission Performance Rate and Goal Computation Technical 

Support Document for Final Rule, which is available in the docket.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 massbased 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. 

 

2 U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for Existing 

Stationary Sources: Electric Utility Generating Units. CO2 Emission Performance Rate and Goal Computation. 
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ES-4 

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. We expect that affected EGUs in each 

state will be able to meet the CO2 emission performance rates because the emission performance 

rates represent the application of the BSER specific to the two categories of affected EGUs. 

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. Rather, the 

impact estimates are instead illustrative of approaches that states may take. 

ES.3 Illustrative Plan Approaches Examined in RIA 

In the final emission guidelines, the EPA has translated the source category-specific CO2

emission performance rates into equivalent state-level rate-based and mass-based CO2 goals in 

order to maximize the range of choices that states will have in developing their plans. Because of 

the range of choices available to states and the lack of a priori knowledge about the specific 

choices states will make in response to the final goals, this RIA presents two scenarios designed 

to achieve these goals, which we term the “rate-based” illustrative plan approach and the “massbased” illustrative plan approach. 

In this final rule, states may use trading or other multi-unit compliance 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 reductions from affected 

fossil fuel-fired EGUs. In addition, 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 modelled implementation plan approaches reflect states and affected EGUs pursuing 

building block strategies such as heat rate improvements, shifting generation to less CO2 –

intensive generation, and increased deployment of renewable energy, which are more completely 

described in Chapter 3. However, the modelled strategies are not limited to the technologies and 

measures included in the BSER. For example, investment in demand-side energy efficiency is an 
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ES-5 

important strategy modelled in the rate-based and mass-based illustrative plan approached. In the 

rate-based approach, energy efficiency activities are modeled as being used by EGUs as a lowcost method of demonstrating compliance with their rate-based emissions standards. In the massbased 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. 

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 ratebased 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. 

It is very important to note that the differences between the analytical results for the ratebased and mass-based illustrative plan approaches presented in this RIA may not be indicative of 

likely differences between the approaches if implemented by states and affected EGUs in 

response to the final guidelines. Rather, the two sets of analyses are intended to illustrate two 

contrasting, stylized implementation approaches to accomplish the emission performance rates 

finalized in the Clean Power Plan Final Rule. In other words, if one approach performs better 

than the other on a given metric during a given time period, this does not imply this will apply in 

all instances in all time periods in all places. 

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 will facilitate compliance with interim and final goals. These 

pre-compliance period activities might include investments in renewable energy or demand-side 

energy efficiency projects, for example, that produce emissions reductions in the compliance 

period. Activities might also include preparatory investments in transmission capacity or 

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 and tribes are on glide paths toward fully meeting the 
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ES-6 

final CO2 emission performance goals. Lastly, the RIA presents costs and benefits for 2030, 

when the emission performance goals are fully achieved. 

ES.4 Emissions Reductions 

Table ES-2 shows the emission reductions associated with the modelled rate-based 

illustrative plan approach. 

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

Illustrative Plan Approach1,2

CO2 

(million 

metric tons) 

SO2 

(thousand 

short tons) 

Annual NOX 

(thousand 

of short tons) 

PM2.5 

(thousands 

short tons) 

2020 Rate-Based Approach 

Base Case 1,955 1,311 1,333 [X] 

Final Guidelines 1,892 1,297 1,282 [X] 

Emissions Change -63 -14 -50 [X] 

2025 Rate-Based Approach 

Base Case 1,964 1,275 1,302 [X] 

Final Guidelines 1,754 1,097 1,138 [X] 

Emissions Change -211 -178 -165 [X] 

2030 Rate-Based Approach 

Base Case 2,021 1,314 1,293 [X] 

Final Guidelines 1,644 996 1,011 [X] 

Emission Change -376 -318 -282 [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. 

In 2020, the EPA estimates that CO2 emissions will be reduced by 63 million metric tons 

under the rate-based scenario compared to base case levels. In 2025, the EPA estimates that CO2

emissions will be reduced by 211 million metric tons under the rate-based approach compared to 

base case levels. CO2 emission reductions increase to 376 million metric tons annually in 2030 

when compared to the base case emissions. Table ES-2 also shows emission reductions for 

criteria air pollutants (in short tons).3

 

3 Emissions estimates of PM2.5 require additional processing of IPM results. Estimates for PM2.5 reductions for 

2025 and 2030 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-7 

Table ES-3 shows the emission reductions associated with the modeled mass-based 

illustrative plan approach. 

Table ES-3. Climate and Air Pollutant Emission Reductions for the Mass-Based 

Illustrative Plan Appproach1,2

CO2 

(million 

metric tons) 

SO2 

(thousand 

short tons) 

Annual NOX 

(thousand 

of short tons) 

PM2.5 

(thousands 

short tons) 

2020 Mass-Based Approach 

Base Case 1,955 1,311 1,333 [X] 

Final Guidelines 1,881 1,257 1,272 [X] 

Emissions Change -74 -54 -60 [X] 

2025 Mass-Based Approach

Base Case 1,964 1,275 1,302 [X] 

Final Guidelines 1,724 1,090 1,100 [X] 

Emissions Change -240 -185 -203 [X] 

2030 Mass-Based Approach

Base Case 2,021 1,314 1,293 [X] 

Final Guidelines 1,646 1,034 1,015 [X] 

Emission Change -375 -280 -278 [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. 

In 2020, the EPA estimates that CO2 emissions will be reduced by 74 million metric tons under 

the mass-based approach compared to base case levels. In 2025, the EPA estimates that CO2

emissions will be reduced by 240 million metric tons under the mass-based approach compared 

to base case levels. CO2 emission reductions increase to 375 million metric tons annually in 2030 

when compared to the base case emissions. Table ES-3 also shows emission reductions for 

criteria air pollutants (in short tons). 

Table ES-4 presents CO2 emission reductions relative to 2005. 

Table ES-4. Projected CO2 Emission Reductions, Relative to 2005 

 CO2 Emissions 

(million metric tons) 

CO2 Emissions: Change 

from 2005 

(million metric tons) 

CO2 Emissions Reductions: 

Percent Change from 2005 

 2005 2020 2025 2030 2020 2025 2030 

Base Case 2,434 -479 -470 -413 -20% -19% -17% 

Rate-based - -542 -680 -790 -22% -28% -32% 

Mass-based - -553 -710 -788 -23% -29% -32% 

Source: Integrated Planning Model, 2015. 

 
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ES-8 

In 2020, the EPA estimates that CO2 emissions will be reduced by 542 million metric tons (22 

percent) under the rate-based approach compared to 2005 levels. In 2025, the EPA estimates that 

CO2 emissions will be reduced by 680 million metric tons (28 percent) under the rate-based 

approach compared to 2005 levels. Under the rate-based approach, CO2 emission reductions 

increase to 790 million metric tons (32 percent) in 2030 when compared to 2005 levels. 

Under the mass-based approach in 2020, the EPA estimates that CO2 emissions will be 

reduced by 553 million metric tons (23 percent) under the rate-based approach compared to 2005 

levels. In 2025, the EPA estimates that CO2 emissions will be reduced by 710 million metric tons 

(29 percent) under the mass-based approach compared to 2005 levels. Under the mass-based 

approach, CO2 emission reductions increase to 788 million metric tons (32 percent) in 2030 

when compared to 2005 levels. 

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, including the cost of demand-side energy efficiency measures and costs associated 

with monitoring, reporting, and recordkeeping requirements (MR&R). 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. 

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. 
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ES-9 

The total compliance cost estimates presented here include the costs associated with monitoring, 

reporting, and recordkeeping.4 The costs for both illustrative plan approaches are reflected in 

Table ES-5 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 $2.5 billion in 2020, $1.0 billion in 2025 and $8.4 billion in 

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

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

guidelines to be $1.4 billion in 2020, $3.0 billion in 2025 and $5.1 billion in 2030, including the 

costs associated with monitoring, reporting, and recordkeeping. 

Table ES-5. 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 $2.5 $1.4 

2025 $1.0 $3.0 

2030 $8.4 $5.1 

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

energy efficiency costs and monitoring, reporting, and recordkeeping costs. See Chapter 3 of this RIA for a more 

complete explanation. 

The costs reported in Table ES-5 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), and MR&R costs. For example, in 2030, 

under the rate-based approach, the incremental electric utility generating costs decline by about 

$18.0 billion from the base case. MR&R requirements in 2030 are estimated at $16.0 million, 

and demand-side energy efficiency costs in 2030 are estimated to be $26.3 billion, split equally 

 

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 $67 million in 2020, $16 million in 2025 and $16 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-10 

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 the mass-based illustrative plan 

approaches. 

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

The compliance costs reported in Table ES-5 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 July 2015), 

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), as well as ecosystem effects and visibility 

 

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 July 2015). Available at: 

<https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-tsd-final-july-2015.pdf> Accessed 7/11/2015. 
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ES-11 

improvement. The omission of these endpoints from the monetized results should not imply that 

the impacts are small or unimportant. Table ES-6 provides the list of the quantified and 

unquantified health and environmental benefits in this analysis. 

Table ES-6. 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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Table ES-6. Continued 

Reduced incidence of 

morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISA2

Chronic lung disease hospital admissions (age > 65) — — NO2 ISA2

Respiratory emergency department visits (all ages) — — NO2 ISA2

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISA2

Acute respiratory symptoms (age 7–14) — — NO2 ISA2

Premature mortality — — NO2 ISA2,3,4

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) — — SO2 ISA2

Asthma emergency department visits (all ages) — — SO2 ISA2

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISA2

Acute respiratory symptoms (age 7–14) — — SO2 ISA2

Premature mortality — — SO2 ISA2,3,4

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

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 SOx ISA2

Tree mortality and decline — — NOx SOx ISA3

Commercial fishing and forestry effects — — NOx SOx ISA3

Recreational demand in terrestrial and aquatic ecosystems — — NOx SOx ISA3

Other non-use effects NOx SOx ISA3

Ecosystem functions (e.g., biogeochemical cycles) — — NOx SOx ISA3

 

 

 
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ES-13 

Table ES-6. Continued 

Reduced effects from 

nutrient enrichment 

Species composition and biodiversity in terrestrial and 

estuarine ecosystems — — NOx SOx ISA3

Coastal eutrophication — — NOx SOx ISA3

Recreational demand in terrestrial and estuarine ecosystems — — NOx SOx ISA3

Other non-use effects NOx SOx ISA3

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 SOx ISA3

Injury to vegetation from NOx exposure — — NOx SOx ISA3

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 
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ES-14 

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 

it sought through a separate comment period on the approach used to develop the SC-CO2

estimates.7

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 SC-CO2 values was estimated using three integrated assessment 

models (DICE, FUND, and PAGE)8

, which the IWG harmonized across three key inputs: the 

probability distribution for equilibrium climate sensitivity; five scenarios for economic, 

population, and emissions growth; and three constant 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 TSD9

 presents 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 SC-CO2 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

 See https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-response-to-comments-final-july-2015.pdf 

8

 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). 

9

 The IWG published the updated TSD in 2013, then issued two minor corrections to it in July 2015. 
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ES-15 

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 Academies of 

Sciences, Engineering, and Medicine (Academies) to ensure that the SC-CO2 estimates continue 

to reflect the best available scientific and economic information on climate change.10 The 

Academies 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.11 Unlike the global SC-CO2 estimates, the air quality 

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

 

10 See <https://www.whitehouse.gov/blog/2015/07/02/estimating-benefits-carbon-dioxide-emissions-reductions>. 

11 We did not estimate the co-benefits associated with reducing direct exposure to SO2 and NOX. 
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ES-16 

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 

BenMAP12 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 

the local variability in population density, meteorology, exposure, baseline health incidence 

rates, or other local factors for any specific location. 

 

12 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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ES-17 

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 

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 
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ES-18 

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-7 (rate-based illustrative plan approach) and Table ES-8 

(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-7. 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% $0.80 $1.5 to $2.6 $1.4 to $2.5 

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

2.5% $4.1 $4.9 to $6.0 $4.8 to $5.9 

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

In 2025 211 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 

In 2030 376 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-8. 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 74 million metric tons CO2 

5% $0.94 $2.9 to $5.7 $2.8 to $5.3 

3% $3.3 $5.3 to $8.1 $5.1 to $7.7 

2.5% $4.9 $6.9 to $9.7 $6.7 to $9.3 

3% (95th percentile) $9.7 $12 to $14 $11 to $14 

In 2025 240 million metric tons CO2 

5% $3.6 $11 to $21 $10 to $19 

3% $12 $19 to $29 $18 to $27 

2.5% $17 $24 to $35 $24 to $33 

3% (95th percentile) $35 $42 to $52 $42 to $51 

In 2030 375 million metric tons CO2 

5% $6.4 $18 to $34 $17 to $32 

3% $20 $32 to $48 $31 to $46 

2.5% $29 $41 to $57 $40 to $55 

3% (95th percentile) $60 $72 to $89 $71 to $86 

*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-9 and ES-10 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), as well as ecosystem effects and visibility 
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ES-21 

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-9. 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 $2.5 $1.0 $8.4 

Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $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 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 percent 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 final emission 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-23 

Table ES-10. 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 $0.9 $3.6 $6.4 

3% discount rate $3.3 $12 $20 

2.5% discount rate $4.9 $17 $29 

95th percentile at 3% 

discount rate $9.6 $35 $60 

 Air Quality Co-benefits Discount Rate 

 3% 7% 3% 7% 3% 7% 

Air Quality Health 

Co-benefits c $2.0 to $4.8 $1.8 to $4.4 $7 to $17 $7 to $16 $12 to $28 $11 to $26 

Compliance Costs d $1.4 $3.0 $5.1 

Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40 

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 percent 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 final emission 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-24 

ES.8 Economic Impacts 

The final emission guidelines have important energy market implications. Table ES-11 

presents a variety of important energy market impacts for 2020, 2025, and 2030 for both the ratebased and mass-based illustrative plan approaches. 

Table ES-11. Summary Table of Important Energy Market Impacts (Percent Change from 

Base Case) 

 Rate-Based Mass-Based 

 2020 2025 2030 2020 2025 2030 

Retail electricity prices 3% 1% 1% 3% 2% 0% 

Price of coal at minemouth -1% -5% -4% -1% -5% -3% 

Coal production for power sector use -5% -14% -25% -7% -17% -24% 

Price of natural gas delivered to power sector 5% -8% 2% 4% -3% -2% 

Natural gas use for electricity generation 3% -1% -1% 5% 0% -4% 

Non-hydro renewable energy capacity 1% -1% 13% -2% -3% 11% 

Energy market impacts from the guidelines are discussed more extensively in Chapter 3 of this 

RIA. 

Additionally, 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 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 
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ES-25 

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 the 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 a net decrease of approximately 25,200 job-years in 2025 for the final guidelines 

under the rate-based illustrative plan approach and approximately 26,000 job-years in 2025 

under the mass-based approach. For 2030 the estimates of the net decrease in job-years is 31,300 

under the rate-based plan, and 33,700 under the mass-based plan. The Agency is also offering an 

illustrative calculation of potential employment effects due to demand-side energy efficiency 

programs. Employment impacts from demand-side energy efficiency programs in 2030 could 

range from approximately 52,000 to 83,000 jobs under the final guidelines. More detail about 

these analyses can be found in Chapter 6 of this RIA.
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ES-26 

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/for-agencies/Social-Cost-ofCarbon-for-RIA.pdf>. 

Docket ID EPA-HQ-OAR-2013-0602, 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 July 2015). Also available at: < 

https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-tsd-final-july-2015.pdf>. 

Accessed July 15, 2015. 

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. 
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ES-27 

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 Management and 

Budget, Office of Science and Technology Policy, and Department of Treasury. Response to 

to Comments: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 

12866. July 2015. Available at: 

<https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-response-to-commentsfinal-july-2015.pdf.> Accessed July 15, 2015. 

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. 

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. 
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ES-28 

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. 

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. 

U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission 

Guidelines for Existing Stationary Sources: Electric Utility Generating Units. CO2 Emission 

Performance Rate and Goal Computation. 

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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1-1 

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.”13 The EPA 

has listed more than 60 stationary source categories under this provision.14 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.15 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 

mechanism for regulating existing sources differs from the one that CAA section 111(b) provides 

 

13 CAA §111(b)(1)(A). 

14 See 40 CFR 60 subparts Cb – OOOO. 

15 CAA §111(b)(1)(B), 111(a)(1).
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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.”16 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.17 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.”18 

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).19 In the 

Endangerment Finding, the Administrator found that the current, elevated concentrations of 

 

16 CAA section 111(d)(2)(A). 

17 CAA section 111(d)(2)(A).

18 National Research Council, Climate Stabilization Targets, p.3. 19 “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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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 

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 
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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 illustrative plan approaches that states 

and affected EGUs may take to accomplish 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. 

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 
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1-5 

(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.20 

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 fossil fuel-fired electric utility steam 

generating unit or stationary combustion turbine that was in operation or had commenced 

construction as of January 8, 2014,21 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 integrated gasification combined cycle (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 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 

 

20 Detailed documentation for IPM v.5.15 is available at: http://www.epa.gov/powersectormodeling 

21 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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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 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 based on the best system of 

emission reduction (BSER) for two subcategories of existing fossil fuel-fired EGUs, fossil fuelfired 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. 

1.4.5 State Plans 
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After the EPA establishes the emission guidelines, each state22 shall then develop, adopt 

and submit a state plan under CAA section 111(d). Each state’s obligation under section 111(d) 

and under these guidelines is to establish a standard of performance for the affected EGUs in its 

jurisdiction in order to implement the BSER. The EPA has developed a BSER and a mechanism 

(e.g. standard of performance (e.g. subcategory specific emission performance rates)) that allows 

affected EGUs in all states to access all the components of the BSER (as well as other alternative 

but acceptable emission reduction opportunities). That standard of performance may expressly 

incorporate the CO2 emission performance rates the EPA has set forth in these guidelines to 

express the BSER. The EPA has also received numerous comments from a wide range of 

stakeholders desiring freedom to consider a wide range of standards of performance to 

implement BSER. Thus, as an alternative to incorporating the CO2 emission performance rates, 

the state may establish standards of performance at different levels for different EGUs. As a 

second alternative, the state may adopt measures apart from, or in addition to, standards of 

performance for its affected EGUs. 

These guidelines include subcategory specific CO2 emission performance rates, as well as 

state-specific rate-based and mass-based CO2 emission goals for affected EGUs that represent 

the equivalent in aggregate of the CO2 emission performance rates applied to the affected EGUs 

in each state. As already stated, the EPA is establishing rate-based CO2 emission goals for each 

state, as well as mass-based CO2 goals that represent the equivalent of each state’s rate based 

CO2 goal, in order to ensure that both states and affected EGUs enjoy the maximum flexibility 

and latitude in meeting the requirements of the emission guidelines and that the BSER is fully 

implemented by each state. 

For its plan, a state will be able to choose to either impose federally enforceable emission 

standards that fully meet the emission guidelines directly on affected EGUs (the “emission 

standards” approach) or use a “state measures" approach, which would be composed, at least in 

part, of measures implemented by the state that are not included as federally enforceable 

components of the plan. A state measures type plan must include a backstop of federally 

 

22 In this section, the term “state” encompasses the 50 states and the District of Columbia, U.S. territories, and any 

Indian tribe that has been approved by the EPA pursuant to 40 CFR 49.9 as eligible to develop and implement a 

CAA section 111(d) plan. 
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enforceable standards on affected EGUs that fully meet the emission guidelines and that would 

be triggered if the state measures fail to result in the affected EGUs achieving on schedule the 

required emission reductions. 

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 Industry Profile 

 Chapter 3, Cost, Emissions, 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 Impact Analysis 

 Chapter 7, Statutory and Executive Order Analyses 

 Chapter 8, Comparison of Benefits and Costs 

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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1-9 

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: ELECTRIC POWER SECTOR 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. 

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 

Capacity refers to the maximum amount of production from an EGU in a typical hour, typically 
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2-2 

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). Net generation is the amount of electricity that is 

available to the grid from the EGU (i.e., excluding the amount of electricity generated but used 

within the generating station for operations). In addition to producing electricity for sale to the 

grid, generators perform other services important to reliable electricity supply, such as providing 

backup generating capacity in the event of unexpected changes in demand or unexpected 

changes in the availability of other generators. Other important services provided by generators 

include facilitating the regulation of the voltage of supplied generation. 

Individual EGUs are not used to generate electricity 100 percent of the time. Individual 

EGUs are periodically not needed to meet the regular daily and seasonal fluctuations of 

electricity demand. Furthermore, EGUs relying on renewable resources such as wind, sunlight 

and surface water to generate electricity are routinely constrained by the availability of adequate 

wind, sunlight or water at different times of the day and season. Units are also unavailable during 

routine and unanticipated outages for maintenance. These factors result in the mix of generating 

capacity types available (e.g., the share of capacity of each type of EGU) being substantially 

different than the mix of the share of total electricity produced by each type of EGU in a given 

season or year. 

Most of the existing capacity generates electricity by creating heat to create high pressure 

steam that is released to rotate turbines which, in turn, create electricity. Natural gas combined 

cycle (NGCC) units have two generating components operating from a single source of heat. The 

first cycle is a gas-fired turbine, which generates electricity directly from the heat of burning 

natural gas. The second cycle reuses the waste heat from the first cycle to generate steam, which 

is then used to generate electricity from a steam turbine. Other EGUs generate electricity by 

using water or wind to rotate turbines, and a variety of other methods including direct 

photovoltaic generation also make up a small, but growing, share of the overall electricity 

supply. The generating capacity includes fossil-fuel-fired units, nuclear units, and hydroelectric 

and other renewable sources (see Table 2-1). Table 2-1 also shows the comparison between the 

generating capacity in 2002 and 2012. 
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2-3 

In 2012 the power sector consisted of over 19,000 generating units with a total capacity23 

of 1,168 GW, an increase of 188 GW (or 19 percent) from the capacity in 2002 (980 GW). The 

188 GW increase 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. 

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 

Capacit

y 

Generator 

Nameplate 

Capacity 

(MW) 

% Total 

Capacit

y 

% 

Increas

e 

Nameplate 

Capacity 

Change 

(MW) 

% of 

Total 

Capacity 

Increase 

Coal 338,199 35% 336,341 29% -1% -1,858 -1% 

Natural Gas1 352,128 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 

natural gas-fired capacity), Gas Turbine (30 percent), Combined Cycle Steam (19 percent), Steam Turbine (17 

percent), and miscellaneous (< 1 percent). 

 

23 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. Unless otherwise indicated, capacity data presented in this 

RIA is installed nameplate capacity (also known as nominal capacity), defined by EIA as “The maximum rated 

output of a generator, prime mover, or other electric power production equipment under specific conditions 

designated by the manufacturer.” Nameplate capacity is consistently reported to regulatory authorities with a 

common definition, where alternate measures of capacity (e.g., net summer capacity and net winter capacity) can 

use a variety of definitions and specified conditions. 
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2-4 

The 19 percent increase in generating capacity is the net impact of newly built generating 

units, retirements of generating units, and a variety of increases and decreases to the nameplate 

capacity of individual existing units due to changes in operating equipment, changes in emission 

controls, 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.24 The overall mix of newly built 

and retired capacity, along with the net effect, is shown on Figure 2-1. 

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

Source: EIA Form 860 

 

24 Partially offset by 87 MW retired older wind or solar capacity. 

‐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 (MW)

Coal Nat Gas Wind & Solar Oil & Other
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2-5 

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 trillion kWh to meet electricity 

demand, a 5 percent increase from 2002 (3,858 trillion 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 72 percent, 

while natural gas generation increased by 60 percent. This reflects both the increase in natural 

gas capacity during that period as well as an increase in the utilization of new and existing gas 

EGUs during that period. Wind generation also grew from a very small portion of the overall 

total in 2002 to 4.1 percent of the 2012 total. 
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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 

and newer. While 55 percent of the coal EGU fleet is over 500 MW per unit, 77 percent of the 
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gas fleet is between 50 and 500 MW per unit. Many of the largest gas units are gas-fired steamgenerating EGUs. 

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.5.14 

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 or 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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2-8 

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 v5.13 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 or 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 v.5.13 reflects 

generating capacity expected to be on-line at 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 bulk transfer of electricity over a network of 

high voltage lines, from electric generators to substations where power is stepped down for local 

distribution. In the U.S. and Canada, there are three separate interconnected networks of high 

voltage transmission lines,25 each operating synchronously. Within each of these transmission 

networks, there are multiple areas where the operation of power plants is monitored and 

 

25 These three network interconnections are the Western Interconnection, comprising the western parts of both the 

US and Canada (approximately the area to the west of the Rocky Mountains), the Eastern Interconnection, 

comprising the eastern parts of both the US and Canada (except those part of eastern Canada that are in the Quebec 

Interconnection), and the Electric Reliability Council of Texas (ERCOT) Interconnection, comprising most of 

Texas.” See map of all NERC interconnections at 

http://www.nerc.com/AboutNERC/keyplayers/Documents/NERC_Interconnections_Color_072512.jpg 
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controlled to ensure that electricity generation and load are kept in balance. In some areas, the 

operation of the transmission system is under the control of a single regional operator; 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. 

Over the last few decades, several jurisdictions in the United States began restructuring the 

power industry to separate transmission and distribution from generation, ownership, and 

operation. Historically, the transmission system had been developed by vertically integrated 

utilities, establishing much of the existing transmission infrastructure. However, as parts of the 

country have restructured the industry, transmission infrastructure has also been developed by 

transmission utilities, electric cooperatives, and merchant transmission companies, among others. 

Distribution, also historically developed by vertically integrated utilities, is now often managed 

by a number of utilities that purchase and sell electricity, but do not generate it. As discussed 

below, electricity restructuring has focused primarily on efforts to reorganize the industry to 

encourage competition in the generation segment of the industry, including ensuring open access 

of generation to the transmission and distribution services needed to deliver power to consumers. 

In many states, such efforts have also included separating generation assets from transmission 

and distribution assets to form distinct economic entities. Transmission and distribution remain 

price-regulated throughout the country based on the cost of service. 

2.3 Sales, Expenses and Prices 

These electric generating sources provide electricity for commercial, industrial and 

residential ultimate customers. Each of the three major ultimate categories consume roughly a 
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quarter to a third of the total electricity produced26 (see Table 2-4). Some of these uses are highly 

variable, such as heating and air conditioning in residential and commercial buildings, while 

others are relatively constant, such as industrial processes that operate 24 hours a day. The 

distribution between the end use categories changed very little between 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 prices for 

residential and commercial customers are the result both of the necessary extensive distribution 

network reaching to virtually every part of the country and every building, and also the fact that 

generating stations are increasingly located relatively far from population centers (which 

increases transmission costs). Industrial customers generally pay the lowest average prices, 

 

26 Transportation (primarily urban and regional electrical trains) is a fourth ultimate customer category which 

accounts less than one percent of electricity consumption. 
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2-12 

reflecting both their proximity to generating stations and the fact that industrial customers 

receive electricity at higher voltages (which makes transmission more efficient and less 

expensive). Industrial customers frequently pay variable prices for electricity, varying by the 

season and time of day, while residential and commercial prices historically have been less 

variable. Overall industrial customer prices are usually 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. In 2011 the national 

average retail electricity price (all sectors) was 9.90 cents/KWh, with a range from 6.44 cents 

(Idaho) to 31.59 (Hawaii). 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 the most 

expensive retail price of electricity in the country. 

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

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)27. 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. 

 

27 Source: Federal Reserve Economic Data, FRB St. Louis. Available at http://research.stlouisfed.org/fred2/. 

.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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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 real28 (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 percent 

and 28 percent lower respectively than in 1960. 

 

28 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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2-15 

 

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
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2-16 

 

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. 

‐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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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 

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 demand growth for generation has been low, and in fact was 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. 
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2-18 

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 demand for electricity generation grew more slowly than both the population and 

GDP, the relative electric intensity of the U.S. economy improved (i.e., less electricity used per 

person and per real dollar of output) during 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 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. 
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2-19 

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 

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 
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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. 

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

Source: EIA 2010. “Status of Electricity Restructuring by State.” Available online at: 

<http://www.eia.gov/cneaf/electricity/page/restructuring/restructure_elect.html>. 
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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 

capacity (MW) in 2002 was 62 percent of the generating capacity owned by traditional utilities, 

35 percent owned by IPPs29, 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 

 

29 IPP data presented in this section include both combined and non-combined heat and power plants. 
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2-22 

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. 
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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

12345

Capacity (MW)

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

12345

Generation (GW)

Generation Mix, 2002 & 2012

Series1 Series2 Series3

Series4 Series5 Series6
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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 2012, 

the electricity generation accounted for 38 percent of national CO2 emissions. Including both 

generation and transmission (a source of SF6), 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 GHG emissions30 from the power sector relative to other major economic sectors. 

Table 2-6 shows the contributions of CO2 and other GHGs from the power sector and other 

major emitting economic sectors. 

 

 

30 CO2 equivalent data in this section are calculated with the IPCC SAR (Second Assessment Report) GWP potential 

factors. 

0

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40,000

60,000

80,000

100,000

120,000

140,000

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180,000

200,000

1 2

New Capacity (MW)

Capacity Built 2002‐2012

by Ownership Type

Series1 Series2 Series3 Series4

0

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15,000

20,000

25,000

30,000

35,000

1 2

Retired Capacity (MW)

Capacity Retirements 2002‐2012

by Ownership Type

Series1 Series2 Series3 Series4
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Table 2-5. Domestic Emissions of Greenhouse Gases, by Economic Sector (million metric 

tonnes of CO2 equivalent) 

 2002 2012 Change Between '02 and '12 

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% 2,064 32% ‐249 ‐11% 48%

Transportation 1,958 28% 1,837 28% ‐121 ‐6% 23%

Industry 1,419 20% 1,278 20% ‐140 ‐10% 27%

Agriculture 561 8% 614 9% 53 10% ‐10%

Commercial 365 5% 353 5% ‐12 ‐3% 2%

Residential 374 5% 321 5% ‐53 ‐14% 10%

US Territories 52 < 1% 58 1% 6 11% ‐1%

Total Emissions 7,041 100% 6,526 100% ‐516 ‐7% 100%

Sinks and Reductions -885 ‐979 ‐94 11%

Net Emissions 6,156 5,546 ‐610 ‐10%

Source: EPA, 2014 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012”, Table 2-12. Includes 

CO2, CH4, N2O and SF6 emissions. 

 

Figure 2-17. Domestic Emissions of Greenhouse Gases from Major Sectors, 2002 and 2012 

(million metric tonnes of CO2 equivalent) 

Source: EPA, 2014 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012”, Table 2-12. 
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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 

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

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

equivalent) 

 

2002 2012 

Change Between '02 

and '12 

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 

GHG 

Emissions 

% Change 

in 

Emissions 

CO2 2,287 98.9% 2,039 98.8% -248 -11% 

 Fossil Fuel 

Combustion 

2,273 98.2% 2,023 98.0% -250 -11% 

 Coal 1,890 81.7% 1,511 73.2% -379 -20% 

 Natural Gas 306 13.22% 492 23.85% 187 61% 

 Petroleum 76.8 3.32% 18.8 0.91% -58.0 -76% 

 Geothermal 0.4 0.02% 0.4 0.02% 0.0 0% 

 Incineration of 

Waste 

11.8 0.51% 12.2 0.59% 0.4 3% 

 Other Process Uses 

of Carbonates 

2.6 0.11% 4.0 0.19% 1.4 54% 

CH4 0.4 0.02% 0.5 0.02% 0.1 25% 

 Stationary 

Combustion* 

0.4 0.02% 0.5 0.02% 0.1 25% 

 Incineration of 

Waste 

+ + 

N2O 12.4 0.54% 18.6 0.90% 6.2 50% 

 Stationary 

Combustion* 

12.0 0.52% 18.3 0.89% 6.3 53% 

 Incineration of 

Waste 

0.4 0.02% 0.4 0.02% 0.0 0% 

SF6 13.3 0.57% 6.0 0.29% -7.3 -55% 

 Electrical 

Transmission and 

Distribution 

13.3 0.57% 6.0 0.29% -7.3 -55% 

Total GHG Emissions 2,313 2,064 -249 -11% 

Source: EPA, 2014 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012”, Table 2-13 
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* 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. 

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

Modeling Application 

Fuel Type Carbon Dioxide (lb/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 to meet a 1,400 lb CO2/MWh emission rate 

include the use of: (1) highly efficiency coal-fired designs (e.g., modern supercritical or ultrasupercritical steam units) with up to 40% natural gas co-firing, (2), integrated coal gasification 

combined cycle (IGCC) with < 10% CCS or co-firing with up to 10 percent natural gas, (3) 

natural gas combined cycle (NGCC) combustion turbine/steam-turbine units, and/or (4) 

conventional coal-fired generation with carbon capture and storage (CCS). While CCS is not 
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included in the BSER framework, it is an emerging technology with both new build and retrofit 

commercial-scale EGUs coming into operation in 2014 and 2015 in the United States and 

Canada. All of these units with CCS have received substantial subsidies to further develop and 

demonstrate the feasibility of CCS at a commercial scale, and the costs of these new units with 

CCS are not indicative of anticipated future costs of new or retrofit CCS units. CCS is briefly 

discussed in this section as existing (but still emerging) technology that may become 

economically viable in the future. 

Investment decisions for the optimal choice in a particular situation of the type of new 

generating capacity capable of meeting the 1,400 lb CO2/MWh standard of performance depend 

in part on the intended primary use of new generating capacity. 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 

typically 30-33 percent but can be has high as 40 percent or more (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. 

Base load 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 

fertilizers and chemical feedstocks for Fisher-Tropsch processes to manufacture liquid 

hydrocarbons (e.g., fuels and lubricants), and CO2 can be captured for use in EOR. Figure 2-18 
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2-29 

shows the array of products (including electricity) and by-products that can be produced in a 

syngas process (NETL). 

Figure 2-18. Marketable products from Syngas Generation 

Source: National Energy Technology Lab. Gasifipedia. Available at http://www.netl.doe.gov/research/coal/energysystems/gasification/gasifipedia/co-generation 

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). Post-combustion capture processes remove CO2 from the exhaust 

gas of a combustion system – such as a utility boiler. It is referred to as “post-combustion 

capture” because the CO2 is the product of the combustion of the primary fuel and the capture 

takes place after the combustion of that fuel. This process is described in more detail in the 

preamble. (See preamble section V.D.) This process is illustrated for a pulverized coal power 
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plant in Figure 2-19. For post-combustion, a station's net generating output will be lower due to 

the energy needs of the capture process. 
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Figure 2-19. 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. 31 For pre-combustion technology, a 

significant amount of energy is needed to gasify the fuel(s). This process is illustrated in Figure 

2-20. 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 

technology, see the “Report of the Interagency Task Force on Carbon Capture and Storage” 

(2010).32 

 

31 Note that pre-combustion CCS is not considered the best system of emission reduction for this standard. This 

information is provided for background purposes. 

32 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 2-20. 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 commercial-scale coal-fired 

capture and storage project for electricity generation began operation at the Boundary Dam 

Power Station in Saskatchewan, Canada. The 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 commercial-scale 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 

captured CO2 is used for an EOR project in southern Saskatchewan. The portion of the CO2 is 
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2-33 

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, there are two commercial-scale CCS facilities nearing completion: 

1. the Kemper County Carbon Dioxide Capture and Storage Project in Mississippi, and 

2. The W.A. Parish Petra Nova CCA Project near Houston, Texas. 

Construction began on the Kemper project in 2010, and the startup is currently scheduled 

for May, 2016. The Kemper 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 be about 800 pounds per MWh produced. The current 

total cost estimate is $5.6 billion, a substantial increase from the original $2.4 billion estimate.33

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.34

The only other commercial-scale electricity power sector CCS project currently under 

construction in the United States 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 

 

33 The Mississippi Public Utilities Staff authorized an independent monitor to conduct a review of the project. The 

findings of the review are provided in a summary report available at: 

http://www.psc.state.ms.us/InsiteConnect/InSiteView.aspx?model=INSITE_CONNECT&queue=CTS_ARCHIVEQ

&docid=328417 

34 Carbon Capture and Sequestration Technologies Program at MIT. Accessed 1/23/2015. 

https://sequestration.mit.edu/tools/projects/kemper.html 
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2-34 

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 Energy), and the project 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.35

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 unmineable 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 DOE’s 2012a Carbon Utilization and Storage Atlas (discussed 

below) are provided in Table 2-8. 

 

35 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 

Sources: EPA 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 Resource (DOE-NETL)36

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 by DOE-NETL not assessed by DOE-NETL

Delaware 40 40

District of Columbia not assessed by DOE-NETL not assessed by DOE-NETL

Florida 102,740 555,010

Georgia 145,340 159,050

Hawaii not assessed by DOE-NETL not assessed by DOE-NETL

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 by DOE-NETL not assessed by DOE-NETL

Maryland 1,860 1,930

Massachusetts not assessed by DOE-NETL not assessed by DOE-NETL

 

36 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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2.6.3 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.37 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.38 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.39 

2.6.4 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.40 More than half of the projects evaluated in the study have been in operation for 

 

37 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). 

38 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/. 

39 Alaska is not shown in the figure; it has deep saline formation storage capacity, geology amenable to EOR 

operations, and potential GS capacity in unmineable coal. 

40 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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2-39 

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 18 states are within 100 kilometers (62 miles) of current EOR operations (see 

Figure 2-14).41 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.42

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 

2013.43 Approximately 70 percent of this total CO2 supplied was produced from natural 

(geologic) CO2 sources, and approximately 30 percent was captured from anthropogenic 

sources.44 

 

41 The distance of 100 kilometers reflects the assumptions in the DOE-NETL cost estimates. 

42 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. 

43 Greenhouse Gas Reporting Program, data reported as of August 18, 2013. 

44 Greenhouse Gas Reporting Program, data reported as of August 18, 2013. 
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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 

significantly increase the application of EOR to areas outside of current operations.45 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. 

2.7 State Policies on GHG and Clean Energy Regulation in the Power Sector 

Several states have also established emission performance standards or other measures to 

limit emissions of GHGs from new EGUs that are comparable to or more stringent than 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 states 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 

 

45 “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. 
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2-41 

previous record). A total of allowances for 15.3 million tons were sold in the March ’15 auction, 

well below the record of 38.7 million tons sold in June ’13 for $3.21. 

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-net. 

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 included 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, 

“Baseload Electric Generation Performance” 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-net. In 2013 the State of 

Washington revised46 the emission limit to 970 lb CO2/MWh-net based on a survey of available 

NGCC generation units commercially available in the United States. 

 

46 Washington Department of Commerce, 2013. “Greenhouse Gas Emission Performance Standard for Baseload 

Electric Generation”. Available at http://www.commerce.wa.gov/Documents/Concise-Expl-Stmt-WSR-13-06-

074.pdf. 
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In 1997 Oregon required a new baseload gas fired power plants to meet a CO2 emission 

standard that was 17 percent below the most efficient NGCC unit operating in the United States. 

In 2000 Oregon established that the effective 17 percent below most efficient was 675 lb 

CO2/MWh-net. 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-net regardless of fuel type, and prohibited 

utilities from entering into long-term purchase agreements for baseload electricity with out-ofstate 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. 

Implementing regulations established CO2 emission standards for new and modified electric 

generators greater than 25 MW. The standards vary based on the type of facility: base load 

facilities must meet a CO2 standard of 925 lb/MWh-net or 120 lb/MMBtu, and peaking facilities 

must meet a CO2 standard of 1,450 lbs/MWh-net or 160 lbs/MMBtu. 

Several other states have enacted CO2 regulations affecting EGUs that do not set emission 

limits, but set other regulatory requirements limiting CO2 emissions from EGUs. For example, 

Montana enacted a law in 2007 requiring the Public Service Commission to limit approvals of 

new equity interests in or leases of a facility used to generate coal-based electricity to facilities 

that capture and sequester at least half of their CO2 emissions. Minnesota enacted the Next 

Generation Energy Act in 2007 requiring increases in power sector greenhouse gas emissions 

from any new large coal energy facilities built in Minnesota or the import of electricity from 

such a facility located out of state to be offset by equivalent emission reductions. New Mexico 

enacted legislation in 2007 authorizing tax credits and cost recovery incentives for qualifying 

coal-fired facilities. To qualify, plants must capture and store emissions so that they emit less 

than 1,100 lbs CO2/MWh, among other requirements. 

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 29 states, the District of Columbia, and 
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2-43 

Guam have enforceable RPS or other mandatory renewable capacity policies, and eight states, 

Puerto Rico, and Guam have voluntary goals.47 These programs vary widely in structure, 

enforcement, and scope. 

2.8 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). 

 

 

47 EIA 2012a 
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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). 

2.9 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, can undergo major price swings 

during short-lived weather events (such as cold snaps leading to short-run spikes in heating 

demand), and has seen a dramatic shift since 2008 due to increased production from shale 

formations . Over the last decade, the annual average nominal price of gas delivered to the power 

sector peaked in 2008 at $9.02/mmBtu and has since fallen dramatically to a low of 
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$3.42/mmBtu in 2012. During that time, the daily price48 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 peaked at 

$9.38/mmBtu in 2008 and has fallen dramatically to a low of $3.36 in 2012. The annual natural 

gas prices in both nominal and real (2011$) terms are in Figure 2-22. A comparison of the trends 

in the real price of natural gas with the real prices of delivered coal and oil are shown in Figure 

2-23. Figure 2-23 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. 

 

48 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. The price of natural gas delivered to electricity 

generation differs substantially in different regions of the country, and can be higher or lower than the Henry Hub 

national benchmark price. 

$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 ($/mmBTU)

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 Marcellus49, 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, there 

is some uncertainty in the estimate of technically recoverable resources, and it 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) of shale gas production, 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.50

EIA’s projections of natural gas conditions did not change substantially in AEO 2014 from 

either the AEO 2012 or 2013, and EIA continues to forecast 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 record51 for domestic natural gas production.52 

 

49 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. 

50 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 

51 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 

52 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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2.10 References 

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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. 

Clean Energy States Alliance (CESA). The State of State Renewable Portfolio Standards. June 

2013. Available Online at 

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. 

Independent Monitor’s Prudency Evaluation Report for the Kemper County IGCC Project. April 

15, 2014. Available online at: 

http://www.psc.state.ms.us/InsiteConnect/InSiteView.aspx?model=INSITE_CONNECT&que

ue=CTS_ARCHIVEQ&docid=328417. 

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 

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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. 
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2-49 

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. 

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 2013.

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. 
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2-50 

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. 

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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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. 

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 

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 Clean Power Plan 

Final 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 approach analyses. The level of demand side 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, 
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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.53 

 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 

price levels.54 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 

 

53 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 

54 See Chapter 10 of EPA’s Base Case using IPM (v5.154) documentation, available at: 

http://www.epa.gov/powersectormodeling 
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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.55 

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 ratepayers 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, 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.56 It is important to note that there is no single CRF factor applied in 

the model; rather, the CRF varies across technologies in the model in order to better simulate 

power sector decisionmaking. 

While the CRF is used to annualize costs within IPM, a discount rate is used to estimate 

the net present value of the intertemporal flow of the annualized capital and operating costs. The 

optimization model then identifies power sector investment decisions that minimize the net 

present value of all costs over the full planning horizon while satisfying a wide range of demand, 

capacity, reliability, emissions, and other constraints. As explained in Chapter 8 of the IPM 

documentation, the discount rate is derived as a weighted average cost of capital which is a 

function of capital structure, post-tax cost of debt, and post-tax cost of equity. While the detailed 

formulation of this rate is presented in the IPM documentation, the rate estimated and used in the 

current analysis is 4.77 percent.It is important to note that this discount rate is selected for the 

purposes of best simulating power sector behavior, and not for the purposes of discounting social 

costs or benefits. 

 

55 See Chapter 9 of EPA’s Base Case using IPM (v5.15) documentation, available at: 

http://www.epa.gov/powersectormodeling 

56 See Chapter 8 of EPA’s Base Case using IPM (v5.15) documentation, available at: 

http://www.epa.gov/powersectormodeling. 
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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 

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. 

3.4 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. As such, the IPM base case 

represents the baseline for this regulatory impact analysis. 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. 
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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 

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.57 

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.58

3.5 State Goals in this Final Rule 

In this final rule, the EPA is establishing CO2 emission performance rates for two 

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

in order to expand the range of choices that states will have in developing their plans. Due to the 

range of choices available to states, and the lack of a priori knowledge about the specific choices 

states will make in response to the final goals, this RIA presents two scenarios designed to 

 

57 Available at: http://www.epa.gov/powersectormodeling/ 

58 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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achieve these goals, which we term the “rate-based” illustrative plan approach and the “massbased” illustrative plan approach. Table 3-1 presents the rate-based and mass-based state goals. 

 
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Table 3-1. Statewide CO2 Emission Performance Goals, Rate-based and Mass-based 

 

Rate-Based 

(Adjusted Output-WeightedAverage Pounds of CO2 Per 

Net MWh From All Affected 

Fossil Fuel-Fired EGUs) 

Mass-Based 

(Adjusted Output-WeightedAverage Metric Tons of CO2 From 

All Affected Fossil Fuel-Fired 

EGUs) 

State Interim Goal Final Goal Interim Goal Final Goal 

Alabama 1,156 1,018 56,432,886 51,599,803 

Arkansas 1,304 1,130 30,554,805 27,507,539 

Arizona 1,173 1,031 29,991,524 27,369,757 

California 906 828 46,289,448 43,915,819 

Colorado 1,362 1,174 30,279,604 27,117,614 

Connecticut 851 786 6,565,914 6,297,085 

Delaware 1,022 916 4,592,743 4,274,388 

Florida 1,026 919 102,493,187 95,337,918 

Lands of the Fort Mojave Tribe 831 771 554,370 533,882 

Georgia 1,197 1,049 46,196,486 42,044,096 

Iowa 1,505 1,283 25,632,850 22,698,346 

Idaho 831 771 1,322,487 1,273,611 

Illinois 1,455 1,245 67,852,887 60,305,548 

Indiana 1,451 1,242 77,664,415 69,047,576 

Kansas 1,518 1,293 22,550,167 19,949,240 

Kentucky 1,509 1,286 64,688,636 57,265,616 

Louisiana 1,292 1,121 35,659,254 32,138,047 

Massachusetts 901 824 11,564,122 10,980,965 

Maryland 1,510 1,287 14,703,722 13,015,622 

Maine 841 779 1,957,557 1,881,144 

Michigan 1,354 1,169 48,129,127 43,130,166 

Minnesota 1,413 1,213 23,071,207 20,572,953 

Missouri 1,490 1,272 56,757,481 50,313,819 

Mississippi 1,060 945 24,799,644 22,955,132 

Montana 1,534 1,305 11,603,144 10,253,749 

Lands of the Navajo Nation 1,534 1,305 22,276,621 19,685,947 

North Carolina 1,311 1,136 51,693,220 46,506,777 

North Dakota 1,534 1,305 21,437,569 18,944,473 

Nebraska 1,522 1,296 18,742,279 16,576,333 

New Hampshire 946 858 3,849,483 3,626,452 

New Jersey 885 812 15,808,468 15,058,657 

New Mexico 1,325 1,146 12,532,374 11,260,240 

Nevada 941 855 13,012,251 12,268,081 

New York 1,024 918 30,475,737 28,355,550 
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Ohio 1,383 1,190 74,861,210 66,921,162 

Oklahoma 1,222 1,068 40,467,213 36,729,354 

Oregon 963 871 7,840,631 7,364,934 

Pennsylvania 1,258 1,095 90,105,397 81,483,381 

Rhode Island 831 771 3,317,849 3,195,228 

South Carolina 1,337 1,156 26,278,911 23,585,275 

South Dakota 1,351 1,166 3,571,517 3,201,472 

Tennessee 1,410 1,211 28,832,545 25,716,586 

Texas 1,187 1,042 188,765,209 171,987,787 

Lands of the Uintah and Ouray 

Reservation 1,534 1,305 2,323,513 2,053,298 

Utah 1,368 1,179 24,098,838 21,570,672 

Virginia 1,046 934 26,833,268 24,886,275 

Washington 1,110 983 10,595,066 9,742,168 

Wisconsin 1,364 1,176 28,355,019 25,388,731 

West Virginia 1,534 1,305 52,687,755 46,560,398 

Wyoming 1,526 1,299 32,456,457 28,697,535 

3.6 Illustrative Plan Approaches Analyzed 

To estimate the costs, benefits, and economic and energy market impacts of 

implementing the CPP guidelines, the EPA modeled two illustrative plan approaches, each at the 

state level, based on a rate-based approach and a mass-based approach. The rate-based plan 

approach 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 plan approach 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 nuclear59 

 Demand-side energy efficiency in addition to levels implicit in base case 

electricity demand. 

In the rate-based illustrative plan approach 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 3-1 below.60 

 

59 Includes three nuclear facilities at which construction has already commenced: Watts Barr (TN), Vogtle (GA), and 

Summer (SC) 

60 In this scenario, energy efficiency/renewable energy 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 energy 

efficiency/renewable energy from states outside of these illustrative regions. 
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Figure 3-1. Illustrative Regions for Demand-Side Energy Efficiency/Renewable Energy 

Procurement Used in this Analysis 

This rate-based implementation plan 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 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, plus generation (or avoided generation) 

from energy efficiency/renewable energy which is procured from within the illustrative regions. 

In this rate-based scenario, specific generation (or avoided generation) from energy 

efficiency/renewable energy 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 

energy efficiency/renewable energy across the illustrative region, no particular energy 

efficiency/renewable energy 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 analysis is an illustrative plan approach 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 approach assumes identical levels of demand-side energy efficiency 

megawatt-hour (MWh) demand reductions and associated costs, which are specified 

exogenously and consistent with the energy efficiency plan scenario performance levels 

described in section 3.6. Details of the implementation of the demand reduction are reported in 

the following section. 

The mass-based scenario presented in this chapter includes a 5 percent set-aside of 

allowances that would be allocated to recognize deployment of new renewable capacity, which is 

represented by lowering the capital cost of new renewable capacity by the estimated value of the 

allowances in the set-aside. The value of the set-aside is estimated in each model run year as the 

total allowances in the set-asides of each state in the contiguous U.S. multiplied by the projected 

average allowance price over the contiguous U.S. for that year. This total value is then assumed 

to apply evenly to all new renewable capacity. 

 The full array of estimates for the benefits, costs, and economic impacts of this action are 

presented for both the illustrative rate-based and mass-based illustrative plan approaches. These 

illustrative plan approaches 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 assumed to 

be adopted in response to the CPP. In other words, aside from investments in energy efficiency, 

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 costeffective 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 
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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. 

As previously noted, the power sector modeling and analysis presented in this chapter is 

intended to be illustrative in nature, and reflects the EPA’s best assessment of likely impacts of 

the CPP under a range of approaches that states may adopt. The modeling is designed to reflect 

the rule’s requirements, including the timing, applicability to sources, and flexibilities across the 

power system as accurately as possible to represent the nature and scope of the CPP. The 

analysis is a reasonable expectation of the incremental effects of the rule, and is consistent with 

past EPA analyses of power sector regulatory requirements (and estimates of the costs and 

benefits). 

For the CPP, the analysis and projections for the year 2025 reflect the impacts across the 

power system of complying with the interim goals, and the analysis and projections for 2030 

reflect the impacts of complying with the final goals. In addition to the 2025 and 2030 

projections, modeling results and projections are also shown for 2020. There is no regulatory 

requirement reflected in the 2020 run-year in IPM, consistent with the final rule. These years 

reflect the basic run-year structure in IPM, as configured by EPA. 

Although the analysis of the CPP does not include estimates of the costs and benefits of 

the CPP across each year of the rule in a year-by-year manner, the EPA has reflected the 

structure of the rule, including the interim and the final state goals of the CPP, in a manner that is 

consistent with the regulatory requirements. This is also consistent with past practice, including 

analysis of the Clean Air Interstate Rule, the Cross State Air Pollution Rule, the NOx SIP Call, 

National Ambient Air Quality Standards, the Acid Rain Program, and state rules. These past 

regulatory and legislative efforts included modeling and analysis in a similar manner, where 

select analytic years reflected projections of policy impacts for rules that include multi-year 

compliance periods. 
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3.7 Demand-Side Energy Efficiency 

3.7.1 Demand-Side Energy Efficiency Improvements (Electricity Demand Reductions)61

The final CPP provides states the flexibility to use demand reductions resulting from 

demand-side energy efficiency measures as a component of their plan 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 rateand 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 “demandside 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 a historical basis62 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. 

Nineteen 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 

 

61 For a more detailed discussion of the demand-side energy efficiency demand reductions and their associated costs, 

refer to U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for 

Existing Stationary Sources: Electric Utility Generating Units. Demand-Side Energy Efficiency. 

62 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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3-14 

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 illustrative plan 

approach 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.63 To reflect the adjusted 

state-level 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 3-2 summarizes the results of the illustrative demand-side energy efficiency plan 

scenario at the national level. 

Table 3-2. 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: U.S. EPA. 2015. Technical Support Document (TSD) for the Final Carbon Pollution Emission Guidelines 

for Existing Stationary Sources: Electric Utility Generating Units. Demand-Side Energy Efficiency. 

 

63 Details and reasoning for this assumption are included in U.S. EPA. 2015. Technical Support Document (TSD) 

for the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating 

Units. Demand-Side Energy Efficiency. 
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3.7.2 Demand-Side Energy Efficiency Costs64

Total costs of achieving the demand-side energy efficiency plan scenario for each year 

were calculated exogenous to the power sector modeling. The power system cost impacts 

resulting from the illustrative plan approach analyses 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 approaches 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 energy efficiency cost includes levelized value of firstyear 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 3-3. The total levelized cost of saved energy was calculated 

based upon the same inputs and using a 3 percent discount rate resulted in national 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.

 

 

64 For a more detailed discussion of the demand-side energy efficiency cost analysis, refer to the Demand-Side 

Energy Efficiency TSD. 
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Table 3-3. 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: U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for 

Existing Stationary Sources: Electric Utility Generating Units. Demand-Side Energy Efficiency. 

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 approaches assumes the recovery of 

these program costs through the following procedure.65 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 3.7.9.66 The U.S. EPA’s Demand-Side Energy Efficiency Technical Support Document 

(U.S. EPA 2015) 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. 

3.8 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 47 states 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 

 

65 The full retail price analysis method is discussed in section 3.7.9 of this chapter. 

66 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. 
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available to them, EPA believes a new system for calculating net energy output is not needed. 

Under the final guidelines, 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. 

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 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 3-4) 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 three 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.67 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 

 

67 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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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 two full time staff 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 seven states over 

a three year period resulted in 3,714 hours per year, per entity. Due to the 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 two full time staff, which totaled 

4,160 hours per year.68 

In addition, in the absence of specific information, EPA assumed that 27 states with 

strong, state-driven energy efficiency programs as documented in the U.S. EPA’s Energy and 

Environment Guide to Action69 would submit a plan under the state measures approach, which 

adds to the state respondent burden. Table 3-4 shows estimates of the annual state and industry 

respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030. 

 

68 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. 

69 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 3-4. Years 2020, 2025 and 2030: Summary of State and 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 

State 

Year 2020 203,840 14,427,298 0 36,015 36,015 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 

Industry 

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 

Total 

Year 2020 799,272 65,553,113 0 1,592,515 1,592,515 67,145,628 

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 

3.9 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. 

3.9.1 Projected Emissions (in the Contiguous U.S.) 

Under the rate-based approach, 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 emissions70 in 2025 and 2030, 

respectively). For the mass-based approach, EPA projects annual CO2 reductions of 4 percent 

below the base case in 2020, 12 percent below the base case in 2025 and 19 percent below base 

 

70 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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case projections in 2030 (reaching 29 percent to 32 percent below 2005 emissions71 in 2025 and 

2030, respectively). 

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

 CO2 Emissions (MM Tonnes) 

CO2 Emissions: Change 

from Base Case (MM 

Tonnes) 

CO2 Emissions: Percent 

Change from Base Case 

 2020 2025 2030 2020 2025 2030 2020 2025 2030 

Base Case 1,955 1,964 2,021 

Rate-based 1,892 1,754 1,644 -63 -211 -376 -3% -11% -19% 

Mass-based 1,881 1,724 1,646 -74 -240 -375 -4% -12% -19% 

Source: Integrated Planning Model run by EPA, 2015 

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

 CO2 Emissions (MM 

Tonnes) 

CO2 Emissions: Change 

from 2005 (MM Tonnes) 

CO2 Emissions: Percent Change 

from 2005 

 2005 2020 2025 2030 2020 2025 2030 

Base Case 2,434 -479 -470 -413 -20% -19% -17% 

Rate-based - -542 -680 -790 -22% -28% -32% 

Mass-based - -553 -710 -788 -23% -29% -32% 

Source: Integrated Planning Model run by EPA, 2015 

Under the rate-based illustrative plan approach, 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 17 percent reduction of mercury in 

2030. Under the mass-based illustrative plan approach, EPA projects a 15 percent reduction of 

SO2, 16 percent reduction of NOX, and a 12 percent reduction of mercury in 2025, and a 24 

percent reduction of SO2, 22 percent reduction of NOX, and a 16 percent reduction of mercury in 

2030. The projected non-CO2 reductions are summarized below in Table 3-7. 

 

 

71 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-based Mass-based Rate-based Mass-based 

2020 

SO2 (thousand tons) 1,311 1,297 1,257 -1.0% -4.1% 

NOX (thousand tons) 1,333 1,282 1,272 -3.8% -4.5% 

Hg (tons) 6.6 6.4 6.4 -2.8% -3.3% 

PM2.5 (thousand tons) [X] [X] [X] [X] [X] 

2025 

SO2 (thousand tons) 1,275 1,097 1,090 -14.0% -14.5% 

NOX (thousand tons) 1,302 1,138 1,100 -12.6% -15.6% 

Hg (tons) 6.6 5.9 5.8 -10.8% -12.2% 

PM2.5 (thousand tons) [X] [X] [X] [X] [X] 

2030 

SO2 (thousand tons) 1,314 996 1,034 -24.2% -21.3% 

NOX (thousand tons) 1,293 1,011 1,015 -21.8% -21.5% 

Hg (tons) 6.8 5.6 5.8 -17.2% -15.6% 

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, 

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 
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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. 

3.9.2 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, including 

the cost of demand-side energy efficiency programs and measures and monitoring, reporting, and 

recordkeeping (MR&R) costs. 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. 72 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 energy efficiency programs assumed in the 

IPM analysis are reported in section 3.6.2. 

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 $8.5 billion in 2030 (Table 3-8). The annual 

compliance cost of the mass-based illustrative plan approach are estimated to be $1.4 billion in 

2020, $3.0 billion in 2025, and $5.1 billion in 2030. The annual compliance cost is the projected 

additional 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,73 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. 

 

 

72 The compliance costs also capture the effect of changes in equilibrium fuel prices on the expenditures of the 

electricity sector to serve demand. 

73 See Chapter 2 of the GHG Mitigation Measures TSD and EPA’s Base Case using IPM (v5.15) documentation, 

available at: http://www.epa.gov/powersectormodeling 
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Table 3-8. Annualized Compliance Costs Including Monitoring, Reporting and 

Recordkeeping Costs Requirements (billions of 2011$) 

 2020 2025 2030 

Rate $2.5 $1.0 $8.4 

Mass $1.4 $3.0 $5.1 

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

energy efficiency costs and monitoring, reporting, and recordkeeping costs. 

In order to contextualize EPA’s projection of the additional costs in 2030 across the two 

illustrative plan approaches 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 final 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 $18 billion (rate) and $21 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 

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-based $166.8 $162.6 $183.3 

Mass-based $165.7 $164.6 $180.1 

Source: Integrated Planning Model run by EPA, 2015
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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 

energy efficiency-related expenditures include costs incurred by parties administering energy 

efficiency programs and costs incurred by participants in those programs. As a result, this 

analysis finds the cost of the rate-based and mass-based illustrative plan approaches in 2030 to 

be $8.4 billion and $[X] billion, respectively. 

3.9.3 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 approaches, 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 percent in Texas, all at a capital cost of $100 per 

kW.74 The option for heat rate improvement is only made available in the illustrative plan 

approaches, in response to the final rule. 

The vast majority of existing coal boilers are projected to adopt the aforementioned heat 

rate improvements. Of the 183 GW of coal projected to operate in 2030, 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 rate-based approach by 2030. Under the mass-based 

approach, EPA projects that 88 GW of the 174 GW of coal projected to operate in 2030 will 

improve operating efficiency by 2030. 

 

74 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 

Mitigation Measures TSD. 
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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 approaches. Under the illustrative rate-based plan 

approach, the average 2030 capacity factor is 69 percent, and under the mass-based approach, the 

average capacity factor for existing coal steam is 75 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 approaches, on average. The 

utilization of existing natural gas combined cycle capacity is lower than the BSER level of 75 

percent75 on an annual average basis in these illustrative plan approaches, 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 two 

(generation shifting) is not deployed to the maximum extent in the illustrative plan approaches. 

See Table 3-10. 

 

 

75 See preamble section V.D. 
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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-based 78% 75% 69% 56% 60% 61% 

Mass-based 78% 75% 75% 56% 58% 54% 

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 approaches presented in this RIA, each state is 

credited for total demand-side energy efficiency implemented in, or procured by, that state, 

consistent in aggregate 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 while they might be directly credited toward the state goals in the 

rate-based illustrative scenario, they are also utilized as a compliance option in the mass-based 

illustrative scenario. Increased deployment results in CO2 reductions in both rate-based and 

mass-based approaches. See sections below discussing projected impacts on generation mix and 

capacity. 

3.9.4 Projected Generation Mix 

Table 3-11 and Figure 3-2 show the generation mix in the base case and under the two 

illustrative plan approaches. 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 15 

percent in 2025, and natural-gas-fired generation from existing combined cycle capacity is 

projected to increase 2 percent relative to the base case. The coal-fired fleet in 2030 generates 22 

percent less than in the base case, while natural-gas-fired generation from existing combined 

cycles increases 5 percent relative to the base case. Gas-fired generation from new combined 

cycle capacity decreases 8 percent and 36 percent relative to the base case in 2025 and 2030, 

respectively. Relative to the base case, generation from non-hydro renewables decreases 3 

percent in 2025 and increases 8 percent in 2030. 
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Table 3-11. Generation Mix (thousand GWh) 

 Base Case Rate-based Mass-based Rate-based Mass-based 

2020 

Coal 1,462 1,391 1,374 -5% -6% 

NG Combined Cycle (existing) 1,111 1,126 1,132 1% 2% 

NG Combined Cycle (new) 33 53 69 61% 111% 

Combustion Turbine 15 20 17 39% 14% 

Oil/Gas Steam 51 51 50 0% -1% 

Non-Hydro Renewables 393 399 385 2% -2% 

Hydro 310 311 310 0% 0% 

Nuclear 798 792 804 -1% 1% 

Other 18 18 18 0% 0% 

Total 4,190 4,160 4,159 -1% -1% 

2025 

Coal 1,428 1,256 1,217 -12% -15% 

NG Combined Cycle (existing) 1,152 1,206 1,179 5% 2% 

NG Combined Cycle (new) 113 53 104 -53% -8% 

Combustion Turbine 23 30 34 31% 46% 

Oil/Gas Steam 39 21 19 -46% -52% 

Non-Hydro Renewables 417 414 404 -1% -3% 

Hydro 340 340 340 0% 0% 

Nuclear 799 791 804 -1% 1% 

Other 17 17 18 0% 0% 

Total 4,328 4,128 4,118 -5% -5% 

2030 

Coal 1,466 1,131 1,144 -23% -22% 

NG Combined Cycle (existing) 1,042 1,230 1,090 18% 5% 

NG Combined Cycle (new) 324 100 207 -69% -36% 

Combustion Turbine 22 27 32 21% 46% 

Oil/Gas Steam 22 11 11 -52% -53% 

Non-Hydro Renewables 450 488 485 9% 8% 

Hydro 340 341 340 0% 0% 

Nuclear 783 777 785 -1% 0% 

Other 17 17 17 0% 0% 

Total 4,467 4,122 4,110 -8% -8% 

Note: “Other” mostly includes generation from MSW and fuel cells. Source: Integrated Planning Model run by 

EPA, 2015 
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Figure 3-2 Generation Mix with the Base Case and 111(d) Options, 2020-2030 (thousand 

GWh) 

Source: Integrated Planning Model run by EPA, 2015 

Under both the rate-based and mass-based approaches, the projected rate of change in 

coal-fired generation is consistent with recent historical declines in coal-fired generation. 

Additionally, under both of these approaches, the trends for all other types will remain consistent 

with what their trends would be in the absence of this rule. Specifically, natural-gas fired 

generation and renewables would be expected to increase without this rule, and both are 

expected to increase under this rule, with renewables increasing at a somewhat greater rate than 

in the absence of this rule; and nuclear, oil-fired, and other types of generation are expected to be 

little impacted by this rule generation mix is consistent with recent declines in coal-fired 

generation and increases in gas-fired generation. See Figures 3-3 and 3-4. 
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Figure 3-3. Nationwide Generation: Historical (1990-2014) and Rate-Based Illustrative 

Plan Approach Projections (2020, 2025, 2030) 

Sources: Historic data (i.e., 1990-2014): U.S. Energy Information Administration, June 2015 Monthly Energy 

Review, Table 7.2a Electricity Net Generation: Total (All Sectors), Available at 

<http://www.eia.gov/totalenergy/data/monthly/>. Projected data (i.e., 2020, 2025, 2030): Integrated Planning Model, 

2015. Notes: Historic and projected data include generation from the power, industrial, and commercial 

sectors. Historic data from U.S. EIA reflects all cogeneration, while projections from the Integrated Planning 

Model reflect net cogeneration. 

Figure 3-4. Nationwide Generation: Historical (1990-2014) and Mass-Based Illustrative 

Plan Approach Projections (2020, 2025, 2030) 
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Sources: Historic data (i.e., 1990-2014): U.S. Energy Information Administration, June 2015 Monthly Energy 

Review, Table 7.2a Electricity Net Generation: Total (All Sectors), Available at 

<http://www.eia.gov/totalenergy/data/monthly/>. Projected data (i.e., 2020, 2025, 2030): Integrated Planning Model, 

2015. Notes: Historic and projected data include generation from the power, industrial, and commercial sectors. 

Historic data from U.S. EIA reflects all cogeneration, while projections from the Integrated Planning Model reflect 

net cogeneration. 

3.9.5 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 29 GW of additional coal-fired capacity is projected to be uneconomic to maintain (about 

14 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-based Mass-based Rate-based Mass-based 

2020 

Coal 208 195 193 -6% -7% 

NG Combined Cycle (existing) 233 231 232 -1% 0% 

NG Combined Cycle (new) 4 7 9 62% 113% 

Combustion Turbine 141 137 137 -3% -3% 

Oil/Gas Steam 88 81 80 -8% -9% 

Non-Hydro Renewables 130 132 128 1% -2% 

Hydro 106 106 106 0% 0% 

Nuclear 100 100 101 -1% 1% 

Other 5 5 5 0% 0% 

Total 1,016 994 992 -2% -2% 

2025 

Coal 208 187 181 -10% -13% 

NG Combined Cycle (existing) 233 231 232 -1% 0% 

NG Combined Cycle (new) 15 7 14 -52% -9% 

Combustion Turbine 143 138 137 -4% -4% 

Oil/Gas Steam 82 71 69 -14% -16% 

Non-Hydro Renewables 139 137 134 -1% -3% 

Hydro 112 112 112 0% 0% 

Nuclear 100 99 101 -1% 1% 

Other 5 5 5 0% 0% 

Total 1,037 988 985 -5% -5% 

2030 

Coal 207 183 174 -11% -16% 

NG Combined Cycle (existing) 233 231 232 -1% 0% 

NG Combined Cycle (new) 44 14 27 -68% -38% 

Combustion Turbine 147 138 136 -6% -7% 

Oil/Gas Steam 82 70 67 -15% -18% 

Non-Hydro Renewables 154 174 171 13% 11% 

Hydro 112 112 112 0% 0% 

Nuclear 99 98 99 -1% 0% 

Other 5 5 5 0% 0% 

Total 1,082 1,025 1,024 -5% -5% 

Source: Integrated Planning Model run by EPA, 2015 

3.9.6 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 the rate-based approach, 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 20 GW (27 percent increase) by 

2030. 

Under the mass-based illustrative scenario, new natural gas combined cycle capacity is 

projected to decrease by 1 GW in 2025 and decrease by 17 GW in 2030 (a 9 percent and 38 

percent decrease relative to the base case). New renewable capacity is projected to decrease 4 

GW (7 percent) relative to the base case in 2025, and increase 18 GW (24 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-based 7.1 7.1 13.9 2.7 -7.8 -30.1 

Mass-based 9.3 13.6 27.2 4.9 -1.3 -16.8 

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-based 40.5 57.4 94.4 1.4 -1.8 20.2 

Mass-based 36.7 54.9 91.9 -2.4 -4.2 17.8 

Source: Integrated Planning Model run by EPA, 2015 
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3.9.7 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-based Mass-based Rate-based Mass-based 

Appalachia 92 71 69 -23% -25% 

Interior 250 242 236 -3% -6% 

West 379 306 293 -19% -23% 

Waste Coal 6 6 6 0% 0% 

Imports 1 1 1 -37% -14% 

Total 729 626 606 -14% -17% 

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, power sector natural 

gas use is projected to decrease by 4.5 percent in 2030. 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 

-based 8.91 9.28 9.59 3.4% -1.0% -1.3% 

-based 9.02 9.39 9.28 4.6% 0.2% -4.5% 

Source: Integrated Planning Model run by EPA, 2015 

3.9.8 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 and Table 3-18 and are attributable to the changes in 

overall power sector demand for each fuel due to the final guidelines. 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 
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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 both the rate-based and mass-based scenarios, pipeline 

capacity construction through 2020 is projected to increase by less than two percent beyond base 

case projections. By 2030, however, the total cumulative pipeline capacity construction built is 

projected to decrease 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-based 1.54 1.58 1.73 2.34 2.35 2.46 

Mass-based 1.54 1.59 1.73 2.35 2.40 2.55 

Rate-based -0.8% -5.0% -3.8% -1.7% -6.2% -8.0% 

Mass-based -0.7% -4.7% -3.2% -1.6% -4.3% -4.6% 

Source: Integrated Planning Model run by EPA, 2015 

Table 3-18. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices 

(2011$/MMBtu) 

Henry Hub Delivered - Electric Power Sector

2020 2025 2020 2025 2020 2025

Base Case 5.20 5.12 6.01 5.25 5.17 5.98 

Rate-based 5.48 4.73 6.21 5.53 4.77 6.13 

Mass-based 5.40 4.97 5.92 5.45 5.00 5.86 

Rate-based 5.4% -7.5% 3.3% 5.3% -7.7% 2.5% 

Mass-based 3.9% -3.0% -1.4% 3.8% -3.2% -2.1% 

Source: Integrated Planning Model run by EPA, 2015 

3.9.9 Projected Retail Electricity Prices 

EPA’s analysis of the illustrative rate-based plan scenario shows an increase in the 

national average (contiguous U.S.) retail electricity price of less than one percent in both 2025 

and 2030, compared to the modeled base case price estimate in those years. Under the illustrative 

mass-based plan scenario, EPA projects an increase in the national average (contiguous U.S.) 

retail electricity price of 2 percent in 2025 and 0.01 percent in 2030. 

Retail electricity prices embody generation, transmission, distribution, taxes, and 

demand-side energy efficiency costs. IPM modeling projects changes in regional wholesale 

power prices and capacity payments related to imposition of the represented CPP scenarios that 

are combined with EIA regional transmission and distribution costs to calculate changes to 

regional retail prices using the Retail Price Model (RPM).76 As described in Section 3.6.2, the 

funding for demand-side energy efficiency (to cover program costs) is typically collected 

through a standard per kWh surcharge to the ratepayer and the regional retail price impacts 

 

76 See documentation available at: http://www.epa.gov/powersectormodeling/ 
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presented here assume that these costs are recovered by utilities in retail rates. This is an 

approximation, since not every utility will pass through the entirety of demand-side energy 

efficiency costs (e.g., distribution only utilities that generate ERCs for demand-side energy 

efficiency, sell them to affected EGUs, and account for this revenue in rate determination). 

Furthermore, this analysis assumes that rate-payers in the state producing zero-emitting 

generation (or avoided generation) bear the costs of such production. However, in practice, if 

such generation is claimed through an ERC in another state, part of its costs may ultimately be 

borne by rate-payers in the ERC-consuming state rather than the state in which that zero-emitting 

generation was located. There are many factors influencing the estimated retail electricity price 

impacts, namely projected changes in generation mix, fuel prices, and development of new 

generating capacity. These projected changes vary regionally under each illustrative plan 

scenario in response to the goals under the two scenarios, and they also vary depending upon 

retail electricity market structure (e.g., cost-of-service vs. competitive). In the mass-based 

approach, treatment of allowance allocations will also have an impact on retail electricity prices. 

In competitive regions, this RIA assumes that allowances are freely allocated to generators who 

then keep 100 percent of the freely allocated allowance value without passing this value through 

to ratepayers through lower retail electricity prices. To the extent that implementing authorities 

choose to require this allowance value to be passed through to ratepayers (such as by allocating 

allowances to load-serving entities who could be subject to such a requirement), retail prices 

would be lower than those shown here. 

 
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Table 3-19. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) 

 2020 Projected Retail Price (cents/kWh) Percent Change from Base Case 

 Base Case Rate-based Mass-based Rate-based Mass-based 

ERCT 9.7 9.9 9.9 2.5% 2.1% 

FRCC 10.5 10.7 10.7 2.0% 1.6% 

MROE 9.9 10.3 10.3 4.2% 3.8% 

MROW 8.7 9.0 9.0 2.8% 2.3% 

NEWE 13.3 14.0 14.0 5.1% 5.5% 

NYCW 17.4 18.3 18.3 5.0% 5.3% 

NYLI 14.4 15.1 15.1 4.6% 5.1% 

NYUP 12.4 13.1 13.1 5.4% 5.3% 

RFCE 11.1 11.8 11.8 6.1% 6.1% 

RFCM 10.4 10.9 10.9 4.3% 4.3% 

RFCW 9.4 9.8 9.8 5.1% 4.8% 

SRDA 8.6 8.8 8.7 2.1% 1.7% 

SRGW 8.6 9.0 9.0 4.1% 4.8% 

SRSE 10.0 10.1 10.1 0.9% 0.5% 

SRCE 8.0 8.1 8.1 1.1% 0.8% 

SRVC 9.8 9.9 9.9 1.5% 1.2% 

SPNO 9.9 9.9 9.9 -0.8% -0.9% 

SPSO 7.9 8.1 8.1 3.2% 2.4% 

AZNM 10.9 11.2 11.2 2.1% 2.1% 

CAMX 14.3 14.8 14.7 3.3% 3.0% 

NWPP 6.9 7.1 7.1 3.2% 2.9% 

RMPA 8.7 9.0 8.9 3.1% 2.9% 

Contiguous U.S. 10.0 10.3 10.3 3.2% 3.0% 

Note: regions pictured on Figure 3-5. 
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Table 3-20. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) 

 2025 Projected Retail Price (cents/kWh) Percent Change from Base Case 

 Base Case Rate-based Mass-based Rate-based Mass-based 

ERCT 10.7 11.1 10.9 3.8% 1.5% 

FRCC 10.2 10.2 10.3 -0.2% 1.0% 

MROE 9.7 10.0 10.0 2.4% 2.6% 

MROW 8.7 9.0 9.0 2.5% 3.1% 

NEWE 12.6 12.4 12.7 -1.3% 0.5% 

NYCW 17.0 16.9 16.9 -0.5% -0.5% 

NYLI 14.0 13.7 13.7 -2.2% -1.7% 

NYUP 11.8 11.7 11.7 -0.8% -1.3% 

RFCE 10.3 10.2 10.5 -0.2% 2.1% 

RFCM 10.4 10.4 10.6 0.5% 1.9% 

RFCW 9.8 9.7 10.1 -1.4% 2.4% 

SRDA 8.6 8.6 8.7 0.0% 1.4% 

SRGW 9.1 9.0 9.3 -0.9% 2.5% 

SRSE 9.6 9.7 9.8 1.4% 2.1% 

SRCE 7.8 8.0 8.0 2.6% 3.0% 

SRVC 9.3 9.5 9.6 1.7% 2.4% 

SPNO 9.8 10.0 10.2 2.9% 4.3% 

SPSO 8.1 8.3 8.4 2.7% 4.4% 

AZNM 10.7 10.9 10.9 2.2% 1.8% 

CAMX 13.2 13.3 13.5 0.8% 2.4% 

NWPP 6.8 6.9 7.0 2.1% 2.7% 

RMPA 8.6 8.7 8.9 2.0% 4.3% 

Contiguous U.S. 9.9 9.9 10.1 0.9% 2.0% 

Note: regions pictured on Figure 3-5. 
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Table 3-21. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices 

(cents/kWh) 

 2030 Projected Retail Price (cents/kWh) Percent Change from Base Case 

 Base Case Rate-based Mass-based Rate-based Mass-based 

ERCT 11.6 11.4 11.3 -1.4% -2.5% 

FRCC 10.3 10.8 10.5 4.6% 2.3% 

MROE 9.7 10.3 10.3 5.9% 6.3% 

MROW 8.9 9.1 9.1 2.7% 2.8% 

NEWE 14.3 13.6 13.4 -5.4% -6.9% 

NYCW 19.2 18.2 18.0 -5.2% -6.4% 

NYLI 16.3 14.8 14.6 -9.0% -10.1% 

NYUP 13.6 12.7 12.5 -7.0% -8.4% 

RFCE 11.3 10.7 10.6 -5.6% -6.5% 

RFCM 10.5 10.8 10.7 3.4% 1.7% 

RFCW 10.4 10.5 10.5 1.2% 0.7% 

SRDA 9.0 9.3 9.2 3.5% 1.9% 

SRGW 9.7 9.6 9.7 -0.6% 0.4% 

SRSE 9.8 10.2 10.0 3.9% 2.1% 

SRCE 7.8 8.1 8.0 4.3% 3.3% 

SRVC 9.3 9.6 9.5 3.2% 2.0% 

SPNO 9.5 9.8 10.1 2.7% 5.8% 

SPSO 8.7 9.0 8.9 3.9% 2.0% 

AZNM 10.9 11.2 11.1 2.3% 2.0% 

CAMX 13.5 13.6 13.7 1.1% 1.4% 

NWPP 6.9 7.0 7.1 2.2% 2.6% 

RMPA 8.9 9.0 9.3 0.7% 3.5% 

Contiguous U.S. 10.3 10.4 10.3 0.8% 0.01% 

Note: regions pictured on Figure 3-5. 

 
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Figure 3-5. Electricity Market Module Regions 

Source: EIA (http://www.eia.gov/forecasts/aeo/pdf/nerc_map.pdf) 

3.9.10 Projected Electricity Bill Impacts 

The electricity price changes addressed in section 3.9.9 combine with the significant 

reductions in electricity demand applied in the illustrative approaches to affect average electricity 

bills. The estimated changes to average bills are summarized in Table 3-22. Under the illustrative 

rate-based plan scenario, EPA estimates an average monthly bill increase of 2.7 percent in 2020 

and an average bill decrease of 3.8 percent in 2025 and 7 percent in 2030. Under the mass-based 

scenario, EPA estimates an average bill increase of 2.4 percent in 2020 and an average bill 

decrease of 2.7 percent in 2025 and 7.7 percent in 2030. These reduced electricity bills reflect the 

combined effects of changes in both average retail rates (driven by compliance approaches taken 

to achieve the state goals) and lower electricity demand (driven by demand-side energy 

efficiency). 

Table 3-22. Projected Changes in Average Electricity Bills 

 2020 2025 2030 

Rate-based 2.7% -3.8% -7.0% 

Mass-based 2.4% -2.7% -7.7% 

3.10 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 
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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 

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. 

3.11 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 demand-side energy 
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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 estimating the demand-side energy 

efficiency costs is discussed in more detail in the Demand-Side Energy Efficiency Technical 

Support Document (TSD). The evaluation, measurement and verification (EM&V) of demandside 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 Demand-Side Energy Efficiency TSD. 

 

Cost estimates for the final emission guidelines are based on rigorous power sector 

modeling using ICF’s Integrated Planning Model.77 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. 

One important element of the final CPP is the flexibility afforded to states as they 

develop requirements for their existing emitting sources. Each state has discretion on how to best 

achieve the standards of performance and/or state goals. As such, states can apply requirements 

to sources that achieve greater reductions than required during the interim period, and use those 

earlier reductions in the final period (i.e., banking of reductions). 

In the analysis and modeling for the RIA, such flexibilities were not explicitly modeled in 

the compliance scenarios. Doing so would require additional assumptions about the specific 

 

77 Full documentation for IPM can be found at <http:// http://www.epa.gov/powersectormodeling>. 
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opportunities states may choose to adopt in their plans, including the form of the standard that 

states apply, the manner in which it is applied, and the economic signal that such a mechanism 

provides to sources over time, such that sources would have an incentive to make greater 

reductions earlier. As previously stated, the analysis in the RIA is intended to be illustrative to 

inform the broad impacts of the rule across the power sector, and not intended to forecast the 

specific approaches that individual states might choose, and how sources might prefer to achieve 

the emission reductions to reflect each state plan in response to particular policy signals or 

requirements. Not representing banking of earlier reductions into the final period is a somewhat 

conservative analytic approach, given that there is inadequate and incomplete information at this 

time regarding state plans. 

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 final emission 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 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 
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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 approaches 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 Demand-Side Energy 

Efficiency 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 wellfunctioning 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 final 

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 

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 
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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.78 Such circumstances are present in the analysis presented in this chapter, whereby in 

some regions the base case and illustrative approaches 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 Demand-Side Energy Efficiency 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 

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 demand-

 

78 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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3-47 

side 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. 

U.S. EPA. 2010. EPA Guidelines for Preparing Economic Analyses, Chapters 8 and Appendix 

A. Available at: <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.htm>. 

Accessed 7/11/2015. 

U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission 

Guidelines for Existing Stationary Sources: Electric Utility Generating Units. Demand-Side 

Energy Efficiency. 

 
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3A-1 

APPENDIX 3A: ANALYSIS OF POTENTIAL UPSTREAM METHANE EMISSIONS 

CHANGES IN NATURAL GAS SYSTEMS AND COAL MINING

The purpose of this appendix is to describe the methodology for estimating upstream 

methane emissions related to natural gas systems and coal mining sectors that may result from 

the illustrative plan approaches examined in the Regulatory Impact Analysis (RIA). The US 

Environmental Protection Agency (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 these upstream changes relative to CO2 emissions reductions 

anticipated at power plants from the illustrative plan approaches examined in the RIA. In 

addition to estimating changes in upstream methane emissions, this assessment included 

estimating CO2 from the flaring of methane, but did not examine other potential changes in other 

upstream greenhouse gas emissions changes from natural gas systems and coal mining sectors. 

The methodologies used to project upstream emissions were previously developed for the 

purpose of the 2014 U.S. Climate Action Report, and were subject to peer review and public 

review as part of the publication of that report. In section 3A.1, the overall approach is described 

in brief. In section 3A.2, results are presented. Section 3A.3 discusses uncertainties and 

limitations of the analysis. Finally, section 3A.4 contains a bibliography of cited resources. In the 

RIA for the Clean Power Plan proposal (in then section 3A.3), we presented the detailed 

methodologies for how methane and flaring-related CO2 projections were estimated for coal 

mining and natural gas systems. We rely on the same methods in this RIA, so we refer the 

interested reader to the proposal RIA79 for the detailed methodological discussion. The 

calculations have been updated to reflect input data from the most recent U.S. GHG Inventory, 

published in April 2015. 

 

79 Clean Power Plan proposal RIA can be found at < http://www2.epa.gov/sites/production/files/2014-

06/documents/20140602ria-clean-power-plan.pdf>. 
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3A-2 

3A.1 General Approach

3A.1.1 Analytical Scope 

Upstream methane and flaring-related CO2 emissions associated with coal mining and 

natural gas systems were estimated for 2025 through 2030 using methodologies developed for 

the 2014 U.S. Climate Action Report (U.S. Department of State 2014). The base year for the 

projections is 2013, as reported in the 2015 U.S. GHG Inventory (EPA 2015a). The projection 

methodologies use activity driver data outputs such as coal and natural gas production from the 

base case and policy scenarios generated by the Integrated Planning Model (IPM), which was 

used in the RIA to model illustrative plan approaches. The projection methodologies use similar 

activity data and emissions factors as are used in the U.S. Greenhouse Gas (GHG) Inventory. 

The projection methodologies estimate reductions associated with both voluntary and 

regulatory programs affecting upstream methane-related emissions. In the case of the voluntary 

programs, the rate of reductions is based on the historical average decrease from these programs 

over recent years. In the case of regulatory reductions, the reductions are based on the reduction 

rates estimated in the RIAs of relevant regulations. The projections include emissions reductions 

projected to result from the 2012 Oil and Natural Gas New Source Performance Standards. The 

methodologies to estimate upstream emissions were subject to expert peer review and public 

review in the context of the 2014 U.S. Climate Action Report. For more information on the 

review, or for the detailed methodologies used for non-CO2 source projections in that report, 

including methane-related emissions from coal production and natural gas systems, see 

“Methodologies for U.S. Greenhouse Gas Emissions Projections: Non-CO2 and Non-Energy CO2

Sources” (EPA, 2013). Uncertainties and limitations are discussed, including a side case which 

incorporates additional geographic information for estimating methane from coal mining. 

The term “upstream emissions” in this document refers to vented, fugitive and flared 

emissions associated with fuel production, processing, transmission, storage, and distribution of 

fuels prior to fuel combustion in electricity plants. For this analysis, the EPA focused on 

upstream methane from the natural gas systems and coal mining sectors. In addition, the analysis 

included CO2 resulting from flaring in natural gas production. This analysis does not assess other 

upstream GHG emissions changes, such as CO2 emissions from the combustion of fuel used in 
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3A-3 

natural gas and coal production activities or other non-combustion CO2 emissions from natural 

gas systems, such as vented CO2 and CO2 emitted from acid-gas removal processes. 

Also, the EPA assessed potential upstream methane emissions from natural gas systems 

and coal mining sectors within the domestic U.S., but did not examine emissions from potential 

changes in upstream emissions generated by changes in natural gas and coal production, 

processing, and transportation activities outside of the US.80 Last, the EPA did not assess 

potential changes in other upstream non-GHG emissions, such as nitrogen oxides, volatile 

organic compounds, and particulate matter. Table 3A-1 presents estimates of the upstream 

emissions discussed in this analyses for 2013, based on the 2015 U.S. GHG Inventory. 

EPA defined the boundaries of this assessment in order to provide targeted insights into the 

potential net change in methane emissions from natural gas systems and coal production 

activities specifically. CO2 emissions from flared methane are included because regulatory and 

voluntary programs influence the rate of methane flaring over time and the CO2 remaining after 

flaring is a methane-related GHG. Because of the multiple strategies adopted in the illustrative 

plan approaches, a more comprehensive assessment of upstream GHG emissions would require 

examination of the broader power sector and related input markets and their potential changes in 

response to the rule. This analysis would be complex and likely subject to data limitations and 

substantial uncertainties. Rather, EPA chose to limit the scope of this upstream analysis to 

evaluate the potential for changes in GHG emissions that may be of significant scale relative to 

the impacts of the rule and for which EPA had previously-reviewed projection techniques, which 

are presented in detail below.

3A.1.2 Coal Mining Source Description 

Within coal mining, this analysis covers fugitive methane emissions from coal mining 

(including pre-mining drainage) and post-mining activities (i.e., coal handling), including both 

underground and surface mining. Emissions from abandoned mines are not included. Energy-

 

80 While the analysis does not estimate methane emissions changes outside of the United States, activity factors 

include imports and exports of natural gas to help estimate domestic methane emissions related to trade of natural 

gas, such as emissions from LNG terminals in the US or from pipelines transporting imported natural gas within the 

US (or transporting natural gas within the US while en route for export). 
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3A-4 

related CO2 emissions, such as emissions from mining equipment and vehicles transporting coal 

are not included. Methane, which is contained within coal seams and the surrounding rock strata, 

is released into the atmosphere when mining operations reduce the pressure above and/or 

surrounding the coal bed. The quantity of methane emitted from these operations is a function of 

two primary factors: coal rank and coal depth. Coal rank is a measure of the carbon content of 

the coal, with higher coal ranks corresponding to higher carbon content and generally higher 

methane content. Pressure increases with depth and prevents methane from migrating to the 

surface; as a result, underground mining operations typically emit more methane than surface 

mining. In addition to emissions from underground and surface mines, post-mining processing of 

coal and abandoned mines also release methane. Post-mining emissions refer to methane retained 

in the coal that is released during processing, storage, and transport of the coal. 

3A.1.3 Natural Gas Systems Source Description 

Within natural gas systems, this analysis covers vented and fugitive methane emissions 

from the production, processing, transmission and storage, and distribution segments of the 

natural gas system. It also includes CO2 from flaring of natural gas. Not included are vented and 

fugitive CO2 emissions from natural gas systems, such as vented CO2 emissions removed during 

natural gas processing, or energy-related CO2 such as emissions from stationary or mobile 

combustion. The U.S. natural gas system encompasses hundreds of thousands of wells, hundreds 

of processing facilities, and over a million miles of transmission and distribution pipelines. 

Methane and non-combustion81 CO2 emissions from natural gas systems are generally processrelated, with normal operations, routine maintenance, and system upsets being the primary 

contributors. There are four primary stages of the natural gas system which are briefly described 

below. 

Production: In this initial stage, wells are used to withdraw raw gas from underground 

formations. Emissions arise from the wells themselves, gathering pipelines, and well-site gas 

treatment facilities (e.g., dehydrators, separators). Major emissions source categories within the 

production stage include pneumatic devices, gas wells with liquids unloading, and gas well 

 

81 In this document, consistent with IPCC accounting terminology, the term “combustion emissions” refers to the 

emissions associated with the combustion of fuel for useful heat and work, while “non-combustion emissions” refers 

to emissions resulting from other activities, including flaring and CO2 removed from raw natural gas. 
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3A-5 

completions and re-completions (i.e., workovers) with hydraulic fracturing (EPA 2013). Flaring 

emissions account for the majority of the non-combustion CO2 emissions within the production 

stage. 

Processing: In this stage, natural gas liquids and various other constituents from the raw gas are 

removed, resulting in “pipeline-quality” gas, which is then injected into the transmission system. 

Fugitive methane emissions from compressors, including compressor seals, are the primary 

emissions source from this stage. In the U.S. GHG Inventory, the majority of non-combustion 

CO2 emissions in the processing stage come from acid gas removal units, which are designed to 

remove CO2 from natural gas. 

Transmission and Storage: Natural gas transmission involves high-pressure, large-diameter 

pipelines that transport gas long distances from field production and processing areas to 

distribution systems or large-volume customers such as power plants or chemical plants. 

Compressor station facilities, which contain large reciprocating and turbine compressors, are 

used to move the gas throughout the U.S. transmission system. Fugitive methane emissions from 

these compressor stations and from metering and regulating stations account for the majority of 

the emissions from this stage. Pneumatic devices and non-combusted engine exhaust are also 

sources of methane emissions from transmission facilities. Natural gas is also injected and stored 

in underground formations, or liquefied and stored in above-ground tanks, during periods of 

lower demand (e.g., summer), and withdrawn, processed, and distributed during periods of 

higher demand (e.g., winter). Compressors and dehydrators are the primary contributors to 

emissions from these storage facilities. Emissions from LNG import terminals are included 

within the transportation and storage stage. 

Distribution: Distribution pipelines take the high-pressure gas from the transmission system at 

“city gate” stations, reduce the pressure, and then distribute the gas through primarily 

underground mains and service lines to individual end users. 

 
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Table 3A-1. Base Year Upstream Methane-Related Emissions in the U.S. GHG Inventory 

Emissions Source 2013 Emissions (million metric tons CO2 Eq.) 

Methane from Coal Mining 64.6 

Underground Mining and Post-Mining 52.8 

Surface Mining and Post-Mining 11.8 

Methane from Natural Gas Systems 157.4 

Production 47.0 

Processing 22.7 

Transmission and Storage 54.4 

Distribution 33.3 

CO2 from flaring of natural gas 15.5 

Source: 2015 U.S. GHG Inventory (EPA, 2015). A Global Warming Potential of 25 was used to convert methane 

emissions to CO2 Eq. 

In Table 3A-1, CO2-equivalent methane emissions are presented using the Fourth 

Assessment Report Global Warming Potential (GWP) of 25. 

3A.1.4 Illustrative Plan Approaches Examined 

States will ultimately determine optimal approaches to comply with the goals established 

in this regulatory action. The RIA depicts illustrative plan approaches for the final emissions 

guidelines, reflecting a rate-based illustrative plan or mass-based illustrative plan approach. 

3A.1.5 Activity Drivers 

IPM-based activity driver projections from base case and illustrative plan approaches 

underlie the estimates of upstream methane emissions. These activity drivers include domestic 

coal and natural gas production, imports and exports, and natural gas consumption. Table 3A-2 

and Table 3A-3 summarize the IPM-based coal and natural gas production activity driver results 

from the baseline and illustrative scenario for the final guidelines.82

Under the final guidelines, both the rate-based and mass-based illustrative plan approaches 

result in reduced coal production and little change in natural gas production. We estimate that the 

illustrative plan approaches will result in reductions in coal production of 5 to 6 percent in 2020, 

12 to 15 percent in 2025 and 21 to 22 percent in 2030, relative to base case coal production. 

 

82 Uncertainties related to activity drivers are discussed in the uncertainties and limitations section. 
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3A-7 

Natural gas production in the illustrative plan approaches change by 1 percent or less in each of 

the years of analysis relative to production in the base case. 

Table 3A-2. Projected Coal Production Impacts 

 

Coal Production 

(million short tons) 

Coal Production Change 

from Base Case 

(million short tons) 

Coal Production Percent 

Change from Base Case 

2020 2025 2030 2020 2025 2030 2020 2025 2030 

Base Case 832.4 828.7 860.1 

Rate-based 791.5 725.7 674.4 -41.0 -103.0 -186.0 -5% -12% -22% 

Mass-based 779.9 705.6 678.9 -52.0 -123.0 -181.0 -6% -15% -21% 

Table 3A-3. Projected Natural Gas Production Impacts 

 

Dry Gas Production 

(trillion cubic feet) 

Dry Gas Production 

Change from Base Case 

(trillion cubic feet) 

Dry Gas Production 

Percent Change from 

Base Case 

 2020 2025 2030 2020 2025 2030 2020 2025 2030 

Base Case 28.9 30.8 33.0 

Rate-based 29.1 30.7 32.9 +0.2 -0.1 -0.1 +1% 0% 0% 

Mass-based 29.2 30.8 32.6 +0.3 +0.0 -0.4 +1% 0% -1% 

3A.2 Results 

The analytical results (Table 3A-4) for the final guidelines indicates decreases in methane 

emissions from coal mining of 6 to 8 million metric tons CO2 Eq. in 2025, and about 12 million 

metric tons CO2 Eq. in 2030. Methane from natural gas systems decreases relative to the base 

case by 0 to 1 million metric tons CO2 Eq. in 2025 and 1 to 2 million metric tons CO2 Eq. in 

2030. CO2 from flaring in natural gas production does not show significant change relative to the 

base case. 

Based on the actions modelled in the illustrative plan approaches, upstream methane 

emissions and CO2 emissions are predicted to decline (see Table 3A-4). The final guidelines are 

predicted to result in a net emissions reduction of about 8 million metric tons CO2 Eq. in 2025 

and a net emissions reduction of 13 to 15 million metric tons CO2 Eq. in 2030. These net 

emissions changes represent the sum of changes in methane from coal mining, methane from 
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3A-8 

natural gas systems, and CO2 from flaring in natural gas production. 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. 

Table 3A-4. Potential Upstream Emissions Changes 

Emissions (million metric tons CO2 Eq.) 

2020 2025 2030 

Rate-based 

Methane from Coal Mining -2.7 -6.8 -12.7 

Methane from Natural Gas Systems +1.0 -0.7 -0.6 

CO2 from NG flaring +0.1 -0.0 -0.1 

Total Methane + CO2 -1.6 -7.6 -13.4 

Mass-based 

Methane from Coal Mining -3.5 -8.2 -12.4 

Methane from Natural Gas Systems +1.3 -0.1 -2.0 

CO2 from NG flaring +0.2 +0.0 -0.3 

Total Methane + CO2 -2.0 -8.2 -14.6 

Note: A Global Warming Potential of 25 was used to convert methane emissions to CO2 Eq. 

3A.3 Uncertainties and Limitations 

Projections of upstream methane emissions and CO2 emitted from flaring of methane are 

subject to a range of uncertainties and limitations. These uncertainties and limitations include 

estimating the effect of the plan approach on activity drivers, uncertainty in base year emissions, 

and uncertainties in changes in emissions factors over relatively long periods of time. For 

example, EPA’s application of IPM relies on EIA projections for coal imports and exports. 

Consequently, coal imports and exports are not able to fully respond within the IPM framework 

to significant fluctuations in power sector coal demand. To the extent international markets may 

be expected to offset reduced domestic coal demand, changes in U.S. upstream emissions as a 

result of the policy scenarios would be smaller than what is presented here. 

Discussion of uncertainty in historical estimates of emissions from coal mining and natural 

gas systems can be found in the 2015 U.S. GHG Inventory. Projected changes in activity drivers 
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3A-9 

and emissions factors are based on a combination of policy, macroeconomic, energy market, and 

technology factors which are uncertain in both baseline and illustrative plan approaches. 

Relatively higher or lower economic growth, or changes in the relative prices or availability of 

various technologies could result in alternative estimates in the net change in upstream methane 

emissions and related CO2 emissions. 

3A.4 References 

IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Available at 

<http://www.ipcc-nggip.iges.or.jp/public/2006gl/>. Accessed June 4, 2015. 

U. S. Department of State. 2014. 2014 U.S. Climate Action Report to the UNFCCC. Available at 

<http://www.state.gov/e/oes/rls/rpts/car6/index.htm>. Accessed June 4, 2015. 

U. S. Environmental Protection Agency (U.S. EPA). 1993. Anthropogenic Methane Emissions in 

the United States, Estimates for 1990: Report to Congress. EPA-430-R-93-003. 

U. S. Environmental Protection Agency (U.S. EPA). 2011. Oil and Natural Gas Sector: 

Standards of Performance for Crude Oil and Natural Gas Production, Transmission, and 

Distribution: Background Technical Support Document for the Proposed Standards. 

Available at <http://epa.gov/airquality/oilandgas/pdfs/20110728tsd.pdf>. Accessed June 4, 

2015. 

U. S. Environmental Protection Agency (U.S. EPA). 2012a. Oil and Natural Gas Sector: New 

Source Performance Standards and National Emission Standards for Hazardous Air 

Pollutants Reviews. 40 CFR Parts 60 and 63 [EPA-HQ-OAR-2010-0505; FRL-9665-1], RIN 

2060-AP76. Available at <http://www.gpo.gov/fdsys/pkg/FR-2012-08-16/pdf/2012-

16806.pdf>. Accessed June 4, 2015. 

U. S. Environmental Protection Agency (U.S. EPA). 2012b. Oil and Natural Gas Sector: 

Standards of Performance for Crude Oil and Natural Gas Production, Transmission, and 

Distribution: Background Supplemental Technical Support Document for the Final New 

Source Performance Standards. Available at 

<http://epa.gov/airquality/oilandgas/pdfs/20120418tsd.pdf>. Accessed June 4, 2015. 

U. S. Environmental Protection Agency (U.S. EPA). 2015. Inventory of U.S. Greenhouse Gas 

Emissions and Sinks: 1990–2013. EPA-430-R-15-004. Available at 

<http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html>. Accessed June 4, 

2015. 
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3A-10 

U. S. Environmental Protection Agency (U.S. EPA). 2013. Methodologies for U.S. Greenhouse 

Gas Emissions Projections: Non-CO2 and Non-Energy CO2 Sources. Available at 

<http://www.state.gov/documents/organization/219472.pdf> Accessed June 4, 2015. 

 
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4-1 

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 

illustrative plan approaches 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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4-2 

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 July 2015) (“current TSD”).83 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). 

 

83 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 July 2015). 

Available at: <https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-tsd-final-july-2015.pdf> Accessed 

7/11/2015. 
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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 July 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.84 

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 

 

84 See https://www.whitehouse.gov/omb/oira/social-cost-of-carbon for both TSDs. 
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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 

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.85

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.86 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 

 

85 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. 

86 Climate change impacts and SCC modeling is an area of active research. For example, see: (1) Howard, Peter, 

“Omitted Damages: What’s Missing from the Social Cost of Carbon.” March 13, 2014, 

http://costofcarbon.org/files/Omitted_Damages_Whats_Missing_From_the_Social_Cost_of_Carbon.pdf; and (2) 

Electric Power Research Institute, “Understanding the Social Cost of carbon: A Technical Assessment,” October 

2014, www.epri.com. 
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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 peerreviewed literature has continued to support this conclusion. For example, 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 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 comments87, the IWG announced plans to obtain expert 

independent advice from the National Academies of Sciences, Engineering, and Medicine 

 

87 See https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-response-to-comments-final-july-2015.pdf 
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(Academies) to ensure that the SC-CO2 estimates continue to reflect the best available scientific 

and economic information on climate change.88 The Academies 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 Academies process, OMB posted a revised TSD that includes two minor 

technical corrections to the current estimates. One technical correction addressed an inadvertent 

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 percent 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$).89 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 

 

88 See https://www.whitehouse.gov/blog/2015/07/02/estimating-benefits-carbon-dioxide-emissions-reductions. 

89 The current version of the TSD is available at: https://www.whitehouse.gov/sites/default/files/omb/inforeg/scctsd-final-july-2015.pdf. 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. 
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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.

90 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 

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 illustrative plan approaches. 

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 Mass-Based 

Million metric tonnes of CO2 reduced 63 74 

5% (average) $0.80 $0.94 

3% (average) $2.8 $3.3 

2.5% (average) $4.1 $4.9 

3% (95th percentile) $8.2 $9.7 

* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting 

of climate impacts. 

 

90 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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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 Mass-Based 

Million metric tonnes of CO2 reduced 211 240 

5% (average) $3.1 $3.6 

3% (average) $10 $12 

2.5% (average) $15 $17 

3% (95th percentile) $31 $35 

* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting 

of climate impacts. 

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 Mass-Based 

Million metric tonnes of CO2 reduced 376 375 

5% (average) $6.4 $6.4 

3% (average) $20 $20 

2.5% (average) $29 $29 

3% (95th percentile) $61 $60 

* 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.91 

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

 

91 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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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 

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 approaches 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 
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PM2.5, as well as directly emitted fine particles.92 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 

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 (Anenberg et al, 2009, Jhun et al, 2014). 

 

92 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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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 

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 approaches 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 rules which require specific emission reductions in a specific sector, for example, the recent 

Mercury and Air Toxics Standards. As such, emission reductions achieved under these types of 

promulgated 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 
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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 

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. In addition to transferring information from other contexts to the context of this 

regulation, we also 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. Thus, to develop estimates of benefits for this RIA, 

we are transferring both the underlying health and economic information from previous studies 

and information on air quality responses to emissions reductions from previous air quality 

modeling. 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,93 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. 

 

93 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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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 

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/m3

 for 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 reducing risks of premature death 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. 
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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 

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. 
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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 

[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).94 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 

 

94 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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4-19 

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). 

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)95 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 $6.3 million (2000$).96 We then adjust this VSL to account for the currency year and to 

 

95 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. 

96 In 1990$, this base VSL is $4.8 million. 
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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 2030.97 

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 

 

97 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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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 

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 
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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. 

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 approaches, respectively, by region (i.e., East, West, and California).98 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 approaches, 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 ratebased and mass-based plan approaches, respectively, in each analysis year.99 Figure 4-1 provides 

a visual representation of the range of estimated PM2.5 and ozone-related co-benefits using 

benefit-per-ton estimates based on concentration-response functions from different studies and 

expert opinion for the illustrative rate-based and mass-based plan approaches evaluated in 2025 

as an illustrative analysis year. Figure 4-2 provides a breakdown of the monetized health cobenefits for the rate-based and mass-based plan approaches evaluated in 2025 as an illustrative 

analysis year by precursor pollutant. 

 

98 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 approach. 

99 Incidence estimates were generated using the same “per ton” approach as used to generate the dollar benefit per 

ton values. See Appendix 4-A for details. 
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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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4-24 

 

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 Illustrative Plan Approach 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 13 50 19 

West 1 1 0 

California 0 0 0 

National Total 14 50 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 Illustrative Plan Approach 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 171 155 67 

West 7 8 3 

California 1 2 0 

National Total 178 165 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 Illustrative Plan Approach 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 306 263 109 

West 11 15 9 

California 1 4 0 

National Total 318 282 118 

*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 Illustrative Plan Approach 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 49 57 22 

West 4 4 1 

California 0 0 0 

National Total 54 60 23 

*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 Illustrative Plan Approach 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 156 169 74 

West 29 34 14 

California 0 0 0 

National Total 185 203 88 

*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 Illustrative Plan Approach 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 243 229 99 

West 36 48 21 

California 1 1 1 

National Total 280 279 121 
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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 Illustrative Plan Approach in 2020 (billions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate 

SO2 $0.44 to $0.99 $0.39 to $0.89 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $0.14 to $0.33 $0.13 to $0.30 

NOx (as Ozone) $0.12 to $0.52 $0.12 to $0.52 

Total $0.70 to $1.8 $0.64 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 Illustrative Plan Approach 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) $0.49 to $2.1 $0.49 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 Illustrative Plan Approach 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 $0.93 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 Illustrative Plan Approach in 2020 (billions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate 

SO2 $1.70 to $3.80 $1.50 to $3.40 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $0.17 to $0.39 $0.16 to $0.36 

NOx (as Ozone) $0.14 to $0.61 $0.14 to $0.61 

Total $2.00 to $4.8 $1.80 to $4.4 

* 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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4-28 

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

Guidelines Mass-based Illustrative Plan Approach in 2025 (billions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate 

SO2 $6.0 to $13 $5.4 to $12 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $0.58 to $1.3 $0.52 to $1.2 

NOx (as Ozone) $0.56 to $2.4 $0.56 to $2.4 

Total $7.1 to $17 $6.5 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). 

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

Guidelines Mass-based Illustrative Plan Approach in 2030 (billions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate 

SO2 $10 to $23 $9.0 to $20 

Directly emitted PM2.5 (EC+OC) 

Directly emitted PM2.5 (crustal) 

NOx (as PM2.5) $0.87 to $2.0 $0.79 to $1.8 

NOx (as Ozone) $0.82 to $3.5 $0.82 to $3.5 

Total $12 to $28 $11 to $26 

* 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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4-29 

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

Plan Approach in 2020* 

PM2.5-related Health Effects

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 64 

Lepeule et al. (2012) (adult) 140 

Woodruff et al. (1997) (infant) 0 

Avoided Morbidity 

Emergency department visits for asthma (all ages) 34 

Acute bronchitis (age 8–12) 94 

Lower respiratory symptoms (age 7–14) 1,200 

Upper respiratory symptoms (asthmatics age 9–11) 1,700 

Minor restricted-activity days (age 18–65) 47,000 

Lost work days (age 18–65) 7,900 

Asthma exacerbation (age 6–18) 4,200 

Hospital admissions—respiratory (all ages) 19 

Hospital admissions—cardiovascular (age > 18) 23 

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 73 

Pooled estimate of 4 studies 8 

 Ozone-related Health Effects 

Avoided Premature Mortality 

Bell et al. (2004) (all ages) 11 

Levy et al. (2005) (all ages) 51 

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 66 

Hospital admissions—respiratory causes (ages < 2) 33 

Emergency room visits for asthma (all ages) 37 

Minor restricted-activity days (ages 18-65) 66,000 

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 incidence-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 incidence-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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4-30 

Table 4-23. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Final Emission Guidelines Rate-based Illustrative Plan 

Approach 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) 380 

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) 530,000 

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 incidence-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 incidence-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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4-31 

Table 4-24. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Final Emission Guidelines Rate-based Illustrative Plan 

Approach 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) 530 

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 incidence-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 incidence-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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4-32 

 

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

Plan Approach in 2020* 

PM2.5-related Health Effects

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 200 

Lepeule et al. (2012) (adult) 460 

Woodruff et al. (1997) (infant) 0 

Avoided Morbidity 

Emergency department visits for asthma (all ages) 110 

Acute bronchitis (age 8–12) 300 

Lower respiratory symptoms (age 7–14) 3,800 

Upper respiratory symptoms (asthmatics age 9–11) 5,500 

Minor restricted-activity days (age 18–65) 150,000 

Lost work days (age 18–65) 25,000 

Asthma exacerbation (age 6–18) 13,000 

Hospital admissions—respiratory (all ages) 59 

Hospital admissions—cardiovascular (age > 18) 73 

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 230 

Pooled estimate of 4 studies 25 

 Ozone-related Health Effects 

Avoided Premature Mortality 

Bell et al. (2004) (all ages) 13 

Levy et al. (2005) (all ages) 61 

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 78 

Hospital admissions—respiratory causes (ages < 2) 40 

Emergency room visits for asthma (all ages) 43 

Minor restricted-activity days (ages 18-65) 78,000 

School absence days 27,000 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional incidence-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 incidence-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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4-33 

Table 4-26. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Final Emission Guidelines Mass-based Illustrative Plan 

Approach in 2025* 

PM2.5-related Health Effects

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 700 

Lepeule et al. (2012) (adult) 1,600 

Woodruff et al. (1997) (infant) 2 

Avoided Morbidity 

Emergency department visits for asthma (all ages) 350 

Acute bronchitis (age 8–12) 1,000 

Lower respiratory symptoms (age 7–14) 13,000 

Upper respiratory symptoms (asthmatics age 9–11) 19,000 

Minor restricted-activity days (age 18–65) 500,000 

Lost work days (age 18–65) 84,000 

Asthma exacerbation (age 6–18) 46,000 

Hospital admissions—respiratory (all ages) 210 

Hospital admissions—cardiovascular (age > 18) 260 

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 810 

Pooled estimate of 4 studies 88 

 Ozone-related Health Effects 

Avoided Premature Mortality 

Bell et al. (2004) (all ages) 51 

Levy et al. (2005) (all ages) 230 

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 320 

Hospital admissions—respiratory causes (ages < 2) 150 

Emergency room visits for asthma (all ages) 160 

Minor restricted-activity days (ages 18-65) 290,000 

School absence days 100,000 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional incidence-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 incidence-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*** 

4-34 

 

Table 4-27. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Final Emission Guidelines Mass-based Illustrative Plan 

Approach in 2030* 

PM2.5-related Health Effects

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 1,200 

Lepeule et al. (2012) (adult) 2,600 

Woodruff et al. (1997) (infant) 2 

Avoided Morbidity 

Emergency department visits for asthma (all ages) 440 

Acute bronchitis (age 8–12) 1,600 

Lower respiratory symptoms (age 7–14) 21,000 

Upper respiratory symptoms (asthmatics age 9–11) 30,000 

Minor restricted-activity days (age 18–65) 790,000 

Lost work days (age 18–65) 130,000 

Asthma exacerbation (age 6–18) 74,000 

Hospital admissions—respiratory (all ages) 360 

Hospital admissions—cardiovascular (age > 18) 430 

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 1,400 

Pooled estimate of 4 studies 150 

 Ozone-related Health Effects 

Avoided Premature Mortality 

Bell et al. (2004) (all ages) 70 

Levy et al. (2005) (all ages) 320 

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 470 

Hospital admissions—respiratory causes (ages < 2) 200 

Emergency room visits for asthma (all ages) 210 

Minor restricted-activity days (ages 18-65) 380,000 

School absence days 130,000 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional incidence-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 incidence-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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4-35 

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

Plan Approaches 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 benefits from reductions in CO2. The monetized co-benefits do not include climate benefits 

from changes in NO2 and SO2or reduced health effects from direct exposure to NO2, SO2, ecosystem effects, or 

visibility impairment. 

PM2.5 Ozone  
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4-36 

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 and Mass-based Illustrative Plan Approaches for 

the 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, 

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

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

SO2 84%

Directly

emitted

PM2.5

(EC+OC) 0%

Directly

emitted

PM2.5

(Crustal) 0%

NOx (as

PM2.5) 8%

NOx (as

Ozone)

8%

Mass‐based

SO2 78%

Directly

emitted

PM2.5

(EC+OC) 0%

Directly

emitted

PM2.5

(Crustal) 0%

NOx (as

PM2.5) 8%

NOx (as

Ozone)

14%

Mass‐based
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4-37 

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 approach, rather 

than the actual co-benefits anticipated from implementing the final emission guidelines. 

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 provides 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 
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4-38 

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 

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. 

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. 
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4-39 

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. 

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.100 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). 

 

100 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 

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 

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 Option 1 State 

Scenario 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/m3

 and one standard deviation below the mean is 11.0 μg/m3. For 

Lepeule et al., the LML is 8 μg/m3

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

4-41 

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

the Option 1 State Scenario 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
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4-42 

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

PM2.5 Exposure in the Option 1 State Scenario 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 benefits associated with the 

decreased emissions of CO2 and co-benefits of reduced exposure to PM2.5 and ozone, but we 

were unable to monetize the co-benefits associated with reducing exposure to mercury, carbon 

monoxide, SO2, and NO2, as well as ecosystem effects and visibility impairment. In addition, 

there are expected to be unquantified health and welfare impacts associated with changes in 

hydrogen chloride. Specifically, we estimated combinations of 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 

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

4-43 

(as recommended by 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.101

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

evaluated in 2025 as an illustrative analysis year using a 3 percent discount rate for both climate 

and health benefits. 

 

101 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. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4-44 

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 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 $3.5 to $4.5 

2.5% $4.1 $4.9 to $6.0 $4.8 to $5.9 

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

Mass-based 74 million metric tons CO2 

5% $0.94 $2.9 to $5.7 $2.8 to $5.3 

3% $3.3 $5.3 to $8.1 $5.1 to $7.7 

2.5% $4.9 $6.9 to 9.7 $6.7 to $9.3 

3% (95th percentile) $9.6 $12 to $14 $11 to $14 

*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 211 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 240 million metric tons CO2 

5% $3.6 $11 to $21 $10 to $19 

3% $12 $19 to $29 $18 to $27 

2.5% $17 $24 to $34 $24 to $33 

3% (95th percentile) $35 $42 to $52 $42 to $51 

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

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 376 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 375 million metric tons CO2 

5% $6.4 $18 to $34 $17 to $32 

3% $20 $32 to $48 $31 to $46 

2.5% $29 $41 to $57 $40 to $55 

3% (95th percentile) $60 $72 to $89 $71 to $86 

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

4-46 

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 Illustrative Plan 

Approaches and Pollutants (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.

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), SO2 and NO2, as well as ecosystem effects, and 

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

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

CO2

62%

SO2

32%

Directly

emitted

PM2.5

(EC+OC)

0%

Directly

emitted

PM2.5

(Crustal)

0%

NOx (as

PM2.5)

3%

NOx (as

Ozone)

3%

Mass‐based

CO2

41%

SO2

47%

Directly

emitted

PM2.5

(EC+OC)

0%

Directly

emitted

PM2.5

(Crustal)

0%

NOx (as

PM2.5)

4%

NOx (as

Ozone)

8%

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

4-47 

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 changes in emissions 

of HAP or reductions in exposures to SO2 and NO2. 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) — — NO2 ISA1

Chronic lung disease hospital admissions (age > 

65) — — NO2 ISA1

Respiratory emergency department visits (all 

ages) — — NO2 ISA1

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISA1

Acute respiratory symptoms (age 7–14) — — NO2 ISA1

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — NO2 ISA2,3

Reduced incidence of 

morbidity from exposure 

to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISA1

Asthma emergency department visits (all ages) — — SO2 ISA1

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISA1

Acute respiratory symptoms (age 7–14) — — SO2 ISA1

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — SO2 ISA1,2

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

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

20002,3

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
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4-48 

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More Information

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 SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

Recreational demand in terrestrial and aquatic 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles) — — NOx SOx ISA2

Reduced effects from 

nutrient enrichment 

Species composition and biodiversity in terrestrial 

and estuarine ecosystems — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in terrestrial and estuarine 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles, fire regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from ambient 

exposure to SO2 and NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

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. 

4.5.1 HAP Impacts 

Due to methodology and resource limitations, we were unable to estimate the impacts 

associated with changes in 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 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4-49 

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). 

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).102 Many of the peer-reviewed articles cited in this section are publications 

originally cited in the Mercury Study.103 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.” 

 

102 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Washington, DC: National 

Academies Press. 

103 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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4-50 

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 

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.104 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. 

 

104 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. 
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4-51 

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).105

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)106 and in IRIS 

(U.S. EPA, 2002).107 The existing evidence supporting the possibility of carcinogenic effects in 

humans from low-dose chronic exposures is tenuous. Multiple human epidemiological studies 

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.108 Concentrations in typical 

 

105 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. 

106 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). 

107 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. 

108 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. 
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4-52 

human exposure environments are much lower than these levels and rarely exceed the reference 

concentration.109The 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.110

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 

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 

 

109 Table of Prioritized Chronic Dose-Response Values: http://www2.epa.gov/sites/production/files/2014-

05/documents/table1.pdf 

110 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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4-53 

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 

identified three short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for other pollutants. We did not quantify these co-benefits due to data 

constraints. 
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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 those associated with 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 

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) 
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Reductions in emissions of NO2 and SO2 will improve the level of visibility throughout the 

United States because these gases (and the particles of nitrate and sulfate formed from these 

gases) impair visibility by scattering and absorbing light (U.S. EPA, 2009). Visibility is also 

referred to as visual air quality (VAQ), and it directly affects people’s enjoyment of a variety of 

daily activities (U.S. EPA, 2009). Good visibility increases quality of life where individuals live 

and work, and where they travel for recreational activities, including sites of unique public value, 

such as the Great Smoky Mountains National Park (U. S. EPA, 2009). 

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 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 

includes those who have angina pectoris (cardiac chest pain), as well as those who have 
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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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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4-66 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004c. 

Advisory Council on Clean Air Compliance Analysis Response to Agency Request on 

Cessation Lag. EPA-COUNCIL-LTR-05-001. December. Available at: 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$Fil

e/council_ltr_05_001.pdf>. Accessed June 4, 2015. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2008. 

Benefits of Reducing Benzene Emissions in Houston, 1990–2020. EPA-COUNCIL-08-001. 

July. Available at: 

<http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$Fi

le/EPA-COUNCIL-08-001-unsigned.pdf>. Accessed June 4, 2015. 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009b. 

Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review 

Draft, December 2008). EPA-COUNCIL-09-008. May. Available at: 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73

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. 
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4-67 

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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4A-1 

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 the contiguous U.S. 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 12km2

 grid 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).111,112

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. 

 

111 When estimating these impacts we apply effect coefficients that relate changes in total PM2.5 mass to the risk of adverse health outcomes; we do not apply effect coefficients 

that are differentiated by PM2.5 species. 

112 Previous RIAs have used earlier versions of the BenMAP software. BenMAP-CE v1.1 provides results consistent 

with earlier versions of BenMAP and is available for download at http://www.epa.gov/air/benmap/. 
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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 domain113 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 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 

Connecticut 1 1 1 

Delaware 4 1 1 

 

113 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) 

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-4 

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 

Utah 22 14 10 
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4A-5 

State 

2011 Base Year 2025 Base Case 

2025 Clean Power Plan 

Proposal 

(Option 1 State) 

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 12km2

 resolution 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 12km2

 resolution 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-1.114 While a small percentage of 

benefits from emissions reductions in a particular region may occur in one of the other regions, 

we selected each region to minimize this percentage. Thus, the benefits per ton in each region 

will represent well the match between where the emissions reductions and air quality benefits are 

occurring. 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$.115

 

114 This aggregation is identified as the shapefile “Report Regions” in BenMAP’s grid definitions. 

115 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-6 

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 

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 
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4A-7 

In this RIA, we estimate emission reductions from EGUs using IPM.116 IPM outputs 

provide endogenously projected unit level emissions of SO2, NOX, CO2, Hg, hydrogen chloride 

(HCl) from EGUs, but carbon monoxide, volatile organic compounds, ammonia and total 

directly emitted PM2.5 and PM10 emissions are post-calculated.117 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.118

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 

 

116 See Chapter 3 of this RIA for additional information regarding the Integrated Planning Model (IPM).

117 Detailed documentation of this post-processing is available at 

http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Methodology.pdf 118 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.). 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4A-8 

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. 

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 because of the additional unquantified uncertainties that 

result from the benefit transfer methods, including those related to the transfer of air quality 

modeling information. 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 (which 

follows the same general methodology as for the benefit-per-ton calculations) for the EGU sector 

in 2020, 2025, and 2030 respectively, for the set of health endpoints used to calculate the benefitper-ton estimates. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4A-9 

Table 4A-3. Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Pro

in 2020 (2011$)* 

Pollutant Discount Rate National Region East West 

SO2 3% $32,000 to $71,000 $33,000 to $75,000 $6,200 to $14,000 

7% $28,000 to $64,000 $30,000 to $68,000 $5,600 to $13,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $140,000 to $310,000 $140,000 to $320,000 $27,000 to $60,000 7% $120,000 to $270,000 $130,000 to $290,000 $24,000 to $54,000 

Directly emitted 

PM2.5 (Crustal) 

3% $22,000 to $49,000 $23,000 to $52,000 $11,000 to $25,000 7% $20,000 to $44,000 $21,000 to $47,000 $9,900 to $22,000 

NOX (as PM2.5) 3% $3,000 to $6,800 $3,100 to $7,000 $0,670 to $1,500 

7% $2,700 to $5,600 $2,800 to $6,300 $0,610 to $1,400 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to tw

particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude o

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. 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 Pro

in 2025 (2011$)* 

Pollutant Discount Rate National Region East West 

SO2 3% $35,000 to $78,000 $37,000 to $83,000 $7,100 to $16,000 

7% $31,000 to $70,000 $33,000 to $75,000 $6,400 to $14,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $150,000 to $340,000 $160,000 to $360,000 $30,000 to $68,000 7% $130,000 to $290,000 $140,000 to $320,000 $27,000 to $61,000 

Directly emitted 

PM2.5 (Crustal) 

3% $24,000 to $55,000 $25,000 to $58,000 $12,000 to $28,000 7% $22,000 to $49,000 $23,000 to $52,000 $11,000 to $25,000 

NOX (as PM2.5) 3% $3,200 to $7,300 $3,300 to $7,500 $0,750 to $1,700 

7% $2,900 to $6,000 $3,000 to $6,800 $0,670 to $1,500 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to tw

particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude o

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4A-10 

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

in 2030 (2011$)* 

Pollutant Discount Rate National Region East West 

SO2 3% $37,000 to $85,000 $40,000 to $89,000 $7,800 to $18,000 

7% $34,000 to $76,000 $36,000 to $81,000 $7,100 to $16,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $160,000 to $360,000 $170,000 to $380,000 $33,000 to $75,000 7% $150,000 to $320,000 $150,000 to $340,000 $30,000 to $68,000 

Directly emitted 

PM2.5 (Crustal) 

3% $26,000 to $59,000 $28,000 to $62,000 $14,000 to $31,000 7% $24,000 to $53,000 $25,000 to $56,000 $13,000 to $28,000 

NOX (as PM2.5) 3% $3,400 to $7,800 $3,500 to $8,000 $0,820 to $1,900 

7% $3,100 to $6,400 $3,200 to $7,200 $0,740 to $1,700 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to tw

particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude o

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** 

4A-11 

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

Plan in 2020* 

Health Endpoint 

East West 

SO2 NOX EC+OC Crustal SO2 NOX EC+OC Crustal SO2 

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 Lepeule et al. (2012) – adult 0.008300 0.000770 0.036000 0.005700 0.001500 0.000170 0.006600 0.002700 0.023000 

Woodruff et al. (1997) – infants 0.000009 0.000001 0.000037 0.000006 0.000002 0.000000 0.000007 0.000003 0.000023 

Morbidity 

Emergency department visits for asthma 0.001900 0.000190 0.007800 0.001300 0.000290 0.000031 0.001200 0.000470 0.005300 Acute bronchitis 0.005400 0.000510 0.023000 0.003700 0.001300 0.000200 0.005200 0.002100 0.019000 Lower respiratory symptoms 0.069000 0.006500 0.300000 0.047000 0.016000 0.002500 0.067000 0.026000 0.240000 Upper respiratory symptoms 0.098000 0.009300 0.420000 0.068000 0.023000 0.003600 0.095000 0.038000 0.340000 Minor restricted-activity days 2.700000 0.250000 11.000000 1.900000 0.580000 0.078000 2.400000 0.920000 9.400000 Lost work days 0.450000 0.043000 1.900000 0.310000 0.098000 0.013000 0.410000 0.160000 1.600000 Asthma exacerbation 0.240000 0.023000 1.000000 0.170000 0.056000 0.008800 0.230000 0.091000 0.840000 Hospital Admissions, Respiratory 0.001100 0.000100 0.004500 0.000720 0.000150 0.000015 0.000640 0.000260 0.002500 Hospital Admissions, Cardiovascular 0.001300 0.000120 0.005600 0.000910 0.000200 0.000019 0.000820 0.000330 0.003000 Non-fatal Heart Attacks (age>18)  Peters et al (2001) 0.004100 0.000390 0.018000 0.002800 0.000650 0.000064 0.002800 0.001200 0.011000  Pooled estimate of 4 studies 0.000450 0.000042 0.001900 0.000310 0.000070 0.000007 0.000300 0.000130 0.001100 

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-t

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates inc

precursor emissions to ambient fine particles. 
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4A-12 

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

Plan in 2025* 

Health Endpoint 

East West 

SO2 NOX EC+OC Crustal SO2 NOX EC+OC Crustal SO2 

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 Lepeule et al. (2012) – adult 0.008900 0.000800 0.038000 0.006200 0.001700 0.000180 0.007300 0.003000 0.026000 

Woodruff et al. (1997) – infants 0.000008 0.000001 0.000035 0.000006 0.000002 0.000000 0.000007 0.000003 0.000022 

Morbidity 

Emergency department visits for asthma 0.002000 0.000200 0.006300 0.001300 0.000320 0.000033 0.001000 0.000510 0.005500 Acute bronchitis 0.005700 0.000520 0.024000 0.003900 0.001300 0.000210 0.005600 0.002200 0.020000 Lower respiratory symptoms 0.072000 0.006700 0.310000 0.050000 0.017000 0.002700 0.071000 0.028000 0.250000 Upper respiratory symptoms 0.100000 0.009600 0.440000 0.071000 0.024000 0.003800 0.100000 0.040000 0.360000 Minor restricted-activity days 2.800000 0.250000 12.000000 1.900000 0.610000 0.083000 2.500000 0.970000 9.600000 Lost work days 0.470000 0.043000 2.000000 0.320000 0.100000 0.014000 0.430000 0.160000 1.600000 Asthma exacerbation 0.250000 0.023000 1.100000 0.170000 0.059000 0.009300 0.250000 0.097000 0.880000 Hospital Admissions, Respiratory 0.001200 0.000110 0.005100 0.000810 0.000180 0.000017 0.000740 0.000300 0.002800 Hospital Admissions, Cardiovascular 0.001400 0.000130 0.006200 0.001000 0.000220 0.000022 0.000930 0.000380 0.003300 Non-fatal Heart Attacks (age>18)  Peters et al (2001) 0.004600 0.000430 0.020000 0.003100 0.000740 0.000071 0.003200 0.001300 0.012000  Pooled estimate of 4 studies 0.000490 0.000046 0.002100 0.000340 0.000080 0.000008 0.000340 0.000140 0.001300 

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-t

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates inc

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 P

Plan in 2030* 

Health Endpoint 

East West 

SO2 NOX EC+OC Crustal SO2 NOX EC+OC Crustal SO2 

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 Lepeule et al. (2012) – adult 0.009600 0.000850 0.041000 0.006700 0.001900 0.000200 0.008100 0.003400 0.029000 

Woodruff et al. (1997) – infants 0.000008 0.000001 0.000033 0.000005 0.000002 0.000000 0.000007 0.000003 0.000021 

Morbidity 

Emergency department visits for asthma 0.001600 0.000160 0.006600 0.001100 0.000260 0.000027 0.001100 0.000420 0.004500 Acute bronchitis 0.005900 0.000540 0.025000 0.004100 0.001400 0.000220 0.005900 0.002300 0.021000 Lower respiratory symptoms 0.075000 0.006800 0.320000 0.052000 0.018000 0.002800 0.075000 0.030000 0.260000 Upper respiratory symptoms 0.110000 0.009800 0.460000 0.074000 0.026000 0.004000 0.110000 0.042000 0.370000 Minor restricted-activity days 2.900000 0.260000 12.000000 2.000000 0.650000 0.088000 2.700000 1.000000 9.800000 Lost work days 0.480000 0.043000 2.000000 0.330000 0.110000 0.015000 0.450000 0.170000 1.700000 Asthma exacerbation 0.260000 0.024000 1.100000 0.180000 0.063000 0.009800 0.260000 0.100000 0.920000 Hospital Admissions, Respiratory 0.001300 0.000120 0.005600 0.000900 0.000200 0.000019 0.000830 0.000340 0.003200 Hospital Admissions, Cardiovascular 0.001600 0.000150 0.006800 0.001100 0.000250 0.000024 0.001000 0.000420 0.003800 Non-fatal Heart Attacks (age>18)  Peters et al (2001) 0.005000 0.000460 0.021000 0.003500 0.000830 0.000079 0.003600 0.001500 0.014000  Pooled estimate of 4 studies 0.000540 0.000049 0.002300 0.000370 0.000090 0.000009 0.000380 0.000160 0.001500 

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-t

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates inc

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-15 

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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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.5 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-18 

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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4A-19 

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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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. In 

addition, EPA is asking the panel to identify potential paths forward for improvements that could 

address the challenges posed when using economy-wide models 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 benefitcost 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 improvements 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 described in more 

detail in Chapter 3. The Retail Price Model (RPM) uses forecast changes in wholesale prices and 
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5-4 

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 2020 2025 2030 

Electricity Price Change 3.2% 0.9% 0.8% 

Delivered Natural Gas Price Change 5.3% -7.7% 2.5% 

   Delivered Coal Price Change -1.7% -6.2% -8.0% 

 

Mass-based 2020 2025 2030 

Electricity Price Change 3.0% 2.0% 0.0% 

Delivered Natural Gas Price Change 3.8% -3.2% -2.1% 

   Delivered Coal Price Change -1.6% -4.3% -4.6% 

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 would l 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.119

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 

 

119 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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5-6 

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.” 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 

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. 
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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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6-1 

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,120 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”).121

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 

 

120 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses. 

121 The employment analysis in this RIA is part of EPA’s ongoing effort to “conduct continuing evaluations of 

potential loss or shifts of employment which may result from the administration or enforcement of [the Act]” 

pursuant to CAA section 321(a).
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employment impacts due to demand-side energy efficiency activities. Section 6.6 offers several 

conclusions. 

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, the 

environmental protection 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.122 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.123,124 Berman and Bui (2001) 

adapt this model to analyze how environmental regulations affect labor demand.125 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.126 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 

 

122 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 

123 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization. 

124 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs). 

125 Morgenstern, Pizer, and Shih (2002) develop a similar model. 

126 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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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 

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 
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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. 

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.127 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.128 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 

 

127 See Ehrenberg & Smith, p. 108. 

128 This discussion draws from Berman and Bui (2001), pp. 293. 
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firms. Therefore, it is important to consider the net effect of compliance actions on employment 

across multiple sectors or industries. 

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.129 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).130 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.131 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. 

 

129 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. 

130 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. 

131 E.g. Graff Zivin and Neidell (2012). 
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To summarize, economic theory provides a framework for analyzing the impacts of 

environmental regulation on employment. The net employment effect incorporates expected 

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.132 This work focuses primarily on effects of employment policies such 

as labor taxes and minimum wages.133 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 

 

132 Again, see Hamermesh (1993) for a detailed treatment. 

133 See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview. 
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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 

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).134 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 

 

134 U.S. EPA (2011b). 
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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.” 

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 
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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 

regulations, including the impact on net national employment.135 Once EPA receives guidance 

from this panel it will carefully consider this input and then decide if and how to proceed on 

economy-wide 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.136 Additionally, some of the analyses predated 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.137 The LIFT model, which is from the Interindustry Forecasting Project 

(Inforum) at the University of Maryland, has been used in the peer-reviewed academic 

literature138 and has also been used to examine the economic impacts of other national policies 

[Meade (2009); Werling (2011)].139 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 

 

135 For further information see: 

http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument 

136 See, for example, comments EPA-HQ-OAR-2013-0602-33150, EPA-HQ-OAR-2013-0602-23140, and EPA-HQOAR-2013-0602-23156, within Docket ID: EPA-HQ-OAR-2013-0602.

137 See comment EPA-HQ-OAR-2013-0602-22960, within Docket ID: EPA-HQ-OAR-2013-0602.

138 See, for example, Almon (1991) and Mccarthy (1991). 

139 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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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.” 

As mentioned previously, EPA is currently engaged in an SAB process on economy-wide 

modeling.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?”140 

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.141 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 

 

140 See p. 10, 

http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED/$File/Charge+Questions+2

-26-15.pdf 

141 For a recent review see Graff-Zivin and Neidell (2013). 
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billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent.” 

(Graff Zivin and Neidell, 2012, p. 3654).142

This section (section 6.3) 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. 

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.143 Installation, maintenance, and repair 

occupations accounted for the largest share of workers (25 percent).144 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 

 

142 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. 

143 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.

144 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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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. 

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 2014, 

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).145 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.146 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. 

 

145 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. 

146 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.”147 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.148

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.149 Based on surveys across the 325 industries it 

identified as potential producers of green goods and services, BLS counts approximately 2.3 

 

147 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>. 

148 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>. 

149 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 2011,150 compared 

to increases of about one percent across all occupations in the entire economy over the same 

period.151 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.152 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. 

 

 

150 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>. 

151U.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. 

152 <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.153

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.154 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. 

 

 

153 The Solar Foundation, National Solar Jobs Census 2014. < http://www.thesolarfoundation.org/national-solarjobs-census-2014/> 

154 <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. Implementing each of the three building blocks used in determining BSER will 

involve changes in the amount of labor needed in different parts of the utility power sector. 

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 increases in 

labor demand during the implementation phase for manufacturing, installing, and operating 

higher efficiency and renewable energy electricity generating assets, as well as making heat rate 

improvements at existing fossil units. Additional supply-side direct employment impacts are the 

reductions in labor demand for labor that would have been used by less efficient or higher 

emitting generating assets. Once implemented, increases in operating efficiency and shifting 

generation to existing or new NGCC units and renewable energy generation will impact the 

utility power sector’s demand for fossil fuels 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 
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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 

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 generation-side employment analysis uses the cost projections from the engineeringbased 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 
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labor categories. This approach is different from the economy-wide 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 supply-side direct impact on labor 

demand in industries closely involved with electricity generation. The engineering approach 

projects labor changes measured as the change in each analysis year in job-years155 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. Some of these employment impacts are 

one-time impacts, such as changes associated with building new NGCC or renewable generating 

units. Other labor impacts will continue, such as changes associated with operating and 

maintaining generating units that will be retired, and labor providing the fuel supply to the retired 

units. 

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).156 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 

 

155 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. 

156 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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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 supply-side 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 renewable generating capacity. In addition to the changes in retirements and 

construction of new units, there are also estimated changes in the utilization of existing 

generating units, and changes in the gas and coal supply sectors. 

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). Fossil fuel utilization will also be impacted. The rate-based control scenario finds 

that in 2025 less coal will be used (102.9 million tons less, a 14.1 percent decrease from the base 

case) and less gas will also be used (0.1 TCF less, a 1.0 percent decrease). 

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 
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to generate electricity. IPM assumes157 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. 

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; 

 

157 Table 4.13, IPM 5.13 Documentation. 
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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 

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 2020-2030, 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 2020-2030, 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. 
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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-Yearsb of 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 multi-year period 

during which construction and HRI installation activities occur. Construction-related figures in table are the average 

annual job-years in each year between the years in the 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 demandside 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. 
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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 a Single Year) 

 Construction-related (One-time) Changes* 

 2018-2020 2021-2025 2026-2030 

Heat Rate Improvement: Total 0 14,900 800 

Boilermakers and General Construction 0 10,700 600 

 Engineering and Management 0 2,700 100 

 Equipment-related 0 1,200 100 

 Material-related 0 300 0 

New Capacity Construction: Total -1,700 -11,100 4,700 

Renewables -1,400 -3,500 21,300 

 Natural Gas -300 -7,600 -16,600 

 

 Recurring Changes** 

 2020 2025 2030 

Operation and Maintenance: Total -11,700 -21,200 -25,000 

Changes in Renewables -900 -1,000 700 

Changes in Gas 500 -400 -2,300 

Retired Coal -9,100 -17,000 -20,800 

Retired Oil and Gas -2,200 -2,800 -2,600 

Fuel Extraction: Total 200 -8,600 -14,300 

Coal -1,800 -8,700 -12,200 

Natural Gas 2,000 100 -2,100 

Supply-Side Employment Impacts - Quantified -13,100 -26,000 -33,700 

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 multi-year period 

during which construction and HRI installation activities occur. Construction-related figures in table are the average 

annual job-years in each year between the years in the 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 demandside 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. 

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 

efficiency, specifically, to support a significant number of jobs in related industries. For more 
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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.158 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 

 

158 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)).159

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 

 

159 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.160 Using 

BLS Green Goods and Services information on 132 energy efficiency industries, as identified by 

BLS,161 we adjusted the list to remove ten industries expected to not be directly affected by the 

rule, e.g. transportation.162 Next we used this detailed list of 122 industries to extract 2011 BLS 

data on green employment.163 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.164 

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.165 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. 

 

160For more details on this survey, see section 6.5.3.1. 

161 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. 

162 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. 

163 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. 

164 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. 

165 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.166 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.167 At the less detailed, 4-digit 

level, 15 industries have data available for both employment and total value of shipments.168

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 

 

166 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. 

167 The 17 industries are: NAICS 321219, 321991, 321992, 327993, 327999, 332913, 332996, 333414, 333415, 

334513, 334514, 334515, 335110, 335222, 335311, 335312, and 335999.  

168 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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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).169 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 Demand-Side Energy Efficiency Technical Support Document170, 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 6-4 and 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 Demand-Side Energy Efficiency TSD 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 milllion dollars in industry 

 

169 See Section 3.6.2, “Demand-Side Energy Efficiency Total Costs”, in this RIA, for more information. 

170 U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelines for 

Existing Stationary Sources: Electric Utility Generating Units. Demand-Side Energy Efficiency. 
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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, 

and are on the smaller end of the range.171 Figure 6.4 shows the range of estimates, including 

EPA low (2.07) and EPA high (3.29). The Department of Commerce report estimates overall 

employment per million dollar values, ranging from 4.65 to 4.85 (Department of Commerce 

2010a).172 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.173 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. 

 

171 See Section 6.5.3 for more information on sources in the literature. 

172 Calculated values using data on employment and shipments reported in Department of Commerce (2010a), Table 

2, p. 12. 

173 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. 

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 (2010), PNNL (2014), DOC (2010)

Demand-Side EE Employment
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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 workers 

employed elsewhere in the economy. For demand-side efficiency activities this potential overstatement 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 
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6-35 

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. 

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 
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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 -8,500 job-years in 

2020, -25,000 job-years in 2025, and -30,900 job-years in 2030 for the rate-based scenario. For 

the mass-based scenario, the EPA estimates that these guidelines could have an employment 

impact of roughly -13,100 job-years in 2020, -26,000 job-years in 2025, and -30,900 job-years in 

2030 (see Tables 6-4 and 6-5). 

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 both the ratebased and mass-based approaches 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 job-years, 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)174, 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-years175 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 HRI176 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; 

 

174 Results for this analysis were developed using various outputs from EPA’s Base Case v.5.15 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/ for more 

information. 

175 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. 

176 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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6A-3 

 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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6A-4 

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.177 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 estimated178 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% 

Materials 10% $424,000 -1.2% 

 

177 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. 

178 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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6A-5 

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.179 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 

 

179 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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6A-6 

annual impacts on construction phase labor. Duration estimates were based on assumptions for 

construction lengths used in EPA’s IPM modeling.180 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; 

 

180 Ibid. 
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6A-7 

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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6A-8 

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.181

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 

 

181 The same specific sources as cited before, however, used workers and total payroll. 
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6A-9 

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.182,183 

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 

 

182 From EIA Annual Energy Review, Coal Mining Productivity Data. Used 2008. 

183 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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6A-10 

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.184 The job-years associated with the pipeline construction 

were included in the natural gas employment estimates. 

 

184 See U.S. EPA. 2011. Technical Support Document for the Mercury and Air Toxics Standards: Employment 

Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015. 
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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 July 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. 

In the final emission guidelines, the EPA has translated the source category-specific CO2

emission performance rates into equivalent state-level rate-based and mass-based CO2 goals in 

order to maximize the range of choices that states will have in developing their plans. Because of 
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7-2 

the range of choices available to states and the lack of a priori knowledge about the specific 

choices states will make in response to the final goals, the Regulatory Impact Analysis (RIA) for 

this rule analyzed two implementation scenarios designed to achieve these goals, which we term 

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

It is very important to note that the differences between the analytical results for the ratebased and mass-based illustrative plan approaches presented in the RIA may not be indicative of 

likely differences between the approaches if implemented by states and affected EGUs in 

response to the final guidelines. Rather, the two sets of analyses are intended to illustrate two 

contrasting, stylized approaches to accomplish the emission performance rates finalized in the 

Clean Power Plan Final Rule. In other words, if one approach performs better than the other on a 

given metric during a given time period, this does not imply this will apply in all instances in all 

time periods in all places. 

Table 7-1 and 7-2 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 

illustrative plan approaches, respectively. 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 (e.g., nitrous oxide and 

methane) 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 final action are substantial, and far exceed the costs. Additional details on benefits, costs, 

and net benefits estimates are provided in this RIA. 

 
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7-3 

Table 7-1. Monetized Benefits, Compliance Costs, and Net Benefits Under the Rate-based 

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 $2.5 $1.0 $8.4 

Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $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 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 percent 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 final emission 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 7-2. Monetized Benefits, Compliance Costs, and Net Benefits under the Mass-based 

Illustrative Plan Approach (billions of 2011$)a

   Mass-Based Approach 

2020 2025 2030 

Climate Benefits b 

5% discount rate $0.9 $3.6 $6.4 

3% discount rate $3.3 $12 $20 

2.5% discount rate $4.9 $17 $29 

95th percentile at 3% 

discount rate $9.6 $35 $60 

 Air Quality Co-benefits Discount Rate 

 3% 7% 3% 7% 3% 7% 

Air Quality Health 

Co-benefits c $2.0 to $4.8 $1.8 to $4.4 $7 to $17 $7 to $16 $12 to $28 $11 to $26 

Compliance Costs d $1.4 $3.0 $5.1 

Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40 

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 percent 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 final emission 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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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 

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. 
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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 54,900 

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 

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. 
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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. 

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. 
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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 

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 
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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. 

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 
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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. 

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 
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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 

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 
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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. 

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 
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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 

(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 
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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 

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, 
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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 

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, 
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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 1 to 2 percent change in retail 

electricity prices on average across the contiguous U.S. in 2025, and a 22 to 23 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 up to 2.5 percent in 2030. 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. 

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 
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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).185 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. 

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. 

 

185 “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 most recent assessments continue to strengthen scientific understanding of climate 

change risks to minority populations and low-income populations in the United States.186 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 (more 

specifically, populations defined jointly by ethnic/racial characteristics and geographic location) 

may be uniquely 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 

 

186 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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7-19 

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,187 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.188 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 

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 overburdened communities. The EPA consulted its May 2015, Guidance on Considering 

Environmental Justice During the Development of Regulatory Actions, when determining what 

considerations to put in place.189 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).

 

187 “National Ambient Air Quality Standards for Particulate Matter, Final Rule,” 78 FR 3086 (Jan. 15, 2013). 

188 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>. 

189 Guidance on Considering Environmental Justice During the Development of Regulatory Actions. 

http://epa.gov/environmentaljustice/resources/policy/considering-ej-in-rulemaking-guide-final.pdf. May 2015. 
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8-1 

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 July 2015) (“current SC-CO2 TSD”) to 

analyze CO2 climate impacts of this rulemaking.190 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 percent 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 

 

190 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 July 2015). 

Available at: <https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-tsd-final-july-2015.pdf>. 
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8-2 

precursors, and 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 mass-based 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), 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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8-3 

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 $2.5 $1.0 $8.4 

Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $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 percent 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 final emission 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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8-4 

Table 8-2. Monetized Benefits, Costs, and Net Benefits Under the Mass-based 

Illustrative Plan Approach Scenario (billions of 2011$) a

   Mass-Based Scenario

2020 2025 2030 

Climate Benefits b 

5% discount rate $0.94 $3.6 $6.4 

3% discount rate $3.3 $12 $20 

2.5% discount rate $4.9 $17 $29 

95th percentile at 

3% discount rate $9.6 $35 $60 

 Air Quality Co-benefits Discount Rate 

 3% 7% 3% 7% 3% 7% 

Air Quality 

Health Cobenefits c

$2.0 to $4.8 $1.8 to $4.4 $7 to $17 $7 to $16 $12 to $28 $11 to $26 

Costs d $1.4 $3.0 $5.1 

Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40 

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 percent 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 final emission 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.191

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... 

 

191 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.192 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 

 

192 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.193 

 

193 See https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-response-to-comments-final-july-2015.pdf 
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8-8 

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 

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 
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8-9 

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. 

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 

Docket ID EPA-HQ-OAR-2013-0602, 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 July 2015). Also available at: 

<https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-tsd-final-july-2015.pdf>. 

Accessed July 15, 2015. 
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8-10 

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 Management and 

Budget, Office of Science and Technology Policy, and Department of Treasury. Response to 

Comments: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 

12866. July 2015. Available at 

<https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-response-to-commentsfinal-july-2015.pdf.> Accessed July 15, 2015. 

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
Technical Support Document (TSD) for the CAA Section 111(d) Emission Guidelines for Existing Power 

Plants 

Docket ID No. EPA-HQ-OAR-2013-0602 

CO2 Emission Performance Rate and 

Goal Computation Technical Support 

Document for CPP Final Rule

U.S Environmental Protection Agency 

Office of Air and Radiation 

August 2015 

 
Still Under Agency Review – Do not Quote, Cite, or Share

2

Category-Specific Performance Rates and State Goal Setting under 111(d) 

This Technical Support Document (TSD) provides information that supports the EPA’s determination of 

category-specific performance rates for fossil steam and stationary combustion turbine technology 

categories as well as the state emission rate and mass goals that encompass the likely affected fossil units 

in a state.1

 Section VI of the preamble discusses the category-specific performance rates more broadly 

along with some of the changes made between proposal and final based on comment. Section VII of the 

preamble describes the expression of the category-specific performance standards through a state goal 

metric reflecting likely covered fossil sources in a state. The Greenhouse Gas (GHG) Mitigation 

Measures TSD for CPP Final Rule explains the technical basis for the development of the Best System of 

Emission Reductions (BSER) that inform the category-specific performance rates and the subsequent 

state goals. This TSD provides detailed explanation of the data and the BSER-based calculations used to 

determine the category-specific performance rates and state goals The TSD is organized as follows: 

1. BSER factors informing the category-specific performance rates and state emission rate goals 

a. Block 1 - Heat rate improvement in the coal steam fleet 

b. Block 2 - Substitute increased generation from lower emitting existing NGCC units 

for reduced generation from higher emitting fossil steam EGUs 

c. Block 3 - Substitute generation from new zero emitting renewable energy (RE) 

generating capacity for reduced generation from higher emitting fossil EGUs 

2. Form of the Category-specific Performance Rates and State Goals 

3. Baseline data used to derive performance rates and state goals 

a. Emissions & Generation Integrated Resource Database (eGRID) 

b. Data sources for affected “under construction” units 

c. Region-level baseline 

4. Methodology for determining category-specific performance rates 

5. Methodology for converting category-specific performance rates into state emission rate goals 

6. Methodology for converting state emission rate goals into mass goals 

7. Appendix (attached Excel Workbook) 

 Appendix 1 – Underlying 2012 unit-level baseline inventory and data 

 Appendix 2 - Units that commenced operation post 2011, but commenced construction 

prior to 1/8/14 

 Appendix 3 – Underlying state-level data, adjustments, and region-level data 

 Appendix 4 – Computation of the category-specific performance rates (Interim and Final) 

 Appendix 5 - Computation of the State Goal (Interim and Final) 

 Appendix 6 –State goal summary table 

 Appendix 7 – Calculation for generation adjustment in hydro-intensive states 

 Appendix 8 - Category-specific emission rates reflecting BB severability 

 Appendix 9 – Description of 111(d) baseline data sources and development 

                                                            

1 The only gas units currently considered likely affected units under the 111(d) applicability criteria are turbines at

NGCC units (both the gas and steam turbines).  The remainder of the document will use the term “NGCC” to

collectively refer to the output from both the steam and gas turbines at a NGCC unit.
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3

In EPA’s technical evaluation, it assessed the cost and potential of each GHG reducing technology were 

identified (see GHG Mitigation Measures TSD). EPA relied on a similar building block structure as 

proposed, but revised the quantification of those building blocks based on comments. These revised 

building blocks levels were used to derive the category-specific performance rates provided in this final 

rule. The category-specific performance rates were then used to derive the state rate and mass goals. 

1. BSER Factors Informing the Category-specific Performance Rates and State Emission Rate 

Goals 

The GHG Mitigation Measures TSD describes three categories of emission reduction measures (building 

blocks) used in determining the category-specific performance rates. That document describes EPA’s 

historical data review and analysis underlying each technology and informing EPA’s assessment of its 

feasibility and cost-effectiveness as part of a BSER. It also explains how EPA made adjustments to the 

building blocks based on comments The technology estimates determined through EPA’s analysis and 

documented in the GHG Mitigation Measures TSD are summarized below. 

Figure 1. 2030 Building Block Potential Identified for Each Region

  

BB1 ‐ HRI

Improvement

for coal fleet

BB2 ‐ TWh of total NGCC

Generation at 75%

Utilization, (Amount of

NGCC Generation Potential

Incremental to Baseline)

BB3 ‐ Incremental

RE Potential (TWh)

Eastern Interconnection 4.3% 988, (253) 438

Western Interconnection 2.1% 306, (108) 161

Texas Interconnection 2.3% 204, (67) 107

*Totals are building block potential only. As evidenced in Section 4-step 8, not all of the building 

block potential is utilized in establishing BSER category-specific rates and state goals. 

The building block data shown above are used to determine category-specific performance rates 

expressed in a lb/MWh rate. As these building blocks reflect both fossil and non-fossil measures, the 

corresponding category-specific performance rates also reflect fossil and non-fossil generation through 

the use of an adjusted emission rate described in the preamble and below. 

2. Form of the Category-specific Performance Rates and State Goals 

As described in Section VI of the preamble, EPA is promulgating a separate emission rate that quantifies 

BSER for each technology category covered under 111(d) applicability. Therefore, while similar 

adjustments are made to the generation levels of affected fossil steam and NGCC generation reflecting the 

building blocks, the adjustments are made and expressed at the technology level rather than the combined 

affected EGU level: 
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4

Exhibit A - Simplified formula demonstrating category-specific emission performance rates 

Final – Affected fossil steam and NGCC Generation treated separately for quantifying BSER

BSER for Fossil Steam = BSER adjusted emissionsfor affected fossil steam sources 

 BSER adjusted generation for affected fossil steam sources + replacement generation 

BSER for NGCC = BSER adjusted emissionsfor affected NGCC sources 

 BSER adjusted generation for affected NGCC sources + replacement generation 

3. Baseline Data Used to Derive Performance Rates and State Goals 

See Section VI.D.2 of the Preamble for a description of EPA’s identification of a baseline data 

Adjustments that the EPA made to the 2012 historical data 

EPA received significant comments regarding unit-level data and applicability status. It has reviewed 

these comments and updated its 2012 unit-level data accordingly to better reflect unit-level operation in 

that year and unit-level applicability status. The updated unit-level data is available in Appendix One and 

reflects changes to unit data based on comments. 

In addition to unit-level data updates, the EPA also made some targeted baseline adjustments at the statelevel to address commenter concerns about the representativeness of baseline year-data, even where 

correctly reported. These are highlighted below, but discussed in more detail in the Preamble Section VI. 

State-level adjustments: 

 EPA adjusted affected fossil baseline generation upwards in states with large hydro generation 

portfolios (adjustment calculations in appendix 7 and applied in appendix 3). 

 EPA adjusted state-level generation upwards in cases where a significant portion of the 

generation portfolio was off line for maintenance (adjustment calculations in appendix 7 and 

applied in appendix 3). 

 EPA adjusted state-level generation upwards to reflect under construction fossil steam and NGCC 

capacity operating at expected technology-class levels (including units commencing operation in 

2012). (List of units available in appendix 2 and adjustment applied in appendix 3). 

Once these adjustments were calculated, EPA summed the baseline data described above at the state and 

regional-level for the following categories. These totals reflect the baseline from which the performance 

rates and state goals are assessed. 

- State and regional-level coal steam generation 

- State and regional-level coal steam emissions 

- State and regional-level oil/gas steam generation 

- State and regional-level oil/gas steam emissions 

- State and regional-level NGCC generation 

- State and regional-level NGCC emissions 

- State and regional-level NGCC capacity 
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5

All generation values are expressed as net generation. Emission rate values are net emission rates and 

expressed as lbs/MWh. The NGCC capacity expressed is net summertime capacity in megawatts. At 

proposal, there was a limited number of high utilization combustion turbines and integrated gasification 

combined cycle units (IGCCs) determined to be likely affected by 111(d) and placed in a separate “other” 

category when calculating state goals. In this final rule, the applicability language has been revised, and 

EPA’s current assessment has not identified any simple-cycle combustion turbines that are likely affected 

units under this rule. The IGCCs that are likely affected by the rule are included with the coal steam 

totals consistent with their fuel use and reporting under subpart Da. 

a. Emissions & Generation Integrated Resource Database (eGRID) 

eGRID is an inventory of environmental attributes of the U.S. electric power system. It is a 

comprehensive source of air emissions data for the electric power sector, based on available plant-specific 

data for all U.S. electricity generating plants that provide power to the electric grid and report data to the 

U.S. government. eGRID integrates many different data sources on power plants and power companies, 

including, but not limited to: the EPA, the Energy Information Administration (EIA), the North American 

Electric Reliability Corporation (NERC), and the Federal Energy Regulatory Commission (FERC). 

Emissions data from the EPA are carefully integrated with generation data from EIA to produce useful 

values like pounds per megawatt-hour (lb/MWh) of emissions, which allows direct comparison of the 

environmental attributes of electricity generation. EPA applied its eGRID methodology for matching the 

publicly available and reported 2012 emissions and generation data. The EPA relies on this most recent 

data to calculate category-specific performance rates and state goals. 

The state and region-level totals for each technology category described in the above bullets are intended 

to reflect the baseline totals for electric generating units (EGUs) that likely meet the applicability criteria 

as described in Preamble Section IV.D. 2

i. b. Data sources for under construction units 

At proposal, EPA relied on its National Electric Energy Data System (NEEDS). NEEDS includes basic 

geographic, operating, capacity, and other data on existing or under construction generating units. 

NEEDS was updated for EPA's new power sector modeling platform v.5.15 reflecting some of the unitlevel information EPA received in the comment period. For a description of the sources used in preparing 

NEEDS v.5.15, see Documentation, Chapter 4: Generating Resources.3

  Several commenters identified 

units that were under construction and likely covered under the rule’s applicability language, but that had 

not been identified at Proposal. Per commenter suggestion, EPA performed an additional review of under 

construction units using EIA 860 data, NEEDS v.5.15, and comments. In most cases, commenter and 

publically available data supported one another. There were several instances where commenter and 

                                                            

2

 The historical baseline development is described in more detail in Appendix 9 

3 Available at http://www.epa.gov/powersectormodeling/
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6

reported data conflicted. In these cases, EPA generally relied on the publically available data to 

determine the likely affected units to ensure consistent treatment across the fleet. 

EPA notes that this baseline inventory does not constitute a final applicability determination. The actual 

inventory of affected units in a future year may vary from the 2012 baseline inventory of likely affected 

units. 

c. Region-level data 

The EPA aggregated unit-level data to the state level for purposes of state-specific emission rate and mass 

goal calculation discussed in Section VII of the Preamble. However, before calculating the state goal and 

mass equivalents, it further aggregated unit-level to the regional level to calculate the category-specific 

performance standards. The regions reflect the Eastern, Western, and Texas Interconnections. These 

regions were used when quantifying the best system of emission reductions in order to capture the 

interstate effects of the building blocks. The rationale for the regional structure is explained in preamble 

section V.A.3.e. For each region, the EPA made BSER-related adjustments to the baseline data to 

determine the effect the three building block abatement measures could have on the average fossil steam 

rate and the average NGCC rate in that region. In making adjustments to region-level data, the EPA is 

simply identifying the BSER reductions that can be achieved on average at the regional level relative to 

baseline level. The EPA is not making any assertions about specific units or plant capability. The EPA 

recognizes the uniqueness and complexity of individual power plants, and is aware that there are sitespecific factors that may prevent some EGUs from achieving performance equal to region-level 

assumptions for a given technology. Likewise, the EPA also recognizes that some EGUs are capable of, 

and regularly do, achieve performance levels that surpass the BSER values assumed. In any case, the 

EPA is not making those unit-level evaluations in this exercise. The EPA is instead attempting to 

quantify what is feasible at the regional level based on application of the BSER values to historical 

regional-level data. Affected EGUs can then meet that emission rate through any particular use of 

abatement measures and/or emission reduction credits that it chooses. Therefore, the ability or inability 

of a specific EGU to under/overachieve the assumed technology value cannot be taken, on its own, as an 

indication of the appropriateness of the category-specific performance standards and the state goals 

estimated using this approach. 

The aggregate baseline generation and emission rates constitute a representative baseline for the power 

fleet for units likely subject to 111(d) applicability criteria. As with other EPA regulations, there may be 

subsequent applicability determinations post rule finalization that arrive at a different status determination 

for a particular unit than the one assumed here. Moreover, the future year inventory of affected units will 

inherently vary due to scheduled fleet turnover. While EPA addressed unit-level data comments, there 

may also be areas where stakeholders disagree over unit-level representation in the baseline. However, it 

is the regional representation of the power sector based on historical data is what ultimately informs the 

category-specific emissions rates. The large population size of units embedded in the aggregate regionallevel values used to quantify emission performance rates limit the ability of any unit-level inventory or 

data discrepancies to introduce a bias that alters this representation. 
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7

EPA received some comment suggesting that it should remove units scheduled to retire from the baseline 

inventory. It also received comments suggesting that they should not be removed. Removing units 

scheduled for retirement would generally result in lower category-specific emission rates and state goals. 

EPA is using 2012 as a representative year among the most recently available data and does not try to 

forecast future generation and emission levels post fleet turnover. Even where fleet turnover is certain, 

(e.g., a scheduled retirement), the impact of that retirement is not. Removing units and generation from 

the baseline inventory without accounting for the shift in generation to other units would understate the 

amount of fossil generation in the baseline and distort its representativeness. Accounting for the shift in 

generation would begin to shift the baseline from a historical-data informed baseline to a projectioninformed baseline. Factoring in retirements and replacing it with projected generation shifts would 

undermine the merits of relying on a historical data set and the certainty of reported data. 
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4. Methodology for Determining Category-specific Emission Performance Rates 

EPA’s methodology for calculating category-specific performance rates is described in the steps below. The implementatioillustrated –using the Eastern Interconnection for year 2030 as an example - in the table below its description.4

 Step 1: Compile 2012 unit-level data, aggregate to state-level, make baseline adjustments, and sum to regional baseline to

The EPA begins the category-specific performance rate calculation by starting with 2012 historical data. The underlying u

data reflects actual emissions and generation reported by the facility (See Appendix 9 for more detailed explanation). EPA 

using the classification system described in Appendix 9, as coal steam, O/G steam, or NGCC. It also flagged units that wer

under construction during the 2012 baseline year. EPA then aggregated the unit-level data to the state level and calculated emission rate for each technology category by dividing the total emissions into the total generation. This reflected the unad

data for units that commenced operation prior to 2012. For states that have likely affected EGUs in two different interconn

segmented these states into their relevant interconnect portions at this step (e.g., the Montana Eastern Interconnection and M

Interconnection). EPA then made the aforementioned adjustments to the state-level values to address concerns addressed b

included adding in the expected generation and emissions from likely affected units considered under construction. The re

following these limited adjustments provided a baseline for all likely affected EGUs. Complete data for these steps is avail

and 3. See the North Carolina example below illustrating the adjustment made to 2012 data reflecting under construction u

EPA received stakeholder comment noting that the Lee and Dan NGCC plants and the Cliffside coal unit 6 commenced op

through 2012 and therefore should be treated as under construction since they were still under construction for part of the y

not representative of a full year’s operation. EPA described in preamble section VI how it incorporated this type of adjustmThe example below illustrates where EPA first identified 2012 data from likely affected units that were not under construct

then identified under construction capacity (columns D and E), and then adjusted the baseline generation values up to reflec

generation values assuming a more representative full-year utilization for these units (columns F & G). 

Table 1. Example of Adjustment to 2012 Data 

4 As described in the GHG Mitigation Measures TSD, the building blocks have different assumed levels over the 2022-2030 time frame 

assumptions. Therefore, the rates described below vary by year and the amount of building block specified for that year.
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A B C D E F 

2012 Data for likely affected 

units (excluding under 

construction) 

Adjustment for Under Construction 

Sources Adjusted Ba

Coal Generation (TWh) 

NGCC Generation (TWh) 

Under Construction Coal Capacity (MW) 

Under Construction NGCC Capacity (MW) 

Coal Generation (TWh) 

NG(T

North Carolina 50.57 15.06 825 2,165 54.92 

15.06 TWh + 8784 hours*2,165 MW*55% capacity factor = 25.52 TWh5

Step 2: Aggregate the adjusted historical emissions and generation to a regional level for coal steam, OG steam, and NGCC 

EPA keeps the fossil steam sources separate from the NGCC sources at this stage to evaluate BSER impacts separately for 

This distinction is illustrated in Exhibit A above. These category-specific values become the basis for calculating the categoperformance emission rates and subsequent state goals. All emission rates provided are on a net basis. 

Table 2. Regional Baseline 

A B C D E F G 

 Coal NGCC OG Steam Interconnection 

Emissions 

(1000 short 

tons) 

Net 

Generation 

(GWh) 

Emissions 

(1000 short 

tons) 

Net 

Generation 

(GWh) 

Emissions 

(1000 short 

tons) 

Net 

Genera

(GWh

Eastern 1,356,050 1,230,434 328,233 734,542 52,979 74,241 

5 As described in the preamble section VI, EPA established a 55% capacity factor as representative of new NGCC units (60% for new co

utilization patterns for units that commenced operation during the previous five years. The 2,165 capacity value reflects summertime 

the L.V Sutton Plant which was also under construction. 8,784 hours are used instead of 8,760 to be consistent with the number of h

for which the baseline is premised. The under construction coal capacity in column D reflects Cliffside 6 which commenced operation 

as under construction per comment recommendation.
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Step 3: Identify category-specific baseline emission rates for fossil steam and NGCC 

Fossil steam sources include both coal steam and oil/gas steam affected sources, whose data is combined to arrive at a fossi

and generation total for each interconnection. This emission rate (in column H) reflects the sum of coal emissions from co

emissions from column F divided into the baseline generation for each technology from columns C & G. Because the BSEreductions in emissions intensity of sources (e.g., heat rate improvements) and reductions in generation of sources (e.g., shirenewable generation), the baseline emission rate and generation for each technology source category are utilized to assess 

the building blocks. 

Table 3. Baseline Category-specific Emission Rates and Generation. 

A B C D E F G H I 

 Coal NGCC OG Steam Fossil Steam Interconnection 

Emissions 

(1000 

short tons) 

Net 

Generation 

(GWh) 

Emissions 

(1000 

short 

tons) 

Net 

Generation 

(GWh) 

Emissions 

(1000 

short 

tons) 

Net 

Generation 

(GWh) 

Emission 

Rate 

(lb/MWh) 

Net 

Generation 

(MWh) 

Em

R

(lb/

Eastern 1,356,050 1,230,434 328,233 734,542 52,979 74,241 2,160 1,304,674 

Eastern Fossil Steam Rate = (coal emissions + OG emissions) = Eastern Fossil steam rate = 1,356,050,000 tons +52,9796,00 Coal gen + OG gen (1,230,434 GWh + 74,241 Eastern NGCC Rate = NGCC emissions = Eastern NGCC Rate = 328,233 tons  NGCC gen 734,542 GWh Step 4: Calculate regional fossil steam emission rate resulting from building block 1 heat rate improvement (HRI). After this baseline data is aggregated for each region, the EPA begins to adjust some of the data values to reflect each build

BSER. The EPA assumes a 2.1% HRI in the Western Interconnection, a 2.3% HRI in the Texas Interconnection, and a 4.3Interconnection applied only to the coal steam fleet. This is reflected by adjusting the coal emissions down by the region-s

and leaving the generation level unchanged. Subsequently, the fossil steam rate for the region is calculated by adding the a
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post HRI assumption (column H) with the baseline OG steam emissions (column D) and dividing into the sum of the coal s

OG steam generation (column E). There is no change in the NGCC rate from this step.

Table 4. Adjusted Fossil Steam Rate Reflecting Building Block 1 

A B C D E F G H I 

 Baseline Coal Baseline OG Steam 

Baseline 

Fossil 

Steam 

BB1

Interconnection 

Emissions 

(1000 

short tons) 

Net 

Generation 

(GWh) 

Emissions 

(1000 

short tons) 

Net 

Generation 

(GWh) 

Emission 

Rate 

(lb/MWh) 

BB1 HRI Level 

Post BB1 Coal Emissions (1000 short tons) 

Fossil Steam 

Emission Rate 

Post BB1 

(lb/MWh) 

Eastern 1,356,050 1,230,434 52,979 74,241 2,160 4.3% 1,297,740 2,071 

When the technology emission rate is recalculated with building block 1 reflected in the adjustment to the region’s coal emfossil steam emission rate drops below its baseline value. Note that the fossil steam rate reflects the aggregation of both co

This is not the final category-specific performance rate, rather it is an adjusted emission rate reflecting the application of bu

moving on to the remaining building blocks. The bold areas in the equation below reflect the values that are adjusted from this step. In this example, the fossil steam rate drops from a baseline value of 2,160 lb/MWh to 2,071 lb/MWh after buildin

Eastern fossil Steam Rate = (coal emissions *(1-HRI%) + (OG emissions) = Eastern fossil steam rate = (1,356,050,000 tons)*0. Coal gen + OG gen (1,230,434 GWh + 74,24Step 5: Calculate regional fossil steam and NGCC generation levels resulting from building block 3 (incremental RE generBuilding Block 3 is based on lower-emitting generation replacing higher emitting generation. The GHG Mitigation Measu

the incremental RE generation potential for each region was derived. As explained in the TSD, the BB3 potential is defineRE generation (incremental relative to 2012 levels). Therefore the computation of category-specific performance rates and 

rule only reflect this incremental RE total. All incremental BB3 RE is assumed to emit zero tons of CO2. 
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For this final rule, EPA assumes that BB3 incremental generation replaces existing fossil generation from the baseline leveimpact on each technology category is estimated on a pro-rata basis where the incremental BB3 generation is first identifieapportioned to replace either fossil steam (column D * column F = column I) or NGCC generation (column E * column F = 

the share of baseline generation each technology category represents. For example, if a region had 100 MWh of potential Bidentified, and baseline fossil steam accounted for 70% of the region’s generation from affected units and NGCC accounted 

MWh of incremental RE identified would be assumed to replace 70 MWh of fossil steam generation and 30 MWh of NGCsteam generation and NGCC generation are decreased by the amount of RE MWh apportioned to that technology (column 

(column C – column J). The total baseline generation (columns B & C) equals the total remaining generation and renewabF, H, I, and J) reflecting that replacement of fossil sources by incremental RE generation. 

Table 5. Adjusted Fossil Steam and NGCC Generation Reflecting Building Block 3 

A B C D E F G H I 

Baseline Gen. BB3 

Interconnection 

Fossil Steam Net Generation (GWh) 

NGCC Net Generation (GWh) 

Fossil Steam Share of Total Fossil Gen. 

NGCC Share of Total Fossil Gen. 

Potential BB3 (GWh) 

Remaining Fossil Steam (GWh) 

Remaining NGCC Gen (GWh) 

BB3 Replacing Fossil Stea(GWh) 

Eastern 1,304,674 734,542 64% 36% 438,445 1,024,161 576,611 280,513 

Eastern Fossil Steam Gen = Baseline Fossil Steam Generation - (Potential BB3 Gen x fossil steam share of total fossil generation) Eastern Fossil steam Gen = 1,304,674 – (438,445 * 64%) = 1,024,161GWh Eastern NGCC Gen = Baseline NGCC Generation - (Potential BB3 Gen x NGCC share of total fossil generation) Eastern NGCC Gen = = 734,542 – (438,445 * 36%) = 576,611 GWh
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Step 6: Calculate regional fossil steam and NGCC generation resulting from building block 2 (incremental NGCC generati

The “Remaining NGCC Generation” field in column C of the below table indicates that there is less NGCC generation – re

- following building block 3 incorporation due to the assumption that some of the incremental RE would replace baseline NMoreover, there is significantly less generation than the potential identified in building block 2 that reflects a 75% utilizatio

implementing building block 3, the NGCC generation levels would be assumed to decrease under a pro-rata replacement apthe GHG Mitigation Measures TSD, the EPA described the abatement potential of replacing higher emitting fossil steam ge

emitting gas generation, identified as building block 2. This step of the rate calculation captures the change in source-categ

associated with building block 2 potential of a 75% potential utilization for the NGCC fleet. To incorporate building block 2, the regional NGCC fleet summertime capacity is multiplied by 8,784 hours (the number oyear) and then by 75% to get total potential net NGCC generation at a 75% capacity factor (column D). However, this 75% 

represents a generation ceiling, and the region’s NGCC generation is only adjusted up to this ceiling to the extent -that such 

increases can replace remaining fossil steam generation. 6 Note that the combined remaining fossil steam and NGCC gener

and G in this table reflect the remaining fossil steam and NGCC generation total after BB3 (columns B and C). Moreover, 

combined with the RE potential assigned to each technology in columns I and J in the previous table sum to the total baselifor each region. 

Table 6. Adjusted Fossil Steam and NGCC Generation Reflecting Replacement by Building Block 3 and Building B

Generation 

A B C D E F 

Post BB 3 BB2 

Region 

Remaining Fossil Steam (GWh) 

Remaining NGCC Gen (GWh) 

NGCC Potential at 75% CF (GWh) 

Difference between NGCC generation levels at 75% utilization and Post BB3 NGCC levels (GWh) 

Remaining Fossil Steam (GWh) 

ReNG(GW

Eastern 1,024,160 576,611 987,956 411,345 612,816 

6 The ceiling in the interim period is less than the 75% utilization level. The BB2 deployment schedule is discussed in the GHG Mitigat
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In the above example, NGCC generation is adjusted upwards by approximately 411 TWh (column E) to 987,956 TWh (co

the NGCC fleet generation at 75% utilization) and the fossil steam generation is adjusted down by that same amount (colum

Eastern Fossil Steam Gen = Post BB3 fossil steam gen. - (NGCC Potential at 75% CF – Post BB3 NGCC Gen)7

Eastern Fossil steam Gen = 1,024,160 – (987,956 – 576,611) = 612,816 GWh Eastern NGCC Gen = Post BB3 NGCC gen + (NGCC Potential at 75% CF – Post BB3 NGCC Gen) Eastern NGCC Gen = 576,611+ (987,956 – 576,611) = 987,956 GWh

Step 7: Determine the adjusted category-specific performance rates for each region. 

Step four estimated the category-specific emission rates post BB1. Steps five and six estimated the category-specific gener

building block 3 and 2, respectively. Combining the adjusted emission rates with the adjusted generation allows EPA to ca

specific adjusted emission rate reflecting the expression of BSER onto the baseline. For the regional fossil steam rate, EPA first calculates the numerator. EPA multiplies the fossil steam emission rate from s(reflecting the heat rate improvement) by the remaining fossil steam generation following step six (column J). For BB3, algeneration was assumed to equal zero so no numerator adjustment was made. As described in the preamble, EPA also capt

7 If (NGCC Potential at 75% CF – Post BB3 NGCC Gen) is greater than post BB3 fossil steam gen, then the fossil steam gen amount is ad

NGCC Gen amount is increased by the Post BB3 fossil steam generation amount that it replaced.
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NGCC generation in the fossil steam rate reflecting the incremental BB2 potential;8 this incremental NGCC generation is d

remaining NGCC subsequent to both blocks 2 and 3 (column K) minus the amount of NGCC generation in the baseline (co

of NGCC generation is multiplied by the NGCC emission rate from step three (column C) to get the amount of incremental assigned to the fossil steam emission rate as part of BB2. These emissions from fossil steam sources along with emissions from its replacement sources are then divided by the total fossil steam generation, the RE generation assigned to fossil steam, and the incremental NGCC defined above. This genera

remaining fossil steam generation post step six (column J), 2) amount of RE generation assigned to fossil steam generation amount of NGCC generation defined above (column K-column E). Dividing this total emissions level by the total generati

regional fossil steam emission rate reflective of BSER. For the regional NGCC emission rate, EPA performs a similar operation. The NGCC generation post step six (column K) NGCC baseline emission rate from step three (column C) to estimate the total amount of NGCC emissions post BB3 and Bare then divided into the sum of the NGCC generation post step six (column K) and the amount of BB3 generation apportio

generation in step five (Column I).10 This regional NGCC rate reflects the adjusted NGCC rate reflecting BSER. 

8 As described in the preamble, EPA reflected the incremental NGCC generation (and corresponding emissions) in the fossil steam rate 

created a parallel compliance structure for quantifying NGCC ERCs which fossil steam sources may use in compliance. This was nece

steam source-category rate that reflected generation shifting due to BB2. 

9 EPA also considered quantifying the amount of NGCC generation assigned to fossil steam generation as post step 6 levels minus post 

have resulted in a lower fossil steam rate. However, this approach would not have resulted in a different stringency because a similar awhen measuring and quantifying NGCC ERCs available for compliance. In other words, there would be a lower rate, but more credits determined that measuring incremental NGCC generation to include in the fossil steam rate was more appropriately done using a baselihistorical generation) as it would facilitate appropriate measurement and identification of NGCC ERCs. EPA took the more conservati

the amount of NGCC generation and emissions reassigned to the fossil steam rate as the difference between post BSER levels and baseconsistency with potential ERC measurement and crediting from a historical baseline level and help ensure that enough ERCs would be 

that compliance route. Therefore, the remaining fossil steam and NGCC generation levels appropriately reflect the full building block tand the portion of the NGCC emissions and generation levels included in the fossil steam rate appropriately reflect the historical measu

10 The full NGCC generation (and corresponding emissions) expected under the BSER calculation is included in the NGCC rate, even t

also reflected in the fossil steam rate. Failing to do so would leave the NGCC sources with a lower rate than what is expected post BB
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Eastern Fossil Steam Gen = (Post BB3&2 fossil steam gen x Post BB1 fossil steam emission rate)+(Incremental NGCC Generation  (Post BB3&2 fossil steam gen + BB3 generation replacing fossil steam + incremental BB2 generatiEastern Fossil steam Gen = (612,816 GWh * 2,071lb/MWh) + ((987,956 GWh - 734,542 GWh)*894 lb/MWh)  612,816 GWh + 280,513 GWh + (987,956 GWh - 734,542 GWh) Eastern NGCC Gen = (Post BB3 NGCC gen x NGCC baseline rate)  (Post BB3 NGCC gen + BB3 generation replacing NGCC gen) Eastern NGCC Gen = (987,956 GWh * 894 lb/MWh)  (987,956 GWh + 157,931 GWh)

Step 8: Identify the least stringent regional rate as the emission performance rate for the technology source category

After completing a regional assessment of building block potential impact on source category-specific rates, EPA evaluated 

NGCC rate for each region to identify the region with the least stringent emission rate. The least stringent (i.e., the highest

the least stringent NGCC emission rate among the three regions are identified and used to establish the source-category em

rates described in the preamble. 

full NGCC generation amount in the NGCC rate, in spite of its replacement impact on fossil steam, recognizes the dual role it has in terresponsibility as an affected source and a mitigation measure under building block 2 that that can offset fossil steam generation.

A B C D E F G H I J 

Interconnection

FossilSteamRate

NGCCRate

Fossil Steam

Gen NGCC Gen

Fossil

Steam

Rate

NGCC

Emission

Rate

BB3 Replacing

Fossil Steam

BB3 Replacing

NGCC

RemainingFossil Steam

RemaiNGCC 

lb/MWh lb/MWh GWh GWh lbs/MWh lbs/MWh GWh GWh GWh GWh 

Eastern 2,160 894 1,304,674 734,542 2,071 894 280,513 157,931 612,816 

Table 7. Adjusted Fossil Steam and NGCC Generation Rates Reflecting all Three Building Blocks

Baseline BB1 HRI BB3 Apportioned to Fossil Post BB2 
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Table 7. Identify Least Stringent Rate for Each Technology

Category (2030)

Adjusted Rates 

Region 

Fossil Steam Rate (lb/MWh) 

NGCC Rate (lb/MWh) 

Eastern Interconnection 1,305 771 Western Interconnection 260 660 Texas Interconnection 450 754 

The completion of the previous steps results in a 2030 emission performance rate for each source category. However, as de

Mitigation Measures TSD, the BB2 and BB3 assumed potential changes for each year from 2022 through 2030. Thus this for each of those years using the corresponding BB2 and BB3 assumptions for that year that reflect the deployment rate for 

This results in a set of decreasing annual adjusted emission rates for the years 2022-2029. However, this rulemaking issues 

emission performance rates for an interim and a final rate. Thus, the interim rate is derived by averaging the annual adjuste2022-2029. Once the interim and final rates are determined, EPA rounds any fractional number up to the nearest integer.  For the Final CPP Rule category-specific rates:  Interim category-specific rate – Average of the adjusted yearly emission rates for the period 2022-2029 

 Final category-specific rate– The 2030 emission rate (as calculated above) becomes the state goal for 2030 and eac11 The region with the least stringent rate can differ by year. For the fossil steam rate, the Eastern Interconnection is the limiting regio

NGCC rate, the Texas Interconnection is the limiting region for 2022 through 2026, and the Eastern Interconnection is the limiting reg

2030.
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The assumptions used to arrive at the category-specific performance rates are not prescriptive of necessary actions that sou

must take. As described in the preamble, these values are used only for calculating the emission performance rates and stat

required to base its state plan on using the same set of measures or the same amount of any measure reflected in these assum

state plan, not these assumptions, determines the range of available measures a source may or must use to comply with the 

performance established for it in the state plan and the extent to which the source may or must rely on any individual measu

5. Methodology for Converting Category-specific Rates into State Emission Rate Goals To calculate a state goal in the final CPP, EPA estimates the affected fleet rate for a state if all likely affected baseline sour

category-specific emission performance rates presented above while generating at the same baseline generation total. For e

calculated a weighted average of the category-specific fossil steam rate and the category-specific NGCC using the state’s blevels for each source category. These aggregate state rates reflect the total emission rate from likely affected units in the s

baseline generation levels while meeting the category-specific rates. For example, the nation-wide 111(d) source category rates determined at the regional level were 1305 lb/MWh and 771 lb/state of Arizona had baseline affected fossil generation consisting of 25.37 TWh of fossil steam generation and 26.78 TWh 

Arizona’s 2030 state goal metric would be calculated as follows: Arizona state goal = (Fossil steam source category rate * Fossil steam baseline share of affected generation) + (NGCC sourNGCC baseline share of affected generation) Arizona State Goal = (48.65%*1,305) + (51.35%*771) = 1,031 lb/MWh. 

2022 2023 2024 2025 2026 2027 2028 2029 2030 Interim Fin

Fossil Steam 1,741 1,681 1,592 1,546 1,500 1,453 1,404 1,355 1,304 1,534 1,

NGCC 898 877 855 836 817 798 789 780 771 832 

Annual Category‐specific Rates
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EPA uses the representative baseline and calculations described above to derive category-specific rates and state emission r

calculated, the system-wide impacts and feasibility of these state goals are further tested using EPA’s power sector modelin6. Methodology for Converting State Emission Rate Goals into State Mass Goals 

The calculation of affected EGU mass goals includes two components. First, it includes the emissions associated with each 

goal, which is the product of the state emission rate goal and 2012 affected EGU generation. Second, it includes the emissiability of affected EGUs to expand output under rate-based compliance if they deployed the amount of RE quantified unde

was not captured in the ultimate quantification of the source category-specific performance rates. The procedure for quantifying this level of building block 3 generation applies to the values and calculations in Appendix 4

from Appendix 4 that displays building block 3 data and goal rates for 2030: Columns V and W in Appendix 4 display the regional fossil steam and NGCC rates after the full application of the building 

rates lower than the source category-specific performance rates (1,305 lbs/MWh for fossil steam and 771 lbs/MWh for NG

region contains more building block 3 generation potential than is necessary to achieve the source category-specific perform

region. In order to quantify the amount of building block 3 generation potential in each region that is beyond the level need
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category-specific performance rates, the EPA designed an optimization algorithm to reduce the region’s building block 3 p

until the regional rate was equal to the corresponding source category-specific performance rate. The optimization algorith

 Minimize ‘Potential BB3’ (column N) in each region12 for each year by changing values for ‘Potential BB3,’ ‘Foss

Fossil,’ and ‘NGCC Share of Total Fossil’ (columns L and M).13

 Subject to the following constraints: 

o ‘Fossil Steam Share of Total Fossil’ and ‘NGCC Share of Total Fossil’ must sum to 100% and neither can 

below 0%. The ‘Share of Total Fossil’ values control how the total amount of building block 3 generation subcategory in each region. For example, an 80% value under ‘Fossil Steam Share of Total Fossil’ indicatbuilding block 3 generation in that particular region is being applied to the fossil steam subcategory.14

o ‘Fossil Steam Rate’ must be less than or equal to the source category-specific performance rate 

o ‘NGCC Rate’ must be less than or equal to the source category-specific performance rate 

After minimizing ‘Potential BB3’ for each region according to the procedure described above, the updated Appendix 4 valu

12 Each row is a different BSER region – row 7 is the Eastern Interconnection, row 8 is the Western Interconnection, and row 9 is the T13 The fossil steam and NGCC shares of total generation are allowed to vary in this computation because under rate‐based compliance

building block 3 that was not captured in the source category‐specific performance rate could be deployed and claimed for complianc

NGCC units, at any possible balance of generation across both steam and NGCC units.14 Note that even when the minimization procedure increases the share of potential BB3 generation assigned to a subcategory of affe

amount of building block 3 generation assigned to that subcategory (i.e., potential BB3 generation multiplied by the share) is always r

value.
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The difference in ‘Potential BB3’ across all regions for 2030 is 166,203,128 MWh, obtained by subtracting the minimum p

the source category-specific performance rates in each region (539,826,984 MWh) from the total potential identified in the building block 3 (706,030,113 MWh)15. It is this difference in “Potential BB3” that was not captured in the ultimate quanti

category-specific performance rates, and that affected EGUs could deploy to expand output and associated emissions under 

Note that the Eastern Interconnection (row 7), as the region whose fossil steam and NGCC rates determined the source cateperformance rates in 2030, requires all of the building block 3 generation potential quantified for that region to achieve the 

However, because the final rule would allow sources in the Eastern Interconnection to claim RE from any region for use in relevant value for this computational procedure to quantify emissions for mass goals (across all states) is the national-level 

BB3” across all regions. Note that in the Texas Interconnection (row 9), the fossil steam rate after the reduction in “Potential BB3” has increased fro1,096 lbs/MWh, which is still below the source category-specific performance rate of 1,305 lbs/MWh. However, the rema

between the regional fossil steam rate and the source category-specific performance rate for steam cannot be addressed by 

the region’s “Potential BB3”, because the region would still need all of the remaining “Potential BB3” generation to satisfy 

specific performance rate for NGCC (as reflected by the “100%” value in column M). The 1,096 lbs/MWh steam rate resu

15 All values rounded to the nearest MWh; for exact values refer to Appendix 5.
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for the Texas Interconnection serves only as an indicator that the computation did not violate the criteria laid out above for block 3 potential that was not captured in the source category-specific performance rates; this value is not used in any compcomputation below to quantify emissions associated with the ability of affected EGUs to expand output if they deployed th

potential. The total amount of building block 3 generation not captured in the source category-specific performance rates for each yea

The next step is to apportion this generation to states on the basis of each state’s 2012 share of affected EGU generation. T

generation total can then be converted into a mass adjustment that reflects the ability of affected EGUs to increase their owthis building block 3 generation under rate-based compliance: Mass Adjustment = State Emission Rate Goal * BB3 Generation Not Captured in Source Category-Specific Performance RThe mass adjustment reflects the ability of affected EGUs to procure incremental RE to increase their own generation and ean applicable rate-based standard. In that rate-based compliance scenario, every zero-emitting MWh added to the denomineffective emission rate would enable that EGU to add another MWh of generation with twice the emissions intensity of the standard, because the average intensity of that emitting MWh combined with the zero-emitting MWh would then equal the standard and thus maintain that EGU’s compliance.16

16 The assumption that one MWh of incremental RE enables one MWh of additional affected EGU generation is consistent with the hi

affected sources over time as well as expected future demand levels.
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As an example, an EGU in Arizona in 2030, if subject to (and already compliant with) an emission rate standard equal to th

2030 of 1,031 lbs/MWh, and assuming it was already generating 1,000 MWh for sake of illustration, would be able increas

lbs, assuming the procurement an incremental MWh of RE enables an additional MWh of affected EGU output: 

ݏ݈ܾ	1,031,000

൅ ܹ݄ܯ	1,000

ݏ݈ܾ	0

ܹ݄ܯ ݏ݈ܾ	1,031 ൌ ܹ݄ܯ	1,002 ݏ݈ܾ	1,033,062 ൌ ܹ݄ܯ	1 ݏ݈ܾሻ2	ݔ	1,031ሺ ൅ ܹ݄ܯ	1

In this illustrative example, this EGU was able to remain compliant at the 1,031 lbs/MWh rate while adding a MWh with eand acquiring an incremental MWh of zero-emitting RE.17 This example shows why the mass adjustment procedure assum

block 3 potential not captured in the source category-specific compliance rates could allow additional emissions of twice th

represented by the applicable state goal. The final step in calculating an affected EGU mass goal is to simply add the mass associated with the state emission rate to described above, using this equation:. Affected EGU Mass Goal = (State Emission Rate Goal * State’s 2012 Affected EGU Generation) + (State Emission Rate GApportioned BB3 Generation Not Captured in Source Category-Specific Performance Rates * 2) As an illustration, Arizona’s 2030 affected EGU mass goal would be calculated as follows: Arizona Affected EGU Mass Goal for 2030 = (1,031 lbs/MWh * 52,154,061 MWh) + (1,031 lbs/MWh * 3,192,419 MWh metric tons Affected EGU mass goal calculations and results are available for each state in Appendix 5. 

17 The emissions quantified through this particular mass adjustment approach could also represent a variety of source‐specific and fle

result if affected EGUs procure incremental RE beyond what is required to demonstrate the source category‐specific performance rat
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7. APPENDIX 

Appendix 1 – Underlying 2012 unit-level baseline inventory and data 

See “Appendix 1-unit-level data” worksheet in the Excel attachment titled “Baseline, Category-specific ratesQuantification” 

Appendix 2 - Units that commenced operation post 2011, but began construction prior to 1/8/14 

See “Appendix 2 – Under construction” worksheet in the Excel attachment titled “Baseline, Category-specifi

Quantification”. Note, this is largely a subset of the Appendix 1 worksheet.

Appendix 3 – Underlying state-level data, adjustments 

See “Appendix 3 – state-level data” worksheet in the Excel attachment titled “Baseline, Category-specific ra

Quantification”

Appendix 4 – Computation of the category-specific performance rates (Interim and Final) 

See “Appendix 4 – category-specific calc.” worksheet in the Excel attachment titled “Baseline, Category-spe

Goal Quantification” 

Appendix 5 - Computation of the state goal (Interim and Final) 

See “Appendix 5 – State Goals” worksheet in the Excel attachment titled “Baseline, Category-specific rates, Quantification” 
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Appendix 6 –State Goals 

State Interim Goal Final Goal State Interim Goal Final Goal

Alabama 1,156 1,018 Nebraska 1,522 1,296

Arizona 1,173 1,031 Nevada 941 855

Arkansas 1,304 1,130 New Hampshire 946 858

California 906 828 New Jersey 885 812

Colorado 1,362 1,174 New Mexico 1,325 1,146Connecticut 851 786 New York 1,024 918

Delaware 1,022 916 North Carolina 1,311 1,136Florida 1,026 919 North Dakota 1,534 1,305Georgia 1,197 1,049 Ohio 1,383 1,190

Idaho 831 771 Oklahoma 1,222 1,068

Illinois 1,455 1,245 Oregon 963 871

Indiana 1,451 1,242 Pennsylvania 1,258 1,095

Iowa 1,505 1,283 Rhode Island 831 771

Kansas 1,518 1,293 South Carolina 1,337 1,156

Kentucky 1,509 1,286 South Dakota 1,351 1,166

Louisiana 1,292 1,121 Tennessee 1,410 1,211

Maine 841 779 Texas 1,187 1,042Maryland 1,510 1,287 Utah 1,368 1,179

Massachusetts 901 824 Virginia 1,046 934

Michigan 1,354 1,169 Washington 1,110 983

Minnesota 1,413 1,213 West Virginia 1,534 1,305

Mississippi 1,060 945 Wisconsin 1,364 1,176

Missouri 1,490 1,272 Wyoming 1,526 1,299

Montana 1,534 1,305
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Appendix 7 –Adjustments to state-level 2012 baseline data 

See the Excel attachment titled “State-level adjustments” 

Hydro adjustment – States suggested that 2012 was an abnormal year for hydro, and because of the predominance of hydro 

made it an outlier year for other generation technologies in the state. EPA assessed this concern for all states using the foll

1) Using EIA 2012 data, identify the % share of total generation coming from hydro in each state 

2) Using EIA 1990-2012 data, identify average hydro generation for a state from 1990-2012 and look at the % difference 

generation levels and the average hydro generation levels 

3) Estimate the increase in affected fossil generation that would occur if the difference between the average hydro year an

was replaced with generation from affected fossil generation. 

EPA determined that hydro intensive states (greater than 10% generation from hydro), that experienced an outlier year in 2

increase in hydro generation relative to observed average between 1990-2012), and that would have their state’s affected fo

significantly affected when assuming average hydro generation levels (an adjustment > 5%) had baseline values that were s

in hydro generation and thus increased the fossil generation in the state from observed 2012 levels to reflect potential gener

average hydro year. Unit-outage adjustment As explained in the Preamble Section VI, EPA did not generally view single unit-outages as problematic to its baseline for 

category rates or state goals. As regional load levels did not change subject to the unit outage, the decrease at a particular u

by the increase in generation from other unit(s) in the same state or region. Therefore, EPA views the regional and state-legeneration totals as robust against unit-level outages. However, it did test for outlier cases where the unit-level outage was 

potentially have a large impact on the state goals that EPA provided in section VII. EPA assess this concern for all units by 

1) Units where the generation in 2012 was less than 25% of its 2010 and 2014 totals collectively (signaling a significa

2) For units meeting the step 1 criteria, EPA identified those where the generation observed in the non-outage years o

was greater than 10% of the state’s total generation (suggesting the replacement generation may be difficult to find 

The only unit that met this criteria was the 900 MW Sherburne county unit 3 coal unit in Minnesota. EPA adjusted the stat

value up to reflect this unit operating in a typical year. 
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Appendix 8 – Category-specific emission rates under building block severability 

See “Appendix 9” worksheet in the Excel attachment titled “BB Severability” which replicates EPA quantifiassuming only building blocks 1 and 2 
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Appendix 9- Description of 111(d) baseline data sources and development Introduction This section describes the methodology used by the EPA to develop the state and region-level baselines used in the goal se

The 111(d) baseline analysis methodology is based largely on the methodology used to develop the Emissions and Generat

Database (eGRID)18, with certain key differences, which are explained in Appendix A. The 111(d) baseline consists of emi

CO2 per megawatt-hour (MWh) of electricity generation. The baseline is constructed by matching electricity generation da

Energy Information Administration (EIA) by power plants on forms EIA-86019 and EIA-92320 with data on CO2 emissions 

plants to the EPA under 40 CFR Part 75.21

The process of matching emissions data to generation data and categorizing the EGUs is described in more detail below. Th

the baseline released for the Clean Power Plan Proposed Rule22 and the Final Rule are discussed in Section C. 

Data Sources The key data sources used in the construction of the 111(d) baseline are listed in Table 1. Table 1. Key data sources used to construct the 111(d) baseline. 

Data Source Key DataEIA‐860 Contains key identifying information, including nameplate capacity, summer

capacity, unit operational status, prime mover type, and fuel type, as well as the

plant’s name and location.

18 U.S. Environmental Protection Agency, Clean Air Markets Division, Emissions and Generation Resource Integrated Database (eGRID

http://www.epa.gov/cleanenergy/energy‐resources/egrid/

19 Energy Information Administration, Form EIA‐860, available at http://www.eia.gov/electricity/data/eia860/20 Energy Information Administration, Form EIA‐923, available at http://www.eia.gov/electricity/data/eia923/21 40 CFR Part 75, available at http://www.ecfr.gov/cgi‐bin/text‐idx?tpl=/ecfrbrowse/Title40/40cfr75_main_02.tpl22 https://www.federalregister.gov/articles/2014/06/18/2014‐13726/carbon‐pollution‐emission‐guidelines‐for‐existing‐stationary‐so

generating
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EIA‐923 Contains information on net electricity generation and fuel use at the generator

level, boiler level, and/or prime mover level.

EPA Part 75 Data Contains information on CO2 emissions and heat input.

EPA Part 75 emissions data is presented at the unit level, where the unit is defined as the fossil fuel‐fired device, which co

boiler (including any heat recovery steam generators (HRSG), if present). EIA data on generation and fuel use is presented 

boiler, prime mover, and plant levels.The 111(d) baseline analysis methodology involves matching EPA emissions data at the unit level (i.e. emissions from boil

EIA generation data at the generator level. However, the data do not always match cleanly between the two data sources

sources identify plants using the Office of Regulatory Information Systems Plant Location code (ORIS code), the EIA data i

with a generator ID, and the EPA data identifies units with a unit ID. The ORIS code generally matches between data sourc

ID from EIA must be matched to the unit ID from EPA based on ORIS code, nameplate capacity, fuel type, prime mover ty

operation.Furthermore, because there are different regulations governing which plants and units must report data to the EIA and th

different numbers of units at each plant between the two data sets. Additionally, existing and proposed plants are requir

860 and 923 to the EIA if the plant’s total generator nameplate capacity is 1 MW or greater and it is capable of supplying 

power from the electricity grid. Plants are required to submit emissions data to EPA under 40 CFR Part 75, generally if a u

with a nameplate capacity of greater than 25 MW which produces electricity for sale.

Baseline Construction Process As discussed above, the construction of the 111(d) baseline involves matching net electricity generation data from EIA wi

emissions from EPA. All of the existing, proposed, and retired units listed in the EIA‐860 serve as the foundation for the ba

universe of units. Electricity generation and CO2 emissions are added to this foundation using the EIA‐923 and EPA Part 75 

Electricity Generation For any given power plant, data on net electricity generation from the EIA‐923 may be available at the unit level for some 

mover level for other units. If unit‐level data are available, the data are used in the baseline. If data are only available at t

then these data are distributed proportionally based on nameplate capacity to the units at that plant with that prime mov
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CO2 Emissions Part 75 emissions data from EPA are matched to the generator‐level data from EIA. When units can be matched exactly b

sources, the unit‐level emissions are used in the baseline. When one unit from the EPA data is associated with more than 

the EIA data (e.g. emissions from a boiler that supplies steam to more than one generator), or if units at a given plant can

between the two data sources, the total emissions may be distributed to generators based on the proportion of namepla

cycle units are considered a single system and emissions from all components are summed and distributed to all generato

of nameplate capacity.

Because there are different regulations governing which plants and units report data to EIA and EPA, there are more unit

than in the EPA data (for example, a unit under 25 MW may not be required to report emissions data under Part 75). To e

units that are listed in the EIA data but not in the EPA data, a fuel‐specific emission factor is multiplied by unit‐level fuel c

British thermal units (mmBtu)). This method is based on the methodology used by the Intergovernmental Panel on Climat

in EPA’s Inventory of U.S. Greenhouse Gas Emissions and Sinks.24 CO2 emission factors for year 2012 are obtained from tw

Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990‐2012, and the emission factors used in the Greenhouse Gas 

which are listed in 40 CFR Part 98.25 The emissions factors used in the 111(d) baseline analysis are listed in Section D. The 

heat input data from EPA Part 75 data, boiler‐level data from the EIA‐923, and prime mover level data from the EIA‐923. D

preferentially in that order (e.g. if heat input data are unavailable from EPA, then boiler‐level data from EIA are used).

Data Corrections When CO2 emissions from EPA are matched with net electricity generation data from EIA, an emissions rate (lbs. CO2 per 

the calculated emissions rate is unreasonably high (>10,000 lbs. CO2 per MWh) or unreasonably low (<500 lbs. CO2 per M

electricity data are calculated based on gross generation data from EPA. Because the EPA data contain gross generation r

23 IPCC, 2007: The Intergovernmental Panel on Climate Change (IPCC), “2006 IPCC Guidelines for

National Greenhouse Gas Inventories”, volume 2 (Energy), April 2007.

http://www.ipccnggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_2_Ch2_Stationary_Combustion.pdf

24 EPA, 2014: U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions

and Sinks: 1990‐2012, Washington, D.C., April 2014. http://epa.gov/climatechange/emissions/usinventoryreport.html

25 TCR, 2014: The Climate Registry 2014 Climate Registry Default Emission Factors, Washington DC,

January 10, 2014. http://www.theclimateregistry.org/tools‐resources/reporting‐protocols/generalreporting‐ 

protocol/
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electricity generation, net generation must be calculated by multiplying gross generation by a unit‐specific net‐gross conv

where a net‐gross conversion factor is not available for a specific unit, the calculation uses a net‐gross conversion factor f

state and with the same prime mover. If the EPA data do not include gross generation for a specific unit, the calculation u

generation from EIA.27 If this correction still results in an emissions rate greater than 10,000 lbs./MWh or less than 500 lb

electricity generation data are left unchanged and the original calculated rate is retained.

In addition, for units that report negative net electricity generation (for example, the facility uses more electricity than it 

emissions, the electricity generation is adjusted using gross electricity generation data as described above. This correction 

estimating a negative emissions rate.

Limited adjustments are also made for several likely affected facilities that had reported summertime capacity significant

nameplate capacity. For these units, EPA replaced the summer capacity value reported in EIA 860 with the nameplate va

Inclusion Criteria In order to calculate the state‐level emission rate for coal steam units, natural gas combined cycle units, and oil and gas st

individual units are categorized according to the nameplate capacity, prime mover type, fuel, and operating status, as sho

Table 2. Criteria for inclusion of units in the 111(d) baseline as likely affected EGUs. 

Category Code Category Inclusion criteriaCOALST Coal steam

units

Steam turbine units (prime mover = ST) with coal as primary fuel

source. Nameplate capacity must be greater than 25 MW.

26 These conversion factors were developed by Ventyx (now called ABB Enterprise Software), a consulting firm that provides informat

the electricity generation sector. The factors are developed using North American Reliability Corporation (NERC) Generating Availabil

which contains data on gross and net generation for units with a nameplate capacity greater than 20 MW. The data provided for this 

ratios of net generation to gross generation.

27 EIA supplied the gross generation data for a subset of generators to EPA, as these data are not publicly available in the EIA‐923 data
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Category Code Category Inclusion criteriaNGCC Natural gas

combinedcycle units

Combined cycle units with natural gas as primary fuel source. If all of

the turbine components of the combined cycle unit (prime mover =

CT) have a nameplate capacity greater than 25 MW, then all of the

steam components (prime mover = CA) are included, regardless of

whether they have a nameplate capacity greater than 25 MW.

Otherwise, only components with a nameplate capacity greater than

25 MW are included.

OGST Oil and gas

steam units

Steam turbine units with oil or gas as primary fuel source. Nameplate

capacity must be greater than 25 MW.

UC Coal –Commenced in

2012

Coal steamunits that

commencedoperations in

2012

Units that would otherwise be classified as COALST, but which

commenced operations in 2012. Determination of when the unitcommenced operations is based on the EIA‐860 and public comments

on the 111(d) baseline developed for the Proposed Rule.

UC NGCC –

Commenced in

2012

NGCC unitsthatcommenced

operations in2012

Units that would otherwise be classified as NGCC, but which

commenced operations in 2012. Determination of when the unit

commenced operations is based on the EIA‐860 and public comments

on the 111(d) baseline developed for the Proposed Rule.

UC‐Coal Coal steam

units that areunderconstruction in2012 or 2013

Units that are under construction in the data year (EIA unit status = U,

V, or TS), but which would likely be considered COALST units if

operational. For the 2012 111(d) baseline, units can be listed as UC‐

Coal if they are under construction in 2012 or 2013.

UC‐NGCC NGCC units

that are under

construction in2012 or 2013

Units that are under construction in the data year (EIA unit status = U,

V, or TS), but which would likely be considered NGCC units if

operational. For the 2012 111(d) baseline, units can be listed as UC‐

NGCC if they are under construction in 2012 or 2013.
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Category Code Category Inclusion criteriaEXCLUDE Units excluded

from the

111(d)

baseline

Units may be excluded from the baseline for several reasons,

including:

 Internal combustion engine units;

 Non‐combustion prime movers, such as photovoltaics, wind

turbines, and hydropower units;

 Units that used less than 10% fossil fuel on a heat input basis

in 2012;

 Non‐operational units, such as units that have retired or are

under construction according to EIA data; or

 Industrial or commercial units, including CHP units and non‐

CHP units that supply less than one‐third of their electricity to

the grid.

*Note also that the inclusion or exclusion of a particular unit in the 111(d) baseline analysis does not necessarily indicate th

applicability criteria in the Final Rule. State-level baseline

The state‐level baseline is created by summing the CO2 emissions and net generation from the generator‐level baseline fo

NGCC, and OGST categories. Units are also grouped by state and North American Electric Reliability Corporation (NERC) re

NERC region data for each plant are taken from the EIA‐860, which lists the Independent System Operator/Regional Trans

(ISO/RTO) region at the plant level. ISO/RTO regions are matched to NERC regions using the relationships in Error! Refere

found..28

The emissions rate is calculated by converting the CO2 emissions from tons to pounds by multiplying by 2,000 and then di

generation.Differences between 111(d) and eGRID Methodologies 

28 There are at least two facilities in Texas (Tenaska Frontier Generating Station and Tenaska Gateway Generating Station) that can su

the Eastern or ERCOT NERC regions. The region that these plants reported in the EIA‐860 is used as the NERC region in the 111(d) bas
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The methodology used to develop the baseline state-level emission rates for the 111(d) analysis is based largely on the met

develop the annual editions of the Emissions and Generation Resource Integrated Database (eGRID), with certain key diffe

however, the methodologies are broadly similar: they both involve matching Part 75 CO2 emissions data from the EPA Cle

Division (CAMD) with data on electricity generation from EIA. Nevertheless, there are specific criteria set forth in the Clenecessitate slight deviations from the eGRID methodology in the 111(d) baseline analysis. In particular, the Clean Power Plan defines specific criteria that dictate which generating units are to be included in the baseGRID methodology is altered slightly to accommodate these inclusion criteria. This Section explains those methodologicaEmissions assigned to boilers eGRID reports emissions at the boiler level and rolled up to the plant level, but the eGRID methodology does not attempt t

boilers to individual generators. Because the baseline is based on generators (e.g. units with a nameplate capacity greater th

level emissions must be assigned to the generators in the 111(d) baseline analysis. Where possible in the 111(d) baseline analysis, the emissions data from EPA are assigned to the generator directly associataccording to data from EIA-860. When the emissions are only available at the plant level, or if one boiler is associated with generator, or if it is unclear which generator is associated with which boiler, the emissions are proportionally distributed to nameplate capacity. Similarly, in the 111(d) baseline analysis, combined cycle units are treated as a single system, and the total emissions from units are distributed to the components (the steam parts and turbine parts) based on proportion of nameplate capacity. Inclusion criteria In order to decide which units are included in the estimation of state-level rates, it is necessary to determine if they meet th

based on unit size and type, operating status, fuel use, electricity sales, and capacity factor. For example, coal units with a n

than 25 MW or with a heat input capacity less than 250 mmBtu/hr. are excluded from the analysis, and therefore the emissi

not used to calculate the state-level rates. In addition, the 111(d) baseline analysis does not include units that use less than 1input basis in 2012, units that are retired or under construction, and certain commercial and industrial units that are not grid 

the data files from the 111(d) baseline analysis still list all of these units, but the “Category” field for these units is listed as In addition, in the methodology to develop eGRID, units with zero generation are not included in the final eGRID files. Sin

files capture the entire universe of units, the files for the 111(d) baseline will list more units than the eGRID files. 
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Outlier emission rates In certain cases, when the emissions data from EPA are matched with generation data from EIA, it appears that a unit has p

zero or negative generation. As a result, the emission rate calculated for this unit will result in a negative value. EPA imple

the 111(d) baseline analysis for affected units with negative or unreasonable (e.g., too low or too high) emission rates. For 

considered rates that were less than 500 lbs. CO2/MWh or greater than 10,000 lbs. CO2/MWh as unreasonable and used the 

available, with a net-gross conversion factors to estimate reasonable generation data. The process is described above in the 

section. If these corrections resulted in emissions rates that were still less than 500 lbs. CO2/MWh or greater than 10,000 lb

the generation data unchanged and retained the original calculated rate. EPA does not use this quality assurance correction 

rates in eGRID. Adjustments to emissions from biomass In eGRID, it is assumed that biomass is carbon neutral and therefore the emissions associated with biomass are adjusted to 

plant file reports both the adjusted and unadjusted emissions, the summary tables are based on adjusted emissions. This adjustment is not made in the 111(d) baseline analysis, although units that use less than 10% fossil fuel on a heat inpu

excluded from the analysis. Tribal lands The 111(d) analysis includes a total of 4 units from Navajo, Ute, and Fort Mojave tribal lands. These units are included in eto states, rather than the appropriate tribal lands. Key Differences between Proposed and Final 111(d) Baselines 

This section outlines the differences between the 111(d) baseline file, created for the Proposed Rule (June 2014, hereafter “

version of the file created for the Final Rule (hereafter “final file”). EPA received public comment on the proposed file and 

accordingly. Two changes to the methodology, based on comment, are used to create the final file are as follows: 

1. Outlier emission rates. 

In addition to this methodological change, EPA also made non-methodological changes to the proposed file when creating 

2. Changes to unit characteristics; 3. Changes to the unit categorization; and 
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4. Changes to emissions data. 

Each of these changes are described in more detail below. Methodological Changes Based on Comment 

1. Outlier emission rates 

In certain cases, when EPA emissions data collected under 40 CFR Part 75 are matched with generation data from EIA, a uemissions, but zero or negative generation. This may occur if a unit uses more power than it generates. As a result, the emithis unit would be negative. To correct this issue, EPA estimated the net electricity generation from these units based on the

net-gross conversion factors. Using this methodology, EPA updated the generation for 95 units with negative generation. O

the criteria for inclusion in the 111(d) baseline analysis. Additionally, EPA also implemented a correction for units with emconsidered unreasonable, either too low or too high. For this analysis EPA used 500 lbs. CO2/MWh as the cutoff for rates t

10,000 lbs. CO2/MWh as the cutoff for rates that are too high. 

For these units EPA applies a correction converting the gross generation to net generation using net-gross conversion facto

Data Corrections section above. If these corrections results in emissions rates that are still less than 500 lbs. CO2/MWH or 

CO2/MWh, EPA leaves the generation data unchanged and retains the original emissions rate. 

Using this methodology, EPA updated the generation for 104 units that have “out-of-range” emission rates. Of these, 10 un

for inclusion in the 111(d) baseline analysis. EPA was not able to apply gross to net conversion on 357 units with emission

bounds. Non-Methodological Changes Based on Comment 

2. Changes to unit characteristics 

In addition to the methodological changes described above, EPA also responded to public comments received on the 111(d

the Proposed Rule. These comments include updating generation data that had been misreported to EIA, changing prime mand changing CHP flags. These changes are detailed for each unit in the unit-level baseline files in the column titled “ChanProposed Rule.” EPA also added a column to the baseline files to indicate whether a unit had commenced operations in tha

column is populated using a combination of public comments and data from EIA on when the unit commenced operations. 
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3. Changes to unit categorization 

For the 2012 baseline final file, EPA made changes to the categorization for coal steam and natural gas combined cycle (N

under construction or commenced operations in 2012. In the proposed file, there are 9 units listed as COALST and 46 units 

commenced operations in 2012. In the final file, EPA changed the category of these units to “UC Coal – commenced in 20

commenced in 2012”, respectively. There are also 4 coal steam units and 55 NGCC units that were under construction in ei

according to EIA data that are in the EXCLUDE category in the proposed file, but are now listed as “UC Coal” or “UC NGthe final file. In addition, the proposed file contained additional categories, including simple-cycle turbines (SST), which are not include

Final Rule. EPA changed the category for these units to EXCLUDE. 

4. Changes to the EPA emissions data 

EPA used an updated version of emissions data collected under 40 CFR Part 75 in the analysis. The EPA pulled the emissio

the proposed file in February 2014, and the data used to create the final file in February 2015. This resulted in changes in e

between the proposed and final files. 
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Emission Factors The emission factors listed in the table below are used in the 111(d) baseline analysis to estimate CO2 emissions, if emissi

not included in the EPA data. CO2 emission factors for year 2012 are obtained from two sources: EPA’s Inventory of U.S. G

Emissions and Sinks: 1990‐2012, and the Greenhouse Gas Reporting Program (40 CFR Part 98).

Fuel

Code Fuel Type Prime Mover Emission Factor (Tons CO2/mmBtu)

AB Agricultural byproducts ST 0.13027BFG Blast furnace gas ST 0.05844BG Bagasse ST 0.13027BIT Bituminous coal ST 0.10282BLQ Black liquor ST 0.10448BU Butane ST 0.07182COG Coke oven gas ST 0.05844DFO Distillate fuel oil #2 ST 0.08152DFO Distillate fuel oil #2 GT 0.08152DFO Distillate fuel oil #2 OT 0.08152DFO Distillate fuel oil #2 CS 0.08152DFO Distillate fuel oil #2 CT 0.08152DFO Distillate fuel oil #2 CC 0.08152DFO Distillate fuel oil #2 IC 0.08152DG Digester gas ST 0.05739DG Digester gas GT 0.05739DG Digester gas OT 0.05739DG Digester gas CS 0.05739DG Digester gas CT 0.05739DG Digester gas CC 0.05739DG Digester gas IC 0.05739DG Digester gas FC 0.05739GEO Geothermal BT 0
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Fuel

Code Fuel Type Prime Mover Emission Factor (Tons CO2/mmBtu)

GEO Geothermal ST 0HY Hydrogen ST 0HY Hydrogen GT 0HY Hydrogen CT 0HY Hydrogen OT 0HY Hydrogen CS 0HY Hydrogen CC 0IGCC Integrated gasification combined cycle burning BIT IG 0.10282JF Jet fuel GT 0.07962JF Jet fuel IC 0.07962JF Jet fuel CC 0.07962KER Kerosene GT 0.08067KER Kerosene IC 0.08067LB Liquid byproduct ST 0.08209LFG Landfill gas ST 0.05739LFG Landfill gas GT 0.05739LFG Landfill gas OT 0.05739LFG Landfill gas CS 0.05739LFG Landfill gas CT 0.05739LFG Landfill gas CC 0.05739LFG Landfill gas FC 0.05739LIG Lignite coal ST 0.10771MH Methanol ST 0.06984MSB MSW biomass component ST 0.10339NG Natural gas ST 0.05844NG Natural gas GT 0.05844NG Natural gas OT 0.05844NG Natural gas CS 0.05844NG Natural gas CT 0.05844
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Fuel

Code Fuel Type Prime Mover Emission Factor (Tons CO2/mmBtu)

NG Natural gas CC 0.05844NG Natural gas IC 0.05844NG Natural gas FC 0.05844OBG Other biomass gas CC 0.05739OBG Other biomass gas GT 0.05739OBG Other biomass gas ST 2.01492OBG Other biomass gas FC 0.05739OBL Other biomass liquid ST 0.08989OBL Other biomass liquid GT 0.08989OBL Other biomass liquid CT 0.08989OBL Other biomass liquid OT 0.08989OBL Other biomass liquid CS 0.08989OBL Other biomass liquid CC 0.08989OBS Other biomass solid ST 0.11632OG Other gas ST 0.05844OG Other gas GT 0.05844OG Other gas CC 0.05844OO Other oil ST 0.08152OTL Other liquid ST 0.08209OTL Other liquid GT 0.08209OTL Other liquid OT 0.08209OTL Other liquid CS 0.08209OTL Other liquid CT 0.08209OTL Other liquid CC 0.08209OTS Other solid ST 0.11289PC Petroleum coke ST 0.11256PC Petroleum coke GT 0.11256PC Petroleum coke CT 0.11256PC Petroleum coke OT 0.11256PC Petroleum coke CS 0.11256
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Fuel

Code Fuel Type Prime Mover Emission Factor (Tons CO2/mmBtu)

PC Petroleum coke CC 0.11256PG Propane gas ST 0.06774PP Paper pellets ST 0.10339PRG Process gas ST 0.05844RFO Residual fuel oil #6 ST 0.08278RFO Residual fuel oil GT 0.08278RFO Residual fuel oil CC 0.08278RG Refinery gas ST 0.07356SC Synthetic coal ST 0.10529SLW Sludge waste ST 0.11632SUB Subbituminous coal ST 0.10711SUN Sun PV 0TDF Tire‐derived fuel ST 0.06376WAT Water HY 0WC Waste coal ST 0.10529WDL Wood liquid ST 0.08989WDS Wood solid ST 0.10339WND Wind WS 0WND Wind WT 0WO Waste oil ST 0.08209WO Waste oil CC 0.08209WO Waste oil GT 0.08209
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Data Codes The following data codes are codes used by the EIA in the EIA-860 and EIA-923 forms to indicate a unit’s prime mover, fu

Prime Mover Code Prime Mover Description BA Energy Storage, Battery BT 

Turbines Used in a Binary Cycle (including those used for geothermal applications) 

CA Combined Cycle Steam Part CC 

Combined Cycle Total Unit (use only for plants/generators that are in planning stage, for which specific generator details cannot be provided) 

CE Energy Storage, Compressed Air CP Energy Storage, Concentrated Solar Power CS 

Combined Cycle Single Shaft (combustion turbine and steam turbine share a single generator) 

CT Combined Cycle Combustion Turbine Part ES Energy Storage, Other FC Fuel Cell FW Energy Storage, Flywheel GT 

Combustion (Gas) Turbine (does not include the combustion turbine part of a combined cycle; see code CT, below) 

HA Hydrokinetic, Axial Flow Turbine HB Hydrokinetic, Wave Buoy HK Hydrokinetic, Other HY 

Hydroelectric Turbine (includes turbines associated with delivery of water by pipeline) 

IC Internal Combustion Engine (diesel, piston, reciprocating) OT Other PS Energy Storage, Reversible Hydraulic Turbine (Pumped Storage) PV Photovoltaic ST 

Steam Turbine, including nuclear, geothermal and solar steam (does not include combined cycle) 

WS Wind Turbine, Offshore 
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WT Wind Turbine, Onshore 

Fuel Type Code Energy Source Description AB Agricultural By-Products ANT Anthracite Coal BFG Blast Furnace Gas BIT Bituminous Coal BLQ Black Liquor DFO Distillate Fuel Oil (including diesel, No. 1, No. 2, and No. 4 fuel oils) GEO Geothermal JF Jet Fuel KER Kerosene LFG Landfill Gas LIG Lignite Coal MSW Municipal Solid Waste MWH Electricity used for energy storage NG Natural Gas NUC Nuclear (including Uranium, Plutonium, and Thorium) OBG Other Biomass Gas (including digester gas, methane, and other biomass gases) OBL Other Biomass Liquids OBS Other Biomass Solids OG Other Gas OTH Other PC Petroleum Coke PG Gaseous Propane PUR Purchased Steam RC Refined Coal RFO Residual Fuel Oil (incl. Nos. 5 & 6 fuel oils, and bunker C fuel oil) SGC Coal-Derived Synthesis Gas SGP Synthesis Gas from Petroleum Coke SLW Sludge Waste SUB Subbituminous Coal SUN Solar 
Still Under Agency Review – Do not Quote, Cite, or Share

Fuel Type Code Energy Source Description TDF Tire-derived Fuels WAT 

Water at a Conventional Hydroelectric Turbine, and water used in Wave Buoy 

Hydrokinetic Technology, Current Hydrokinetic Technology, and Tidal Hydrokinetic Technology 

WC 

Waste/Other Coal (incl. anthracite culm, bituminous gob, fine coal, lignite waste, 

waste coal) 

WDL 

Wood Waste Liquids excluding Black Liquor (including red liquor, sludge wood, spent sulfite liquor, and other wood-based liquids) 

WDS 

Wood/Wood Waste Solids (incl. paper pellets, railroad ties, utility poles, wood 

chips, bark, and wood waste solids) 

WH 

Waste heat not directly attributed to a fuel source (WH should only be reported when the fuel source is undetermined, and for combined cycle steam turbines that 

do not have supplemental firing.) 

WND Wind WO 

Waste/Other Oil (including crude oil, liquid butane, liquid propane, naphtha, oil 

waste, re-refined motor oil, sludge oil, tar oil, or other petroleum-based liquid wastes) 

Unit Status Code Status Code Description IP 

Planned new generator canceled, indefinitely postponed, or no longer in resource 

plan 

L 

Regulatory approvals pending. Not under construction but site preparation could 

be underway 

OA 

Out of service – was not used for some or all of the reporting period but is expected to be returned to service in the next calendar year. 

OP 

Operating - in service (commercial operation) and producing some electricity. Includes peaking units that are run on an as needed (intermittent or seasonal) 

basis. 

OS 

Out of service – was not used for some or all of the reporting period and is NOT expected to be returned to service in the next calendar year. 

OT Other 
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P 

Planned for installation but regulatory approvals not initiated; Not under construction 

RE Retired - no longer in service and not expected to be returned to service. SB 

Standby/Backup - available for service but not normally used (has little or no generation during the year) for this reporting period. 

T 

Regulatory approvals received. Not under construction but site preparation could 

be underway 

TS 

Construction complete, but not yet in commercial operation (including low power testing of nuclear units) 

U 

Under construction, less than or equal to 50 percent complete (based on construction time to date of operation) 

V 

Under construction, more than 50 percent complete (based on construction time to 

date of operation) 

Description of Baseline Data Fields The following table provides a description of the data fields in the 111(d) baseline file with an indication of the data source

field. 

Field Description SourceCategory Category based on the inclusion criteria of each generator ‐‐ State State in which the plant is located EIA‐860Plant Name Plant name EIA‐860ORIS Code EIA Office of Regulatory Information Systems Plant Location plant or facility code EIA‐860Generator ID Generator identification code EIA‐860Fuel type Primary fuel type of the generator EIA‐860Prime mover type

The engine, turbine, water wheel, or similar

machine that drives an electric generator; or, for

reporting purposes, a device that converts

EIA‐860
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Field Description Source

energy to electricity directly (e.g., photovoltaicsolar and fuel cells).

Nameplate Capacity

(MW)

The full capacity value of power output from the

generator

EIA‐860

Summer Nameplate

Capacity (MW)

The full capacity value of power output from the

generator during the summer EIA‐860

Electric Generation

(MWh) Net electricity generation of the unit EIA‐923, EPA Part 75 data

Carbon DioxideEmissions (tons)

The annual carbon dioxide emissions from each

generator in tons

EPA Part 75 data,EIA‐923

UNITKEEP (CA<25 part

of CC with CT>25)

If all of the turbine parts (prime mover =CT) of

an NGCC system have a nameplate capacity >

25MW, then all of the steam parts (prime mover

= CA) are included in the baseline, regardless of

whether they have a nameplate capacity

>25MW. In this case, the UNITKEEP field will be

equal to 1. It will be blank otherwise.

‐‐ 

Source Category

The type of industry in which the generator is

located. Options include electric utility,

independent power producer (IPP), industrial, or

commercial.

EIA‐860

Cogn Flag Y/N Indicates the cogeneration status of each generator – yes (Y) or no (N). EIA‐860Unit Status The operating status of the generator EIA‐860Unit Retirement Year The actual or planned retirement year of the

generator

EIA‐860

Exclusion Description Description of why the generator was excluded in the “Category” field ‐‐ CommencedOperations in Data Year

If the generator commenced operations within

the data year, the field is marked “Yes.” This

field is left blank for all other generators.

EIA‐860

Changes from File for

Proposed Rule

Description of changes made to the data that

differ from the baseline file created for the ‐‐ 
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Field Description Source

Proposed Rule, including changes made based

on public comments.

NERC Interconnection NERC region in which the plant is located EIA‐860