**EO12866_ CPP 2060 AR33 RIA Final_20150626** 



# Regulatory Impact Analysis

**for the Clean Power Plan**  **Final Rule** ***E.O. 12866 Review

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ii [ This page intentionally left blank ]***E.O. 12866 Review

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iii EPA - 452/R - 15 - 003 July 2015



## Regulatory Im **pact 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

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iv **CONTACT INFORMATION** This document has been prepared b y 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 Pol icy . Contributions to this report were also made by I CF International and RTI International .***E.O. 12866 Review

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v 

### T **ABLE OF** 

### C **ONTENTS L**  **IST OF T**  **ABLES** ................................ ................................ ................................ ............................... x **L IST OF**  **F IGURES**  ................................ ................................ ................................ ......................... xvi **A CRONYMS**  ................................ ................................ ................................ ................................ xviii **E XECUTIVE**  **S UMMARY**  ................................ ................................ ................................ .................... 1 ES.1 Background and Context ................................ ................................ ........................ 1 ES.2 Summary

of Clean Power Plan Final Rule ................................ ............................. 1 ES.3 Illustrative Plan Approaches Examined in RIA ................................ ...................... 4 ES.4 Emissions Reductions ................................ ................................ ............................. 5 ES.5 Costs ................................ ................................ ................................ ....................... 7 ES.6 Monetized Climate Benefits and Health Co - benefits ................................ ............. 9 ES.6.1 Estimating Global Climate Benefits ................................ ................................ . 13 ES 6.2 Estimating Air Quality Health Co - Benefits ................................ ...................... 15 ES 6.3 Combined Benefits Estimates ................................ ................................ ........... 18 ES.7 Net Benefits ................................ ................................ ................................ .......... 20 ES.8 Econo mic Impacts ................................ ................................ ................................ 24 ES.9 Employ

ment Impacts ................................ ................................ ............................ 25 ES.10 References ................................ ................................ ................................ ............. 26 **C HAPTER**  **1: I**  **NTRODUCTION AND B**  **ACKGROUND FOR THE C**  **LEAN P**  **OWER P**  **LAN** ........................ 1 1.1 Introduction ................................ ................................ ................................ .................... 1 1.2 Legal, Scientific and Economic Basis for this Rulemaking ................................ .......... 1 1.2.1 Statutory

Requirement ................................ ................................ ........................ 1 1.2.2 Health and Welfare Impacts from Climate Change ................................ ............ 2 1.2.3 Market Failure ................................ ................................ ................................ .... 3 1.3 Summary

of Regulatory

Analy sis ................................ ................................ .................. 4 1.4 Background for the Final Emission Guidelines ................................ ............................. 5 1.4.1 Base Case and Years of Analy sis ................................ ................................ ....... 5 1.4.2 Definition of Affected Sources ................................ ................................ ........... 5 1.4.3 Regulated Pollutant ................................ ................................ ............................. 6 1.4.4 Emission Guidelines ................................ ................................ ........................... 6 1.5 Organization of the Regulatory Impact Analy sis ................................ ........................... 7 1.6 References ................................ ................................ ................................ ...................... 7 **C HAPTER**  **2: I**  **NDUSTRY P**  **ROFILE** ................................ ................................ ................................ .... 1 2. 1 Introduction ................................ ................................ ................................ .................... 1 2.2 Power Sector Overview ................................ ................................ ................................ . 1 2.2.1 Generation ................................ ................................ ................................ .......... 1 2.2.2 Transmission ................................ ................................ ................................ ....... 8 2.2.3 Dis tribution ................................ ................................ ................................ ......... 9***E.O. 12866 Review

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vi 2.3 Sales, Expenses and Prices ................................ ................................ ............................ 9 2.3.1 Electricity

Prices ................................ ................................ ............................... 10 2.3.2 Prices of Fossil Fuels Used for Generating Electricity ................................ ..... 15 2.3.3 Changes in Electricity Intensity of the U.S. Economy

Between 2002 to 2012 16 2.4 Deregulation and Restructuring ................................ ................................ ................... 17 2.5 Emissions of Greenhouse Gases from Electric Utilities ................................ .............. 22 2.6 Carbon Dioxide Control Technologies ................................ ................................ ........ 25 2.6.1 Carbon Capture and Storage ................................ ................................ ............. 26 2.6.2 Geologic and Geographic Considerations for Geologic Sequestration ................ 31 2.7.2 Availability

of geologic sequestration in deep saline formations ..................... 35 2.7.3 Availability

of CO 2 storage via enhanced oil recovery (EOR) ........................ 35 2.8 GHG and Clean Energy Regulation in the Power Sector ................................ ............ 37 2.8.1 State Policies ................................ ................................ ................................ ..... 37 2.8.2 Federal Policies ................................ ................................ ................................ . 39 2.9 Revenues and Expenses ................................ ................................ ............................... 42 2.10 Natural Gas Market ................................ ................................ ............................... 43 2.11 References ................................ ................................ ................................ ............. 47 **C HAPTER**  **3: C**  **OST ,**  **E MISSIONS**  **, E**  **CONOMIC ,**  **AND E**  **NERGY I**  **MPACTS** ................................ ............ 1 3.1 Introduction ................................ ................................ ................................ .................... 1 3.2 Overview ................................ ................................ ................................ ........................ 1 3.3 Power Sector Modelling Framework ................................ ................................ ............. 1 3.3.1 Recent Updates to EPA’s Base Case using IPM (v.5.15) ................................ .. 4 3.4 State Goals in this Final Rule ................................ ................................ ........................ 5 3.5 Illustrative Plan Approaches Anal yz ed ................................ ................................ .......... 7 3.6 Demand - Side Energy Efficiency ................................ ................................ ................. 11 3.6.1 Demand - Side Energy Efficiency

Improvements (Electricity Demand Reductions) ................................ ................................ ................................ .................. 11 3.6.2 Demand - Side Energy Efficiency

Costs ................................ ............................ 13 3.7 Projected Power Sector Impacts ................................ ................................ .................. 14 3.7.1 Projected Emissions (in the Contiguous U.S.) ................................ ................. 14 3.7.2 Projected Compliance Costs (in the Contiguous U.S.) ................................ ..... 17 3.7.3 Projected Compliance Actions for Emissions Reductions ............................... 19 3.7.4 Projected Generation Mix ................................ ................................ ................. 21 3.7.5 Projected Incremental Retirements ................................ ................................ ... 25 3.7.6 Projected Capacity Additions ................................ ................................ ........... 26 3.7.7 Projected Coal Production and Natural Gas Use for the Electric Power Sector 28 3.7.8 Projected Fuel Price, Market, and Infrastructure Impacts ................................ 28 3.8 Projected Primary

PM Emissions from Power Plants ................................ .................. 30 3.9 Limitations of Anal ys is ................................ ................................ ................................ 31 3.10 Significant Energy Impacts ................................ ................................ .......................... 32 3.11 Monitoring, Reporting, and Recordkeeping Costs ................................ ...................... 33 3.12 Social Costs ................................ ................................ ................................ .................. 36 3.13 References ................................ ................................ ................................ .................... 39***E.O. 12866 Review

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vii **C HAPTER**  **4: E**  **STIMATED C**  **LIMATE B**  **ENEFITS AND H**  **UMAN H**  **EALTH C**  **O -**  **BENEFITS** .................... 1 4.1 Introduction ................................ ................................ ................................ .................... 1 4.2 Estimated Climate Benefits from CO 2 ................................ ................................ ........... 1 4.2.1 Climate Change Impacts ................................ ................................ ..................... 2 4.2.2 Social Cost of Carbon ................................ ................................ ......................... 3 4. 3 Estimated Human Health Co - Benefits ................................ ................................ ......... 10 4.3.1 Health Impact Assessment for PM 2.5 and Ozone ................................ .............. 12 4.3.2 Economic Valuation for Health Co - benefits ................................ .................... 18 4.3.3 Benefit - per - ton Estimates for PM 2.5 ................................ ................................ . 20 4.3.4 Benefit - per - ton Estimates for Ozone ................................ ................................ 21 4.3.5 Estimated Health Co - Benefits Results ................................ ............................. 22 4.3.6 Characterization of Uncertainty in the Estimated Health Co - benefits ............. 36 4.4 Combined Climate Benefits and Health Co - benefits Estimates ................................ .. 42 4.5 Unquantified C o - benefits ................................ ................................ ............................. 46 4.5.1 HAP Co - benefits ................................ ................................ ............................... 48 4.5.2 Additional NO 2 Health Co - Benefits ................................ ................................ . 51 4.5.3 Additional SO 2 Health Co - Benefits ................................ ................................ .. 52 4.5.4 Additional NO 2 and SO 2 Welfare Co - Benefits ................................ ................. 53 4.5.5 Ozone Welfare Co - Benefits ................................ ................................ .............. 54 4.5.6 Carbon Monoxide Co - Benefits ................................ ................................ ......... 54 4.5.7 Visibility

Impairment Co - Benefits ................................ ................................ ... 55 4.6 References ................................ ................................ ................................ .................... 55 **A PPENDIX**  **4A: G** **ENERATING R** **EGIONAL B**  **ENEFIT -**  **PER -**  **T ON**  **E STIMATES**  ................................ .... 1 4A.1 Overview of Benefit - per - Ton Estimates ................................ ................................ . 1 4A.2 Air Quality

Modeling for the Proposed Clean Power Plan ................................ .... 2 4A.3 Regional PM 2.5 Benefit - per - Ton Estimates for EGUs Derived from Air Quality Modeling of the Proposed Clean Power Plan ................................ .......................... 6 4A.4 Regional Ozone Benefit - per - Ton Estimates ................................ ......................... 15 4A.6 References ................................ ................................ ................................ ............. 18 **C HAPTER**  **5: E**  **CONOMIC I**  **MPACTS –**  **M ARKETS**  **O UTSIDE THE**  **U TILITY**  **P OWER**  **S ECTOR**  ............ 1 5.1 Introduction ................................ ................................ ................................ .................... 1 5.2 Methods ................................ ................................ ................................ .......................... 2 5.3 Summary

of Secondary

Market Impacts of Energy Price Changes ............................... 3 5.3.1 Share of Total Production Costs ................................ ................................ ......... 5 5.3.2 Ability

to Substitute between Inputs to the Produc tion Process ......................... 5 5.3.3 Availability

of Substitute Goods and Services ................................ ................... 5 5.4 Effect of Changes in Input Demand from Electricity Sector ................................ ......... 6 5.5 Conclusions ................................ ................................ ................................ .................... 6 5.6 References ................................ ................................ ................................ ...................... 7 **C HAPTER**  **6: E**  **MPLOYMENT I**  **MPACT A** **NALYSIS** ................................ ................................ ............. 1***E.O. 12866 Review

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viii 6.1 Introduction ................................ ................................ ................................ .................... 1 6.2 Economic Theory

and Employ

ment ................................ ................................ .............. 2 6.3 Current State of Knowledge Based on the Peer- Reviewed Literature ........................... 6 6.3.1 Regulated Sector ................................ ................................ ................................ . 7 6.3.2 Economy- Wide ................................ ................................ ................................ ......... 8 6.3.3 Labor Supply

Impacts ................................ ................................ ....................... 10 6.4 Recent Employ

ment Trends ................................ ................................ ......................... 11 6.4.1 Electric Power Generation ................................ ................................ ................ 11 6.4.2 Fossil Fuel Extraction ................................ ................................ ....................... 12 6.4.3 Clean Energy

Employ ment Trends ................................ ................................ ... 14 6.5 Projected Sectoral Employment Changes due to the Final Emission Guidelines ........ 18 6.5.1 Projected Changes in Employ ment in Electricity Generation and Fossil Fuel Extraction ................................ ................................ ................................ ............. 19 6.5.2 Projected Changes in Employ ment in Demand - Side Energy Efficiency Activities ................................ ................................ ................................ ...................... 25 6.6 Conclusion ................................ ................................ ................................ ................... 33 6.7 References ................................ ................................ ................................ .................... 35 **A PPENDIX**  **6A: E**  **STIMATING S**  **UPPLY S**  **IDE E**  **MPLOYMENT IMPACTS** ................................ ............. 1 6A.1 General Approach ................................ ................................ ................................ ... 1 6A.1.1 Employ

ment Effects Included In the Analy sis ................................ ................... 2 6A.2 Employ

ment Changes due to Heat Rate Improvements ................................ ......... 3 6A.2.1 Employ

ment Changes Due to Building (or Avoiding) New Generation Capacity ................................ ................................ ................................ ......................... 5 6A.2.2 Employ

ment Chan ges due to Coal and Oil/Gas Retirements ............................. 8 6A.2.3 Employ

ment Changes due to Coal and Oil/Gas Retirements ............................. 8 **C HAPTER**  **7: S**  **TATUTORY AND E**  **XECUTIVE O**  **RDER A** **NALYSIS** ................................ ...................... 1 7.1 Executive Order 12866: Regulatory Planning and Review, and Executive Order 13563: Improving Regulation and Regulatory Review ................................ ........................ 1 7.2 Paperwork Reduction Act (PRA) ................................ ................................ ................... 3 7.3 Regulatory

Flexibility

Act (RFA) ................................ ................................ .................. 5 7.4 Unfunded Mandates Reform Act (UMRA) ................................ ................................ ... 6 7.5 Executive Order 13132: Federalism ................................ ................................ .............. 6 7.6 Executive Order 13175: Consultation and Coordination with Indian Tribal Governments ................................ ................................ ................................ ....................... 11 7.7 Executive Order 13045: Protection of Children from Environmental Health Risks and Safety Risks ................................ ................................ ................................ .................. 13 7.8 Executive Order 13211: Actions Concerning Regulations That Significantly Affect Energy

Suppl y, Distribution, or Use ................................ ................................ ........ 14 7.9 National Technology Transfer and Advancement Act ................................ ................ 14 7.10 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and L ow - Income Populations ................................ .......................... 15 7.11 Congressional Review Act (CRA) ................................ ................................ ............... 18***E.O. 12866 Review

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ix **C HAPTER**  **8: C**  **OMPARISON OF B**  **ENEFITS AND C**  **OSTS** ................................ ................................ .... 1 8.1 Comparison of Benefits and Costs ................................ ................................ ................. 1 8.2 Uncertainty Analysis ................................ ................................ ................................ ...... 5 8.2.1 Uncertainty in Costs and Illustrative Plan Approaches ................................ ...... 5 8.2.2 Uncertainty Associated with Estimating the Social Cost of Carbon .................. 6 8.2.3 Uncertainty Associated with PM 2.5 and Ozone Health Co - Benefits Assessment ................................ ................................ ................................ ..................... 7 8.3 References ................................ ................................ ................................ ...................... 9***E.O. 12866 Review

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x 

### L **IST OF** 

### T **AB LES**  Table ES - 1. Climate and Air Pollutant Emission Reductions for the Rate - Based Illustrative Plan Approach 1,2

................................ ................................ ................................ 6 Table ES - 2. Climate and Air Pollutant Emission Reductions for the Mass - Based Illustrative Plan Appproach 1,2

................................ ................................ .............................. 7 Table ES - 3. Compliance Costs for the Illustrative Rate - Based and Mass - Based Plan Approaches ................................ ................................ ................................ .......................... 8 Table ES - 4. Quantified and Unquantified Benefits ................................ ................................ ... 11 Table ES - 5. Combined Estimates of Climate Benefits and Health Co - Benefits for Rate Based Approach (billions of 2011$)* ................................ ................................ ................ 19 Table ES - 6. Combined Estimates of Climate Benefits and Health Co - benefits for Mass Based Approach (billions of 2011$)* ................................ ................................ ................ 20 Table ES - 7. Monetized Benefi ts, Compliance Costs, and Net Benefits Under the Rate based Illustrative Plan Approach (billions of 2011$) a ................................ ...................... 22 Table ES - 8. Moneti zed Benefits, Compliance Costs, and Net Benefits under the Mass based Illustrative plan approach (billions of 2011$) a ................................ ....................... 23 Table 2 - 1. Existing Electricity Generating Capacity

by Energy Source, 2002 and 2012 ........ 3 Table 2 - 2. Net Generation in 2002 and 2012 (Trillion kWh = TWh) ................................ ...... 5 Table 2 - 3. Coal and Natural Gas Generating Units, by Size, Age, Capacity , and Thermal Effi ciency

(Heat Rate) ................................ ................................ ........................... 6 Table 2 - 4. Total U.S. Electric Power Industry Retail Sales in 2012 (billion kWh) ............... 10 Table 2 - 5. Domestic Emissions of Greenhouse Gases, by Economic Sector (million metric tonnes of CO 2 equivalent) ................................ ................................ ....................... 23 Table 2 - 6. Greenhouse Gas Emissions from the Electricity Sector (Generation, Transmission and Distribution), 2002 and 2012 (million metric tonnes of CO 2 equiv alent) ................................ ................................ ................................ .......................... 24 Table 2 - 7. Fossil Fuel Emission Factors in EPA Base Case 5.14 IPM Power Sector Modeling Application ................................ ................................ ................................ ........ 25 Table 2 - 8. Total CO 2 Storage Resource ................................ ................................ ................. 33***E.O. 12866 Review

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xi Table 2 - 9. Revenue and Expense Statistics for Major U.S. Investor- Owned Electric Utilities for 2010 (nominal $millions) ................................ ................................ ............... 43 Table 3 - 1. Rate - Based and Mass - Based State Goals ................................ ............................... 6 Table 3 - 3. Demand - Side Energy Efficiency

Plan Scenario: Net Cumulative Demand Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales) ................................ 12 Table 3 - 4. Annualized Cost of Demand - Side Energy Efficiency

Plan Scenario [Contiguous U.S.] (at discount rates of 3 percent and 7 percent, billions 2011$) ............. 14 Table 3 - 5. Projected CO 2 Emission Impacts, Relative to Base Case ................................ ..... 15 Table 3 - 6. Projected CO 2 Emission Impacts, Relative to 2005 ................................ ............. 15 Table 3 - 7. Projected Non - CO 2 Emission Impacts, 2020 - 2030 ................................ ...................... 16 Table 3 - 8. Annualized Compliance Costs (billions of 2011$) ................................ ............... 18 Table 3 - 9. Total Power Sector Generating Costs (IPM) (billions 2011$) ............................. 19 Table 3 - 10. Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined Cycle Capacity ................................ ................................ ................................ . 21 Table 3 - 11. Generation Mix (thousand GWh) ................................ ................................ ......... 2 3 Table 3 - 12. Total Generation Capacity by 2020 - 2030 (GW) ................................ .................. 26 Table 3 - 13. Proj ected Capacity Additions, Gas (GW) ................................ ............................. 27 Table 3 - 14. Projected Capacity Additions, Renewable (GW) ................................ ................. 27 Table 3 - 15. Coal Production for the Electric Power Sector, 2025 ................................ ........... 28 Table 3 - 16. Power Sector Gas Use ................................ ................................ .......................... 28 Table 3 - 17. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu) ...... 30 Table 3 - 19. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2011$/MMBtu) ................................ ................................ ................................ ................. 30 Table 3 - 25. Years 2020, 2025 and 2030: Summary of State Annual Respondent Burden and Cost of Reporting and Recordkeeping Requirements (2011$) ................................ ... 35 Table 3 - 26. Years 2020, 2025 and 2030: Summary of Industry Annual Respondent Burden and Cost of Reporting and Recordkeeping Requirements (2011$) ...................... 35 Table 3 - 27. Years 2020, 2025 and 2030: Summary of Territories Annual Respondent Burden and Cost of Reporting and Recordkeeping Requirements (2011$) ...................... 36***E.O. 12866 Review

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xii Table 3 - 28. Annualized Compliance Costs Including Monitoring, Reporting and Recordkeeping Costs Requirements (billions of 2011 $) ................................ ................... 36 Table 4 - 1. Climate Effects ................................ ................................ ................................ ....... 2 Table 4 - 2. Social Cost of CO 2 , 2015 - 2050 (in 2011$)* ................................ ........................... 8 Table 4 - 3. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission Guidelines in 2020 (billions of 2011$)* ................................ .............................. 8 Table 4 - 4. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission Guidelines in 2025 (billions of 2011$)* ................................ .............................. 8 Table 4 - 5. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission Guidelines in 2030 (billions of 2011$)* ................................ .............................. 9 Table 4 - 6. Human Health Effects of Ambient PM 2.5 and Ozone ................................ ........... 14 Table 4 - 7. Summary of Regional PM 2.5 Benefit - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)* ............................ 23 Table 4 - 8. Summary of Regional PM 2.5 Benefit - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)* ............................ 23 Table 4 - 9. Summary of Regional PM 2.5 Benefit - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030 (2011$)* ............................ 24 Table 4 - 10. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Rate - based Scenario in 2020 (thousands of short tons)* ................................ ................... 24 Table 4 - 11. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Rate - based Scenario in 2025 (thousands of short tons)* ................................ ................... 24 Table 4 - 12. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Rate - based Scenario in 2030 (thousands of short tons)* ................................ ................... 25 Table 4 - 13. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Mass - based Scenario in 2020 (thousands of short tons)* ................................ .................. 25 Table 4 - 14. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Mass - based Scenario in 2025 (thousands of short tons)* ................................ .................. 25 Table 4 - 15. Emis sion Reductions of Criteria Pollutants for the Final Emission Guidelines Mass - based Scenario in 2030 (thousands of short tons)* ................................ .................. 25 Table 4 - 16. Summary of Estimated Monetized Health Co - Benefits for the Final Emission Guidelines Rate - based Scenario in 2020 (billions of 2011$) * ......................... 26***E.O. 12866 Review

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xiii Table 4 - 17. Summary of Estimated Monetized Health Co - Benefits for the Final Emission Guidelines Rate - based Scenario in 2025 (billions of 2011$) * ......................... 26 Table 4 - 18. Summary of Estimated Monetized Health Co - Benefits for the Final Emission Guidelines Rate - based Scenario in 2030 (billions of 2011$) * ......................... 27 Table 4 - 19. Summary of Estimated Monetized Health Co - Benefits for the Final Emission Guidelines Mass - based Scenario in 2020 (billions of 2011$) * ........................ 27 Table 4 - 20. Summary of Estimated Monetized Health Co - Benefits for the Final Emission Guidelines Mass - based Scenario in 2025 (billions of 2011$) * ........................ 28 Table 4 - 21. Summary of Estimated Monetized Health Co - Benefits for the Final Emission Guidelines Mass - based Scenario in 2030 (billions of 2 011$) * ........................ 28 Table 4 - 22. Summary of Avoided Health Incidences from PM 2.5 - Related and Ozone Related Co - benefits for the Final Emissi on Guidelines Rate - based Scenario in 2020* .... 29 Table 4 - 23. Summary of Avoided Health Incidences from PM 2.5 - Related and Ozone Related Co - benefits for Final Emission Guidelines Rate - based Scenario in 2025* .......... 30 Table 4 - 24. Summary of Avoided Health In cidences from PM 2.5 - Related and Ozone Related Co - Benefits for Final Emission Guidelines Rate - based Scenario in 2030* ......... 31 Table 4 - 25. Summary of Avoided Health Incidences from PM 2.5 - Related and Ozone Related Co - benefits for the Final Emission Guidelines Mass - based Scenario in 2020* 32 Table 4 - 26. Summary of Avoided Health Incidences from PM 2.5 - Related and Ozone Related Co - benefits for Final Emission Guidelines Mass - based Scenario in 2025* ......... 33 Table 4 - 27. Summary of Avoided Health Incidences from PM 2.5 - Related and Ozone Related Co - Benefits for Final Emission Guidelines Mass - based Scenario in 2030* ........ 34 Table 4 - 28. Population Exposure in the Clean Power Plan Proposal Modeling (used to generate the benefit - per - ton estimates) Above and Below Various Concentrations Benchmarks in the Underlying Epidemiology Studies * ................................ ................... 40 Table 4 - 29. Combined Climate Benefits and Health Co - Benefits for Final Emission Guidelines in 2020 (billions of 2011$)* ................................ ................................ ............ 44 Table 4 - 30. Combined Climate Benefits and Health Co - Benefits for Final Emissio n Guidelines in 2025 (billions of 2011$)* ................................ ................................ ............ 44 Table 4 - 31. Combined Climate Benefits and Health Co - Benefits for Final Emission Guid elines in 2030 (billions of 2011$)* ................................ ................................ ............ 45 Table 4 - 32. Unquantified Health and Welfare Co - benefits Categories ................................ ... 46***E.O. 12866 Review

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xiv Table 4A - 3. Summary of Regional PM 2.5 Benefit - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)* ............................ 10 Table 4A - 4. Summary of Regional PM 2.5 Benefit - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)* ............................ 10 Table 4A - 5. Summary of Regional PM 2.5 Benefit - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 20 30 (2011$)* ............................ 11 Table 4A - 6. Summary of Regional PM 2.5 In cidence - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020* ................................ .......... 12 Table 4A - 7. Summary of Regional PM 2.5 In cidence - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025* ................................ .......... 13 Table 4A - 8. Summary of Regional PM 2.5 In cidence - per - Ton Estimates Based on Air Quality Mod eling from Proposed Clean Power Plan in 2030* ................................ .......... 14 Table 4A - 9. Summary of Regional Ozone Benefit - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)* ............................ 16 Table 4A - 10. Summary of Regional Ozone Benefit - per - Ton Estimat es Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)* ............................ 16 Table 4A - 11. Summary of Regional Ozone Benefit - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030 (2011$)* ............................ 16 Table 4A - 12. Summary of Region al Ozone In cidence - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020* ................................ .......... 17 Table 4A - 13. Summary of Regional Ozone In cidence - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025* ................................ .......... 17 Table 4A - 14. Summary of Regional Ozone In cidence - per - Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030* ................................ .......... 17 Table 5 - 1. Estimated Percentage Changes in Average Energy Prices by Energy Ty pe for the Final Emission Guidelines, Rate - based and Mass - bas ed Illustrative Plan Approaches ................................ ................................ ................................ .......................... 4 Table 6 - 1. U. S. Green Goods and Services (GGS) Emplo ym ent (annual average) .............. 16 Table 6 - 2. Renewable Electricity Generation - Related Employ ment ................................ ..... 17 Table 6 - 3. Energy

and Resources Efficiency- Related Employment ................................ ...... 18 Table 6 - 4. Engineering - Based a Changes in Labor Utilization, Rate - based Scenario (Number of Job - Years b of Employ ment in a Single Year) ................................ ................ 24***E.O. 12866 Review

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xv Table 6 - 5. Engineering - Based a Changes in Labor Utilization, Mass - Based Illustrative Plan Approach (Number of Job - Years of Emplo yment in Year) ................................ ...... 25 Table 6 - 6. Estimated Demand - Side Energy Efficiency

Employ ment Impacts: Target 1 percent Growth in Energy Efficiency ................................ ................................ ................ 30 Table 6A - 1. Labor Productivity Growth Rate due to Heat Rate Improvement .......................... 4 Table 6A - 2. Capital Charge Rate and Duration Assumptions ................................ .................... 6 Table 6A - 3. Expenditure Breakdown due to New Generating Capacity ................................ .... 6 Table 6A - 4. Labor Productivity due to New Generating Capacity ................................ ............. 7 Table 6A - 5. Average FOM Cost A ssumptions ................................ ................................ ........... 8 Table 6A - 6. Labor Productivity due to New Generating Capacity ................................ ............. 9***E.O. 12866 Review

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xvi 

### L **IST OF** 

### F **IGURES** Figure 2 - 1. New Build and Retired Capacity (MW) b y Fuel Type, 2002 - 2012 ....................... 4 Figure 2 - 2. Cumulative Distribution in 2010 of Coal and Natural Gas Electricity Capacity and Generation, by

Age ................................ ................................ ........................ 7 Figure 2 - 3. Fossil Fuel - Fired Electricity Generating Facilities, by Size ................................ ... 8 Figure 2 - 4. Average Retail Electricity Price b y State (cents/kWh), 2011 ............................... 11 Figure 2 - 5. Nominal National Average Electricity Prices for Three Major End - Use Categories ................................ ................................ ................................ .......................... 12 Figure 2 - 6. Relative Increases in Nominal National Avera ge Electricity Prices for Major End - Use Categories, With Inflation Indices ................................ ................................ ...... 13 Figure 2 - 7. Real National Average Electricity Prices (2011$) for Three Major End - Use Categories ................................ ................................ ................................ .......................... 14 Figure 2 - 8. Relative Change in Real National Average Electricity Prices (2011$) fo r Three Major End - Use Categories ................................ ................................ ...................... 15 Figure 2 - 9. Relative Real Prices of Fossil Fuels for Electricity Generation; Change in National Average Real Price per MBtu Delivered to EGU ................................ ............... 15 Figure 2 - 10. Relative Growth of Electricity Generation, Population and Real GDP Since 2002 16 Figure 2 - 11. Relative Change of Real GDP, Population and Electricity Generation Intensity Since 2002 ................................ ................................ ................................ ........... 17 Figure 2 - 12. Status of State Electricity Industry Restructuring Activities ................................ 19 Figures 2 - 13 and 2 - 14. Capacity and Generation Mix by

Ownership Ty

pe, 2002 & 2012 ........ 21 Figures 2 - 15 and 2 - 16. Generation Capacity Built and Retired between 2002 and 2012 by Ownership Ty

pe 22 Figure 2 - 17. Domestic Emissions of Greenhouse Gases from Major Sectors, 2002 and 2013 (million metric tonnes of CO 2 equivalent) ................................ ................................ 23 Figure 2 - 18. Post - Combustion CO 2 Capture for a Pulverized Coal Power Plant ..................... 27 Figure 2 - 18. Pre - Combustion CO 2 Capture for an IGCC Power Plant ................................ ..... 29 Figure 2 - 19. Geologic Sequestration in the Continental United States ................................ ..... 32***E.O. 12866 Review

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xvii Figure 2 - 20. Relative Change Nominal and Real (2011$) Prices of Natural Gas Delivered to the Power Sector ($/MMBtu) ................................ ................................ ........................ 44 Figure 2 - 21. Relative Change in Real (2011$) Prices of Fossil Fuels Delivered to the Power Sector ($/MMBtu) ................................ ................................ ................................ .. 45 Figure 3 - 3. Illustrative Regions for EE/RE Procurement Used in this Analy sis ....................... 9 Figure 3 - 4. Generation Mix with the Base Case and 111(d) Options, 2020 - 2030 (thousand GWh) ................................ ................................ ................................ ................. 24 Figure 4 - 1. Monetized Health Co - benefits of Rate - based and Mass - based Scenarios for the Final Emission Guidelines in 2025 * ................................ ................................ ........... 35 Figure 4 - 2. Breakdown of Monetized Health Co - benefits by Precursor Pollutant at a 3% Discount Rate for Rate - based Scenario for Final Emission Guidelines in 2025 * ............. 36 Figure 4 - 3. Percentage of Adult Population (age 30+) b y Annual Mean PM2.5 Exposure in the Clean Power Plan Proposal Modeling (used to generate the benefit - per - ton estimates) ................................ ................................ ................................ ........................... 41 Figure 4 - 4. Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM 2.5 Exposure in the Clean Power Plan Proposal Modeling (used to generate the benefit - per - ton estimates) ................................ ................................ ................................ .. 42 Figure 4 - 5. Breakdown of Combined Monetized Climate and Health Co - benefits of Final Emission Guidelines in 2025 for Rate - based and Mass - based Scenarios and Pollutant (3% discount rate)* ................................ ................................ ............................. 45 Figure 4A - 1. Regional Breakdown ................................ ................................ .............................. 7 Figure 6.1. Electric Power Industry Employ ment ................................ ................................ ... 12 Figure 6.2. Coal Production Employ ment ................................ ................................ ............... 13 Figure 6.3. Oil and Gas Production Employ ment ................................ ................................ ... 14 Figure 6.4. Demand - Side Energy Efficiency

Employ ment: Jobs per One Million Dollars (2011$) ................................ ................................ ................................ ............................... 32***E.O. 12866 Review

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xviii 

### A **CRONYMS** 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 DI CE Dynamic Int egrated 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 Inf ormation 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 G as***E.O. 12866 Review

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xix GS Geologic Sequestration Gt Gigaton H 2 S Hydrogen Sulfide HAP Hazardous air pollutant HCl Hydrogen chloride HFC Hydrofluorocarbons HIA Health impact assessment IARC International Agency for Research on Cancer IAM Integrated Assess ment 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 MECS A 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 Qual ity 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 Syst em 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***E.O. 12866 Review

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xx OF A Overfire Air OMB Office of Management and Budget PAGE Policy

Analy sis of the Greenhouse Gas Effect Model PFC Perfluorocarbons PM 2.5 Fine Particulate Matter ppm Parts per Million PRA Paperwork Reduction Act PSD Prevention of Significant Deterioration RCSP Regional Carbon Sequestration Partne rships RADS Relative Airway s Dysfunction Sy ndrome RES Renewable Electricity Standards RFA Regulatory F lexibility

Act RGGI Regional Greenhouse Gas Initiative RIA Regulatory Impact Analy sis RPS Renewable Portfolio Standards SAB - CASAC Science Advisory Board Clean Air Scientific Advisory Committee SAB - HES Science Advisory Board Health Effects Subcommittee of the Advisory Council on Clean Air Compliance SAB - EEAC Science Advisory Board Environmental Economics Advisory Committee SBA Small Business Administration SBREFA Small Business Regulatory Enforcement Fairness Act SCC Social Cost of Carbon SCPC Super Critical Pulverized Coal SCR Selective Catalytic Reduction SF 6 Sulfur Hexafluoride SIP State Implementation Plan SO 2 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***E.O. 12866 Review

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ES - 1 

### E **XECUTIVE** 

### S **UMMARY** This Regulatory

Impact Analy sis (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 guid elines ” or the “Clean Power Plan Final Rule”). **ES.1 Background and Context**  The emission of greenhouse gas es (GHGs) threatens Americans' health and welfare by leading to long - lasting changes in our climate. Carbon dioxide (CO 2 ) is the primary greenhouse g as pollutant, accounting for roughl y 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, b y far, the largest emitters of GHGs, primaril y in the form of CO 2 , among stationary sources in the U.S. In this action, the Environmental Protection Agency (EPA) is establishing final emission guidelines for states, U.S. territories, and tribes to follow in developing plans to reduce greenhouse gas emi ssions from existing fossil fuel - fired EGUs. Specifically, the EPA is establishing: 1) CO 2 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 CO 2 emission performance rates. This final rule will continue progress already underway in the U.S. to reduce CO 2 emissions from the uti lity power s ector . **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 sy stem 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***E.O. 12866 Review

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ES - 2 Administrator determines has been adequatel y demonstrated. 1 To de termine the BSER for reducing CO 2 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 CO 2 emissions from fossil fuel - fired E GUs. Overall, the BSER is based on a range of measures that f

all into three main categories (or “building blocks”) that have been amply demonstrated via their current widespr ead 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. S ubstituting increased generation from new low - and certain zero - emitting generating units, including renewable energy , for reduced generation from affected fossil fuel - fired generating units. Specificall y, the EPA is esta blishing CO 2 emission performance rates for two source categories of existing fossil fuel - fired EGUs, fossil fuel - fired electric utility steam generating units and stationary

combustion turbines. Also, s tates , U.S. territories, and tribes with one or more affected EGUs will be required to develop and implement plans to ensure that their affected EGUs, individually , in aggregate, or in combination with other mea sures undertaken b y the state, achieve the equivalent of the final CO 2 emission performance rates by 2030 . The EPA derived statewide rate - based CO 2 emissions performance goals as a weighted average of the uniform rate goals with weights based on baseline generation for the two types of units (fossil steam and NGCC) in the state. This blended rate reflects the aggregate emission rate a state may

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

1 Under CAA section 111(a)(1) and (d), the EPA is authorized to determine the BSER and to calculate the amount of emission reduction achievable through applying the BSER. The state is authorized to identify the standard or standards of performance that refle cts 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.***E.O. 12866 Review

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ES - 3 performance rates. The Clean Power Plan Final Rule also establishes an 8 - year interim compliance period that begins in 2022 with a glide path for meeting interim CO 2 emission performance rates separated into three steps: 2022 - 2024, 2025 - 2027, and 2028 - 2029. This results in interim and final statewide goal values unique to each state’s historical blend of fossil steam and NGCC generation. Chapter 3 presents finalized st ate goals. The EPA is providing equivalent mass - based statewide CO 2 emission performance goals for each state, which are presented in Chapter 3 . The mass - based statewide CO 2 emission performance goals are based on the tonnage totals from the numerator of the statewide rate - based CO 2 emission performance goals. These mass levels are then adjusted to account for the ability of affected EGUs to increase utilization under rate - based compliance b y engaging in emission reduction strategies that are in excess of what is necessary to demonstrate compliance if affected EGUs choose to replace their own generation. 2 The final guidelines are structured so that states and affected EGUs are not required to use each and every

one of the measures that the EPA determines c onstitute 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 combi nation of measures it prefers in order to achieve the CO 2 emission performance rates for its affected EGUs or meet the equivalent statewide rate - or mass - based CO 2 goal. Thus, a state could choose to achieve more reductions from one measure encompassed by the BSER and less from another, or it could choose to include measures such as demand - side energy efficiency programs that are not part of the EPA’s BSER determination . Expressing the states’ obligation in terms of a state - level goal that can be met in aggregate, including through the adoption of CO 2 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 precisel y because the approach mirrors the foundation of the BSER approach itself, namel y the interconnected nature of the electricity sy stem in which affected EGUs operate. Given the flexibilities afforded states in complying with

2 For more detail on the translation methodology, please refer to the technical support document, CO 2 Emission Performance Rate and Goal Computation TSD for Final Rule, which is available in the docket.***E.O. 12866 Review

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ES - 4 the emissio n guidelines, the benefits, cost and economic impacts reported in this RIA are not definitive estimates, but are instead illustrative of a pproaches that states may take. **ES.3 Illustrative Plan Approaches**  **Examined in RIA** This RIA depicts two illustrative pl an approaches d esigned to achieve these goals, which we term the “rate - based approach ” and “mass - based approach ”. In the rate - based illustrative plan approach, states within each interconnect region are assumed to collaborate in order meet the CO 2 emission performance g oals . In the mass - based illustrative plan approach , affected sources within each state are a ssumed to collaborate to meet the mass - based CO 2 emission performance goals. The equilibrium distribution of the mass levels across the existing fossil fuel - fired EGUs is modeled to yield a least - cost outcome, such that the marginal compliance cost of reducing a ton of emissions is equal across all of the existing fossil fuel - fired EGUs within each region. This equilibrium outcome holds for all years of the interim period and for each period after 2030. The anal ys is for both illustrative plan approaches includes the assumptions regarding policy design noted in Chapter 3, but does not incorporate any additional flexibilities that states are able to use in developing policy mechanism s to achieve the state goals. Alternative compliance approaches other than those modelled are also possible, which may have different levels and distributions of emissions and electricity generation as well as costs . While IPM finds a least cost way

to achieve the state goals implemented through the rate - based or mass - based emissions constraints imposed in the illustrative plan approaches , individual states or multi - state regional groups may

develop alternate approaches to achieve their state goals. Meanwhile, because IP M models the power sector within the 48 contiguous states, including areas of Indian Country , and the District of Columbia, the EPA performed supplementary analy sis to account for the potential benefits and costs of the Clean Power Plan Final Rule for Alaska, Hawaii , and U.S. t erritories with affected EGUs. To present a complete picture of costs and benefits of the final emission guidelines, this RIA presents results for the anal ys is years 2020, 2025, and 2030. While 2020 is before the first ye ar of the interim compliance period (2022), the EPA expects states and affected EGUs to perform voluntary

activities that that facilitate compliance with interim and final goals. These pre - compliance period activities might include investments in demand - side energy efficiency***E.O. 12866 Review

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ES - 5 projects, for example, that produce emissions reductions in the compliance period. Activities might also include preparatory investments in monitoring, reporting, and recordkeeping s ys tems. As a result, there are likely to be benefits and costs in 2020, so these are reported in the illustrative analy sis 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 compli ance time - frame as states, U.S. territories, and tribes are on glide paths toward fully meeting the final CO 2 emission performance rates. Lastl y, the RIA presents costs and benefits for 2030, when the emission performance rates are fully achieved. **ES. 4**  **Emi ssions Reductions**  T able ES - 1 shows the emission reductions associated with the modelled rate - based illustrative plan approach . In 2020, the EPA estimates that CO 2 emissions will be reduced b y 109 million short tons under the rate - based scenario compared to base case levels. In 2025, the EPA estimates that CO 2 emissions will be reduced b y 430 million short tons under the rate - based approach compared to base case levels. CO 2 emission reductions increase to 559 million short tons annually

in 2030 when compared to the base case emissions. Table ES - 1 also shows emission reductions for criteria air pollutants. 3

3 Emissions estimates of PM 2.5 require additiona l processing of IPM results. These results will be provided in the RIA released at the signature of the final rule. However, the SO 2 and NO X reductions account fo r 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.***E.O. 12866 Review

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ES - 6 **Table ES -**  **1 .**  **Climate and Air Pollutant Emission Reductions for the Rate**  **- Based** 

**Illustrative Plan Approach 1**  **,2**

CO 2 (million short tons) SO 2

(thousands of tons) Annual NO X

(thousands of tons) PM 2.5

(thousands of tons) **2020 Rate**  **- Based Approach**  Base Case 2,220 1,350 1,420 [X] Final Guidelines 2,110 1,300 1,330 [X] Emissions Change 109 49 85 [X] **2025 Rate**  **- Based**  **Approach** Base Case 2,300 1,410 1,430 [X] Final Guidelines 1,870 1,080 1,090 [X] Emissions Change 430 331 347 [X] **2030 Rate**  **- Based Approach**  Base Case 2,370 1,430 1,420 [X] Final Guidelines 1,810 1,030 1,020 [X] Emission Change 559 395 396 [X] Source: Integrated Planning Model, 2015. 1 CO 2 emission reductions are used to estimate the climate benefits of the guidelines. SO 2 , NOx, and directly emitted PM 2.5 emission reductions are relevant for estimating air quality health co - benefits of the final guide lines. 2 Emissions rounded to three significant digits. Emissions may not sum due to rounding. T able ES - 2 shows the emission reductions associated with the modeled mass - based illustrative plan approach . In 2020, the EPA estimates that CO 2 emissions will be reduced b y [X] million short tons under the mass - based approach compared to base case levels. In 2025, the EPA estimates that CO 2 emissions will be reduced by [X] million short tons under the mass based approach compared to base case lev els. CO 2 emission reductions increase to [X] million short tons annually

in 2030 when compared to the base case emissions. Table ES - 2 also shows emission reductions for criteria air pollutants.***E.O. 12866 Review

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ES - 7 **Table ES -**  **2 .**  **Climate and Air Pollutant Emission Reductio ns f**  **or the Mass -**  **Based Illustrative Plan Appproach**  **1 ,2** 

CO 2 (million short tons) SO 2

(thousands of tons) Annual NO X

(thousands of tons) PM 2.5

(thousands of tons) **2020 Mass -**  **Based Approach** Base Case [X] [X] [X] [X] Final Guidelines [X] [X] [X] [X] Emissions Change [X] [X] [X] [X] **2 025 Mass**  **- Based Approach**  Base Case [X] [X] [X] [X] Final Guidelines [X] [X] [X] [X] Emissions Change [X] [X] [X] [X] **2 030 Mass**  **- Based Approach**  Base Case [X] [X] [X] [X] Final Guidelines [X] [X] [X] [X] Emission Change [X] [X] [X] [X] Source: Integrated Planning Model, 2015. 1 CO 2 emission reductions are used to estimate the climate benefits of the guidelines. SO 2 , NOx, and directly emitted PM 2.5 emission reductions are relevant for estimating air quality health co - benefits of the final guidelines. 2 Emissions rounded to three significant digits. Emissions may not sum due to rounding. Comparing the rate - based and mass - based illustrative plan approaches indicates... **ES.5 Costs**  The compliance cost estimated for this final action are represented in this anal ys is as the change in electric power generation costs between the base case and illustrative plan approach policy cases , inclusive of the cost of demand - side EE measures . The modelled implementation plan a pproaches reflect states pursuing strategies which are not limited to the technologies and measures included in the BSER to meet the final emission guidelines. Additionally , the compliance cost estimates include the costs of implementing demand - side energy efficiency measures. In the rate - based approach, energy efficiency

activities are modeled as being used b y EGUs as a low - cost method of demonstrating compliance with their rate - based emissions standards. In the mass - based approach, energy efficiency

activ ities are assumed to be adopted b y 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 app roaches. The compliance assumptions, and therefore the projected “compliance costs” set forth in this analy sis, are illustrative in nature and do not represent the full suite of compliance flexibilities states may ultimately

pursue.***E.O. 12866 Review

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ES - 8 The annual incremental cost is the projected additional cost of compl yi ng with the final rule in the year anal yz ed and includes the net change in the annualized cost of capital investment in new generating sources and heat rate improvements at coal - fired steam generating units , the change in the ongoing costs of operating pollution controls, shifts between or amongst various fuels, demand - side energy efficiency

measures, and other actions associated with compliance. The total compliance cost estimates presented here include illus trative compliance cost estimates for Alaska, Hawaii, and U.S. territories, as well as estimates of the c osts associated with monitoring, reporting, and recordkeeping (MR&R). 4 The costs for both illustrative plan approaches are reflected in Table ES - 3 belo w and discussed more extensively in Chapter 3 of this RIA. All dollar estimates are in 2011 dollars. The EPA estimates the annual inc remental compliance cost for the rate - based approach for final emission guidelines to be $ 2.4 billion in 2020, $ 1.1 billion in 2025 and $ 8.4 billion in 20 30 , including the costs associated with monitoring, reporting, and recordkeeping. 5 The EPA estimates the annual inc remental compliance cost for the mass - based approach for final emission guidelines to be $ [X] billion in 2020, $[X] billion in 2025 and $ [X] billion in 2030, including the costs associated with monitoring, reporting, and recordkeeping. **Table ES -**  **3 .**  **Compliance Costs for the Illustrative Rate**  **- Based and Mass**  **- Based Plan** 

**Approaches Incremental Cost from** **Base C ase (billions of 2011$)**  **Rate -**  **based Approach**  **Mass -**  **based Approach**  2020 $ 2.4 $[X] 2025 $ 1 . 1 $[X] 2030 $ 8 . 4 $[X] Source: Integrated Planning Model, 2015, with post -processing to account for exogenous demand - side management energy efficiency costs. See Chapter 3 of this RIA for a mo re com plete explanation. Compliance costs shown include monitoring, reporting, and reco rdkeeping costs. Costs currently do not include costs for Alaska, Hawaii, and U.S. Territories.

4 These costs are estimated outside of the IPM modelling framework as I PM only models the contiguous U.S. and does not incorporate monitoring, reporting, and recordkeeping requirements specific to the Clean Power Plan Final Rules. 5 The MR&R costs estimates are $68 million in 2020, $17 mi llion in 2 025 and $17 million in 2030 and are assumed to be the same for both rate - based and mass - based illustrative plan approaches . Note the MR&R costs in 2020 are related to facilities setting up net energy output monitoring and upgrading data acquisition system s .***E.O. 12866 Review

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ES - 9 The costs reported in Table ES - 3 represent the estimated incremental electric utility generating costs changes from the base case plus the estimates of demand - side energy efficiency program costs (which are paid by electric utilities), demand - side energy efficiency

participant costs (which are paid b y electric utility consumers) , cost estimates for Alaska, Hawaii, and U.S. Territories, and MR&R costs . For example , in 2030 , under the rate - based approach, the incremental electric utility generating costs decline by $17.9 billion from the base case. The costs associated with illustrative plan approaches for Alaska, Hawaii, and U.S. territories in 2030 are estimated at $[X]. MR&R requirements in 2030 are estimated at $17 million. Demand - side energy effic iency costs in 2030 are estimated to be $ 26.3 billion , split equally between program and participants us ing a 3 percent discount rate ( see Chapter 3 of this RIA for more details on these estimates). The s e 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 ye ar of anal ys is for the rate - based and ma ss - based illustrative plan approaches. Comparing the rate - based and mass - based illustrative plan approaches indicates... The compliance costs reported in Table ES - 3 are not social costs. These costs represent the estimated expenditures incurred b y EGUs and s tates 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, respectivel y, of this RIA. **ES.6 Monetized Climate Benefits and Health Co**  **- benefits**  Implementing the final emission guidelines is expected to reduce emissions of CO 2 and have ancillary

emission reductions (i.e., co - benefits) of SO 2 , NO 2 , and directly emitted PM 2.5 , which would lead to lower ambient concentrations of PM 2.5 and ozone. The climate benefits estimates have been calculated using the estimated values of m arginal climate impacts presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866 (May 2013, Revised June 2015) ,***E.O. 12866 Review

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ES - 10 henceforth denoted as the current SC - CO 2 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 co benefits associated with the final emission guidelines, including reducing exposure to SO 2 , NO X , and hazardous air pollutants (e.g., mercury and h ydrogen chloride), as well as ecos ys tem effects and visibility improvement . The omission of these endpoints from the monetized result s should not imply that the impacts are small or unimportant. Table ES - 4 provides the list of the quantified and unquantified health and environmental benefits in this analysis.

6 Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Enviro nmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Domestic Policy Council, Environmental Protection Agency, National Economic Council, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised June 2015).***E.O. 12866 Review

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ES - 11 **Table ES -**  **4 .**  **Quantified and Unquantified Benefits** Benefits Category Speci fic Effect Effect Has Been Quantified Effect Has

Been Monetized More Information Improved Environment Reduced climate effects Global climate impacts from CO 2 — 1  SC - CO 2 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) — — IPCC 2 Improved Human Health (co - benefits) Reduced incidence of premature mortality from exposure to PM 2.5 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 PM 2.5 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 ISA 3 Other respiratory effects (e.g., pulmonary function, non asthma ER visits, non - bronchitis chronic diseases, other ages and populations) — — PM ISA 3 Reproductive and developmental effects (e.g., low birth weight, pre - term births, etc.) — — PM ISA 3,4 Cancer, mutagenicity, and genotoxicity effects

— — PM ISA 3,4 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 day s (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 ISA 3 Cardiovascular and nervous system effects

— — Ozone ISA 3 Reproductive and developmental effects

— — Ozone ISA 3,4***E.O. 12866 Review

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ES - 12 **Table ES -**  **4 . Continued**  Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages)

— — NO 2 ISA2 Chronic lung disease hospital admissions (age > 65)

— — NO 2 ISA2 Respiratory emergency department visits (all ages)

— — NO 2 ISA2 Asthma exacerbation (asthmatics age 4

– 18)

— — NO 2 ISA2 Acute respiratory symptoms (age 7

– 14)

— — NO 2 ISA2 Premature mortality

— — NO 2 ISA2,3,4 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA 3,4 Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65)

— — SO 2 ISA2

Asthma emergency department visits (all ages)

— — SO 2 ISA2 Asthma exacerbation (asthmatics age 4

– 12)

— — SO 2 ISA2 Acute respiratory symptoms (age 7

– 14)

— — SO 2 ISA2 Premature mortality

— — SO 2 ISA2,3,4 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA2,3 Reduced incidence of morbidity from exposure to methylmercury Neurologic effects

—IQ loss

— — IRIS; NRC, 2000 2 Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 3 Cardiovascular effects

— — IRIS; NRC, 2000 3,4 Genotoxic, immunologic, and other toxic effects

— — IRIS; NRC, 2000 3,4 Reduced incidence of morbidity from exposure to HAP Effects associated with exposure to hydrogen chloride

— — ATSDR, IRIS 2,3 Improved Environment (co - benefits) Reduced visibility impairment Visibility in Class 1 areas

— — PM ISA2 Visibility in residential areas

— — PM ISA2 Reduced effects on materials Household soiling

— — PM ISA2,3 Materials damage (e.g., corrosion, increased wear)

— — PM ISA 3 Reduced PM deposition (metals and organics) Effects on Individual organisms and ecosystems

— — PM ISA 3

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 ISA 3 Carbon sequestration in terrestrial ecosystems

— — Ozone ISA2 Recreational demand associated with forest aesthetics

— — Ozone ISA 3 Other non - use effects Ozone ISA 3 Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf - gas exchange, community composition) — — Ozone ISA 3

Reduced effects from acid deposition Recreational fishing

— — NO x SO x ISA2 Tree mortality and decline

— — NO x SO x ISA 3 Commercial fishing and forestry effects

— — NO x SO x ISA 3 Recreational demand in terrestrial and aquatic ecosystems

— — NO x SO x ISA 3 Other non - use effects NO x SO x ISA 3 Ecosystem functions (e.g., biogeochemical cycles)

— — NO x SO x ISA 3

**Table ES -**  **4 . Continued** ***E.O. 12866 Review

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ES - 13 Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA 3 Coastal eutrophication

— — NO x SO x ISA 3 Recreational demand in terrestrial and estuarine ecosystems

— — NO x SO x ISA 3 Other non - use effects NO x SO x ISA 3 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA 3 Reduced vegetation effects from exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 3 Injury to vegetation from NO x exposure — — NO x SO x ISA 3 Reduced ecosystem effects from exposure to methylmercury Effects on fish, birds, and mammals (e.g., reproductive effects) — — Mercury Study RTC 3 Commercial, subsistence and recreational fishing

— — Mercury Study RTC 2 1 The global climate and related impacts of CO 2 emissions changes, such as sea level rise, are estimated within each integrated assessment model as part of the calculation of the SC - CO 2 . 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 CO 2 emissions. 2 We assess these co - benefits qualitatively due to data and resource limitations for this analysis. 3 We assess these co - benefits qualitati vely 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 associatio n. ES.6.1 Estimating Global Climate Benefits We estimate the global social benefits of CO 2 emission reductions expected from this rulemaking using the SC - CO 2 estimates presented in the current SC - CO 2 TSD. We refer to these estimates, which were developed by the U.S. government, as “SC - CO 2 estimates” for the remainder of this document. The SC - CO 2 is a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given ye ar. 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 ty pically 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 CO 2 emissions). The SC - CO 2 estimates used in this analysis have been developed over many ye ars, using the best science available, and with input from the public. The EPA and other federal agencies have considered the extensive public comments on way s to improve SC - CO 2 estimation received via the notice and comment period that was part of numerous rul emakings. In addition, OMB’s Office of Information and Regulatory Affairs recently issued a response to the public comments***E.O. 12866 Review

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ES - 14 it sought through a separate comment period on the approach used to develop the SC - CO 2 estimates. An interagency working group (IWG) that included the EPA and other executive branch entities used three integrated assessment models (IAMs) to develop SC - CO 2 estimates and recommended four global values for use in regulatory anal ys es. The SC - CO 2 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 highl y interconnected. Therefore, the SC - CO 2 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 IW G first released the estimates in February 2010 and updated them in 2013 using new versions of each IA M. The general approach to estimating the SC - CO 2 values in 2010 and 2013 was to run the three integrated assessment models (DICE, FUND, and PAGE) 7 using the following three inputs in each model: a probabilistic distribution for climate sensitivity ; five approach s capturing economic, population, and emission trajectories; and constant annual discount rates. The 2010 SC - CO 2 Technical Support Document ( 2010 SC - CO 2 TSD) provides a complete discussion of the methodology and the current SC - CO 2 TSD 8 presents and discusses the updated estimates. The four SC - CO 2 estimates are as follows: $13, $4 5 , $6 6 , and $1 3 0 per metric ton of CO 2 emissions in the year 2020 (2011$), and each estimate increases over time. These SC CO 2 estimates are associated with different discount rates. The first three estimates are the model average at 5 percent discount rate, 3 p ercent, and 2.5 percent, respectively , and the fourth estimate is the 95 th percentile at 3 percent.

7 The full models names are as follows: Dynamic Integrated Climate and Economy (DICE); Climate Framework for Uncertainty, Negotiation, and Distribution (FUND); and Policy Analysis of the Greenhouse Gas Effect (PAGE). 8 The IWG published the updated TSD in 2013, then issued two minor corrections to it in June 2015.***E.O. 12866 Review

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ES - 15 The 2010 SC - CO 2 TSD noted a number of limitations to the SC - CO 2 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 assumption s regarding risk aversion. Currently integrated assessment models do not assign value to all of the important phy sical, 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 understandabl y lags behind the most recent research. In particular, the IPCC Fourth Assessment Report concluded that “It is very likely that [SC - CO 2 estimates] underestimate th e 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 CO 2 emission reductions to inform the bene fit - cost analy sis. In addition, a fter careful evaluation of the full range of comments submitted to OMB ’s Office of Information and Regulatory Affairs , the IW G continues to recommend the use of the se SC - CO 2 estimates in regulatory impact anal ys is. With th e release of the response to comments, the IW G announced plans to obtain expert independent advice from the National Academy of Sciences’ National Research Council (NRC) to ensure that the SC - CO 2 estimates continue to reflect the best available scientific and economic information on climate change. The NRC process will be informed by the public comments received and focus on the technical merits and challenges of potential approaches to improving the SC - CO 2 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., SO 2 , NOx, and directly

emitted particles), which in turn would lower ambient concentrations of PM 2.5 and ozone. This co - benefits analy sis quantifies the monetized benefits associated with the reduced exposure to these two pollutants. 9 Unlike the global SC - CO 2 estimates, the air quality health co - benefits are only estimated for the conti guous U.S. 10 The estimates of monetized PM 2.5 9 We did not estimate the co - benefits associated with reducing direct exposure to SO 2 and NOx. 10 We do not hav e emission reduction information or air quality modeling available to estimate the air pollution health co - benefits in Alaska and Hawaii anticipated from implementation of the final emission guidelines.***E.O. 12866 Review

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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

s ymptoms to heart attacks (U.S. EPA, 2012). The estimates of mone tized 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 Lev y 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 PM 2.5 and ozone co - benefits in this RIA. Benefit - per - ton approaches appl y 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 u se 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. Specificall y, in this analysis, we multiplied the benefit - per - ton estimates by the corresponding emission reductions that were generated from air quality modeling o f the proposed Clean Power Plan. Similar to the co - benefits anal ys is conducted for the RIA for this rule at proposal, we generated regional benefit - per - ton estimates by aggregating the impacts in BenMAP 11 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 exactl y match the emission reductions in this rulemaking, and thus they may not reflect

11 BenMAP is a computer program developed by the EPA that calculates the number and economic value of air pollution - related deaths and illnesses. The software incorporates a database that includes many of the concentration response relationships, population files, and health and economic data needed to quanti fy these impacts.***E.O. 12866 Review

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ES - 17 the local variability in population density, meteorology, exposure, baseline health incidence rates , or other local factors for any specific location. Our estimate of the monetized co - benefits is based on the EPA’s interpretation of the best available scientific literature (U.S. EPA, 2009) and methods and supported by the EPA’s Science Advisory Board and the NAS (NRC, 2002 ). Below are key assumptions underlying the estimates for PM 2.5 - related premature mortality , which accounts for 98 percent of the monetized PM 2.5 health co - benefits: 1. We assume that all fine particles, regardless of the ir chemical composition, are equally potent in causing premature mortality . This is an important assumption, because PM 2.5 varies considerabl y in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effe ct estimates b y particle ty pe. The PM I SA concluded that “many constituents of PM 2.5 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 analy sis. Thus, the estimates include health co - benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the National Ambient Air Quality Standard for fine particle s 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. Specificall y, we assume that some of the incidences of premature mortality related to PM 2.5 exposures occur in a distributed fashion over the 20 years following exposure ba sed on the advice of the SAB - HES (U.S. EPA - SAB, 2004c), which affects the valuation of mortality co benefits at different discount rates. Every benefits anal ys is examining the potential effects of a change in environmental protection requirements is limite d, to some extent, by data gaps, model capabilities (such as geographic coverage) and uncertainties in the underlying scientific and economic studies used to***E.O. 12866 Review

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ES - 18 configure the benefit and cost models. Inaddition, g iven 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 a pproaches that states may take. Despite these uncertainties, we believe this analy sis provides a reasonable indication of the expected health co benefits of the air quality emission reductions for the final emission guidelines under a set of reasonable assumptions. This analy sis does not include the type of detailed uncertainty assessment found in the 2012 PM 2.5 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 o f 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 - CO 2 values with health co - benefits values at the 3 percent and 7 percent discount rates. Different di scount rates are applied to SC - CO 2 than to the health co - benefit estimates; because CO 2 emissions are long - lived and subsequent damages occur over many years. Moreover, several discount rates are applied to SC - CO 2 because the literature shows that the esti mate of SC - CO 2 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 - CO 2 at a 3 percent discount rate but emphasized the importance of considering all four SC - CO 2 estimates. Table ES - 5 (rate - based illustrative plan approach) and Table ES - 6 (mass - based illustrative plan approach) provide the combined climate benefits and health co benefits 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.***E.O. 12866 Review

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ES - 19 **Table ES -**  **5 .**  **Combined Estimates of Climate Benefits and Health Co -**  **Benefits for Rate**  ****

**Based Approach (billions of 2011$)** * **SC -**  **CO 2**  **Discount Rate and Statistic  ** Climate** **Benefits Only**  **C limate Benefits plus Health Co**  **- b**  **enefits**

**(Disc ount Rate Applied to Health Co**  **- b**  **enefits) 3%**  **7% In 2020**  63 million metric tons CO2 5% $0.8 $1. 5 to $2. 6 $1.4 to $2. 5 3% $2.8 $3. 5 to $4. 6 $3.4 to $4. 5 2.5% $4.1 $4.8 to $5. 9 $4. 8 to $5. 8 3% (95 th percentile) $8.2 $8. 9 to $ 10 $8.8 to $9. 9 **In 2025** 21 0 million metric tons CO2 5% $3.1 $11 to $21 $10 to $19 3% $10 $18 to $28 $17 to $26 2.5% $15 $23 to $33 $22 to $31 3% (95 th percentile) $31 $38 to $49 $38 to $47 **In 2030** 37 7 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% (95 th percentile) $6 1 $75 to $9 5 $7 4 to $92 * B enefit estimates are for affected sources in contiguous states and have been rounded to two significant figures. Climate benefits are based on reductions in CO 2 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 PM 2.5 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)). T he mo netized health co - benefits do not include reduced health effects from direct exposure to NOx, SO 2 , 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.***E.O. 12866 Review

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ES - 20 **Table ES -**  **6. Combined Estimates of**  **Climate Benefits and Health Co -**  **b enefits for**  **Mass**  

** Based Approach** **(billions of 2011$)*** **SC -**  **CO 2**  **Discount Rate and Statistic  ** Climate** **Benefits Only**  **C limate Benefits plus Health Co**  **- b**  **enefits**

**(Disc ount Rate Applied to Health Co**  **- b**  **enefits) 3%**  **7% In 2020**  [X] million metric tonnes CO 2 5% $[X] $[X] to $[X] $[X] to $[X] 3% $[X] $[X] to $[X] $[X] to $[X] 2.5% $[X] $[X] to $[X] $[X] to $[X] 3% (95 th percentile) $[X] $[X] to $[X] $[X] to $[X] **In 2025** [X] million metric tonnes CO 2 5% $[X] $[X] to $[X] $[X] to $[X] 3% $[X] $[X] to $[X] $[X] to $[X] 2.5% $[X] $[X] to $[X] $[X] to $[X] 3% (95 th percentile) $[X] $[X] to $[X] $[X] to $[X] **In 2030** [X] million metric tonnes CO 2 5% $[X] $[X] to $[X] $[X] to $[X] 3% $[X] $[X] to $[X] $[X] to $[X] 2.5% $[X] $[X] to $[X] $[X] to $[X] 3% (95 th percentile) $[X] $[X] to $[X] $[X] to $[X] *All benefit estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 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 PM 2.5 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)). T he mon etized health co - benefits do not include reduced health eff ects from direct exposure to NO X , SO 2 , and HAP; ecosystem effects; or visibility impairment. See Chapter 4 for mo re information about these estimates and for more information regarding the uncertainty in these estimates. **Unless otherwise specified, it is the model average. Comparing the rate - based and mass - based illustrative plan approaches indicates... **ES.7 Net Benefits**  Table ES - 7 and ES - 8 provide the estimates of the climate benefits, health co - benefits, compliance costs and net benefits of the final emission guidelines for rate - based and mass - based approaches, respectivel y . 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 CO 2 emissions do not include important impacts like ocean acidification or potential tipping points in natural or managed ec os ys tems. Unquantified benefits also include climate benefits from reducing emissions of non - CO 2 greenhouse gases and co - benefits from reducing exposure to SO 2 , NO X , and hazardous air pollutants (e.g., mercury and h ydrogen chloride), as well as ecos ys tem e ffects***E.O. 12866 Review

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ES - 21 and visibility

impairment. Upon considering these limitations and uncertainties, it remains clear that the benefits of this final rule are substantial and far outweigh the costs.***E.O. 12866 Review

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ES - 22 **Table ES -**  **7 .**  **Monetized Benefits, Compli ance 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 $0.8 0 $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 $6 1 Air Quality Co - benefits Discount Rate 3% 7% 3% 7% 3% 7% **Air Quality Health Co**  **- benefits**  **c** $0.7 to $1.8 $0.6 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31 **Compliance Costs d**  $ 2.4 $1. 1 $ 8 .4 **Net Benefits e**  $1.1 to $2. 2 $1. 0 to $2. 1 $1 7 to $ 27 $1 6 to $2 5 $2 6 to $4 6 $25 to $4 3

**Non -**  **Monetized Benefits**  Non - monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in emissions of NO X , SO 2 , PM, and mercury and HCl Visibility impairment a All are rounded to two significant figures, so figures may not sum. b The climate benefit estimate in this summary table reflects global impacts from CO 2 emission changes and does not account for changes in non - CO 2 GHG emissions. Also, different discount rates are applied to SC - CO 2 than to the other estimates because CO 2 emissions are long - lived and subsequent damages occur over many years. The benefit estimates in this table are based on the average SC - CO 2 estimated for a 3% discount rate, however we emphasize the im portance and value of considering the full range of SC - CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three SC - CO 2 estimates (model average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC - CO 2 estimates are year - specific and inc rease over time. c The air quality health co - benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration - response functions from different epi demiology studies. The reduction in premature fatalities each year accounts for over 98 percent of total mo netized co - benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent i n 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 PM 2.5 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 ar e approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the proposed guidelines and a discount rate of approximately 5 percent. This estimate also includes monitoring, recordkeeping, and reporting costs and demand - side energy efficiency program and participant costs. e The estimates of net benefits in this summary table are calculated using the global SC - CO 2 at a 3 percent discount rate (model average). The RIA includes combined climate and health estimates base d on additional discount rates.***E.O. 12866 Review

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ES - 23 **Table ES -**  **8 .**  **Monetized Benefits, Compli ance 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 $[X] $[X] $[X] 3% discount rate $ [X] $ [X] $ [X] 2.5% discount rate $ [X] $ [X] $ [X] 95th percentile at 3% discount rate $ [X] $ [X] $ [X] Air Quality Co - benefits Discount Rate 3% 7% 3% 7% 3% 7% **Air Quality Health Co**  **- benefits**  **c** $[X] to $[X] $[X] to $[X] $[X] to $[X] $[X] to $[X] $[X] to $[X] $[X] to $[X] **Compliance Costs d**  $[X] $[X] $[X] **Net Benefits e**  $[X] to $[X] $[X] to $[X] $[X] to $[X] $[X] to $[X] $[X] to $[X] $[X] to $[X]

**Non -**  **Monetized Benefits**  Non - monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in emissions of NO X , SO 2 , PM, and mercury and HCl Visibility improvement a All are rounded to two significant figures, so figures may not sum. b The climate benefit estimate in this summary table reflects global impacts from CO 2 emission changes and does not account for changes in non - CO 2 GHG emissions. Also, different discount rates are applied to SC - CO 2 than to the other estimates because CO 2 emissions are long - lived and subsequent damages occur over many years. The benefit estimates in this table are based on the average SC - CO 2 estimated for a 3% discount rate, however we emph asize the im portance and value of considering the full range of SC - CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three SC - CO 2 estimates (model average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th perce ntile at 3 percent). The SC - CO 2 estimates are year - specific and increase over time. c The air quality health co - benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range refl ects the use of concentration - response functions from different epidemiology studies. The reduction in premature fatalities each year accounts for over 98 percent of total mo netized co - benefits from PM 2.5 and ozone. These models assume that all fine partic les, 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 i n 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 ben efits of the rule over the full compliance period. d Total costs are approximated by the illustrative compliance costs estimated using the Integrated Planning Model for the proposed guidelines and a discount rate of approximately 5 percent. This estimate a lso 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 - CO 2 at a 3 percent discount rate (model average). The RIA includes combined climate and health estimates based on additional discount rates.***E.O. 12866 Review

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ES - 24 **ES. 8**  **Economic Impacts** The final emission guidelines have important energy market implications. Average nationwide retail electricity prices are projected to i ncrease/decrease roughly [X] percent in 202 0, increase/decrease roughl y [X] percent in 2025 , and increase/decrease roughl y [X] percent in 2030 (contiguous U.S.), compared to base case price estimates modeled for these same years. Average monthly

electricity

bills are anticipated to increase/decrease b y about [X] percent in 202 0 , increase/decrease by about [X] percent in 2025 , and increase/decrease by about [X] percent by

2030 , compared to base case price estimates modeled for these same years. The average delivered coal price to the power se ctor is projected to decrease by about [X] percent in 202 0, [X] percent in 2025 , and roughly [X] percent in 2030, relative to the base case. The EPA projects coal production for use b y the power sector, a large comp onent of total coal production, will decline by about [X] percent in 2025 and by about [ X] percent in 2030, relative to base case levels. The EPA also projects that the electric power sector - delivered natural gas prices will increase b y about [X] percent in 202 0, increase b y about [X] percent in 2025 , and increase b y about [X] percent in 2030, relative to base case levels. Natural gas use for electricity generation will increase b y as much as [X] trillion cubic feet (TCF) in 2025 relative to the base case , declining over time. Renewable energy

capacity is anticipated to increase by roughly [X] GW in 2025 and by [X] GW in 2030 under the final guidelines. Energy market impacts from the guidelines are discussed more extensively in Chapter 3 of this RIA. C hanges in suppl y or demand for electricity, natural gas, and coal can impact markets for goods and services produced b y sectors that use these energy inputs in the production process or that supply

those sectors. Changes in cost of production may result in c hanges 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 flexibi lity, and states implementing the guidelines may choose to mitigate***E.O. 12866 Review

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ES - 25 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**  **Im pacts**  Executive Order 13563 directs federal agencies to consider the effect of regulations on job creation and employment. According to the Executive Order, “our regulatory s ys tem 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 anal ys es have not ty picall y included a separate anal ysis of regulation - induced employ ment impacts, we ty

picall y conduct employ ment anal ys es. During the current economic recovery , employ ment 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 employ ment impacts is difficult. The EPA’s illustrative employ ment anal ys is includes an estimate of projected employ ment impa cts associated with these guidelines for the utility power sector , coal and natural gas production, and demand - side energy

efficiency

activities. These projections are derived, in part, from a detailed model of the utility

power sector used for this regulatory anal ys is, and U.S government data on employ ment and labor productivity . In the electricity , coal, and natural gas sectors, the EPA estimates that these guidelines could result in an increase of approximately [X] to [X] job - ye ars in 2025 for the final guidelines under the rate - based illustrative plan approach and [X] to [X] job - years in 2025 under the mass based approach . The Agency is also offering an illustrative calculation of potential employ ment effects due to demand - side energy efficiency

program s. Employ

ment impacts in 2025 could be an increase of approximately [X] jobs for the final guidelines. More detail about these anal ys es can be found in Chapter 6 of this RIA.***E.O. 12866 Review

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ES - 26 **ES.1 0**  **References** Bell, M.L., A. McDermott, S.L . Zeger, J.M. Sarnet, and F. Domin ici . 2004. “Ozone and Short Term 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 Anal ys is 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>. Docket ID EPA - HQ - OAR - 2013 - 0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analy sis 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 Transportati on, Domestic Policy Council, Environmental Protection Agency, National Economic Council, Office of Management and Budget, Office of Science and Technology Policy , and Department of Treasury (May 2013, Revised November 2013). Also available at: <http://www. whitehouse.gov/sites/default/files/omb/assets/inforeg/technical - update - social cost - of - carbon - for - regulator - impact - analy sis.pdf>. Fann, N., K.R. Baker , and C.M. Fulcher. 2012. “Characterizing the PM 2.5 - 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. HEIResearch Report, 140, Health Effects Institute, Boston, MA. Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC . Available at: <http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_s ynt hesis_report.htm>. Accessed June 6, 2015.***E.O. 12866 Review

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ES - 27 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. Lev y, J.I. , S.M. Chemerynski , and J.A. Sarnat. 2005. “Oz one Exposure and Mortality : An Empiric Bayes Metaregression Analy sis.” Epidemiology. 16(4): 458 - 68. National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee on the Toxicological Effects of Methylmercury .” Board on Environment al 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 Protectio n Agency (U.S. EPA). 2008 a . 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/rec ordisplay .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 Divisio n, Air Benefit and Cost Group Research Triangle Park, NC. March. Available at: < http://www.epa.gov/ttnecas1/regdata/RIAs/6 - ozoneriachapter6.pdf>. Accessed June 4, 2015. U.S. EPA. 2008c. Integrated Science Assessment for Oxides of Nitrogen: Health Criteria (Final Report). Research Triangle Park, NC: National Center for Environmental Assessment. July . Available at < http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=194645>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessm ent 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.***E.O. 12866 Review

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ES - 28 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 . Av ailable at: <http://www.epa.gov/ttnecas1/regdata/RIAs/s3 - supplemental_anal ys is - 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. EPA 452/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 Di vision, Research Triangle Park. Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay .cfm?deid=247492#Download>. Accessed June 4, 2015. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “ The r elationship b etween s elected causes of postneonatal infant mortality and particulate air pollution in the United States.” Environmental Health Perspectives. 105 (6): 608 - 612.***E.O. 12866 Review

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1 - 1 

### C **HAPTER** 

### 1 **: I**  **NTRODUCTION AND** 

### B **ACKGROUND FOR THE** 

### C **LEAN** 

### P **OWER** 

### P **LAN 1.1**  **Introduction** This document presents estimates of potential benefits, costs, and economic impacts of illustrative approaches states m ay 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 significantl y to, air pollution which may

reasonably

be anticipated to endanger public health or welfare.” 12 The EPA has listed more than 60 stationary source categorie s under this provision. 13 Once the EPA lists a source category , the EPA must, under CAA section 111(b)(1)(B), establish “standards of performance” for emissions of air pollutants from new sources in the source categories. 14 These standards are known as new s ource performance standards (NSPS), and they are national requirements that appl y directly to the sources subject to them. When the EPA establishes NSPS for new sources in a particular source category, the EPA is also required, under CAA section 111(d)(1) , to prescribe regulations for states to submit plans regulating existing sources in that source category for any air pollutant that, in general, is not regulated under the CAA section 109 requirements for the NAAQS or regulated under the CAA section 112 requirements for hazardous air pollutants (HAP). CAA section 111(d)’s

12 CAA §111(b)(1)(A). 13 See 40 CFR 60 subparts Cb

– OOOO. 14 CAA §111(b)(1)(B), 111(a)(1).***E.O. 12866 Review

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1 - 2 mechanism for regulating existing sources differs from the one that CAA section 111(b) provides for new sources because CAA section 111(d) contemplates states submitting plans that establ ish “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 limit ation achievable from the “best s ys tem of emission reduction,” considering costs and other factors, that “the Administrator determines has been adequatel y demonstrated.” CAA section 111(d)(1) grants states the authority, in appl yi ng a standard of performan ce to a particular source, to take into account the source’s remaining useful life or other factors. Under CAA section 111(d), a state must submit its plan to the EPA for approval, and the EPA must approve the state plan if it is “satisfactory .” 15 If a state does not submit a plan, or if the EPA does not approve a state’s plan, then the EPA must establish a plan for that state. 16 Once a state receives the EPA’s approval of its plan, the provisions in the plan become federall y 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 CO 2 from the bur ning of fossil fuels have ushered in a new epoch where human activities will largely determine the evolution of Earth’s climate. Because CO 2 in the atmosphere is long lived, it can effectivel y lock Earth and future generations into a range of impacts, some of which could become very severe. Therefore, emission reduction choices made today matter in determining impacts experienced not just over the next few decades, but in the coming centuries and millennia.” 17

15 CAA section 111(d)(2)(A). 16 CAA section 111(d)(2)(A). 17 National Research Council, Climate Stabilization Targets, p.3.***E.O. 12866 Review

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1 - 3 In 2009, based on a large body of robust and co mpelling scientific evidence, the EPA Administrator issued the Endangerment Finding under CAA section 202(a)(1). 18 Inthe Endangerment Finding, the Administrator found that the current, elevated concentrations of GHGs in the atmosphere

—already at levels unp recedented 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 Rec onsideration 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, CO 2 concentrations, and sea level rise. Additionally , a number of m ajor scientific assessments have been released that improve understanding of the climate sy stem and strengthen the case that GHGs endanger public health and welfare both for current and future generations. These assessments are from the Intergovernmental P anel on Climate Change (IPCC), the U.S. Global Change Research Program (USGCRP), and the N ational Research Council (NRC). These and other assessments are discussed in more detail in the p reamble 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” whereb y the market does not internalize the full opportunity cost of production borne b y s ociety 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 o f the goods produced through the polluting process. For this regulatory

action the good produced is electricity . These social costs associated with the health and welfare impacts are referred to as negative externalities. If a fossil fuel - fired electricity producer pollutes the atmosphere when it generates electricity, this cost will be borne not by the polluting firm but by society as a whole. The equilibrium market price of electricity

18 “Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”).***E.O. 12866 Review

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1 - 4 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 electrici ty may be produced from fossil fuel - fired EGUs than would occur if they

had to account for the full opportunity cost of production including the negative externality . Consequently , absent a regulation on emissions, the composition of the fleet of EGUs used to generate electricity may not be socially optimal, and the marginal social cost of the last unit of electricity produced will exceed its marginal social benefit. This regulation will address this market failure by beginning to internalize the negative e xternality by reducing CO 2 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 Anal ys es,” the EPA prepared this RIA for this “ significant regulatory action. ” This action is an economicall y significant regulatory action because it is expected to have an annual effect on the economy of $100 million or more or adversel y affect in a material way the econom y, a sector of the econom y, 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, employ ment, and markets outside the electricity sector. In evaluating the impacts of the final guidelines, we anal yz e d a number of uncertainties . For example, the analy sis includes an evaluation of two approaches to formulate state emission performance goals, a rate - based and a mass - based approach. The RIA also examines key uncertainties in the estimated benefits of reducing carbon dioxide and other air pollutants. For a further discussion of key

evaluations of uncertainty in the regulatory

anal ys es for this rulemaking, see Chapter 8 of this RIA.***E.O. 12866 Review

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1 - 5 **1.4 Background for the**  **Final Emission Guidelines** 1 . 4 .1 Base Case and Years of Analysis The rule anal yz ed in this RIA finalizes emission guidelines for states to limit CO 2 emissions from certain existing EGUs. The base case for this analysis, which uses the Integrated Planning Model (IPM), includes state r ules that have been finalized and/or approved by a state’s legislature or environmental agencies, as well as final federal rules. The IP M Base Case v.5.15 includes the Cross - State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Rule (MATS), the prop osed 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 legall y binding and enforceable commitments for GHG reductions considered in the base case are discussed in the documentation for IPM. 19 Costs and benefits are presented for illustrative plan approaches for th e analy sis 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 anal yz ed. Analy ses of energy , economic, and employ

ment impacts are presented for illustrative plan approaches in 2020, 2025, and 2030. All dollar estimates are presented in 2011 dollars. 1.4.2 Definition of Affected Sources For the emission guidelines, an affected EGU is any steam generating unit, integrated gasification comb ined c ycle ( IGCC ) , or stationary combustion turbine that was in operation or had commenced construction as of January 8, 2014, 20 and that meets the following criteria , which differ depending on the ty pe of unit . To be an affected source, such a unit, if it is a steam generating unit or IGCC, must serve a generator capable of selling greater than 25 MW to a utility power distribution sy stem and have a base load rating greater than 260 GJ/h (250 MMBtu/h) heat input of fossil fuel (either alone or in combinatio n with any other fuel). If such a

19 Detailed documentation for IPM v . 5.1 5 is available at: http://www.epa.gov/power sectormodeling 20 Under Section 111(a) of the CAA, determination of affected sources is based on the date that the EPA proposes action on such sources. January 8, 2014 is the date the proposed GHG standards of performance for new fossil fuel fired EGUs wer e published in the Federal Register (79 FR 1430).***E.O. 12866 Review

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1 - 6 unit is a stationary

combustion turbine, the unit must meet the definition of a combined cycle or combined heat and power combustion turbine, serve a generator capable of selling greater than 25 MW to a utility

power distribution system, and have a base load rating of greater than 260 GJ/h (250 MMBtu/h). Certain EGUs are exempt from inclusion in a state plan. For specifics on these criteria see section IV of the preamble. When considering and understanding applicability, the following definitions may be helpful. Simple cycle combustion turbine means any stationary

combustion turbine which does not recover heat from the combustion turbine engine exhaust gases for purposes other than enhancing the performance of the stationary combustion turbine itself. Combined cycle combustion turbine means any stationary

combustion turbine which recovers heat from the combustion turbine engine exhaust gases to generate steam that is used to create additional electric power output in a steam turbine. Combined heat and power (CHP) combustion turbine means any stationary combustion turbine which recovers heat from the combustion turbine engine exhaust gases to heat water or another medium, generate steam for useful purposes other than exclusively

for additional electric generation, or directl y 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 CO 2 emissions from fossil fuel - fired power plants in the U.S. because they are the largest domestic stationary source of emissions of carbon dioxide (CO 2 ), 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 CO 2 emissions. 1.4.4 Emission Guide lines In this action, the Environmental Protection Agency (EPA) is establishing final emission guidelines for states, U.S. territories, and tribes to follow in developing plans to reduce greenhouse gas emissions from existing fossil fuel - fired electric gen erating units (EGUs). Specificall y, the EPA is establishing: 1) carbon dioxide (CO 2 ) emission performance rates for two source categories of existing fossil fuel - fired EGUs, fossil fuel - fired electric utility steam generating units and stationary combustio n turbines, and 2) guidelines for the development,***E.O. 12866 Review

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1 - 7 submittal and implementation of state plans that implement the CO 2 emission performance rates. This final rule will continue progress already underway in the U.S. to reduce CO 2 emissions from the utility

p ower s ector . **1.5 O**  **rganization of the Regulatory Impact Analysis** This report presents the EPA’s anal ys is of the potential benefits, costs, and other economic effects of the final emission guidelines to fulfill the requirements of an RIA. This RIA includes the following chapters:

 Chapter 2, Electric Power Sector Profile

 Chapter 3, Control Strategies, Cost, Economic, and Energy Impacts  Chapter 4 , Estimated Climate Benefits and Health Co - benefits

 Chapter 5, Economic Impacts

– Markets Outside the Electricity Se ctor  Chapter 6 , Employ

ment  Chapter 7 , Statutory and Executive Order Analy ses  Chapter 8 , Summary of Benefits and Cost of the Final Regulation **1.6 References**  40 CFR Chapter I [EPA

–HQ

–OAR

–2009

–0171; FRL

– 9091

– 8] RI

N 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 En dangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act.” Melillo, J.M., T.C. Richm ond, and G. W. Yohe, Eds., 2014: Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. G lobal 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 A cademies Press, 2011.***E.O. 12866 Review

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1 - 8 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.***E.O. 12866 Review

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2 - 1 

### C **HAPTER** 

### 2 **: I**  **NDUSTRY** 

### P **ROFILE 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 e xisting 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 ty

pes of generation. These changes are the result of multiple factors in the power sector, including normal replacements of older generating units with new units, changes in the electricity intensity of the US economy , growth and regional changes in the US population, technological improvements in electricity generation from both existing and new units, changes in the prices and availability of different fuels, and substantial growth in electricity generation by

renewable and unconventional methods. Many

of these trends will continue to contribute to the evolution of the power sector in the future. The evolving economics of the power sector, in particular the increased natural gas supply and subsequent relatively low natural gas prices, have resulted in more gas being utilized as base load energy in addition to supplying electricity during peak load. This chapter presents data on the evolution of the power sector from 2002 through 2012. Projections of new capacity and the impact of this rule on these new sources are discussed in more detail in Cha pter 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 de livery of electricity to consumers. There are two important aspects of electricity generation; capacity and net generation. Generating***E.O. 12866 Review

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2 - 2 Capacity refers to the EGUs that are available for use in a given year, typically measured in megawatts (MW) or gigawatts (1 GW = 1000 MW). Electricity Generation refers to the amount of electricity

actuall y produced b y EGUs, measured in kilowatt - hours (kWh) or gigawatt - hours (GWh = 1 million kWh). Individual EGUs are not used to generate electricity 100 percent of the time. Not only are units unavailable during routine and unanticipated outages for maintenance, but individual EGUs are also periodicall y not needed to meet the regular dail y and seasonal fluctuations of electricity demand. Furthermore, EGUs rel yi ng on renewable resources such as wind, sunlight and surface water to generate electricity are routinel y constrained by the availability of adequate wind, sunlight or water at different times of the day and season. These factors result in the mix of generating capacity

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

different than the mix of the share of total electricity produced by each type of EGU in a given season or ye ar. Most of the existing capacity for generating electricity does so b y creating heat to create high pressure steam that is released to rotate turbines which, in turn, create electricity. Other units generate electricity by using water or wind to rotate turbines, and a variety of other methods also make up a small p art of the overall electricity suppl y. 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 2 012. In 2012 the power sector consists of over 19,000 generating units with a total capacity21 of 1,168 GW, an increase of 19 percent from the capacity in 2002 (989 GW). The 188 GW increase in capacity consisted primarily of natural gas fired EGUs (134 GW) and wind generators (55 GW), with substantially

smaller net increases and decreases in other typ es of generating units.

21 As with all data presented in this section, this includes generating capacity not only at EGUs primarily operated to supply electricity to the grid, but also generating capacity at commercial and industri al facilities that produce both electricity used onsite as well as dispatched to the grid.***E.O. 12866 Review

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2 - 3 **Table 2 -**  **1. Existing Electricity Generating Capacity by Energy Source, 2002 and 2012**  **2002 2012**  **Change Between '02 and '12 Energy Source**  **Generator**

**Nam eplate**  **Capacity (M**  **W) % Total** 

**Capacity Generator** 

**Nam eplate**  **Capacity (M**  **W) % Total** 

**Capacity %** 

**Increase Nam**  **eplate**

**Capacity Change**  **(M W)**  **% of**

**Total Capacity**  **Increase** Coal 338,199 35% 336,341 29% - 1% - 1,858 - 1% Natural Gas 1 352,128 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 capacity), Gas Turbine (30 percent), Combined Cycle Steam (19 percent), Steam Turbin e (17 percent), and miscellaneous (< 1 percent). The 19 percent increase in generating capacity increase is the net impact of newly built generating units, retirements of generating units, and a variety of increases and decreases to the rated capacity of individual existing units due to unit modifications, changes in emission control equipment, etc. During the period 2002 to 2012, a total of 315,752 MW of new generating capacity was built and brought online, and 64,763 MW existing units were retired. The n et 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 newl y 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 b y 2,807 MW of solar. 22 The overall mix of newly built and retired capacity , along with the net effect, is shown on Figure 2 - 1.

22 Partially offset by 87 MW retired wind or solar capacity (87 MW).***E.O. 12866 Review

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2 - 4 **Figure 2**  **- 1**  **. New Build and Retired Capacity (MW) by Fuel Type, 2002**  **- 2012**  Source: EIA Form 860 Not displayed: wind and solar retirements = 87 MW, net change in coal capacity = - 56 MW In 2012, electric generating sources produced a net 4,058 billion kWh to meet electricity demand, a 5 percent increase from 2002 (3,858 billion kWh). As presented in Table 2 - 2, almost 70 percent of electricity

in 2012 was produced through the combustion of fossil fuels, primarily coal and natural gas, with coal accounting for the largest single share. Although the share of the total generation from fossil fuels in 2012 (67 percent) was onl y 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 b y 18 percent and petroleum generation by 63 percent, while natural gas generation increased b y 61 percent. This reflects both the increase in natural gas capacity

during that period as well as an increase in the utilization of new and ex isting gas EGUs during that period. Wind generation also grew from a very small portion of the overall total in 2002 to 4.1 percent of the 2012 total.

-50,000 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000

New Build Retirement Net ChangeChangeinCapacity(MW)

Coal Nat Gas Wind & Solar Oil & Other***E.O. 12866 Review

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2 - 5 **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 typicall y operate throughout all hours of the ye ar. The coal units meet the part of demand that is relativel y 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 earl y in the mornin g, 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 generall y smaller***E.O. 12866 Review

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2 - 6 and newer. While 55 percent of the coal EGU fleet is over 500 MW per unit, 77 percent of the gas fleet is between 50 and 500 MW per unit. **Table 2 -**  **3. Coal and Natural Gas Generating Units, by Size, Age, Capacity, and** 

**Thermal Efficiency (Heat Rate)** Unit Size Grouping (MW) No.

Units % of All

Units Avg.

Age Avg. Net

Summer Capacity (MW) Total Net

Summer Capacity (MW) % Total

Capacity Avg. Heat

Rate (Btu/kWh) COAL 0

– 24 223 18% 40.7 11.4 2,538 1% 11,733 25

– 49 108 9% 44.2 36.7 3,963 1% 11,990 50

– 99 157 12% 49.0 74.1 11,627 4% 11,883 100 - 149 128 10% 50.6 122.7 15,710 5% 10,971 150 - 249 181 14% 48.7 190.4 34,454 11% 10,620 250 - 499 205 16% 38.4 356.2 73,030 23% 10,502 500 - 749 187 15% 35.4 604.6 113,056 36% 10,231 750 - 999 57 5% 31.4 823.9 46,963 15% 9,942 1000 - 1500 11 1% 35.7 1259.1 13,850 4% 9,732 Total Coal 1257 100% 42.6 250.7 315,191 100% 11,013 NATURAL GAS 0

– 24 1992 37% 37.6 7.0 13,863 3% 13,531 25

– 49 410 8% 21.8 125.0 51,247 12% 9,690 50

– 99 962 18% 15.6 174.2 167,536 39% 8,489 100 - 149 802 15% 23.4 39.9 31,982 8% 11,765 150 - 249 167 3% 28.7 342.4 57,179 13% 9,311 250 - 499 982 18% 24.6 71.1 69,788 16% 12,083 500 - 749 37 1% 40.0 588.8 21,785 5% 11,569 750 - 1000 14 0.3% 35.9 820.9 11,492 3% 10,478 Total Gas 5366 100% 27.7 79.2 424,872 100% 11,652 Source: National Electric Energy Data System (NEEDS) v.5.13 Note: The average heat rate reported is the mean of the heat rate of the units in each size category (as opposed to a generation - weighted or capacity - weighted average heat rate.) A lower heat rate indicates a higher level of fuel efficiency. Table is limited to coal - steam units in operation in 2013 or earlier, and excludes those units in NEEDS with planned retirements in 2014 - 2015. In terms of the age of the generating units, 50 percent of the total coal generating capacity has been in service for more than 38 ye ars, while 50 percent of the natural gas capacity has been in service less than 15 ye ars. Figure 2 - 2 presents the cumulative age distributions of the co al and***E.O. 12866 Review

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2 - 7 gas fleets, highlighting the pronounced differences in the ages of the fleets of these two types of fossil - fuel generating capacity . Figure 2 - 2 also includes the distribution of generation.

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

**Capacity and Generation, by Age** Source: National Electric Energy Data System (NEEDS) v.5.13 Not displayed: coal units (376 MW total, 1 percent of total) and gas units (62 MW, < .01 percent of total)) over 70 years old for clarity. F igure is limited to coal - steam units in NEEDS in operation in 2013 or earlier (excludes ~2,100 MW of coal - fired IGCC and fossil waste capacity), and excludes those units in NEEDS with planned retirements in 2014 - 2015. The locations of existing fossil unit s in EPA”s National Electric Energy Data Sy stem (NEEDS) v.5.13 are shown in Figure 2 - 3.***E.O. 12866 Review

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2 - 8 **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 c apacity at facilities in the NEEDS v.5.13 IPM frame. NEEDS reflects available fossil capacity on - line by the end of 2015. This includes planned new builds already under construction and planned retirements. In areas with a dense concentration of facilities , some facilities may be obscured. **2.2.2 Transmission**  Transmission is the term used to describe the movement of electricity over a network of high voltage lines, from electric generators to substations where power is stepped down for local distribution. Inthe U.S. and Canada, there are three separate interconnected networks of high voltage transmission lines, 23 each operating s yn chronously . Within each of these transmission networks, there are multiple areas where the operation of power plants is monitored and controlled to ensure that electricity generation and load are kept in balance. In some areas, the

23 These three network interconnections are the western US and Canada, corresponding approximate ly to the area we st of the Rocky Mountains; eastern US and Canada, not including most of Texas; and a third network operating in mo st of Texas. These are commonly referred to as the Western Interconnect Region, Eastern Interconnect Region, and ERCOT, respe ctively.***E.O. 12866 Review

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2 - 9 operation of the transmission sy stem is under the control of a single regional operator; in others, individual utilities coordinate the operations of their generation, transmission, and distribution sy stems 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 sy stem and step it down to lower voltage levels to match the needs of customers. The transmission and distribution sy stem is the classic example of a natural monopoly , in part because it is not practical to have more than one set of lines running from the electricity generating sources to substations or from substations to residences and businesses. Transmission has generally been developed by

the larger verticall y integrated utilities that ty picall y operate generation and distribution networks. Often distribution is handled b y a large number of utilities that purchase and sell electricity, but do not g enerate it. Over the last couple of decades, several jurisdictions in the United States began restructuring the power industry to separate transmission and distribution from generation, ownership, and operation. As discussed below, electricity

restructurin g 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 consumer s. 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 s ervice. **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 roughl y a quarter to a third of the total electricity produced 24 (see Table 2 - 4). Some of these uses are highl y variable, such as heating and air conditioning in residential and commercial buildings, while

24 Transportation (primarily urban and regional electrical trains) is a fourth ultimate customer category which accounts less than one percent of electricity consumption.***E.O. 12866 Review

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2 - 10 others are relativel y 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)** 

**Sa le**  **s/ Dir**  **ec t**  **Us e**  **(Billion kW h)**  **Sha re of** 

**To ta**  **l End Us**  **e Sale**  **s/ Dir**  **ec t** 

**Us e**  **(Billion kW**  **h) Sha**  **re of**

**T ot**  **al En**  **d U se**  Re sid en ti al 1, 265 35% 1, 375 35. 9% Com mer ci al 1, 104 30% 1, 327 34. 6% In dus tr ial 990 27% 986 25. 7% Tr an sp ort at io n NA 7 0. 2% Ot he r 106 3% NA **To ta**  **l** 3, 465 95% 3, 695 96% 166 5% 138 4% **3, 632**  100% **3, 832**  100% **Sa le**  **s**

**Dir ec** **t Us**  **e To**  **ta l E**  **nd U se**  **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 b y state and region of the country. Electricity prices are ty picall y highest for residential and commercial customers because of the relatively high costs of distributing electricity to individual homes and commercial establishments. The high distribution costs are the result both of the necessary distribution network reaching to virtually every part of the country , and also that generating stations are increasingl y located relatively far from population centers. Industrial customers generall y pay the lowest average prices, reflecting both their proximity

to generating stations and the fact that industrial customers receive electricity at higher voltages (which makes transmission more efficient and less expensive). Industrial customers frequently

pay variable prices for electricity , varying by the season and time of day , while residential and commercial prices historically have been less variable. Overall industrial***E.O. 12866 Review

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2 - 11 customer prices are usually considerable closer to the wholesale marginal cost of generating electricity than residential and commercial prices. On a state - by- state basis, all retail electricity prices vary

considerabl y. The Northeast, California and Alaska have average retail prices that can be as much as double those of other states (see Figure 2 - 4), and Hawaii has electricity.

**Figure 2 -**  **4 .**  **Average Retail Electricity Price by State (cents/kWh), 2011** Average national overall retail electricity prices increased between 2002 and 2012 by 36.7 percent in nom inal (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.***E.O. 12866 Review

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2 - 12 **Figure 2 -**  **5 .**  **Nominal National Average Electricity Prices for Three Major End -**  **Use**

**Categories** Source: EIA AEO 2012, Table 2.4 Electricity

prices for all three end - use cat egories increased more than overall inflation through this period, measured b y either the GDP implicit price deflator (23.5 percent) or the consumer price index (CPI - U, which increased b y 27.7 percent). Most of these electricity price increases occurred be tween 2002 and 2008; since 2008 nominal electricity prices have been relativel y stable while overall inflation continued to increase. The increase in nominal electricity prices for the major end use categories, as well as increases in the GDP price and CPI - U indices for comparison, are shown in Figure 2 - 6.

.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

2002 2004 2006 2008 2010 2012ElectricityPrice(Cents/kWh,nominal$)

Residential Commercial Industrial Total***E.O. 12866 Review

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2 - 13 **Figure 2 -**  **6 .**  **Relative Increases in Nominal National Average Electricity Prices for Major**

**End -**  **Use Categories, With Inflation Indices** The real (inflation - adjusted) change in average national electricity p rices can be calculated using the GDP implicit price deflator. Figure 2 - 7 shows real 25 (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 alway s 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 relativel y unchanged from 1960, residential and commercial real prices are 23% and 28% lower respectivel y than in 1960.

25 All prices in this section are estimated as real 2011 prices adjusted using the GDP implicit price deflator unless otherwise indicated.***E.O. 12866 Review

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2 - 14 **Figure 2 -**  **7 .**  **Real National Average Electricity Prices (2011$) for Three Major End -**  **Use**

**Categories** Source: EIA Monthly Energy Revie w, A pril 2015, Table 9.8 0 2 4 6 8 10 12 14 16 18

1960 1970 1980 1990 2000 2010RealElectricityPrices(2011$,¢/kWh) 

### Real Electricity Prices, 1960 - 2014 (including taxes)

Residential Commercial Industrial Total

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% 60%

1960 1970 1980 1990 2000 2010ChangeinRealElectricityPrices 

### Relative Change in Electricity Prices,

1960 - 2014 (including taxes)

Residential Commercial Industrial Total***E.O. 12866 Review

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2 - 15 **Figure 2 -**  **8 .**  **Relative Change in Real National Average Electricity Prices (2011$) for**

**Three Major End -**  **Use Categories** Source: EIA Monthly Energy Revie w, A pril 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 b y 54 percent, while the real price of natural gas decreased by 22 percent. The real price of oil increased b y 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 b y 23 percent over 2002 prices. Figure 2 - 9 shows the relative changes in real p rice of all 3 fossil fuels between 2002 and 2012.

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

**National Average Real Price per MBtu Delivered to EGU** Source: EIA AEO 2012, Table 9.9***E.O. 12866 Review

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2 - 16 **2.3.3 Changes in Electricity I**  **ntensity 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 b y 4.9 percent over that period, the growth of generation is lower than both the population growth (9.2 percent) and real GDP growth (19.8 percent). Figure 2 - 10 shows the growth of electricity generation, population and real GDP during this period.

**Figure 2 -**  **10 .**  **Relative Growth of Electricity Generation, Po**  **pulation and Real GDP Since**

**2002** Sources: U.S. EIA Monthly Energy Review, December 2014. Table 7.2a Electricity Net Generation: Total (All Sectors). U.S. Census. Because electricity generation grew slower than both the population and GDP, the relative e lectric 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 b y 10.9 percent, increasing from $44,900 (in 2011$) per person in 200 2 to $49,800/person in 2012. At the same time electricity

generation per capita decreased b y 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 efficienc y 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***E.O. 12866 Review

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2 - 17 and 2 - 11 clearl y show the effects of the 2007

– 2009 recession on both GDP and electricity generation, as well as the effects of the subsequent economic recovery .

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

**Intensity Since 2002** Sources: U .S. EIA Monthly Energy Review, December 2014. Table 7.2a Electricity Net Generation: Total (All Sectors). U.S. Census **2.4 Deregulation and Restructuring**  The process of restructuring and deregulation of wholesale and retail electric markets has changed the structure of the electric power industry . In addition to reorganizing asset management between companies, restructuring sought a functional unbundling of the generation, transmission, distribution, and ancillary services the power sector has historically provided, with the aim of enhancing competition in the generation segment of the industry . Beginning in the 1970s, government policy shifted against traditional regulatory approaches and in favor of deregulation for many important industries, including tra nsportation (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***E.O. 12866 Review

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2 - 18 choices, the economic incentive to provide least - cost electric rates through market competition, reduced costs of combustion turbin e technology that opened the door for more companies to sell power with smaller investments, and complexity of monitoring utilities’ cost of service and establishing cost - based rates for various customer classes. Deregulation and market restructuring in th e 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 re tail 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 b y the Federal Energy Regulatory Commission (FERC) fo r actions aimed at wider restructuring, and no additional states have begun retail deregulation activity since that time.***E.O. 12866 Review

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2 - 19 **Figure 2 -**  **1 2**  **. Status of State Electricity Industry Restructuring Activities**  Source: EIA 2010. “Status of Electricity Restructuring by State.” Available at <http://www.eia.gov/cneaf/electricity/page/restructuring/restructure_elect.html. One major effect of the restructuring and deregulation of the power sector was a significant change in ty

pe 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, transmissio n and distribution in their designated areas, and prices were set b y 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 b y competitive companies known as Independent Power Producers (IPP). The resulting generating sector differed b y 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 largel y in place. The resulting ownership mix of generating***E.O. 12866 Review

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2 - 20 capacity (MW) in 2002 was 62 percent of the generating capacity owned by traditional utilities, 35 percent owned b y IPPs 26 , and 3 perc ent 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 b y 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 ty pes. Figures 2 - 13 and 2 - 14 present the mixes in 2002 and 2012. A portion of the shift of capacity and generation is due to sales and transfers of generation assets from traditional utilities to IPPs, rather than strictly the result of newl y built units.

26 IPP data presented in this section include both combined and non - com bined heat and power plants.***E.O. 12866 Review

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

**2012** The mix of capacity b y fuel types that have been built and retired between 2002 and 2012 also varies significantl y by type of ownership. F igure 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 capaci ty was primary

(60 %) steam and combustion turbines.

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000

1 2 3 4 5Capacity(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

1 2 3 4 5Generation(GW) 

### Generation Mix, 2002 & 2012

Series1 Series2 Series3 Series4 Series5 Series6***E.O. 12866 Review

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2 - 22 **Figures 2 -**  **15 and 2**  **- 1**  **6. Generation Capacity Built and Retired**  **b etween 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 CO 2 in particular, but also contributes to emissions of sulfur hexafluoride (SF 6 ), CH 4 , and N 2 O. In 201 3 , the electricity generation ac counted for 38 percent of national CO 2 emissions. Including both generation and transmission (a source of SF6) and generation, the power sector accounted for 31 percent of total nationwide greenhouse gas emissions (measured in CO 2 equivalent) . Table 2 - 5 an d Figure 2 - 1 7 show the CO 2 emissions from the power sector relative to other major economic sectors. Table 2 - 6 shows the contributions of CO 2 and other GHGs from the power and other major emitting economic sectors.

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

1 2NewCapacity(MW) 

### Capacity Built 2002 - 2012 by Ownership T

ype

Series1 Series2 Series3 Series4 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000

1 2RetiredCapacity(MW) 

### Capacity Retirements 2002 - 2012 by Ownership T

ype

Series1 Series2 Series3 Series4***E.O. 12866 Review

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2 - 23 **Table 2 -**  **5 .**  **Domestic Emissions of Green house Gases, by Economic Sector (million metric** 

**tonnes of CO 2**  **equivalent)**

**2002 201**  **3 Change Between '02 and '1**  **3**

**Sector/Source GHG** 

**Emissions % Total** 

**GHG Emissions**  **GHG**

**Emissions % Total** 

**GHG Emissions**  **Change in**

**Emissions % Change** 

**in Emissions**  **% of**

**Total Change in**  **Emissions** Electric Power Industry 2,313 33% 2,077 31% - 236 - 10% 64% Transportation 1,958 28% 1,806 27% - 151 - 8% 41% Industry 1,419 20% 1,392 21% - 27 - 2% 7% Agriculture 561 8% 587 9% 26 5% - 7% Commercial 365 5% 401 6% 37 10% - 10% Residential 374 5% 375 6% 1 0% 0% US Territories 52 < 1% 35 < 1% - 17 - 33% 5% **Total Emissions 7,041**  **100% 6,673**  **100% -**  **368 -**  **5% 100%**  Sinks and Reductions - 885 - 882 4 0 % **Net Emissions 6,156**  **5,791 -**  **368 -**  **6%** Source: EPA, 201 5 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 2012”, Table ES - 6 . Includes CO 2 , CH 4 , N 2 O and SF 6 emissions.

**Figure 2 -**  **1 7.**  **Domestic Emissions of Greenhouse Gases from Major Sectors, 2002 and 201 3** 

**(million metric tonnes of CO 2**  **equivalent)**

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

2002 2013GHGEmissions(MillionTonnes)

Electric Power Industry Transportation Industry Ag riculture Commercial Residential***E.O. 12866 Review

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2 - 24 Source: EPA, 201 5 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 201 3 ”, Table ES - 6 **Table 2 -**  **6 .**  **Greenhouse Gas Emissions from the Electricity Sector (Generation,**

**Transmission and Distribution), 2002 and 2012 (million metric tonnes of CO 2**  **equiva lent)**  **2002 201**  **3 Change Between '02 and**  **'1 3**  **Gas/Fuel Type or Source GHG**  **Emissions % of** 

**Total GHG**  **Emissions from**  **Power Sector**  **GHG**

**Emissions % of Total** 

**GHG Emissions**  **from Power**  **Sector Change in** 

**Emissions % Change** 

**in Emissions** 

CO 2 2,287 98.9% 2,052 98.8% - 235 - 10% Fossil Fuel Com bustion 2,273 98.2% 2,040 98.2% - 233 - 10% Coal 1,890 81.7% 1,575 75.8% - 315 - 17% Natural Gas 306 13.22% 442 21.28% 136 45% Petroleum 76.8 3.32% 22.4 1.08% - 54.4 - 71% Geothermal 0.4 0.02% 0.4 0.02% 0.0 0% Incineration of Waste 11.8 0.51% 10.1 0.49% - 1.7 - 14% Other Process Uses of Carbonates 2.6 0.11% 2.2 0.11% - 0.4 - 15% CH 4 0.4 0.02% 0.4 0.02% 0.0 0% Stationary Com bustion* 0.4 0.02% 0.4 0.02% 0.0 0% Incineration of Waste + + N 2 O 12.4 0.54% 19.4 0.93% 7.0 56% Stationary Com bustion* 12.0 0.52% 19.1 0.92% 7.1 59% Incineration of Waste 0.4 0.02% 0.3 0.01% - 0.1 - 25% SF 6 13.3 0.57% 5.1 0.25% - 8.2 - 62% Electrical Transmission and Distribution 13.3 0.57% 5.1 0.25% - 8.2 - 62% **Total GHG Emissions 2,313**  **2,064 -**  **236 -**  **10%** Source: EPA, 2015 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 201 3 ”, Table 2 - 1 1 * Includes only stationary combustion emissions related to the generation of electricity. ** SF 6 is not covered by this rule, which specifically regulates GHG emissions from combustion. + Does not exceed 0.05 Tg CO 2 Eq. or 0.05 percent. The amount of CO 2 emitted during the combustion of fossil fuels varies according to the carbon content and heating value of the fuel used. The CO 2 emis sion factors used in IPM v5.14***E.O. 12866 Review

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2 - 25 (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 CO 2 during combustion. Coal emits around 1.7 times as much carbon per uni t of energy when burned a s natural gas (EPA 2013). **Table 2 -**  **7 .**  **Fossil Fuel Emission Factors in EPA Base Case 5.14 IPM Power Sector**

**Modeling Application** Fuel T ype Carbon Dioxide (lbs/MMBtu) Coal Bituminous 202.8

– 209.6 Subbituminous 209.2

– 215.8 Lignite 212.6

– 219. Natural Gas 117.1 Fuel Oil Distillate 161.4 Residual 161.4

– 173.9 Biomass 195 Waste Fuels Waste Coal 204.7 Petroleum Coke 225.1 Fossil Waste 321.1 Non - Fossil Waste 0 Tires 189.5 Municipal Solid Waste 91.9 Source: Documentation for IPM Base Case v.5.13, Table 11 - 5. The emission factors used in Base Case 5.14 are identical to the emission factors in IPM Base Case 5.13. Note: CO 2 emissions presented here for biomass account for combustion only and do not reflect lifecycle emissions fr om

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 significantl y reducing the CO 2 emissions of new fossil fuel combustion sources intended for intermediate and baseload operations include the use of: carbon capture and storage (CCS), higher- efficiency designs (e.g., supercritical or ultrasupercritical steam units, integrated gasification combined cycle (IGCC), or c ombined - cycle combustion - turbine/steam - turbine units), and/or lower - emitting fuels (e.g. natural gas rather than coal).***E.O. 12866 Review

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2 - 26 Daily peak electricity demands, involving operation for relativel y few hours per ye ar, are often most economicall y met by simple - cycle c ombustion turbines (CT). Stationary CTs used for power generation can be installed quickly , at relatively low capital cost. They

can be remotel y started and loaded quickly , and can follow rapid demand changes. Full - load efficiencies of large current techno logy

CTs are 30 - 33 percent (high heating value basis), as compared to efficiencies of 50 percent or more for new combined - cycle units that recover and use the exhaust heat otherwise wasted from a CT . A simple - cycle CT’s lower efficiency causes it to burn much more fuel to produce a MWh of electricity than a combined - cycle unit. Thus, when burning natural gas its CO 2 emission rate per MWh could be 40 - 60 percent higher than a more efficient NGCC unit. Baseload electricity

demand can be met with NGCC generation, coal and other fossil - fired steam generation, and IGCC technology , as well as generation from sources that do not emit CO 2 , such as nuclear and hydro. IGCC employ s the use of a gasifier to transform fossil fuels into sy nthesis gas (“s yn gas”) and heat. The s yn gas is used to fuel a combined cycle generator, and the heat from the s yn gas conversion can produce steam for the steam turbine portion of the combined cycle generator. Electricity can be generated through this IGCC process somewhat more efficientl y than through conventional boiler - steam generators. Additionally , with gasification, some of the sy ngas can be converted into other marketable products such as fertilizer, and CO 2 can be captured for use in EOR. **2.6.1 Carbon Capture and Storage**  CCS can be achieved through either pre - combustion or post - combustion capture of CO 2 from a gas stream associated with the fuel combusted. Furthermore, CCS can be designed and operated for full capture of the CO 2 in the gas stream (i.e., above 90 percent) or for partial capture (below 90 percent). For post - combustion capture, CO 2 is stripped from the flue gas b y passing the flue gas through a liquid absorbent which selectivel y reacts with the gaseous carbon dioxide to remove it from the combustion gas stream. The ab sorbent, upon saturation, transfers to a downstream operation which regenerates the absorbent by desorbing the CO 2 back to gaseous form. The absorbent recycles back into the process to repeat the capture cycle while the removed carbon dioxide is compressed , sent to storage and sequestered. This process is illustrated for a pulverized coal power plant in Figure 2 - 18 . For post - combustion, a station's net generating output could be 20 - 30 percent lower due to the energy needs of the capture process.***E.O. 12866 Review

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2 - 27 **Figure 2 -**  **18. Post**  **- Combustion CO**  **2 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 (“s yn gas”) under heat and pressure and some percentage of the carbon contained in the s yn gas is captured before combustion. For pre - combustion technology, a significant amount of energy is needed to gasify the fuel(s). This proce ss is illustrated in Figure 2 - 18 . Application of post - combustion CCS with IGCC can be designed to use no water - gas shift, or single - or two - stage shift processes, to obtain vary ing percentages of CO 2 removal

– from a “partial capture” percentage to 90 percent “full capture.” Pre - combustion CCS ty picall y has a lesser impact on net energy output than does post - combustion CCS. For more detail on CCS***E.O. 12866 Review

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2 - 28 technology , see the “Report of the Interagency Task Force on Carbon Capture and Storage” (2010). 27

27 For more information on the cost and performance of CCS, see http://www.netl.doe.gov/energy analyses/baseline_studies.html .***E.O. 12866 Review

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2 - 29 **Figure 2 -**  **18. Pre**  **- Combustion CO**  **2 Captu**  **re for an IGCC Power Plant** Source: Interagency Task Force on Carbon Capture and Storage 2010 Carbon capture technology has been successfull y applied since 1930 on several smaller scale industrial facilities and more recently in a number of demonstration p hase projects worldwide for power sector applications. In October 2014 the first industrial - scale coal capture and storage demonstration project for electricit y generation began operation at the Boundary Dam Power Station in Saskatchewan, Canada. The Bound ary Dam CCS project is the first industrial - scale electric power project in the world to begin operation. Boundary Dam Station is owned b y the Province of Saskatchewan, and operated by SaskPower, a provincially owned corporation that is the primary electri c utility in the Province. The demonstration project retrofit Unit 3 (a 130 MW, coal fired built in 1970, and rebuilt in 2013) at a total cost of approximately $1.5 billion (Canadian, or about $1.2 billion US), including a partial subsidy of $240 million ( Canadian) b y the Canadian federal government. The carbon capture sys tem is a post combustion process designed to capture 90 percent of the CO2 emitted by Unit #3. Retrofitting the carbon capture system reduced the capacity o f the unit to 110 MW. The majori ty of the***E.O. 12866 Review

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2 - 30 captured CO 2 is used for an EOR project in southern Saskatchewan. The portion of the CO2 is being stored in a nearb y research and monitoring geological storage facility, where the captured CO 2 will be injected 3.4 kilometers underground into a sandstone formation located below the major coal field suppl yi ng lignite to Unit # 3. The remaining captured CO2 will be injected into deep saline formations. In the United States the Kemper County Carbon Dioxide Capture and Storage Project in Mississippi. Construction began in 2010, and the startup is currentl y scheduled for May , 2016. The Kemper County

CCS project is constructing a new 524 MW lignite unit as well as a 58 MW natural gas unit. Mississippi Power (a division of Southern Power) is building and will operate the Kemper County project. The control s ys tem is designed to capture 65 percent of the CO 2 generated b y the plant, and is projected to capture 3.5 million tons of CO 2 per year. The resulting CO 2 emission rate is expected to ~800 pounds per MWh produced. The current total cost estimate is $5.6 billion, a substantial increase from the original $2.4 billion estimate. The construction has received a $270 million grant from the US Department of En ergy , and $133 million in investment tax credits from the Internal Revenue Service. The captured CO 2 will be transported via a 60 mile pipeline and used for EOR projects in mature Mississippi oil fields. 28 The only other industrial - scale electricity power s ector CCS project currently under construction is the W.A. Parish Petra Nova CCS Project near Houston, Texas. The Parish Petra project is a 50/50 partnership between NRG Energy (an integrated electricity company generating and suppl yi ng 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 slipstre am of the flue gas, and is designed to capture 90 percent of the CO 2 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 i s being built on - site to provide both electricity and steam to the CCS unit. The total cost of the CCS project is estimated to be $1 billion (including a $167 million grant from the US Department of

28 Carbon Capture and Sequestration Technologies Program at MIT. Accessed 1/23/2015. https://sequestration.mit.edu/to ols/projects/kemper.html***E.O. 12866 Review

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2 - 31 Energy), and is expected to extract 1.4

– 1.6 million ton s of CO 2 per year. The construction contract was awarded in July, 2014, and operation is expected to begin in early 2016. The CO 2 will be piped 85 miles to a reservoir for EOR in the West Ranch Oil Field. 29 **2.6 .2**  **Geologic and Geographic Considerations for Geologic Sequestration**  Geologic sequestration (GS) (i.e., long - term containment of a CO 2 stream in subsurface geologic formations) is technicall y feasible and available throughout most of the United States. GS is feasible in different ty pes of geologic for

mations including deep saline formations (formations with high salinity formation fluids) or in oil and gas formations, such as where injected CO 2 increases oil production efficiency through a process referred to as enhanced oil recovery (EOR). CO 2 may

als o be used for other ty

pes of enhanced recovery , such as for natural gas production. Reservoirs, such as un - mineable coal seams, also offer the potential for geologic storage. The geographic availability of deep saline formations, EOR, and un - mineable coal seams is shown in Figure 2 - 14. Estimates of CO 2 storage resources b y state compiled by the DOE’s National Carbon Sequestration Database and Geographic Information Sy stem (NATCARB) and published in a Carbon Utilization and Storage Atlas (discussed below) are provided in Table 2 - 8.

29 US DOE (2010) “Recovery Act: W.A. Parish Post - Com bustion CO 2 Capture and Sequestration Project”. http://www.netl.doe.gov/research/proj?k=FE0003311 Accessed 1/23/2015***E.O. 12866 Review

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2 - 32 **Figure 2 -**  **1 9.**  **Geologic Sequestration in the Continental United States** Source: EPA 2013: Data sources: EPA’s Greenhouse Gas Reporting Program; Department of Energy, NATCARB; Department of Transportation, National Pipeline Manageme nt System.***E.O. 12866 Review

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2 - 33 **Table 2 -**  **8 .**  **Total CO 2**  **Storage Resource 30**  **Million Metric Tons***

**State Low Estim**  **ate High Estimate**  Alabama 122,490 694,380 Alaska 8,640 19,750 Arizona 130 1,170 Arkansas 6,180 63,670 California 33,890 420,630 Colorado 37,610 357,190 Connecticut not assessed not assessed Delaware 40 40 District o f Columbia not assessed not assessed Florida 102,740 555,010 Georgia 145,340 159,050 Hawaii not assessed not assessed Idaho 40 390 Illinois 10,020 116,820 Indiana 32,020 68,210 Iowa 10 50 Kansas 10,880 86,340 Kentucky 2,920 7,650 Louisiana 169,500 2,103,980 Maine not assessed not assessed Maryland 1,860 1,930 Massachusetts not assessed not assessed

30 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory (NETL).***E.O. 12866 Review

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2 - 34 **Table 2 -**  **8. Total CO**  **2 Storage Resource, continued**  **Million Metric Tons***

**State Low Estim**  **ate 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 CO 2 storage resource. States that have not yet been assessed by the RCSPs have been identified.***E.O. 12866 Review

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2 - 35 **2.7.2 Availability of geologic sequestration in deep saline formations**  DOE and the United States Geological Survey (USGS) have independently conducted preliminary

anal ys es of the availability and potential CO 2 sequestration capacity of deep saline formations in the United States. DOE estimates are compiled b y the DOE’s National Carbon Sequestration Database and Geographic Information Sy stem (NATCARB) using volume tric models and published in a Carbon Utilization and Storage Atlas. 31 DOE estimates that areas of the United States with appropriate geology have a sequestration potential of at least 2,035 billion metric tons of CO 2 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 CO 2 in U.S. onshore areas and state waters using probabilistic assessment. 32 The USGS estimates a mean of 3,000 billion metric tons of subsurface CO 2 sequestration potential, including saline and oil and gas reservoirs, across the basins studied in the United States. As shown in Figure 2 - 14, there are 39 states for which onshore and offshore deep saline formation storage capacity has been identified. 33 **2.7.3 Availability of CO** **2 storage via enhanced oil recovery (EOR)**  Although the regulatory

impact anal ys is for this rule relies on GS in deep saline formations, the EPA also recognizes the potential for securel y sequestering CO 2 via EOR. EOR has been successfull y used at numerous production fields throughout the United States to increase oil recovery . The oil indus try in the United States has over 40 years of experience with EOR. An oil industry

study in 2014 identified more than 125 EOR projects in 98 fields in the United States. 34 More than half of the projects evaluated in the study have been in operation for

31 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S. Department of Energy, Office of Fossil Energy, National En ergy Technology Laboratory (NETL). 32 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resources

—Results: U.S. Geological Survey Circular 1386, p. 41, http://pubs. usgs.gov/circ/1386/. 33 Alaska is not shown in the figure; it has deep saline formation storage capacity, geology amenable to EOR operations, and potential GS capacity in un - mineable coal. 34 Koottungal, Leena, 2014, 2014 Worldwide EOR Survey, Oil & Gas Journal, Volume 112, Issue 4, April 7, 2014 (corrected tables appear in Volume 112, Issue 5, May 5, 2014).***E.O. 12866 Review

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2 - 36 more than 10 years, and many have been in operation for more than 30 ye ars. This experience provides a strong foundation for demonstrating successful CO 2 injection and monitoring technologies, which are needed for safe and secure GS that can be used for deploy ment of CCS across geographically

diverse areas. Currently , 12 states have active EOR operations and most have developed an extensive CO 2 infrastructure, including pipelines, to support the continued operation and growth of EOR. An additional 23 states are within 150 miles of current EOR operations. See Figure 2 - 14. The vast majority

of EOR is conducted in oil reservoirs in the Permian Basin, which extends through southwest Texas and southeast New Mexico. States where EOR is utilized include Alabama, Colorado, L ouisiana, Michigan, Mississippi, New Mexico, Oklahoma, Texas, Utah, and Wyoming. At the project level, the volume of CO 2 already injected for EOR and the duration of operations are of similar magnitude to the duration and volume of CO 2 expected to be captured from fossil fuel - fired EGUs. The volume of CO 2 used in EOR operations can be large (e.g., 55 million tons of CO 2 were stored in the SACROC unit in the Permian Basin over 35 years), and operations at a single oil field may last for decades, injecting into multiple parts of the field. 35 According to data reported to the EPA’s Greenhouse Gas Reporting Program (GHGRP), approximately

60 million metric tons of CO 2 were supplied to EOR in the United States in 2013. 36 Approximately

70 percent of this total CO 2 supplied was produced from natural (geologic) CO 2 sources, and approximately 30 percent was captured from anthropogenic sources. 37 A DOE - sponsored study

has anal yz ed the geographic availability of appl yi ng EOR in 11 major oil producing regions of the United States and found that there is an opportunity to

35 Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. “Evaluation of CO 2 trapping mechanisms at the SACROC northern platform, Permian basin, Texas, site of 35 years of CO 2 injection.” American Journal of Science 310. (2010): 282 - 324. 36 Greenhouse Gas Reporting Program, data reported as of August 18, 2013. 37 Greenhouse Gas Reporting Program, data reported as of August 18, 2013.***E.O. 12866 Review

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2 - 37 significantl y increase the application of EOR to areas outside of current operations. 38 DOE sponsored geologic and engineering anal ys es show that expanding EOR operations into areas additional to the ca

pacity already identified and applying new methods and techniques over the next 20 years could utilize 18 billion metric tons of anthropogenic CO 2 and increase total oil production by

67 billion barrels. The availability of anthropogenic CO 2 in areas outsi de of current sources could drive new EOR projects b y making more CO 2 locally available. **2.8 GHG and Clean Energy Regulation in the Power Sector**  **2.8.1 State Policies**  Several states have also established emission performance standards or other measures to limit emissions of GHGs from new EGUs that are comparable to this rulemaking. In 2003, then - Governor George Pataki sent a letter to his counterparts in the Northeast and Mid - Atlantic inviting them to participate in the development of a regional cap - and - tr ade program addressing power plant CO 2 emissions. This program, known as the Regional Greenhouse Gas Initiative (RGGI), began in 2009 and sets a regional CO 2 cap for participating states. The currentl y participating states 39 include: Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. The cap covers CO 2 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 em issions 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 annuall y from 2015 to 2020. RGGICO 2 allowances are sold in a quarterl y auction. RGGI conducted their 27 th quarterly allowance auction in March, 2015 the market clearing price was $5.41 per ton of CO 2 for current allowances, which was a record high price (the February ’15 price of $5.21 was the previous record). A total of allowances for 15.3 million tons were sold in the March ’15 auction,

38 “I mp rovi ng Domestic Energy Security and Lowering CO 2 Emissions with “Next Generation” CO 2 - Enhanced Oil Recovery”, Advanced Resources International, Inc. (ARI), 2011. Available at: http://www.netl.doe.gov/research/energy - analysis/publications/details?pub=df02ffba - 6 b4b - 4721 - a7b4 - 04a505a19185. 39 New Jersey was originally a part of RGGI, but opted to leave the program in 2011, reducing the number of participating states to 9.***E.O. 12866 Review

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2 - 38 well below the record of 38.7 million tons sold in June ’13 for $3.21. The RGGI CO 2 emissions cap was reduced from 188 million tons (in a 10 state region) in 2009 - 2011 , and reduced again to 165 million tons (in a 9 state region) in 2012 - 2014. The cap is further reduced by 2.5 percent each ye ar from 2014 through 2020. In September 2006, California Governor Schwarzenegger signed into law Senate Bill 1368. The law limits long - term investments in baseload generation by the state's utilities to power plants that meet an emissions performance standard jointly established by

the California Energy Commission and the California Public Utilities Commission. The Energy Commission has designed regulations that establish a standard for new and exi sting baseload generation owned b y, or under long - term contract to publicly owned utilities, of 1,100 lb CO 2 /MWh. In 2006 Governor Schwarzenegger also signed into law Assembly Bill 32, the Global Warming Solutions Act of 2006. This act includes a multi - sec tor GHG cap - and - trade program which covers approximately 85 percent of the state GHG emissions. EGUs are includes in phase I of the program, which began in 2013. Phase II begins in 2020 and includes upstream sources. The cap is based on a 2 percent reducti on from total 2012 expected emissions, and declines 2 percent annuall y 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

marke t. The final 2014 market price for 2014 carbon allowances was $12.38/tonne of carbon. On April 17, 2015 the 2015 allowance futures price was $12.65/tonne, and the spot price was $12.46/tonne. In May 2007, Washington Governor Gregoire signed Substitute Senate Bill 6001, which established statewide GHG emissions reduction goals, and imposed an emission standard that applies to any

baseload electric generation that commenced operation after June 1, 2008 and is located in Washington, whether or not that generat ion serves load located within the state. Baseload generation facilities must initially comply with an emission limit of 1,100 lb CO 2 /MWh. In July 2009, Oregon Governor Kulongoski signed Senate Bill 101, which mandated that facilities generating baseload e lectricity , whether gas - or coal - fired, must have emissions equal to or less than 1,100 lb CO 2 /MWh, and prohibited utilities from entering into long - term purchase***E.O. 12866 Review

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2 - 39 agreements for baseload electricity with out - of - state facilities that do not meet that standard. Natural gas - and petroleum distillate - fired facilities that are primarily used to serve peak demand or to integrate energy

from renewable resources are specifically exempted from the performance standard. In August 2011, New York Governor Cuomo signed the Power NY Act of 2011. This regulation establishes CO 2 emission standards for new and modified electric generators greater than 25 MW. The standards vary based on the typ e of facility : baseload facilities must meet a CO 2 standard of 925 lb/MWh or 120 lb /MMBtu, and peaking facilities must meet a CO 2 standard of 1,450 lbs/MWh or 160 lbs/MMBtu. Additionally , most states have implemented Renewable Portfolio Standards (RPS), or Renewable Electricity Standards (RES). These programs are designed to increase the renewable share of a state’s total electricity generation. Currently

30 states and the District of Columbia have enforceable RPS or other mandatory renewable capacity

policies, and 7 states have voluntary goals. 40 These programs vary widely in structure, enforcement, and scope. **2.8.2 Federal Policies**  In April 2007, the Supreme Court concluded that GHGs met the CAA definition of an air pollutant, giving the EPA the authority to regulate GHGs under the CAA contingent upon an agency determination that GHG emissions from new motor vehicles cause or contribute to air pollution that may

reasonably

be anticipated to endanger public health or welfare. This decision to regulate GHG emissions for motor vehicles set the stage for the determination of whether other sources of GHG emissions, including stationary sources, would need to be regulated as well. In response to the FY2008 Consolidated Appropriations Act (H.R. 2764; Public L aw 110

– 161), the EPA issued the Mandatory Reporting of Greenhouse Gases Rule (74 FR 56 20) which required reporting of GHG data and other relevant information from fossil fuel suppliers and industrial gas suppliers, direct greenhouse gas emitters, and manufacturers of heavy- duty and

40 EIA 2012a***E.O. 12866 Review

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2 - 40 off - road vehicles and engines. The purpose of the rule was to collect accurate and timely GHG data to inform future policy decisions. As such, it did not require that sources control greenhouse gases, but sources above certain threshold levels must monitor and report emissions. In August 2007, the EPA issued a prevention of significant deterioration (PSD) permit to Deseret Power Electric Cooperative, authorizing it to construct a new waste - coal - fired EGU near its existing Bonanza Power Plant, in Bonanza, Utah. The permit did not include emissions control requirements for CO 2 . The EPA acknowledged the Supreme Court decision, but found that decision alone did not require PSD permits to include limits on CO 2 emissions. Sierra Club challenged the Deseret permit. In November 2008, the Environmental Appeals Board (EAB) re manded the permit to the EPA to reconsider “whether or not to impose a CO 2 BACT (best available control technology) limit in light of the ‘subject to regulation’ definition under the CAA.” The remand was based in part on EAB’s finding that there was not an established EPA interpretation of the regulatory phrase “subject to regulation.” In December 2008, the Administrator issued a memo indicating that the PSD Permitting Program would appl y to pollutants that are subject to either a provision in the CAA or a regulation adopted b y the EPA under the CAA that requires actual control of emissions of that pollutant. The memo further explained that pollutants for which the EPA regulations only require monitoring or reporting, such as the provisions for CO 2 in the A cid Rain Program, are not subject to PSD permitting. Fifteen organizations petitioned the EPA for reconsideration, prompting the agency to issue a revised finding in March 2009. After reviewing comments, the EPA affirmed the position that PSD permitting is not triggered for a pollutant such as GHGs until a final nationwide rule requires actual control of emissions of the pollutant. For GHGs, this meant January 2011 when the first national rule limiting GHG emissions for cars and light trucks was scheduled t o take effect. Therefore, a permit issued after January 2, 2011, would have to address GHG emissions. The Administrator signed two distinct findings in December 2009 regarding greenhouse gases under section 202(a) of the Clean Air Act. The endangerment finding indicated that current and projected concentrations of the six key well - mixed greenhouse gases

— CO 2 , CH 4 , N2 O, hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and SF 6 — in the atmosphere threaten the***E.O. 12866 Review

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2 - 41 public health and welfare of current and future generations. These greenhouse gases have long lifetimes and, as a result, become homogeneously distributed through the lower level of the Earth’s atmosphere (IPCC, 2001). This differentiates them from other greenhouse gases that are not homogeneousl y distributed in the atmosphere. The cause and contribute finding indicated that the combined emissions of these well - mixed greenhouse gases from new motor vehicles and new motor vehicle engines contribute to the greenhouse gas pollution which threatens public h ealth and welfare. Both findings were published in the Federal Register on December 15, 2009 (Docket ID EPA - HQ - OAR - 2009 - 0171). These findings did not themselves impose any requirements on any industry or other entities, but allowed the EPA to regulate gree nhouse gases under the CAA (see preamble section II.E for regulatory background). This action was a prerequisite to implementing the EPA's proposed greenhouse gas emission standards for light duty vehicles, which was finalized in January 2010. Once a pollutant is regulated under the CAA, it is subject to permitting requirements under the PSD and Title V programs. The 2009 Endangerment Finding and a denial of reconsideration were challenged in a lawsuit; on June 26, 2012, the DC Circuit Court upheld the Endangerment Finding and the Reconsideration Denial, ruling that the Finding was neither arbitrary nor capricious, was consistent with Massachusetts v. EPA, and was adequatel y supported by the administrative record. The Court found that the EPA had based its d ecision on “substantial scientific evidence,” noted that the EPA’s reliance on assessments was consistent with the methods decision - makers often use to make a science - based judgment, and stated that “EPA’s interpretation of the governing CAA provisions is unambiguousl y correct.” In May 2010, the EPA issued the final Tailoring Rule which set thresholds for GHG emissions that define when permits under the New Source Review and Title V Operating Permit programs are required for new and existing industrial faci lities. Facilities responsible for nearly 70 percent of the national GHG emissions from stationary sources, including EGUs, were subject to permitting requirements under the rule. This rule was upheld by the D.C. Circuit in 2012. The EPA entered into a pro posed settlement agreement in December 2010 to issue rules that will address greenhouse gas emissions from fossil fuel - fired power plants. On March 27, 2012, EPA proposed NSPS for CO 2 emissions from new source natural gas, coal, and other solid fossil - fire d EGUs. After consideration of information provided in more than 2.7 million***E.O. 12866 Review

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2 - 42 comments on the March 27, 2012 proposal, as well as consideration of continuing changes in the electricity sector, the EPA determined that revisions in its initially proposed approach were warranted. EPA replaced that prior proposal with a new proposal for NSPS for CO 2 emissions from EGUs on September 20, 2013; this action finalizes that rulemaking. Existing source standards are not addressed in this action. Details of the settlement agreement can be found on the EPA website. 41 **2.9 Revenues and Expenses**  Due to lower retail electricity sales, total utility

operating revenues declined in 2012 to $271 billion from a peak of almost $300 billion in 2008. Despite revenues not returning to 2008 levels in 2012, operating expenses were appreciably lower and as a result, net income also rose in comparison to 2008 (see Table 2 - 9). Recent economic events have put downward pressure on electricity demand, thus dampening electricity prices and consumption (utility revenues), but have also reduced the price and cost of fossil fuels and other expenses. In 2012 electricity generation was 1.28 percent below the generation in 2011, and has declined in 4 of the past 5 ye ars. Table 2 - 9 shows that i nvestor - 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 ye ar for the period 2002 to 2012 (average: 11.9 percent range: 10.6 percent to 13.32 percent).

41 http://www.epa.gov/airquality/ghgsettlement.html***E.O. 12866 Review

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2 - 43 **Table 2**  **- 9**  **. Revenue and Expense Statistics for Major U.S. Investor**  **- Owned Electric Utilities** 

**for 2010 (nominal $millions) 2002**  **2008 2012**  **Utility Operating Revenues 219,609**  **298,962 270,912**  Electric Utility 200,360 266,124 249,166 Other Utility 19,250 32,838 21,745 **Utility Operating Expenses 189,062**  **267,263 235,694**  Electric Utility 171,604 236,572 220,722 Operation 116,660 175,887 152,379 Production 90,715 140,974 111,714 Cost of Fuel 24,149 47,337 38,998 Purchased Power 58,810 84,724 54,570 Other 7,776 8,937 18,146 Transmission 3,560 6,950 7,183 Distribution 3,117 3,997 4,181 Customer Accounts 4,168 5,286 5,086 Customer Service 1,820 3,567 5,640 Sales 264 225 221 Admin. and General 13,018 14,718 18,353 Maintenance 10,861 14,192 15,489 Depreciation 16,199 19,049 23,677 Taxes and Other 26,716 26,202 29,177 Other Utility 17,457 30,692 14,972 **Net Utility Operating Income 30,548**  **31,699 35,218**  Source: Table 8.3, EIA Electric Power Annual, 2012 Note: This data does not include information for public utilities, nor for Independent Power Producers (IPPs). **2.10 Natural Gas Market**  The natural gas market in the United States has historically experienced significant price volatility from year to year, between seasons within a year, and can undergo major price swings during short - lived weather events (such as cold snaps leading to short - run spikes in heating demand). Over the period last decade, the annual average nominal price of gas delivered to the power sector have ranged from $3.4 2/MM Btu (in 2012) to $9.02/MM Btu (in 2008). During that***E.O. 12866 Review

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2 - 44 time, the daily

price 42 of natural gas reached as high as $18.48/MM Btu and as low as $2.03. Adjusting for inflation using the GDP implicit price deflator, in $2011 the annual average price of natural gas delivered to the power sector ranged from $9.38 (in 2008) to $3.36 (in 2012). The annual natural gas prices in both nominal and real (2011$) terms are in Figure 2 - 20 . A comparison of the trends in the real price of natural gas with the real price of de livered coal and oil are shown in Figur e 2 - 21 . Figure 16 shows that while the real price of coal and oil increased from 2002 to 2012 (+54 percent and +203 percent respectivel y), the real price of natural gas declined b y 22 percent in the same period. Most of the decline in real natural gas prices occurred between 2008 (the peak price ye ar) and 2012, during which real gas prices declined b y 64 percent while coal and oil prices both increased b y 9 percent. The sharp decline in natural gas prices from 2008 to 2012 was primaril y caused b y 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 ($/** **MM Btu)**  Source: http://www.eia.gov/totalenergy/data/monthly/#prices . Downloaded 2/15/2015.

42 Henry Hub daily prices. Henry Hub is a major gas distribution hub in Louisiana; Henry Hub prices are generally seen as the primary metric for national gas prices for all end uses. $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00 $7.00 $8.00 $9.00 $10.00

2002 2004 2006 2008 2010 2012NaturalGasPrices($/mmBTU)

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2 - 45 **Figure 2 -**  **21. Relative Change in Real (2011$) Prices of Fossil Fuels Delivered to the Power** 

**Sector ($/ MM**  **Btu)** Source: http://www.eia.gov/totalenergy/data/monthly/#prices . Downloaded 2/15/2015. Current and projected natural gas prices are considerabl y 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 substantiall y i ncreased th e suppl y of economicall y recoverable natural gas. According to AEO 2012 (EIA 2012): Shale gas refers to natural gas that is trapped within shale formations. Shales are fine grained sedimentary

rocks that can be rich sources of petroleum and natural gas. Ov er 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 natur al 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 technicall y recoverable dry natural gas resources as of January 1, 2 012. Proven reserves make up 15 percent of the technicall y -50% 0% 50% 100% 150% 200% 250%

2002 2004 2006 2008 2010 2012 Coal Oil Gas Average***E.O. 12866 Review

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2 - 46 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 fo rmations, especially the Marcellus 43 , are so large that only small portions of the entire formations have been intensivel y production - tested. Furthermore, estimates from the Marcellus and other emerging fields with few wells already drilled are likely to sh ift significantl y over time as new geological and production information becomes available. Consequently , the estimate of technically

recoverable resources is highl y uncertain, and is regularly updated as more information is gained through drilling and pro duction. At the 2012 rate of U.S. consumption (about 25.6 Tcf per ye ar), 2,266 Tcf of natural gas is enough to suppl y nearl y 90 years o f use. The AEO 2014 estimate of the shale gas resource base is modestly

higher than the AEO 2012 estimate (2,214 Tcf) sh ale gas production estimates, driven by lower drilling costs and continued drilling in shale play s with high concentrations of natural gas liquids and crude oil, which have a higher value in energy equivalent terms than dry natural gas. 44 EIA’s projections of natural gas conditions did not change substantiall y in AEO 2014 from either the AEO 2012 or 2013, and EIA is still forecasting abundant reserves consistent with the above findings. Recent historical data reported to EIA is also consistent with these trends, with 2014 being the highest year on record45 for domestic natural gas production. 46

43 The Marcellus formation, underlying most of Pennsylvania and West Virginia, along with portions of New York and Ohio, in 2014 produced 36% of the U.S. total natural gas extracted from shale formations. 44 For more information, see: http://www.eia.gov/forecasts/archive/aeo11/IF_all.cfm#prospectshale ; http://www.eia.gov/energy_in_brief/about_shale_gas.cfm 45 The total dry gas production in 2012 from the lower 48 states, including both onshore and offshore production, wa s 23.97 Tcf, a 1.5% increase from 2013 and a 7.9% total increase from 2011 46 http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2014&subject= 8 - AEO2014&table=72 - AEO2014®ion=0 - 0& cases=ref2014 - d102413a***E.O. 12866 Review

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2 - 47 **2.11 References**  Advanced Resources International. 2011. Improving Domestic Energy Security and Lowering CO2 Emissions with “Next Generation” CO2 -Enhanced Oil Recovery (CO2 -EOR) . Available at <http://www.netl.doe.gov/research/energy- analy sis/publications/details?pub=df02ffba 6b4b - 4721 - a7b4 - 04a505a19185 >. Accessed June 9 , 2015. Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. 2010. Evaluation of CO 2 trapping mechanisms at the SACROC northern platform, Permian basin, Texas, site of 35 ye ars of CO 2 injection . American Journal of Science 310: 282 - 324. Interagenc y Task Force on Carbon Capture and Storage. Report of the Interagency Task Force on Carbon Capture and Storage . August 2010. Available at http://www.epa.gov/climatechange/downloads/CCS - Task - Force - Report - 2010.pdf>. Accessed June 9, 2015. Intergovernmental Panel on Climate Change. 2001. Climate Change 2001: The Scientific Basis . Available at <http://www.grida.no/publications/other/ipcc_tar/?src=/climate/ipcc_tar/wg1/218.htm>. Accessed June 9, 2015. International Energy

Agency (I EA). 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 W orldwide EOR Survey , Oil & Gas Journal, Volume 112, I ssue 4, April 7, 2014 (corrected tables appear in Volume 112, Issue 5, May 5, 2014). National Energy

Technology

L aboratory

(NETL). 2008. Reducing CO2 Emissions by Improving the Efficiency of Existing Coal- fired Power Plant Fleet . Available at <http://www.netl.doe.gov/energy- anal ys es/pubs/CFPP%2 0Efficiency- FINAL.pdf>. Accessed June 9, 2015. National Energy

Technology

L aboratory

(NETL). 2012. The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition . Available at <http://www.netl.doe.gov/technologies/carbon_seq/refshelf/atlas IV/> . Accessed June 9, 2015. National Energy

Technology

L aboratory

(NETL). 2013. Energy Analyses: Cost and Performance Baselines for Fossil Energy Plants. Available at <http://www.netl.doe.gov/energy- anal ys es/baseline_studies.html>. Accessed June 9, 2015.***E.O. 12866 Review

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2 - 48 Paci fic 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 Jun e 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

Inf ormation 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 A nnual 2009 . Available at <http://www.eia.gov/electricity /annual/archive/03482009.pdf>. Accessed June 9, 2015. U.S. Energy

Information Administration (U.S. EIA). 2013. Electric Power Annual 2011 . Available at <http://www.eia.gov/electricity /annual/>. Accessed June 9, 2015. U.S. Energy

Information Administration (U.S. EIA). 2010a. “Status of Electricity Restructuring by State.” Available at <http://www.eia.gov/cneaf/electricity /page/restructuring/restructure_elect.html>. Accessed June 9, 2015. U .S. Energy

Information Administration (U.S. EIA). 2010b. AEO 2010 Retrospective Review . Available at <http://www.eia.gov/forecasts/aeo/retrospective/>. Accessed June 9, 2015. U.S. Energy

Information Administration (U.S. EIA). 2010c. Annual Energy Outlook 2010 . Available at <http://www.eia.gov/oiaf/archive/aeo10/index.html>. Accessed June 9, 2015. U.S. Energy

Information Administration (U.S. EIA). 2010d. Annual Energy Review 2010 . Available at <http://www.eia.gov/totalenergy /data/annual/pdf/aer.pdf>. Access ed June 9, 2015. U.S. Energy

Information Administration (U.S. EIA). 2011. Annual Energy Outlook 2011 . Available at <http://www.eia.gov/forecasts/archive/aeo11/>. Accessed June 9, 2015. U.S. Energy

Information Administration (U.S. EIA). 2012. Annual Energy Outlook 2012 (Early Release) . Available at <http://www.eia.gov/forecasts/aeo/>. Accessed June 9, 2015.***E.O. 12866 Review

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2 - 49 U.S. Energy

Information Administration (U.S. EIA). Today in Energy: Most states have Renewable Portfolio Standards. 2012a. Available at <http://www.eia.g ov/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 Administratio n (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

–201 1 . 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 carb on dioxide storage resources

– Results: U.S. Geological Survey Circular 1386 . Available at <http://pubs.usgs.gov/circ/1386/>. Accessed June 9, 2015.***E.O. 12866 Review

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3 - 1 

### C **HAPTER** 

### 3: **C OST**  

### , **E MISSIONS**  

### , **E CONOMIC**  

### , **AND** 

### E **NERGY** 

### I **MPACTS 3.1**  **Introduction** This chapter reports the compliance cost, emissions, economic, and energy impact anal ys is performed for the Clean Power Plan Final Rule . EPA used the Integrated Planning Model (IPM), developed by I CF International , to conduct most of the anal ysis discussed in this Chapter . IPM is a dy

namic linear programming model that can be used to examine air pollution control policies for CO 2 , SO 2 , NO X , Hg, HCl, and other air pollutants throughout the contiguous United Stat es for the entire power s ys tem . The IPM electricity demand projections are based on projections from the Energy Information Administration (EIA), adjusted for demand - side energy efficiency measures that can be reasonably anticipated to occur under the Clean Power Plan. To estimate comp liance costs, emissions, and other impacts for states and territories with power markets not represented in IPM, EPA performed supplementary analy sis to account for the potential benefits and costs of the Clean Power Plan Final Rule for Alaska, Hawaii, and U.S. territories with affected EGUs . This chapter combines IPM anal ys is of the continental U.S. with the supplementary

anal ys is performed for Alaska, Hawaii, and U.S. Territories. Supplementary estimates of monitoring, recordkeeping, and reporting costs a re reported in this chapter as well. The sum of emission reductions across these regions informs the benefits anal ys es presented elsewhere in this RIA. **3.2 Overview**  This chapter of the RIA presents illustrative anal yses of the final rule b y m aking assumpti ons about the possible approaches that States might pursue as they develop their state plans. Over the last decade, EPA has conducted extensive analy ses of regulatory actions affecting the power sector. These efforts support the Agency ’s understanding of k ey 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 I CF Consul ting, 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***E.O. 12866 Review

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3 - 2 policies throughout the contiguous United States for the entire electric power s ys tem. EPA used IPM to project likely

future electricity

market conditions with and without the rule. Additional demand - side energy

efficiency measures that may be adopted in response to the regulation, and the resulting changes to future demand projections, are also accounted for in the illustrative plan scenario s. The level of energy efficiency- driven reductions in electricity demand , and the ir associated costs , are reported in s ection 3.6. IPM is a multi - regional, dynamic, deterministic linear programming model of the contiguous U.S. electric power sector. It provides forecasts of least cost capacity expansion, electricity dispatch, and emission control strategies while meeting energy demand and environmental, transm ission, 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 policie s. The model is designed to reflect electricity markets as accuratel y as possible. EPA uses the best available information from utilities, industry experts, gas and coal market experts, financial institutions, and government statistics as the basis for the detailed power sector modeling in IPM. The model documentation provides additional information on the assumptions discussed here as well as all other model assumptions and inputs. 47 Although the Agency

typ ically focuses on broad sy stem effects when assess ing the economic impacts of a particular policy , EPA’s application of IPM includes a detailed and sophisticated regional representation of key variables affecting power sector behavior. The model incorporates a detailed representation of the fossil - fuel su ppl y s ystem that is used to forecast equilibrium fuel prices. The model includes an endogenous representation of the North American natural gas suppl y s ys tem through a natural gas module that reflects a partial supply /demand equilibrium of the North Americ an gas market accounting for varying levels of potential power sector and non - power sector gas demand and corresponding gas production and

47 Detailed information and documentation of EPA’s Base Case using IPM (v5.15), including all the underlying assumptions, data sources, and architecture parameters can be found on EPA’s website at: http://www.epa.gov/powersectormodeling***E.O. 12866 Review

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3 - 3 price levels. 48 This module consists of 118 supply , demand, and storage nodes and 15 liquefied natural gas re - gasification facility locations that are tied together by a series of linkages (i.e., pipelines) that represent the North American natural gas transmission and distribution network. IPM also endogenously

models the partial equilibrium of coal supply and EGU coal d emand levels throughout the contiguous U.S., taking into account assumed non - power sector demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal transport network, which consists of over four thousand linkages representing rail, barge, and truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough bottom - up, mine - by- mine approach that depicts the coal choices and associated supply costs that power plants would face if selecting that coal over the modeling time horizon. The IPM documentation outlines the methods and data used to quantify the economically

recoverable coal reserves, characterize their cost, and build the 36 coal regions’ suppl y curves. 49 EPA has used IPM extensively over th e past two decades to anal yz e 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 b y the model, public interest groups, and other developers of U.S. electricity sector models. The feedback that the Agency receives provides a highl y- detailed review of key input assumptions, model representation, and modeling results. IPM has received extensive review by 48 See Chapter 10 of E PA’s Base Case using IPM (v5.15 4 ) documentation, available at: http://www.epa.gov/powersectormodeling 49 See Chapter 9 of EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/powersectormodeling***E.O. 12866 Review

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3 - 4 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 periodicall y conducted. The model has also undergone considerable interagency

scrutiny when it was used to conduct over a dozen legislative analy ses (performed at Congressional request) over the past decade. The Agency has also used the model in a number of comparati ve modeling exercises sponsored by Stanford University’s Energy Modeling Forum over the past 15 ye ars. IPM has also been employed b y states (e.g., for RGGI, the Western Regional Air Partnership, Ozone Transport Assessment Group), other Federal and state ag encies, environmental groups, and industry , all of whom subject the model to their own review procedures. 3.3.1 Recent Updates to EPA’s Base Case using IPM (v.5.15) The “Base Case” for this anal ys is is a business - as - usual scenario that would be expected un der market and regulatory conditions in the absence of this rule. EPA frequently updates the IPM base case to reflect the latest available electricity demand forecasts, as well as, expected costs and availability

of new and existing generating resources, f uels, and emissions control technologies. EPA’s IPM modeling platform used to anal yz e this final rule (v.5.1 5 ) incorporates updates to the version of the model used to anal yz e 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 suppl y. Additional updates, based on the most up - to date 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 clearl y delineated in March 2015. This update also includes two non - air federal rules a ffecting EGUs: Cooling Water Intakes (3 16(b)) Rule and Combustion Residuals from Electric Utilities (CCR). Additionally, all new capacity projected by

the model is compliant***E.O. 12866 Review

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3 - 5 with Clean Air Act 111(b) standards. For a detailed account of all updates made to the v.5.15 modeling platform, see the Incremental Documentation for EPA Base Case v.5.15 Using IPM. 50 EPA also updated the National Electric Energy Data Sy stem (NEEDS ). This database contains the unit - level data that is used to construct the "model" plants that represent existing and committed units in EPA modeling applications of IPM. NEEDS includes detailed information on each individual EGU, including geographic, operating, air emissions, and other data on every generating units in the contiguous U.S. 51 To estimate compliance costs, emissions and other impacts for states and territories with power markets not represented in IPM, EPA performed supplementary analy sis to account for the potential benefits and costs of the Clean Power Plan Final Rule for Alaska, Hawaii, and U.S. territories with a ffected EGUs . **3.4 State Goals in this Final Rule**  In this final rule , the EPA is establish ing CO 2 emission performance rate s for each of two source categories of fossil fuel - fired EGUs -- fossil fuel - fired electric steam generating units and stationary combustion turbines. The EPA has translated the source category - specific CO 2 emission performance rates into equivalent state - level rate - based and mass - based CO 2 goals to afford states a range of choices , including application of statewide measures , for developing their plans. For modeling purposes, the illustrative rule impacts shown in the RIA reflect an application of both the state - wide rate - based and mass - based goals in each state.

50 Available at: http://www.epa.go v/powersectormodeling/ 51 The NEEDS database can be found on the EPA’s website for the Base Case using IPM (v5.1 5 ), <http://www.epa. gov/powersectormodeling >.***E.O. 12866 Review

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3 - 6 **Table 3 -**  **1. Rate**  **- Based and Mass**  **- Based**  **State Goals Statewide**  **Rate -**  **Based CO 2**  **Emission Perform**  **ance G oals (Adjusted Output**  **- Weighted**  **** **Average Pounds of CO 2**  **Per Net MWh From All**  **Affected Fossil Fuel -**  **Fired EGUs) State**  **Interim Goal Final Goal**  **Interim Goal Final Goal**  Alaska Alabama 1,174 1,018 Arkansas 1,319 1,130 Arizona 1,190 1,031 Califo rnia 925 828 Colorado 1,377 1,174 Connecticut 870 786 Delaware 1,040 916 Florida 1,044 919 Fort Mojave Georgia 1,214 1,049 Hawaii Iowa 1,519 1,283 Idaho 851 771 Illinois 1,470 1,245 Indiana 1,466 1,242 Kansas 1,532 1,293 Kentucky 1,523 1,286 Louisiana 1,308 1,121 Massachusetts 920 824 Maryland 1,524 1,287 Maine 861 778 Michigan 1,370 1,169 Minnesota 1,428 1,213 Missouri 1,504 1,272 Mississippi 1,078 945 Montana 1,548 1,305 Navajo North Carolina 1,327 1,135 North Dakota 1,548 1,305 Nebraska 1,536 1,296 New Hampshire 964 858 New Jersey 904 811 New Mexico 1,341 1,146 Nevada 960 854 New York 1,042 918 Ohio 1,398 1,190***E.O. 12866 Review

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3 - 7 **Statewide Rate**  **- Based CO**  **2 Emission**  **Perform ance G**  **oals (Adjusted Output -**  **Weighted**   ** Average Pounds of CO** **2 Per Net MWh From**  **All Affected Fossil Fuel**  **- Fired EGUs)**  **State Interim Goal**  **Final Goal Interim Goal**  **Final Goal** Oklahoma 1,239 1,068 Oregon 982 871 Pennsylvania 1,274 1,095 Rhode Island 851 771 South Carolina 1,353 1,156 South Dakota 1,367 1,166 Tennessee 1,425 1,211 Texas 1,204 1,041 Ute Utah 1,383 1,179 Virginia 1,064 934 Washington 1,128 983 Wisconsin 1,379 1,176 West Virginia 1,548 1,305 Wyoming 1,541 1,300 **3.5 Illustrative Plan Approaches**  **Analyzed** To estimate the costs, benefits, and impacts of implementing the CPP guidelines, the EPA modeled two illustrative plan scenarios, each at the state level, based on a rate - based approach and a mass - based approach. The rate - based scenario requires affected s ources in each state to achieve a single average emissions rate in each period as represented b y the state - wide goals. The mass - based scenario requires affected sources in each state to limit their aggregate emissions not to exceed the mass goal for that s tate. In each of these scenarios, affected EGUs include:

 E xisting fossil steam boilers with nameplate capacity greater than 25 MW  E xisting 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***E.O. 12866 Review

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3 - 8  Under - construction nuclear 52

 Demand - side energy

efficiency

in addition to levels implicit in base case electricity demand. In the illustrative rate - based plan scenario anal yz ed 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 ra te is achieved, and/or 2) include in the average emissions rate calculation new renewable generation or demand - side energy

efficiency

located outside of the state but within each of the illustrative Interconnection - based regions shown in Figure 3 - 3 below. 53

52 Includes three nuclear facilities at which construction has already commenced: Watts Barr (TN), Vogtle (GA), and Summer (SC) 53 In this scenario, EE/RE procurement is limited to within one of the three illustrative regions, as a conservative mo deling approach. Since the interconnects do not always follow state borders, certain states that fall into mo re than one region were grouped in regions where there was a majority of geographic territory (area) or generation. In practice, sources in states that have adopted rate - based plans may be able to procure EE/RE from states outside of these illustrative reg ions.***E.O. 12866 Review

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3 - 9 **Figure 3 -**  **3. Illustrative Regions for EE/RE Procurement Used in this Analysis**  This rate - based implementation approach enables some sources to emit at rates higher than their applicable state goal, as long as there is corresponding generation coming from af fected sources in that state (or new renewables or demand - side EE located in any state within the illustrative region for EE/RE procurement) that emits at a lower rate, such that each state’s goal (in lbs/MWh) is met across all affected sources located within the state including generation (or avoided generation) from EE/RE procurement across the illustrative regions. In this rate based scenario, specific generation (or avoided generation) from EE/RE procurement may onl y be used once for compliance toward a state goal; in other words, while emitting sources in all states may

avail themselves of qualifying EE/RE across the illustrative region, no particular EE/RE MWh can be claimed by more than one emitter as part of reaching a state goal. While the final rule enables states to achieve their mass goals with the flexibility of interstate trading, this RIA presents an illustrative scenario that conservatively assumes that each state achieves its goal independentl y. 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.***E.O. 12866 Review

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3 - 10 Each illustrative plan scenario assumes identical levels of DS - EE megawatt - hour (MWh) savings and associated costs, which are specified exogenousl y and consistent with the EE plan scenario performance levels described in section 3.6. EPA has specified and imposed EE - related costs and changes in future electricity demand when modeling the illustrative plan scenarios presented for this rule. Details of the implementation of the demand reduction are reported in the following section. The full array

of estimates of the benefits, costs, and economic impacts of this proposed action are pre sented for both the illustrative rate - and mass - based scenarios. These illustrative plan scenarios are designed to reflect, to the extent possible, the scope and nature of the CPP guidelines. However, there is considerable uncertainty with regard to the re gulatory 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 anal ys is of the benefits, costs, and relevant impacts of the r ule 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 anal ys is does not specify any particular CO 2 reduction measure to occur, with the exception of the level of demand - side energy efficiency

(EE) assumed to be adopted in response to the CPP. In other words, aside from investments in EE, the analy sis allows the power s ys tem the flexibility to respond to average emissions rate or mass constraints on affected sources in the illustrative scenarios to achieve the goals in the most cost - effective manner determined b y IP M , as specified below. While IPM produces a cost - minimizing solution to achieve the state goals imposed in the illustrative scenarios, there may be yet lower - cost approaches that the states may adopt to achieve their state goals inasmuch as states and sources take advantage of emission reduction opportunities in practice, and flexibilities afforded under the final rule, that are not represented in this analy sis and would yield different cost and emissions outcomes .***E.O. 12866 Review

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3 - 11 **3.6 Demand**  **- Side Energy Efficiency**  3.6.1 Demand

- Side Energy Efficiency Improvements (Electricity Demand Reductions) 54 The final CPP provides states the flexibility to use demand reductions resulting from demand - side energy

efficiency

measures as a component of their compliance strategy , either direc tly recognized towards compliance with a rate - based goal or as a complementary approach for achieving a mass - based goal. The EPA has included in our illustrative plan scenarios (both rate - and mass - based) a level of demand reduction that could be achieved, and the associated costs incurred, through implementation of demand - side energy efficiency

measures. This “demand - side energy

efficiency

plan scenario” represent s a level of performance that has already been demonstrated or is required b y 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 enc ouraged in response to the final CPP. For the illustrative demand - side energy efficiency plan scenario, electricity demand reductions for each state for each ye ar are developed by ramp ing up from an historical basis 55 to a target annual incremental demand reduction rate of 1. 0 percent of electricity demand over a period of ye ars starting in 20 20, and maintaining that rate throughout the modeling horizon . Twenty leading states have either achieved, or have established requirements that will lead them to achieve, this rate of incremental electricity demand reduction on an annual basis. Based on historic performance and existing state requirements, for each state the pace of improvement from the state’s historical incremental demand reduction rate is set at 0.2 percent per year, beginning in 2020, until the target rate of 1.0 percent is achieved. States already at or above the

54 For a more detailed discussion of the demand - side energy efficiency demand reductions and their associated costs , refer to the Greenhouse Gas Abatement Measures TSD . 55 The historical basis of the percentage of reduced electricity consumption diffe rs for each state and is drawn from the data reported in Energy Information Administration (EIA) Form 861, 201 3 , available at http://www.eia.gov/electricity/data/eia861/.***E.O. 12866 Review

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3 - 12 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 ye ar, is used to derive cumulative annual electricity demand reduction s based upon information about the average life of energy

efficiency measures and the distribution of measure lives across energy efficien cy programs. The cumulative annual electricity demand reduction derived using this methodology is used to adjust base case electricity demand levels in the power sector compliance modeling. To reflect the implementation of the illustrative energy efficien cy plan scenario in modeling, the IPM base case electricity demand was adjusted exogenously to reflect the estimated future - ye ar demand reductions calculated as described above. S tate - level demand reductions were scaled up to account for transmission losse s and applied to base case generation demand i n each model ye ar to derive adjusted demand for each state , reflecting the energy efficiency plan scenario energy reductions . The demand adjustments were applied proportionall y across all segments (peak and non - peak) of the load duration curve. T o reflect the adjusted state level demand with in IPM model regions that cros s 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 opportunitie s. Table 3 - 3 summarizes the results of the demand - side energy efficiency

illustrative plan scenario at the national level. **Table 3**  **- 3**  **. Demand**  **- Side Energy Efficiency Plan Scenario:**  **Net Cumulative Demand** 

**Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales)**  2020 2025 2030 Net Cumulative Demand Reduction (GWh) 23,150 194,126 327,092 Net Cumulative Demand Reduction as Percent of BAU Sales 0.59% 4.81% 7.83% Source: Greenhouse Gas Abatement Measures TSD***E.O. 12866 Review

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3 - 13 3.6.2 Demand - Side Energy Efficiency

Costs 56 Total costs of achieving the demand - side energy efficiency scenario for each year were calculated exogenous to the power sector modeling . The power s ys tem cost impacts resulting from the illustrative plan scenario were captured within IPM and include the effects of reduced demand levels driven b y the energy efficiency

scenario discussed above. The integration of the exogenousl y calculated demand - side energy efficiency

scenario costs with the power s ys tem cost impacts of the illustrative plan scenario are discussed in section 3.7.2 . In addition to the demand reduction results , the demand - side energy efficiency cost s w ere based upon an estimate of the total first - ye ar 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 - ye ar costs for each ye ar 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 ye ar for each state. For example, the 2025 estimate of annualized EE cost includes levelized value of first - year costs for energy efficiency

investments made in 2020 through 2025. The annualized costs rise in each anal ys is year as additional first - year costs are incurred. The annualized cost results are summarized below in Table 3 - 4. The total levelized cost of saved energy was calculated based upon the same inputs and using a 3 percent discount rate resulted in average values of 9.2 cents per kWh in 2020, 8.6 cents per kWh in 2025, and 8.1 cents per kWh in 2030.

56 For a more detailed discussion of the demand - side energy efficiency cost analysis , refer to the Greenhouse Gas Abatement Measures TSD.***E.O. 12866 Review

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3 - 14 **Table 3 -**  **4. Annualized Cost of Demand**  **- Side Energy Efficiency Plan Scenario**  **[Contiguous**

**U.S.] (at discount rates of 3 percent and 7 percent, billions 2011$)**  Discount Rate 2018 2020 2025 2030

at 3 percent 0 . 0 2.1 16.7 2 6 .3 at 7 percent 0.0 2. 6 20.6 3 2.5 Source: Greenhouse Gas Abatement Measures TSD The funding for demand - side energy efficiency

programs (to cover program costs) is ty pical ly collected through a standard per kWh sur charge to the ratepayer ; the regional retail price impacts anal yz ed from this RIA’s illustrative plan scenarios assumes the recovery of these program costs through the following procedure . 57 For each state , the first - ye ar energy efficiency program costs are calculated for each ye ar . These cost s were distributed between the applicable IPM regions using an approach based on reported sales in 2013 as a prox y for the distribution of energy efficiency

investment opportunities. These regionalized energy efficiency

program cos ts were then incorporated into the regional retail price calculation as discussed in section 3.7.9 . 58 Chapter 5 of the GHG Abatement Measures TSD provides complete details on the calculations of annualized costs and first - ye ar costs as well as comprehensi ve results (b y state, by year) for the illustrative demand - side energy efficiency

plan scenario . **3.7 Projected**  **Power Sector Impacts**  The following sections present projected impacts from the two illustrative scenarios described above. The tables present imp acts from 2020 (prior to the initial compliance ye ar), 2025 (representative of the interim compliance period), and 2030 (representative of the final compliance period). The narrative focuses on results during the initial and final compliance periods. 3.7.1 Projected Emissions (in the Contiguous U.S.) Under the rate - based scenario, EPA projects annual CO 2 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 2 8 percent to 32 percent below 2005 emissions 59 in 2025 and 2030, respectivel y). For the mass - based scenario, EPA projects annual CO 2 reductions of [X] percent 57 The full retail price analysis method is discussed in section 3.7.9 of this chapter. 58 The effect on equilibrium supply and demand of electricity due to changing retail rates to fund energy efficiency p rograms is not captured in the IPM modeling. 59 For purposes of these calculations, EPA has used historical CO 2 emissions from eG RID for 2005, w hich reports EGU emissions as 2,434 million metric tonnes in the contiguous US.***E.O. 12866 Review

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3 - 15 below the base case in 2020, [X] percent below the base case in 2025 and [X] percent below base ca se projections in 2030 (reaching [X] percent to [X] percent below 2005 emissions 60 in 2025 and 2030, respectivel y). **Table 3 -**  **5 .**  **Projected CO 2**  **Emission Impacts , Relative to Base Case** 

**CO 2**  **Emissions (MM Tonnes) CO**  **2 Emissions Reductions**  **from Base**  **Case (MM Tonnes) CO**  **2 Emissions Reductions:**  **Percent Change from Base** 

**Case 2020**  **2025 2030**  **2020 2025**  **2030 2020**  **2025 2030**  Base Case 1,955 1,964 2,021 Rate 1,892 1,754 1,644 63 210 377 3% 11% 19% Mass [X] [X] [X] [X] [X] [X] [X] [X] [X] Source: Integrated Planning Model run by EPA, 2015 **Table 3 -**  **6 .**  **Projected CO 2**  **Emission Impacts , Relative to 2005** 

**CO 2**  **Emissions (MM Tonnes) CO**  **2 Emissions Reductions**  **from 2005 (MM Tonnes)**  **CO 2**  **Emissions Reductions: Percent Change from** **2005 2020**  **2025 2030**  **2020 2025**  **2030 2020**  **2025 2030**  Base Case 1,955 1,964 2,021 Rate 1,892 1,754 1,644 542 680 790 22% 28% 32% Mass [X] [X] [X] [X] [X] [X] [X] [X] [X] Source: Integrated Planning Model run by EPA, 2015 Under the rate - based illustrative scenario, EPA projects a 14 percent reduction of SO 2 , 13 percent reduction of NO X , and a 11 percent reduction of mercury in 2025, and a 24 percent reduction of SO 2 , 22 percent reduction of NO X , and a 27 percent reduction of mercury in 2030. Under the mass - based illustrative scenario, EPA projects a [X] percent reduction of SO 2 , [X] percent reduction of NO X , and a [X] percent reduction of mercury in 2025, and a [X] percent reduction of SO 2 , [X] percent reduction of NO X , and a [X] percent reduction of mercury in 2030. The projected non - CO 2 reductions are summarized below in Table 3 - 7.

60 For purposes of these calculations, EPA has used historical CO 2 emissions from eG RID for 2005, w hich reports EGU emissions as 2,434 million metric tonnes in the contiguous US.***E.O. 12866 Review

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3 - 16 **Table 3 -**  **7. Projected Non -**  **CO 2**  **Emission Impacts, 2020 -**  **2030** Base Case Rate Mass Rate Mass 2020 SO 2 (thousand tons) 1,311 1,298 [X] - 1.0% [X] NO X (thousand tons) 1,333 1,282 [X] - 3.8% [X] Hg (tons) 6.6 6.4 [X] - 2.8% [X] PM 2.5 (thousand tons) [X] [X] [X] [X] [X] 2025 SO 2 (thousand tons) 1,275 1,097 [X] - 13.9% [X] NO X (thousand tons) 1,302 1,138 [X] - 12.6% [X] Hg (tons) 6.6 5.9 [X] - 10.8% [X] PM 2.5 (thousand tons) [X] [X] [X] [X] [X] 2030 SO 2 (thousand tons) 1,314 996 [X] - 24.2% [X] NO X (thousand tons) 1,293 1,010 [X] - 21.9% [X] Hg (tons) 6.8 5.6 [X] - 17.3% [X] PM 2.5 (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 - CO 2 emissions changes or CO 2 emissions changes outside the electricity sector for the final emissions guidelines, the Agency has anal yz ed the potential changes in upstream methane emissions from the natural gas and coal production se ctors 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 CO 2 emissions reductions anticipated at power plants. This assessment included CO 2 emissions f rom the flaring of methane, but did not evaluate potential changes in other combustion - related CO 2 emissions, such as emissions associated with drilling, mining,***E.O. 12866 Review

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3 - 17 processing, and transportation in the natural gas and coal production sectors. This analy sis f ound that the net upstream methane emissions from natural gas s ys tems and coal mines and CO 2 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 CO 2 emissions from power plants. The projections include voluntary and regulatory activities to reduce emissions from coal mining and natural gas and oil s ys tems, including the 2012 Oil and Natural Gas NSPS. In addition, the EP A 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 sy stems. The technical details supp orting this anal ys is can be found in the Appendix to this chapter. 3.7.2 Projected Compliance Costs (in the Contiguous U.S.) The power industry ’s “compliance costs” are represented in this analy sis as the change in electric power generation costs between the base case and illustrative CPP scenarios, inclusive of the cost of demand - side EE measures. The s ys tem costs reflect the least cost power s ys tem outcome in which the sector employ s 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 compl ying with state goals, including the total demand - side energy efficiency costs. 61 The compliance costs for the final emissions guidelin e s for EGUs in the contiguous U.S. states is forecast using IPM . The cost o f demand - side EE programs assumed in the IPM analy sis are reported in section 3.6.2. The compliance costs for non - contiguous states and territories are reported in section 3.X. EPA projects that the annual compliance cost of the rate - based illustrative pl an scenario are $2.4 billion in 2020, $1.1 billion in 2025, and $8.4 billion in 2030. The annual compliance cost of the mass - based illustrative plan scenario are estimated to be [X] billion in 2020, $[X] billion in 2025, and $ [X] billion in 2030. The annual compliance cost is the projected additional

61 The compliance costs also capture the effect of changes in equilibrium fuel prices on the expenditures of the electricity sector to serve demand.***E.O. 12866 Review

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3 - 18 cost of complying with the rule in the year anal yz ed and includes the net change in the annualized cost of capital investment in new generating sources and heat rate improvements at coal steam facilities, 62 the change in the ongoing costs of operating pollution controls, the change in expenditures on various fuels (inclusive of changes in the price of these fuels), demand - side energy efficiency

measures, and other actions associated with compliance. **Table 3 -**  **8. A**  **nnualized Compliance Costs (billions of 2011$) 2020**  **2025 2030**  Rate $2.4 $1.1 $8.4 Mass [X] [X] [X] * These costs do not include monitoring, reporting, and recordkeeping (MM&R) costs. For more information on MM&R costs, see section 3.11. Source: Integrated Planning Model run by EPA, 2015 and with post -processing to account for exogenous demand side manag ement energy efficiency costs. In order to contextualize EPA’s projection of the additional costs in 2030 across the two illustrative plan scenari os 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 electri city to end use consumers. In 2014, according to EIA, the power sector generated $389 billion in revenue from retail sales of electricity . For context, the projected costs of compliance with the proposed rule amount to a 4 percent increase in the cost o f m eeting electricity demand, while securing public health and welfare benefits that are several times greater (as described in Chapter s 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 b y $15.7 billion (rate) and $[X] billion (mass) from the base case in 2030. This incremental decline is a net outcome of two simultaneous effects that move in opposite directions. First, imposing the CO 2 constraints represented b y each illustrative plan scenario on electric generators would, other things equal, result in an incremental increase in 62 See Chapter 2 of the Greenhouse Gas Abatement Measures TSD and EPA’s Base Case using IPM (v5.1 5 ) documentation, available at: http://www.epa.gov/powersectormodeling***E.O. 12866 Review

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3 - 19 expenditures to supply

any given level of electricity . However, once electricity demand is reduced to reflect demand - si de energy efficiency

improvements, there is a substantial reduction in the expenditures needed to supply a correspondingl y lower amount of electricity demand. **Table 3 -**  **9. Total Power Sector Generating Costs (IPM) (billions 2011$)**  **2020 2025**  **2030** Base Case $166.5 $178.3 $201.3 Rate $166.8 $162.7 $183.4 Mass Source: Integrated Planning Model run by EPA, 201 5 In order to reflect the full compliance cost attributable to the CPP scenarios, it is necessary to include the annualized exp enditures 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 $2 6 .3 billion in 20 3 0 (using a 3 percent discount rate) . The EE - related expenditures include costs incurred b y parties administering EE programs and costs incurred b y participants in those programs. As a result, this analysis finds the cost of the rate based and mass - based illustrative plan scenarios in 20 3 0 to be $ 8.4 billion and $[ X ] billion, respectivel y. 3.7.3 Projected Compliance Actions for Emissions Reductions Heat Rate Improvements (HRI) EPA anal ys is 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 foss il fuel results in a lower output - based CO 2 emissions rate (lbs/MWh), as well as a lower variable cost of electricity generation. In the modeling conducted for these illustrative plan scenarios, coal boilers have the choice to improve heat rates b y 4.3 percent in the eastern illustrative compliance region, 2.1 percent in the western illustrative compliance region, and 2.3***E.O. 12866 Review

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3 - 20 percent in Texas, all at a capital cost of $100 per kW. 63 The option for heat rate improvement is only made available in the illustrative plan scenarios, in response to the final rule. The vast majority

of existing coal boilers are projected to adopt the aforementioned heat rate improvements. EPA projects that 99 GW of existing coal steam capacity (greater than 25 MW) will improve operating efficiency (i.e., reduce the average net heat rate) under the rate based scenario b y 2030. Under the mass - based scenario, EPA projects that [X] GW of existing coal steam capacity with improve operating efficiency b y 2030. Generation Shifting Another appro ach for reducing the average emission rate from existing units is to shift some generation from more CO 2 - intensive generation to less CO 2 - intensive generation. Compared to the base case, existing coal steam capacity is, on average, projected to operate at a lower capacity factor for both illustrative plan scenarios. Under the illustrative rate - based plan scenario, the average 2030 capacity factor is 69 percent, and under the mass - based scenario, the average capacity factor for existing coal steam is [X] percent. Existing natural gas combined cycle units, which are less carbon - intensive than coal steam capacity on an output basis, operate at noticeabl y higher capacity factor under both illustrative plan scenarios, on average. See Table 3 - 10. The utilization of existing natural gas combined cycle capacity is lower than the BSER level of 75 percent on an annual average basis in these illustrative plan scenarios, reflecting the fact that, in practice, the most cost - effective CO 2 reduction strategies to meet each state’s goal may not require that each building block be achieved in entirety. For example, building block 2 (generation shifting) is not deployed to the maximum extent in the illustrative plan scenarios.

63 T he 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 abi lity to improve heat rates, s ee GHG Abatement Measures TSD .***E.O. 12866 Review

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3 - 21 **Table 3 -**  **10. Projected Capacity Factor of Existi**  **ng Coal Steam and Natural Gas**

**Combined Cycle Capacity**

**Existing Coal Steam Existing Natural Gas Combined Cycle**  **2020 2025**  **2030 2020**  **2025 2030**  Base Case 77% 76% 79% 54% 56% 51% Rate 78% 75% 69% 56% 59% 61% Mass [X] [X] [X] [X] [X] [X] Source: Integrated Planning Model run by EPA, 2015 Demand - Side Energy

Efficiency Another approach for reducing emissions from affected EGUs is to consider reductions in demand attributable to demand - side energy efficiency

measures as discussed in section 3.6. In the illustrative plan scenario s presented in this RIA, each state is credited for total demand - side energy efficiency

consistent with the state - by- state demand reductions that are represented by the demand - side energy

efficiency

scenario discussed in se ction 3.6.1. Deploy ment of Cleaner Generating Technologies Another key

opportunity to reduce emissions from existing sources is to build more lower - or zero - emitting generating resources, in particular renewable energy. These sources of electricity, including wind and solar, can displace higher emitting existing sources and are also directly credited toward the state goals in the rate - based illustrative scenario. Increased deploy ment results in CO 2 reductions in both rate - based and mass - based scenarios. 3.7.4 Projected Generation Mix Table 3 - 3 and Figure 3 - 4 show the generation mix in the base case and under the two illustrative plan scenarios. In both scenarios, total generation declines relative to the base case as a result of the reduction in total dem and attributable to the demand - side energy efficiency applied in the illustrative scenarios, b y 5 percent in 2025 and 8 percent in 2030. Under the rate - based scenario, c oal - 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 in creases 18 percent relative to the base case. Gas - fired generation from new combined cycle capacity***E.O. 12866 Review

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3 - 22 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 renewable s decreases 1 percent in 2025 and increases 9 percent in 2030. Similarly, under the mass - based scenario, c oal - fired generation is projected to decline [X] percent in 2025, and natural - gas - fired generation from existing combined cycle capacity is projected to increase [X] percent relative to the base case . The coal - fired fleet in 2030 generates [X] percent less than in the base case, while natural - gas - fired generation from existing combined cycles increases [X] percent relative to the base case. Gas - fired generation from new combined cycle capacity

changes [X] percent relative to the base case. Relative to the base case, generation from non - hydro renewables increases [X] percent in 2025 and [X] percent in 2030.***E.O. 12866 Review

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3 - 23 **Table 3 -**  **11. Generation Mix (thousand GWh)**  B ase Case Rate Mass Rate Mass 2020 Coal 1,462 1,391 - 5% NG Combined Cycle (existing) 1,111 1,125 1% NG Combined Cycle (new) 33 54 63% Com bustion Turbine 15 20 39% Oil/Gas Steam 51 50 0% Non - Hydro Renewables 393 399 2% Hydro 310 311 0% Nuclear 798 792 - 1% Other 18 18 0% Total 4,190 4,160 - 1% 2025 Coal 1,428 1,256 - 12% NG Combined Cycle (existing) 1,152 1,206 5% NG Combined Cycle (new) 113 54 - 53% Com bustion Turbine 23 30 31% Oil/Gas Steam 39 21 - 46% Non - Hydro Renewables 417 414 - 1% Hydro 340 340 0% Nuclear 799 791 - 1% Other 17 17 0% Total 4,328 4,129 - 5% 2030 Coal 1,466 1,131 - 23% NG Combined Cycle (existing) 1,042 1,231 18% NG Combined Cycle (new) 324 101 - 69% Com bustion Turbine 22 26 21% Oil/Gas Steam 22 11 - 52% Non - Hydro Renewables 450 489 9% Hydro 340 341 0% Nuclear 783 777 - 1% Other 17 17 0% Total 4,467 4,124 - 8% Note: “Other” mostly includes generation from MSW and fuel cells. Source: Integrated Planning Model run by EPA, 2015***E.O. 12866 Review

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3 - 24 **Figure 3 -**  **4. Generation Mix with the Base Case and 111(d) Options,**  **2020 -**  **2030 (thousand GWh)** Source: Integrated Planning Model run by EPA, 2015***E.O. 12866 Review

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3 - 25 3.7.5 Projected Incremental Retirements Relative to the base case, about 23 GW of additional coal - fired capacity is projected to be uneconomic to maintain ( about 11 percent of all coal - fired capacity projected to be in service in the base case) b y 2025 under the rate - based illustrative scenario. Under the mass - based scenario, about [X] GW of additional coal - fired capacity is projected to be uneconomic to maintain ( about [X] percent of all coal - fired capacity projected to be in service in the base case) b y 2025. Capacity changes from the base case are shown in Table 3 - 12.***E.O. 12866 Review

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3 - 26 **Table 3 -**  **12. Total Generation Capacity by 2020**  **- 2030 (GW)**  Base Case Rate Mass Rate Mass 2020 Coal 208 195 - 6% NG Combined Cycle (existing) 233 231 - 1% NG Combined Cycle (new) 4 7 64% Com bustion Turbine 141 137 - 3% Oil/Gas Steam 88 81 - 8% Non - Hydro Renewables 130 132 1% Hydro 106 106 0% Nuclear 100 100 - 1% Other 5 5 0% Total 1,016 994 - 2% 2025 Coal 208 187 - 10% NG Combined Cycle (existing) 233 231 - 1% NG Combined Cycle (new) 15 7 - 52% Com bustion Turbine 143 138 - 4% Oil/Gas Steam 82 71 - 14% Non - Hydro Renewables 139 137 - 1% Hydro 112 112 0% Nuclear 100 99 - 1% Other 5 5 0% Total 1,037 988 - 5% 2030 Coal 207 183 - 11% NG Combined Cycle (existing) 233 231 - 1% NG Combined Cycle (new) 44 14 - 68% Com bustion Turbine 147 138 - 6% Oil/Gas Steam 82 70 - 16% Non - Hydro Renewables 154 174 13% Hydro 112 112 0% Nuclear 99 98 - 1% Other 5 5 0% Total 1,082 1,025 - 5% Source: Integrated Planning Model run by EPA, 2015 3.7.6 Projected Capacity Additions Due largel y 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 ba se case over***E.O. 12866 Review

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3 - 27 the period covered b y the rule. While this new NGCC capacity cannot be directl y counted towards the average emissions rate used for compliance in this scenario, it can displace some generation from covered sources and thus indirectly lower the average emissions rate from covered sources. Conversely , the EPA projects an overall increase in new renewable capacity. New non - hydro renewables are able to contribute their generation to the average emissions rate in each state or region. Under the rate - based illustrative scenario, new natural gas combined cycle capacity is projected to decrease b y 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 b y 21 GW (28 percent increase) b y 2030. Under the mass - based illustrative scenario, new natural gas combined cycle capacity is projected to [X] in 2025 and [X] in 2030 ([X] percent and [X] percent relative to the base case). New renewable capacity is projected to [X] (X percent) relative to the base case in 2025, and [X] ([X] percent increase) b y 2030. **Table 3 -**  **13. Projected Capacity Additions, Gas (GW)** 

Cumulative Capacity Additions: Gas

Com bined Cycle Incremental Cumulative Capacity Additions: Gas Combined Cycle **2020 2025**  **2030 2020**  **2025 2030**  Base Case 4.4 14.9 44.0 Rate 7.1 7.2 14.0 2.8 - 7.7 - 30.0 Mass Source: Integrated Planning Model run by EPA, 2015 **Table 3 -**  **14. Projected Capacity Additions, Renewable (GW)**  Cumulative Capacity Additions:

Renewables Incremental Cumulative Capacity Additions: Renewables **2020 2025**  **2030 2020**  **2025 2030**  Base Case 39.1 59.1 74.1 Rate 40.5 57.4 94.6 1.4 - 1.8 20.5 Mass Source: Integrated Planning Model run by EPA, 2015***E.O. 12866 Review

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3 - 28 3.7.7 Projected Coal Production and Natural Gas Use for the Electric Power Sector Coal production is projected to decrease in 2025 and beyond in the illustrative scenarios due to (1) improved heat rates (g enerating efficiency) at existing coal units, (2) electricity demand reduction attributable to demand - side energy efficiency improvements, and (3) a shift in generation from coal to less - carbon intensive generation. As shown in Table 3 - 15, the largest decrease in coal production is projected to occur in the western region. **Table 3 -**  **15 .**  **Coal Production for the Electric Power Sector, 2025** Coal Production (MM Tons) Percent Change from Base Case Base Case Rate Mass Rate Mass Appalachia 92 71 - 23% Interior 250 242 - 3% West 379 306 - 19% Waste Coal 6 6 0% Im ports 1 1 - 37% Total 729 626 - 14% Source: Integrated Planning Model run by EPA, 2015 Power sector natural gas use is projected to decrease by about 1 percent in 2025 and 2030 under the rate - based illustrative plan scenario. In the mass - based scenario, [X]. These trends are consistent with the change in generation mix described above in Section 3.7.4. **Table 3 -**  **16. Power Sector Gas Use**  Power Sector Gas Use (TCF) Percent Change in Power Sector Gas Use 2020 2025 2030 2020 2025 2030 Base Case 8.62 9.38 9.72 Rate 8.91 9.28 9.60 3.4% - 1.0% - 1.2% Mass Source: Integrated Planning Model run by EPA, 2015 3.7.8 Projected Fuel Price , Market, and Infrastructure Impacts The impacts of the two illustrative plan scenarios on coal and natural gas prices before shipment are shown below in Table 3 - 17, Table 3 - 18, Table 3 - 19, and Table 3 - 20, and are attributable to the changes in overall power sector demand for each fuel due to the CPP.***E.O. 12866 Review

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3 - 29 Coal demand decreases by 2030, resulting in a decrease in the price of coal delivered to the electric power sector. In 2030, gas demand and price decrease below the base case projections, due to the cumulative impact of demand - side energy efficiency improvements and the consequent reduced overall electricity demand. IPM modeling of natural gas prices uses both short - and long - term price signals to balance suppl y 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 historicall y experienced some degree of price volatility from year to ye ar, 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 successfull y align immediate supply and d emand needs. However, end - use consumers are typically shielded from experiencing these rapid fluctuations in natural gas prices b y retail rate regulation and b y 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

experi enced consumer price impacts related to natural gas, but a reflection of expected average price changes over the period of time represented b y the modeling horizon. There are very

small changes to natural gas pipeline infrastructure needs over time, in res ponse to the illustrative plan scenarios. These changes, compared to historical deplo ym ent of new infrastructure, are very modest. In the rate - based scenario, pipeline capacity construction through 2020 is projected to increase 1 percent beyond base case p rojections. By 2025 and 2030, however, the total cumulative pipeline capacity construction built is projected to decrease b y up to 5 percent compared to the base case, consistent with the projected decrease in total demand and natural gas use. The projecte d increase in pipeline capacity in the near term is largel y the result of building pipeline capacity a few ye ars earlier than projected in the base case.***E.O. 12866 Review

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3 - 30 **Table 3 -**  **17. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu)**  Minemouth Delive red - Electric Power Sector 2020 2025 2030 2020 2025 2030 Base Case 1.55 1.67 1.79 2.38 2.50 2.68 Rate 1.54 1.58 1.73 2.34 2.35 2.46 Mass Rate - 0.8% - 5.0% - 3.8% - 1.7% - 6.2% - 8.0% Mass Source: Integrated Planning Model run by EPA, 2015 **Table 3 -**  **19. Projected**  **Average Henry Hub (spot) and Delivered Natural Gas Pric es** 

**(2011$/MMBtu)**

Source: Integrated Planning Model run by EPA, 2015 **3.8 Projected Primary PM Emissions from Power Plants**  IPM is not configured to endogenousl y model primary PM emissions from power plants. These emissions are calculated as a function of IPM outputs, emission factors and control configuration. IPM - projected fuel use (heat input) is multiplied by PM emission factors (based in part on the presence of PM - relevant pollution control devices) to determine PM emissions. Primary PM emissions are calculated by adding the filterable PM and condensable PM emissions. Filterable PM emissions for each unit are based on historical information regarding existing emissions controls and ty pes 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 ty pe, sulfur content of the fuel, and SO 2 /HCl and PM control configurations. Although EPA’s anal ys is 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 SO 3 and H 2 SO 4 ) in units with SCRs depends on a number of***E.O. 12866 Review

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3 - 31 factors, including coal sulfur content, combustion conditions and characteristics of the catal ys t used in the SCR, and is likely to vary widel y from unit to unit. SCRs are generall y designed and operated to minimize increases in condensable PM. This limitation means that IPM post processing 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 emissio ns from IPM, see “IPM ORL File Generation Methodology” (March, 2011), available in the docket. **3.9 Limitations of Analysis**  EPA ’ s modeling is based on expert judgment of various input assumptions for variables whose outcomes are in fact uncertain. As a general matter, the Agency reviews the best available information from engineering studies of air pollution controls , the ability to improve operating efficiency , and new capacity construction costs to support a reasonable modeling framework for anal yz ing 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 t o achieve demand - side energy efficiency

improvements. The IPM - projected annualized estimates of private compliance costs provided in this analy sis are meant to show the increase in production ( generating) costs to the power sector in response to the final rule . To estimate these annualized costs, the EPA uses a conventional and widel y- accepted approach that applie s a capital recovery factor (CRF) multiplier to capital investments and add s that to the annual incremental operating expenses. The CRF is derived from estimates of the cost of capital (private discount rate), the amount of insurance coverage required, local property taxes, and the life of capital. 64 The demand - side energy

efficiency

costs are developed based on a review of energy efficiency data an d studies, and expert judgment. The EPA recognizes that significant variation exists in these anal ys es reflecting data and methodological limitations. The method used for

64 See Chapter 8 of EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/powersectormodeling .***E.O. 12866 Review

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3 - 32 estimating the demand - side energy efficiency

costs is discussed in more detail in the Greenhouse Gas Abatement Measures TSD. The evaluation, measurement and verification (EM&V) of demand - side energy

efficiency

is addressed in the section VIII , State Plans, of the preamble for the final rule . The base case electricity demand in IPM v.5.15 i s calibrated to reference case demand in AEO 2015. AEO 2015 demand may reflect, to some extent, a continuation of the impacts of state demand - side energy

efficiency

policies but does not explicitly represent the most significant existing state policies in this area (e.g., energy efficiency

resource standards). To some degree the implicit representation of state policies in the EPA’s base case alters the impacts assessment, but the direction and magnitude of change is not known with certainty. This issue is discussed in the Greenhouse Gas Abatement Measures TSD. The modeling of these policy scenarios does not account for an additional compliance flexibility allowed in the final rule whereby “overcompliance” with state goals (more reductions than required) during the interim period can be “banked” for compliance with state goals in the final period. Cost estimates for the proposed rule are based on rigorous power sector modeling using IC F ’ s Integrated Planning Model. 65 IP M assumes “ perfect foresight ” of market conditions over the time horizon modeled; t o the extent that utilities and/or energy regulators misjudge future conditions affecting the economics of pollution control, costs may be understated as well. Furthermore, IPM does not represent electricity m arke ts in Alaska, Hawaii, and U.S. territories outside the contiguous U.S. and therefore the costs (and benefits) that may be expected from the proposed rule in this states and territories are not accounted for in the compliance cost modeling. **3.10 Significan**  **t Energy Impacts** The CPP, under both illustrative plan scenarios analyzed, would have a significant impact according to E.O. 13211: Actions that Significantly Affect Energy

Suppl y, Distribution, or Use. Under the rate - based illustrative scenario, EPA proje cts that approximately 23 GW of additional

65 Full documentation for IPM can be found at <http:// http://www.epa.gov/p owersectormodeling>.***E.O. 12866 Review

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3 - 33 coal - fired generation (about 11 percent of all coal - fired capacity and 2.2 percent of total generation capacity

in 2025) may

be removed from operation by 2025. EPA also projects the average delivered coal price decreases by 6.2 percent with decreased production of 103 million tons (14 percent of US production) in 2025 and that electric power sector delivered natural gas prices will decrease b y about 7.7 percent with decreased power sector consumption of 95 billio n cubic feet (BCF) in 2025. Average retail electricity prices are projected to increase in the contiguous U.S. by [X] percent in 2025. **3.11 Monitoring, Reporting, and Recordkeeping Costs**  EPA projected monitoring, reporting and recordkeeping costs for both state entities and affected EGUs for the compliance years 2020, 2025 and 2030. In calculating the costs for state entities, EPA estimated personnel costs to oversee compliance, and review and report annually to EPA on program progress relative to meeting the state’s reduction goal. To calculate the national costs, EPA estimated that 49 states, 2 territories and 1,052 facilities would be affected. The EPA estimated that the majority of the cost to EGUs would be in calculating net energy output, which is need ed whether the state plan utilizes a rate - based or a mass - based limit. Since the majority of EGUs do have some energy usage meters or other equipment available to them, EPA believes a new s ys tem for calculating net energy output is not needed. Under the CP P, states are required to use monitoring and reporting requirements for their affected EGUs to ensure that the sources are meeting the appropriate CO2 emission performance rates or emission goals. In general, the EPA has made it a priority to streamline reporting and monitoring requirements. Inthis rule, the EPA is making implementation as efficient as possible for both the states and the affected EGUs b y allowing state plans to utilize the current monitoring and recordkeeping requirements and pathway s 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 b y a 111(d) state plan to monitor CO2 emissions and report that data using the Emissions Collection and Monitoring Plan Sy stem (ECMPS). Additionally since the CO2 hourly

data is already

reported to the EPA’s ECMPS there is no additional burden associated with the repo rting of that data. Since the ECMPS pathway is already

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3 - 34 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 th e Acid Rain Program does not require net energy output to be reported, there is some additional burden (Shown in Table 3 - 25 ) in updating an affected EGUs monitoring s ys tem to be able to report the associated net energy

output of an affected EGU. The EPA es timates that it would take 3 working months for a technician to retrofit any existing energy

meters to meet the requirements set in the state plan. Additionally EPA believes that 50 hours will be needed for each EGU operator to read the rule and understand how the facility will compl y with the rule , based on an average reading rate of 100 words per minute and a projected rule word count of 300,000 words . 66 Also, after all modifications are made at a facility to measure net energy

output, each EGU’s Data Acq uisition Sy stem (DAS) would need to be upgraded to suppl y the rate - based emissions value to either the state or EPA’s Emissions Collection and Monitoring Plan Sy stem (ECMPS). Note the costs to develop net energy output monitoring and to upgrade each facili ty ’s DAS s ys tem are one - time costs incurred in 2020. Recordkeeping and reporting costs substantially decrease for the period 2021 - 2030. The projected costs for 2020, 2025, and 2030 are summarized below. In calculating the cost for states to comply , EPA estimates that each state will rely on the equivalent of 2 full time staff (1 each from the energy and air offices) to oversee program implementation, assess progress, develop possible contingency measures, perform state plan revisions and host the subsequent public meetings if revisions are indeed needed, download data from the ECMPS for their annual reporting and develop their annual EPA report. The burden estimate was based on an anal ys is of similar tasks performed under the Regional Haze Program, whereb y s tates 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 estim ate of 78,000 hours of labor performed b y 7 states over a 3 ye ar period resulted in 3,714 hours per ye ar, per entity . Due to the

66 According to one source, the average person can proofread at about 200 words per minute on paper and 180 words per minute on a monitor. (Reference: Ziefle, M. (December 1988). “Effects of Display Resolution on Visual Performance.” Human Factors 40 (4): 554 - 68). Due to the highly technical nature of the rule requirements in subpart UUUU, a more conservative estimate of 100 words per minute was used to determine the burden estimate for reading and understanding rule requirements.***E.O. 12866 Review

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3 - 35 nature of this final rule whereb y we believe the air office and the energy office will both be involved in performing the above - mentioned tasks, we rounded up to the equivalent of 2, full time staff, which totaled 4,160 hours per ye ar. 67 Inaddition, in the absence of specific information, EPA assumed that 27 states with strong, state - driven EE programs as documented in the Energy and Environment Guide to Action 68 would submit a plan under the state measures approach , which adds to the state respondent burden . Table 3 - 2 5 shows the annual state respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030. Table 3 - 26 shows the annual industry respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030. Table 3 - 27 shows the annual territories respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030. **Table 3 -**  **25. Years**  **2020, 2025 and 2030: Summary of State Annual Respondent Burden**

**and Cost of Reporting and Recordkeeping Requirements (2011$) Nationwide**  **Totals Total Annual** 

**Labor Burden (Hours)**  **Total**

**Annual Labor Costs**  **Total**

**Annualized Capital Costs**  **Total**

**Annual O&M**  **Costs Total** 

**Annualized Costs**  **Total**

**Annual Respondent**  **Costs** Year 2020 203,840 14,427,298 0 36,015 36,015 11 14,463,313 Year 2025 225,920 15,990,066 0 24,500 24,500 16,014,566 Year 2030 225,920 15,990,066 0 24,500 24,500 16,014,566 **Table 3 -**  **26. Years**  **2020, 2025 and 2030: Summary of Industry Annual Respondent**

**Burden and Cost of Reporting and Recordkeeping Requirements (2011$) Nationwide**  **Totals Total Annual** 

**Labor Burden (Hours)**  **Total**

**Annual Labor Costs**  **Total**

**Annualized Capital Costs**  **Total**

**Annual O&M**  **Cost s**  **Total**

**Annualized Costs**  **Total**

**Annual Respondent**  **Costs** Year 2020 595,432 51,125,815 0 1,556,500 1,556,500 52,682,315 Year 2025 0 0 0 0 0 0 Year 2030 0 0 0 0 0 0

67 Ren ew al of the ICR for the Regional Haze Rule, Section 6(a) Tables 1 through 4 based on 7 states’ burden. EPA HQ - OAR - 2003 - 0162 - 0001. 68 U.S. Environmental Protection Agency. Energy and Environment Guide t o 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/statelocalclim ate/resources/action - guide.html.***E.O. 12866 Review

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3 - 36 **Table 3 -**  **27. Years 2020, 2025 and 2030: Summary of Territories Annual Respondent** 

**Burden and Cost of Reporting and Recordkeeping Requirements**  **(2011$) Nationwide**  **Totals Total Annual** 

**Labor Burden (Hours)**  **Total**

**Annual Labor Costs**  **Total**

**Annualized Capital Costs**  **Total**

**Annual O&M**  **Costs Total** 

**Annualized Costs**  **Total**

**Annual Respondent**  **Costs** Year 2020 8,320 588,869 0 1,470 1,470 590,339 Year 2025 8,960 634,167 0 1,000 1,000 635,167 Year 2030 8,960 634,167 0 1,000 1,000 635,167 The annual costs of the rate - based and mass - based illustrative plan scenarios, including monitoring reporting and recordkeeping costs, are shown in Table 3 - 28 below. **Table 3 -**  **28. Annualized Compliance Costs Including Monitoring,**  **Reporting and**

**Recordkeeping Costs Requirements (billions of 2011$)**  **2020 2025**  **2030** Rate 2 .5 1.1 8.5 Mass [X] [X] [X] Source: Integrated Planning Model run by EPA, 201 5 and GHG Abatement Measures TSD. Monitoring, reporting and recordkeeping costs calculated outside IPM. **3.12 Social Costs**  As discussed in the EPA Guidelines for Preparing Economic Anal ys es , 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 n ot 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 necessaril y be equivalent to the expenditures associated with compliance. Nonetheless, here we use compliance costs as a prox y for social costs. This section provides a qualitative discussion of the relationship between soc ial costs and compliance cost estimates presented in this chapter. The cost estimates f

or the illustrative plan scenarios presented in this c hapter are the sum of expenditures on demand - side energy efficiency and the change in expenditures required b y the electricity sector to comply with the proposed guidelines. These two components are estimated separately . The expenditures required to achieve the assumed demand reductions through demand - side energy

efficiency

programs are estimated using historical data , anal ys is, and expert***E.O. 12866 Review

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3 - 37 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 cau sed b y the demand - side energy efficiency m easure s and the increase in electricity production costs required to achieve the additional emission reductions necessary to comply

with the state goals. As described in section 3.6.1, the illustrative plan scenarios assume that, in achieving their goals, demand - side energy efficiency

measures are adopted which lead to demand reductions in each year represented b y the illustrative energy efficiency plan scenario. The estimated expenditures required to achiev e those demand reductions through demand - side energy efficiency are presented in this chapter and detailed in Chapter 5 of the GHG Abatement Measures TSD. The social cost of achieving these energy savings comes in the form of increased expenditures on technologies and/or services that are required to lower electricity consumption beyond the business as usual . Under the assumption of complete and well - functioning markets the expenditures required to reduce electricity consumption on the margin will represent society ’s opportunity

cost of the resources required to produce the energy savings . Due to the flexibility

held by states in implementing their compliance with the proposed standards these energy

efficiency

expenditures may

be borne by

end - users through direct participant expenditures or electricity rate increases, or by

producers through reductions in their profits. While the allocation of these expenditures between consumers and producers is important for understanding the distributional impact of potential compliance strategies, it does not necessaril y 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 dis tributional 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 servic es 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 d eemed less desirable, then there would be an additional welfare loss that should be accounted for in social costs but is not necessaril y captured in the measure of***E.O. 12866 Review

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3 - 38 expenditures. However, there is an analogous possibility that in some cases the quality of s ervices, outside of the energy savings, provided by

the more energy

efficient products and practices are deemed more desirable b y some end - users. For example, weatherization of buildings to reduced electricity demand associated with cooling will likely hav e a significant impact on natural gas use associated with heating. In either case these real welfare impacts are not fully captured b y 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 hy pothesis for the energy paradox. The energy paradox is the observation that end - users do not alway s purchase products that are more energy e fficient when the additional cost is less than the reduction in the net present value of expected electricity expenditures achieved by those products. 69 Such circumstances are present in the analy sis presented in this chapter, whereby in some regions the ba se case and illustrative scenarios suggest that cost of reducing demand through energy

efficiency

programs is less than the retail electricity price. In addition to heterogeneity

in product services and consumer preferences, there are other explanations fo r the energy paradox, falling both within and outside the neoclassical rational expectations paradigm that is used in benefit/cost analy sis. Chapter 5 of the GHG Abatement Measures TSD discusses the energy

paradox and provides additional hy pothesis 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

b e 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 properl y 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 complian ce cost reported in this chapter is the change in resource cost (i.e., expenditures) required b y the electricity sector to fulfill the remaining demand while 69 An analogous situation is present when some EGUs have assumed to have the ability to make heat rate im provements at a capital cost that is less than the anticipated fuel expenditure savings.***E.O. 12866 Review

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3 - 39 making additional CO 2 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 side energy

efficiency

measures and improvements in heat rate. By shi fting 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 Proje ct Evaluation , Edwin Elgar Press, Cheltenham UK. US EPA (2010) EPA Guidelines for Preparing Economic Analy ses , Chapters 8 and Appendix A. http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.html***E.O. 12866 Review

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4 - 1 

### C **HAPTER** 

### 4 **: E**  **STIMATED** 

### C **LIMATE** 

### B **ENEFITS AND** 

### H **UMAN** 

### H **EALTH** 

### C **O** 

### - **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 (CO 2 ) and have ancillary

human health benefits (i.e., co - benefits) associated wi th 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 (PM 2.5 ) and ozone by reducing emissions of precursor pollutants (i.e., sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and directly emitted PM 2.5 ) . Data, resource, and methodological limitations prevent the EPA from monetizing the benefits from several important co - benefit categories, including reducing direct exposure to SO 2 , NO 2 , and hazardous air pollutants (HAP), as well as ecos ys tem effects and visibility impairment. We qualitatively

discuss these unquantified benefits in this chapter. This chapter provides estimates of the monetize d climate benefits and air quality health co benefits associated with emission reductions for the illustrative rate - based and mass - based scenario s across several anal ys is years and discount rates. The estimated benefits as sociated 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 CO**  **2** The primary

goal of the final emission guidelines is to reduce emi ssions of CO 2 . 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 CO 2 using the Social Cost of Carbon (SC - CO 2 ), a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given ye ar . Table 4 - 1 summarizes the quantified and unquantified climate benefits in this anal ys is.***E.O. 12866 Review

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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 Im proved Environment Reduced climate effects Global climate impacts from CO 2 — 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) — — IPCC 2

1 The global climate and related impacts of CO 2 emissions changes, such as sea level rise, are estimated within each integrated assessment model as part of the calculation of the SC - CO 2 . The resulting monetized damages, which are relevant for conducting the benefit - cost analysis, are used in this RIA t o estimate the welfare effects of quantified changes in CO 2 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 address es the negative externalities caused b y ai r 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 ecos ys tems, and impacts on indigenous communities. The preamble also summarize s 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 publi c 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***E.O. 12866 Review

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4 - 3 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 elderl y 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 previousl y 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 CO 2 emission reductions expected from the final emission guidelines using the SC - CO 2 estimates presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (May 2013, Revised June 2015) (“cur rent TSD”) . 70 We refer to these estimates, which were developed by the U.S. government, as “SC - CO 2 estimates.” The SC - CO 2 is a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given ye ar. 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

s ys tem costs , such as reduced costs for heating and increased costs for air co nditioning. It is ty

pically 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 CO 2 emissions ) .

70 Docket ID EPA - HQ - OAR - 2013 - 0495, Technical Support Document: T echnical 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 Pol icy , and Department of Treasury (May 2013, Revised June 201 5 ). Available at:***E.O. 12866 Review

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4 - 4 The SC - CO 2 estimates used in this analysis were deve loped over many years, using the best science available, and with input from the public. Specificall y, an interagency working group (IWG) that included the EPA and other executive branch agencies and offices used three integrated assessment models (IAMs) t o develop the SC - CO 2 estimates and recommended four global values for use in regulatory anal ys es. The SC - CO 2 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 - CO 2 estimates in June 2015. The SC - CO 2 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 - CO 2 . 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, rel yi ng on the model developers’ be st estimates and judgments, as informed b y 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 acros s all three models. In addition, a common range of scenarios for the socioeconomic parameters and emissions forecasts were used in all three models. Finall y, the marginal damage estimates from the three models were estimated using a consistent range of dis count rates, 2.5, 3.0, and 5.0 percent. See the 2010 TSD for a complete discussion of the methods used to develop the estimates and the key uncertainties, and the current TSD for the latest estimates. The SC - CO 2 estimates represent global measures because of the distinctive nature of the climate change, which is highl y 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 - CO 2 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 highl y interconnected econom y, such that impacts on the other side o f the world can affect our econom y. This means that the true costs of climate change to the U.S. are larger than the direct***E.O. 12866 Review

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4 - 5 impacts that simply

occur within the U.S. Third, climate change represents a classic public goods problem because each country ’s red uctions benefit everyone else and no country can be excluded from enjo yi ng 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 mutuall y 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 compel ling 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, particularl y in the areas of national security, international trade, public health and humanitarian concerns. 71 The 2010 TSD noted a number of limitations to the SC - C O 2 analy sis, including the incomplete way

in which the integrated assessment models 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 phy sical, ecological, and economic impacts of climate change recognized in the climate change literature due to a lack o f precise information on the nature of damages and because the science incorporated into these models understandabl y lags behind the most recent research. The limited amount of research linking climate impacts to economic damages makes the modeling exercis e even more difficult. These individual limitations do not all work in the same direction in terms of their influence on the SC - CO 2 estimates, though taken together they suggest that the SC - CO 2 estimates are likel y 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 likel y that [SC - CO 2 estimates] underestimate the damage costs because they

cannot include many non - quantifiable impacts.” Since then, the peer - reviewed literature has continued to support this conclusion. For example, the IPCC Fifth Assessment 71 See Endangerment and Caus e or Contribute Findings for Greenhouse Gase s Under Section 202(a) of the Clean Air Act , 74 Fed. Reg. 66,496, 66,535 (Dec. 15, 2009) and National Research Council 2013a.***E.O. 12866 Review

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4 - 6 report observed that SC - CO 2 estimates continue to omit various impacts that would likely increase damages. The 95th percentile estimate was included in the recommended range for regulatory

impact anal ys is to address these concerns. The EPA and other agencies have continued to consider feedback on the SC - CO 2 estimates from stakeholders through a range of channels, inclu ding public comments on this rulemaking and others that use the SC - CO 2 in supporting analyses and through regular interactions with stakeholders and research anal ys ts implementing the SC - CO 2 methodology used b y the interagency

working group. The SC - CO 2 comments received on this rulemaking covered a wide range of topics including the technical details of the modeling conducted to develop the SC - CO 2 estimates, the aggregation and presentation of the SC - CO 2 estimates, and the process b y which the SC - CO 2 estimates were derived. Many but not all commenters were supportive of the SC CO 2 and its application to this rulemaking . Commenters also provided constructive recommendations for potential opportunities to improve the SC - CO 2 estimates in future updates . The EPA Response to Comments document provides a summary and response to the SC - CO 2 comments submitted to this rulemaking. Many of the comments EPA received were similar to those that OMB’s Office of In formation 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 - CO 2 estimates in regulatory impact anal ys is. With the release of the response to comments, the IWG announced plans to obtain expert independent advice from the National Academy of Sciences’ National Research Council (NRC) to ensure that the SC - CO 2 esti mates continue to reflect the best available scientific and economic information on climate change. The NRC process will be informed by the public comments received and focus on the technical merits and challenges of potential approaches to improving the S C - CO 2 estimates in future updates. Concurrent with OMB’s publication of the response to comments on SC - CO 2 and announcement of the NRC process, OMB posted a revised TSD that includes two minor technical corrections to the current estimates. One technical correction addressed an inadvertent***E.O. 12866 Review

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4 - 7 omission of climate change damages in the last ye ar of anal ys is (2300) in one model and the second addressed a minor indexing error in another model. On average the revised SC - CO 2 estimates are one dollar less than the m ean SC - CO 2 estimates reported in the November 2013 revision to the May

2013 TSD. The change in the estimates associated with the 95th percentile estimates when using a 3% discount rate is slightly larger, as those estimates are heavil y influenced b y the re sults from the model that was affected b y the indexing error. The four SC - CO 2 estimates are as follows: $13, $45, $66, and $130 per metric ton of CO 2 emissions in the year 2020 (2011 $). 72 The first three values are based on the average SC - CO 2 from the three IAMs, at discount rates of 5 , 3, and 2.5 percent, respectively . SC - CO 2 estimates for several discount rates are included because the literature shows that the SC - CO 2 is quite sensitive to assumptions about the discount rate, and because no consensus ex ists on the appropriate rate to use in an intergenerational context (where costs and benefits are incurred b y different generations). The fourth value is the 95 th percentile of the SC - CO 2 from all three models at a 3 percent discount rate. It is included t o represent higher- than - expected impacts from temperature change further out in the tails of the SC - CO 2 distribution (representing less likel y, but potentially

catastrophic, outcomes). Table 4 - 2 presents the gl obal SC - CO 2 estimates for the years 2015 to 20 50. Inorder to calculate the dollar value for emission reductions, the SC - CO 2 estimate for each emissions year would be applied to changes in CO 2 emissions for that year, and then discounted back to the anal ys is year using the same discount rate used to estimate the SC - CO 2 . 73 The SC - CO 2 increases over time because future emissions are expected to produce larger incremental damages as phy sical and economic sy stems become more stressed in response to greater climate change. Note that the interagency group es timated the growth rate of the SC - CO 2 directly using the three

72 The current version of the TSD is available at: [X] . The 2010 and 2013 TSDs present SCC in 2007$. The estimates were adjusted to 2011$ using GDP Implicit Price Deflator, http://www.gpo.gov/fdsys/pkg/ECONI - 2013 - 02/pdf/ECONI - 2013 - 02 - Pg3. pdf . 73 This analysis considered the climate impacts of only CO 2 emission change. As discussed below , the climate im pacts of other pollutants were not calculated for the proposed guidelines. While CO 2 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.***E.O. 12866 Review

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4 - 8 integrated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. Tables 4 - 3 through 4 - 5 report the incremental climate benefits estimated in three analysis years (2020, 2025, and 2030) for the rate - based and mass - based scenarios. **Table 4 -**  **2. Social Cost of CO**  **2 , 2015**  **- 2050 (in 2011$)** * Year Discount Rate and Statistic

5% Average 3% Average 2.5% Average 3% (95th percentile) 2015 $12 $38 $59 $11 0 2020 $13 $45 $66 $13 0 2025 $15 $49 $72 $1 50 2030 $17 $53 $77 $16 0 2035 $19 $58 $83 $18 0 2040 $22 $64 $89 $19 0 2045 $24 $68 $94 $2 10 2050 $28 $73 $10 0 $2 30 * The SC - C O 2 values vary depending on the year of CO 2 emissions and are defined in real terms , i.e., adjusted for inflation using the GDP implicit price deflator . These SC - C O 2 values are stated in $/metric ton and rounded to two significant figures . **Table 4 -**  **3. Estimated Global Climate Benefits of CO**  **2 Reductions for the Final Emission** 

**Guidelines in 2020 (billions of 2011$)*** Discount rate and statistic **Rate -**  **Based Scenario Mass**  **- Based Scenario**  Million metric tonnes of CO 2 reduced 63

5% (average) $0.80 3% (average) $2.8 2.5% (average) $4.1 3% (95 th percentile) $8.2 * The SC - C O 2 values are dollar - year and emissions - year specific. SC - C O 2 values represent only a partial accounting of climate impacts. **Table 4 -**  **4. Estimated Global Climate Benefits of CO**  **2 Reductions for the**  **Final Emission**

**Guidelines in 2025 (** **billions of 2011$)** * Discount rate and statistic **Rate -**  **Based Scenario Mass**  **- Based Scenario**  Million metric tonnes of CO 2 reduced 210

5% (average) $ 3.1 3% (average) $ 10 2.5% (average) $ 15 3% (95 th percentile) $ 31 * The SC - C O 2 values are dollar - year and emissions - year specific. SC - C O 2 values represent only a partial accounting of climate impacts.***E.O. 12866 Review

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4 - 9 **Table 4 -**  **5. Estimated**  **Global Climate Benefits of CO 2**  **Reductions for the Final Emission** 

**Guidelines in 2030 (** **billions of 2011$)** * Discount rate and statistic **Rate -**  **Based Scenario Mass**  **- Based Scenario**  Million metric tonnes of CO 2 reduced 377

5% (average) $ 6.4 3% (average) $2 0 2.5% (average) $ 2 9 3% (95 th percentile) $ 61 * The SC - C O 2 values are dollar - year and emissions - year specific. SC - C O 2 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 CO 2 emissions only . Implementing these final emission guidelines, however, will have an impact on the emissions of other pollutants that w ould affect the climate. Both predicting reductions in emissions and estimating the climate impacts of these other pollut ants , however, is complex . The climate impacts of these other pollutants have not been calculated for the final emission guidelines. 74 The other emission s 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 incre ases 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 coo ling, but reductions in the other aerosol species and precursors are projected to lead to warming. Therefore, changes in non - CO 2 pollutants could potentially augment or offset the climate benefits calculated here. These pollutants can act in different way s 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. Inaddition, these aerosols are thought to affect cli mate indirectly

by altering properties of clouds. Black carbon can also deposit on snow and ice, darkening these surfaces and accelerating melting. In terms of lifetime, while carbon dioxide emissions can increase concentrations in the atmosphere for hundreds or thousands of 74 The SC - C O 2 estimates used in this analysis are desi gned to assess the climate benefits associated with changes in CO 2 emissions only.***E.O. 12866 Review

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4 - 10 ye ars, many

of these other pollutants are short lived and remain in the atmosphere for short periods of time ranging from day s 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 anal yz ed the potential changes in upstream methane emissions from the natural gas and coal production sectors that may result from the illustrative plan scenari o s 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 CO 2 emissions reductions anticipated at power plants. This assessment included CO 2 emissions from the flaring of methane, but did not evaluate potential changes in other combustion - related CO 2 emissions, such as emissions associated with drilling, mining, processing, and transportation in the natural gas and coal production sectors. This analy sis found that the net upstream CH 4 emissions from natural gas sy stems and coal mines and CO 2 emissions from flaring of methane will likely

decrease under the final emission guidelines. Furthermore, the analy sis suggests that the changes in upstream methane emissions are small relative to the changes in direct emissions from power plants. **4.3 Estimated Hum**  **an Health Co -**  **Benefits** In addition to reducing emissions of CO 2 , implementing these final emission guidelines is expected to reduce emissions of SO 2 and NO X , which are precursors to formation of ambient PM 2.5 , as well as directly emitted fine particles. 75 Th erefore , reducing these emissions would also reduce human exposure to ambient PM 2.5 and the incidence of PM 2.5 - related health effects. In addition, in the presence of sunlight, NO X and VOCs can undergo a chemical reaction in the atmosphere to form ozone. D epending on localized concentrations of volatile organic compounds (VOCs), reducing NO X emissions would also reduce human exposure to ozone and the incidence

75 We estimate the health co - benefits associated with emission reductions of two categories of directly emitted particles: elemental carbon plus organic carbon (EC+OC) and cr ustal. 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

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4 - 11 of ozone - related health effects. Although we do not have sufficient data to quantify these impacts in this analy sis, reducing emissions of SO 2 and NOx would also reduce ambient exposure to SO 2 and NO 2 and their associated health effects , respectively . In this section, we provide an overview of the monetized PM 2.5 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 b y previous EPA rulemakings, including MATS. There are several important considerations in assessing the air quality- related health co benefits 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 co benefits. It is unclear how PM 2.5 - 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 PM 2.5 exposure. Secon d, the estimated health co - benefits also do not consider temperature modification of PM 2.5 and ozone risks ( Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b ). Third, the estimated climate benefits reported in this RIA reflect global benefits, while the estimat ed health co - benefits are calculated for the contiguous U.S. only . Excluding temperature modification of air pollution risks and international air quality- related health benefits likely leads to underestimation of quantified health co - benefits . Fourth, as noted earlier, we do not estimate the climate benefits associated with reductions in PM and O 3 precursors. Implementing the final emission guidelines may lead to reductions in ambient PM 2.5 concentrations below the National Ambient Air Quality Standards (NAAQS) for PM and ozone in some areas and assist other areas with attaining the se 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 anal ytical 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 unkno wn controls, incremental to implementation of existing regulations and***E.O. 12866 Review

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4 - 12 controls need ed to attain the current standards. In short, NAAQS RIAs hy pothesize, but do not predict, the reduction strategies that States may choose to enact when implementing a revis ed NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, the EPA’s NAAQS RIAs are merel y 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 RIA s . The emissions reductions from implementing the final emission guidelines will decrease the remaining amount of emissions reductio ns needed in non - attainment areas and reduce the costs and bene fits attributable to meeting the NAAQS. Similar to NAAQS RIAs, the emission reduction scenario s estimated for the final emission guidelines are also illustrative. In contrast to NAAQS RIAs, all of the emission reductions for the illustrative plan scenario s would occur in one well - characterized sector (i.e., the EGU sector). In general, the EPA is more confident in the magnitude and location of the emission reductions for implementation rules, which ty pically require specific emission reductions in a specifi c sector. As such, emission reductions achieved under promulgated implementation rules will ultimately be reflected in the baseline of future NAAQS analy ses, which would reduce the incremental costs and benefits associated with attaining revised future NAA QS. The EPA does not re - issue illustrative RIAs outside of the rulemaking process that retroactivel y update the baseline to account for implementation rules promulgated after an RIA was completed. For more information on the relationship between illustrative anal ys es, such as for the NAAQS and this final emission guidelines , and implementation rules, please see s ection 1.3 of the PM NAAQS RIA (U.S. EPA, 2012a). 4.3.1 Health Impact Assessment for PM 2.5 and Ozone The Integrated Science Assessment for Particulate Matter (PM I SA) (U.S. EPA, 2009 b ) identified the human health effects associated with ambient PM 2.5 exposure, which include premature mort ality

and a variety of morbidity effects associated with acute and chronic exposures. Simi larly, the Integrated Science Assessment for Ozone and Related Photochemical Oxidants (Ozone I SA) (U.S. EPA, 2013b) identified the human health effects associated with***E.O. 12866 Review

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4 - 13 ambient ozone exposure , which include premature mort ality and a variety of morbidity eff ects associated with acute and chronic exposures. Table 4 - 6 identifies the quantified and unquantified co - benefit categorie s captured in the EPA’s health co - benefits estimates for reduced exposure to ambient PM 2.5 and ozone. Although the table below does n ot list unquantified health effects such as those associated with exposure to SO 2 , NO 2 , 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.***E.O. 12866 Review

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4 - 14 **Table 4 -**  **6. Human Health Effects of Ambient PM**  **2.5 and Ozone**  Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information Im proved Human Health Reduced incidence of premature mortality from exposure to PM 2.5 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 mo rbidity from exposure to PM 2.5 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 ISA 1 Emergency room visits for cardiovascular effects (all ages) — — PM ISA 1 Strokes and cerebrovascular disease (age 50 - 79)

— — PM ISA 1 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 mo rtality 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 ISA 1

Reduced incidence of mo rbidity 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 ISA 1 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.***E.O. 12866 Review

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4 - 15 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 th ose changes assuming independence of the values for those individual endpoints. Because the EPA rarel y has the time or resources to perform new research to measure directly , either health outcomes or their values for regulatory anal ys es, 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 anal ys is. W e use a “benefit - per - ton” approach to estim ate the PM 2.5 and ozone co - benefits in this RIA . Benefit - p er - ton approaches appl y 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 mod elin g is available. This section describes the underlying basis for the health and economic valuation estimates that inform the benefit - per - t on estimates, and the subsequent section provides an overview of the benefit - per - ton estimates, 76 which are described in detail in the appendix to this chapter. The benefit - per - ton approach we use in this RIA relies on estimates of human health responses to exposure to PM and ozone obtained from the peer - reviewed scientific literature . These estimates are used in conjunctio n 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 se ctions provide an overview of the health impact assessment (HIA) methodology and additional details on several key elements. The HIA quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM 2.5 and ozo ne. We use the environmental Benefits Mapping and Analy sis Program

– Community Edition (BenMAP - CE ) (version 1.1 ) to sy stematize health impact anal ys es b y appl ying a database of key input parameters, including population projections, health impact functions , and valuation functions (Abt Associates, 2012). For this 76 We have updated the benefit - per - ton estimates since the proposal RIA. In this RIA, we apply benefit -per - ton estimates that were derived from air quality modeling of the proposed Clean Power Plan (Option 1 State).***E.O. 12866 Review

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4 - 16 assessment, the HIA is limited to those health effects that are directl y linked to ambient PM 2.5 and ozone concentrations. There may be other indirect health impacts associated with reducing emissions, such as occupational health exposures. Epidemiological studies generall y provide estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 μg/m 3 for PM 2.5 or ppb for ozone). These relative risks can be used to develop risk coefficients that relate a unit reduction in PM 2.5 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 informatio n regarding the epidemiology studies and risk coefficients applied in this analy sis, and we briefl y elaborate on adult premature mortality below. The size of the mortality

effect estimates from epidemiological studies, the serious nature of the effect itse lf, and the high monetary

value ascribed to prolonging life make mortality risk reduction the most significant health endpoint quantified in this analysis. 4.3.1.1 Mortality Concentration -Response Functions for PM 2.5 Considering a substantial body of publi shed scientific literature and reflecting thousands of epidemiology , toxicology , and clinical studies, the PM I SA documents the association between elevated PM 2.5 concentrations and adverse health effects, including increased premature mortality (U.S. EPA, 2009 b ). The PM I SA, 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 an d short - term exposure to PM 2.5 based on the entire body of scientific evidence. The PM I SA also concluded that the scientific literature supports the use of a no - threshold log linear model to portray

the PM - mortality

concentration - response relationship whi le recognizing potential uncertainty about the exact shape of the concentration - response function. Inaddition to adult mortality

discussed in more detail below, we use effect coefficients from Woodruff et al. (1997) to estimate PM - related infant mortality . For adult PM - related mortality , we use the effect coefficients from the most recent epidemiology

studies examining two large population cohorts: the American Cancer Society cohort (Krewski et al. , 2009 ) and the Harvard Six Cities cohort (Lepeule et al, 2 012). 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***E.O. 12866 Review

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4 - 17 support from a number of additional cohort studies. The SAB's H ealth Effects Subcommittee (SAB - HES) also supported using these two cohorts for anal ys es 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 - benef its estimates based on benefits - per - ton derived using relative risk estimates from both these cohorts. As a characterization of uncertainty regarding the adult PM 2.5 - mortality relationship, the EPA graphicall y presents the PM 2.5 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 de scribing uncertainty in estimates of the reduction in mortality among the adult U.S. population resulting from reductions in ambient annual average PM 2.5 concentrations . In that study , twelve experts provided independent opinions regarding the PM 2.5 - mortal ity 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 base d 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 PM 2.5 on mortality , not whether such an effect existed” and that the expert elicitation “supports the conclusion that the benefits of PM 2.5 control are very likel y to be substantial”. Although it is possible that newer scientific literature could revise the experts’ quantitative responses if elicited again, we believe that these general conclusions are unlikely to change. 4.3.1.2 Mortality Concentration -Response Functions for Ozone In 2008, the National Academies of Science (NRC, 2008) issued a series of recommendations to the EPA regarding the quantification and valuation of ozone - related short term 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 als o recommended that “...the greatest emphasis be placed on the multicity and NMMAPS***E.O. 12866 Review

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4 - 18 [National Morbidity, Mortality, and Air Pollution Study ] studies without exclusion of the meta anal ys es” (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 anal ys is, the Schwartz (2005) multi - city study , the Huang et al. (2005) multi - city study as well as effect estimates from the three meta - anal ys es (Bell e t al. ( 2005 ) , Lev y et al. ( 2005 ), and Ito et al. ( 2005) ) . These studies are consistent with the studies used in the Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d). 77 For simplicity, we report the ozone mortality estimates in this RIA as a range reflecting application of dollar- per - ton estimates based on Bell et al. (2004) and Lev y et al. (2005) to represent the lowest and the highest co - benefits estimates based on the se six ozone mortality studies. In addition, we graphicall y present in Figure 4 - 1 the estimated co - benefits based on dollar- per - ton estimates derived from all six studies mentioned above a s 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 generall y lower the risk of future adverse health effects b y 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 generall y not available, so we use the cost of treating or mitigating the effect . These cost - of - illness (COI) estimates generall y (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 anal ys is are provided in Table 5 - 9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a).

77 Since the EPA received NAS advice, the A gency 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, d o not reflect the mo st 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.***E.O. 12866 Review

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4 - 19 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 o f premature mortality is the subject of continuing discussion within the economics and public policy anal ys is community . Following the advice of the SAB’s Environmental Economics Advisory Committee (SAB - EEAC), the EPA currentl y 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 b y 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 th is 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 appl y the VSL that was v etted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014 ) 78 while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 79 We then adjust this VSLto account for the currency year and to account for income growth from 1990 to the anal ysis year. S pecifically , the VSL s applied in this anal ys is in 2011$ after adjusting for income growth are $9.9 million for 2020 and $10.1 million for 2025 and 2030 . 80

78 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), the EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming. 79 In 1990$, this base VSL is $4.8 million. 80 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2030 estimates use income grow th only through 2024 and are therefore likely underestimates.***E.O. 12866 Review

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4 - 20 The Agency

is committed to using scientificall y s ound, appropriatel y reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB EEAC’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 recommendation s were transmitted on July 29, 2011 (U.S. EPA - SAB, 2011). The EPA is taking SAB’s recommendations under advisement . In valuing PM 2.5 - related premature mortality , we discount the value of premature mortality occurring in future ye ars 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 ye ars 2 to 5, and 20 percent over the years 6 to 20 after the reduction in PM 2.5 (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 anal ys is year, the estimated ozone - related co - benefits are identical for all discount rate s. 4.3.3 Benefit-

per- ton Estimates for PM 2.5 W e used a “benefit - per - ton” approach to estimate the PM 2.5 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 PM 2.5 (or PM 2.5 precursor such as NO X or SO 2 ) from a specified source. Specificall y, in this analy sis, 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***E.O. 12866 Review

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4 - 21 modeling of the proposed Clean Power Plan . Similar to the proposal, we generate d regional benefit - per - ton estimates by aggregating the impacts i n 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 - to n estimates have inherent limitations. Specificall y, all benefit - per - ton estimates reflect the geographic distribution of the modeled proposal , which may not match the emission reductions anticipated b y the final emission guidelines , and they may not refle ct 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 ty pes 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 PM 2.5 leve ls, which drive population exposure. 4.3.4 Benefit-

per- ton Estimates for Ozone Similar to PM 2.5 , 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 PM 2.5 estimates, we generated regional benefit - per - ton estimates for ozone based on air quality

modeling for the pro posed Clean Power Plan . In contrast to the PM 2.5 estimates, the ozone estimates are not based on changes to annual emissions. Instead, the regional estimates (i.e., East, West, and California) correspond to NO X emissions from U.S. EGUs during the ozone - season (May to September) . Because we estimate ozone health impacts from May to September only , this approach underestimates ozone co - benefits in areas with a longer ozone season such as southern California and Texas. These estimates assume that EGU attributable ozone formation at the regional - level is due to NOx alone. Because EGUs emit little VOC relative to NO X 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 .***E.O. 12866 Review

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4 - 22 4.3.5 Estimated Health Co -Benefits Results Ta ble s 4 - 7 through 4 - 9 provide the regional benefit - per - ton estimates for three anal ys is ye ars: 2020, 2025, and 2030. Tables 4 - 10 through 4 - 1 2 and 4 - 13 through 4 - 15 provide the emission reductions estimated to occur in each analy sis year for the rate - based and mass - based illustrative plan scenario s, respectivel y, by region (i.e., East, West, and California) . 81 Tables 4 - 1 6 through 4 - 1 8 and 4 - 19 through 4 - 21 summarize the national monetized PM and ozone - related health co - benefits estimated to occur in each analy sis year for the illustrative rate - based and mass - based plan scenarios, respectivel y, by precursor pollutant using discount rates of 3 percent and 7 percent . Tables 4 - 22 through 4 - 24 and 4 - 25 through 4 - 27 - provide national summaries of the reductions in estimated health incidences associated with the illustrative rate - based and mass based plan scenarios, respectivel y, in each anal ys is year . Figure 4 - 1 provides a visual representation of the range of estimated PM 2.5 and ozone - related co - benefits using bene fit - per ton estimates based on concentration - response functions from different studies and expert opinion for the illustrative rate - based and mass - based plan scenarios evaluated in 2025 as an illustrative analy sis year. Figure 4 - 2 provide s a breakdown of the monetized health co - benefits for the rate - based and mass - based plan scenarios evaluated in 2025 as an illustrative anal ys is ye ar by precursor pollutant .

81 See Chapter 3 of this RIA for more information regarding the expected emiss ion reductions used to calculate the health co - benefits in this chapter. Chapter 3 also provides more information regarding the illustrative plan scenario.***E.O. 12866 Review

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4 - 23 **Table 4 -**  **7. Summary of Regional PM**  **2.5 Benefit** **- per**  **- Ton Estimates**  **Based on Air Quality**

**Modeling f** 

**rom P**  **roposed Clean Power Plan in 2020 (2011$)** * Pollutant Discount Rate Regional East West California SO 2 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 PM 2.5 (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 PM 2.5 (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 NO X (as PM 2.5 ) 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 NO X (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 PM 2.5 and ozone. All estimates are rounded to two significant figures. The mo netized co - benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , 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 PM 2.5 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 NO X emissions. Ozone co - benefits occur in an alysis 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 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approx imately - 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 PM**  **2.5 Benefit** **- per**  **- Ton Estimates**  **Based on Air Quality**

**Modeling from Proposed Clean Power Plan in 2025 (20**  **11$)*** Pollutant Discount Rate Regional East West California SO 2 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 PM 2.5 (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 PM 2.5 (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 NO X (as PM 2.5 ) 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 NO X (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 PM 2.5 and ozone. All estimates are rounded to two significant figures. The mo netized co - benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , 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 PM 2.5 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 NO X 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 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately - 90 percent to +180 percent of the centra l estimates based on Krewski et al. (2009) and Lepeule et al. (2012).***E.O. 12866 Review

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4 - 24 **Table 4**  **- 9.**  **Summary of Regional PM 2.5**  **Benefit -**  **per -**  **Ton Estimates Based on Air Quality** 

**Modeling from Proposed Clean Power Plan in 2030 (2011$)** * Pollutant Discount

Rate Regional

East West California SO 2 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 PM 2.5 (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 PM 2.5 (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 NO X (as PM 2.5 ) 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 NO X (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 mo rtality for PM 2.5 and ozone. All estimates are rounded to two significant figures. The mo netized co - benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , 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 PM 2.5 concentrations , which drive population exposure. The monetized co - benefits incorporate the conversion from precurs or emissions to ambient fine particles and ozone. Benefit -per - ton estimates for ozone are based on ozone season NO X 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 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). **Table 4 -**  **10. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines** 

**Rate -**  **based Scenario in 2020 (thousands of short tons)*** Region SO 2 All - year NOx Ozone - Season NOx Directly emitted

PM 2.5 (EC+OC) Directly emitted

PM 2.5 ( crustal ) East 12 50 19 West 1 1 0 California 0 0 0 National Total 13 51 19 *All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national total. **Table 4 -**  **11. Emission Reductions of**  **Criteria Pollutants for the Final Emission Guidelines**

**Rate -**  **based Scenario in 2025 (thousands of short tons)*** Region SO 2 All - year NOx Ozone - Season NOx Directly emitted

PM 2.5 (EC+OC) Directly emitted

PM 2.5 ( crustal ) East 170 155 67 West 7 8 3 California 1 2 0 National Total 178 164 70 *All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national total.***E.O. 12866 Review

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4 - 25 **Table 4 -**  **12. Emission Reductions of Criteria Pollutants for the Final Emission**  **Guidelines**

**Rate -**  **based Scenario in 2030 (thousands of short tons)*** Region SO 2 All - year NOx Ozone - Season NOx Directly emitted

PM 2.5 (EC+OC) Directly emitted

PM 2.5 ( crustal ) East 307 264 109 West 11 15 9 California 1 4 0 National Total 319 283 118 *All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national total. **Table 4 -**  **13. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines** 

**Mass -**  **based Scenario in 2020 (thousands of short tons)** * Region SO 2 All - year NOx Ozone - Season NOx Directly emitted

PM 2.5 (EC+OC) Directly emitted

PM 2.5 ( crustal ) East West California National Total *All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national total. **Table 4 -**  **14. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines** 

**Mass -**  **based Scenario in 2025 (thousands of short tons)*** Region SO 2 All - year NOx Ozone - Season NOx Directly emitted

PM 2.5 (EC+OC) Directly emitted

PM 2.5 ( crustal ) East West California National Total *All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national total. **Table 4 -**  **15. Emission**  **Reductions of Criteria Pollutants for the Final Emission Guidelines**

**Mass -**  **based Scenario in 2030 (thousands of short tons)*** Region SO 2 All - year NOx Ozone - Season NOx Directly emitted

PM 2.5 (EC+OC) Directly emitted

PM 2.5 ( crustal ) East West California National Total***E.O. 12866 Review

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4 - 26 *All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national total.

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

**Guidelines Rate**  **- based Scenario in 2020 (**  **billions of 2011$) ** * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $0.41 to $0.92 $0.37 to $0.83 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $0.15 to $0.33 $0.13 to $0.30 NOx (as Ozone) $0.12 to $0.53 $0.12 to $0.53

**Total** $0.67 to $1.8 $0.62 to $1.7 * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated mo netized co - benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , 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 PM 2.5 levels, which drive population exposure. The monetized co - benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co - benefits for PM 2.5 precursors are based on regional benefit -per - ton estimates. Co benefits fo r 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 95 th percentile confidence interval for monetized PM 2.5 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**  **- Bene**  **fits for the Final Emission**

**Guidelines Rate -**  **based Scenario in 2025 (billions of 2011$)** ***** Pollutant 3% Discount Rate 7% Discount Rate SO 2 $6.4 to $14 $5.7 to $13 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $0.56 to $1.3 $0.50 to $1.1 NOx (as Ozone) $0.48 to $2.1 $0.48 to $2.1

**Total** $7.4 to $18 $6.7 to $16 * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated mo netized co - benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , 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 PM 2.5 levels, which drive population exposure. The monetized co - benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co - benefits for PM 2.5 precursors are based on regional benefit -p er - 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. Confidence intervals are unavailable for this analysis because of the benefit -per - ton me thodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).***E.O. 12866 Review

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4 - 27 **Table 4 -**  **18 .**  **Summary of E stimated Monetized Health Co**  **- Benefits for the Final Emission** 

**Guidelines Rate -**  **based Scenario in 20 30 (b**  **illions of 2011$) *** Pollutant 3% Discount Rate 7% Discount Rate SO 2 $12 to $28 $11 to $25 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) $1.0 to $2.3 $0.94 to $2.1 NOx (as Ozone) $0.86 to $3.7 $0.86 to $3.7

**Total** $14 to $34 $13 to $31 * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated mo netized co - benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , 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 PM 2.5 levels, which drive population exposure. The monetized co - benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co - benefits for PM 2.5 precursors 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. Confidence intervals are unavailable for this analysis because of the benefit -per - ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012) . **Table 4 -**  **19 .**  **Summary of Estimated Monetized Health Co -**  **Benefits for the Final Emission**

**Guidelines Mass**  **- based Scenario in 2020 (b**  **illions of 2011$) *** Pollutant 3% Discount Rate 7% Discount Rate SO 2 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) NOx (as Ozone)

**Total** * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated mo netized co - benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , 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 PM 2.5 levels, whic h drive population exposure. The monetized co - benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co - benefits for PM 2.5 precursors are based on regional benefit -per - ton estimates. Co benefits for ozone are base d 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 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).***E.O. 12866 Review

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4 - 28 **Table 4 -**  **20. Summary of Estimated Monetized Health Co**  **- Ben**  **efits for the Final Emission**

**Guidelines Mass -**  **based Scenario in 2025 (billions of 2011$)** ***** Pollutant 3% Discount Rate 7% Discount Rate SO 2 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) NOx (as Ozone)

**Total** * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated mo netized co - benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility im pairment. 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 PM 2.5 levels, which drive population exposure. The monetized co - benefits incorpo rate the conversion from precursor emissions to ambient fine particles and ozone. Co - benefits for PM 2.5 precursors are based on regional benefit -per - ton estimates. Co benefits for ozone are based on ozone season NOx emissions. Ozone co - benefits occur in an alysis 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 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approx imately - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). **Table 4 -**  **21 .**  **Summary of Estimated Monetized Health Co -**  **Benefits for the Final Emission**

**Guidelines Mass**  **- based Scenario in 2030 (b**  **illions o f 2011$) ** * Pollutant 3% Discount Rate 7% Discount Rate SO 2 Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (crustal) NOx (as PM 2.5 ) NOx (as Ozone)

**Total** * All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated mo netized co - benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility im pairment. 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 PM 2.5 levels, which drive population exposure. The monetized co - benefits incorpo rate the conversion from precursor emissions to ambient fine particles and ozone. Co - benefits for PM 2.5 precursors are based on regional benefit -per - ton estimates. Co benefits for ozone are based on ozone season NOx emissions. Ozone co - benefits occur in an alysis 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 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approx imately - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).***E.O. 12866 Review

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4 - 29 **Table 4 -**  **22. Summary of Avoided Health Incidences from PM**  **2.5 -**  **Related and Ozone**  

** Related Co** **- benefits for the Final Emission Guidelines Ra**  **te -**  **based Scenario in 2020** * **PM 2.5**  **- related Health**  **Effects** Avoided Premature Mortality Krewski et al. (2009) (adult) 60 Lepeule et al. (2012) (adult) 140 Woodruff et al. (1997) (infant) 0 Avoided Morbidity Emergency department visits for asthma (all ages) 32 Ac ute bronchitis (age 8

– 12) 89 Lo wer respiratory symptoms (age 7

– 14) 1,100 Upper respiratory symptoms (asthmatics age 9

– 11) 1,600 Minor restricted - activity days (age 18

– 65) 44,000 Lost work days (age 18

– 65) 7,500 Asthma exacerbation (age 6

– 18) 4,000 Hospital admissions

—

respiratory (all ages) 18 Hospital admissions

—cardiovascular (age > 18) 22 Non -Fatal Heart Attacks (age >18) Peters et al. (2001) 69 Pooled estimate of 4 studies 7 **Ozone -**  **r elated Health**  **Effects** Avoided Premature Mortality Bell et al. (2004) (all ages) 11 Levy et al. (2005) (all ages) 52 Avoided Morbidity Hospital admissions

—

respiratory causes (ages > 65) 67 Hospital admissions

—

respiratory causes (ages < 2) 34 Emergency room visits for asthma (all ages) 37 Minor restricted - activity days (ages 18 - 65) 68,000 School absence days 23,000 * All estimates are rounded to whole numbers with two significant figures. Co - benefits for PM 2.5 precursors are based on regional benefit -per - ton estimates for all precursors . Co - benefits for ozone are based on ozone season NOx emissi ons. Confidence interv als are unavailable for this analysis because of the benefit -per - ton methodology. In general, the 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krew ski et al. (2009) and ±46 percent based on Lepeule et al. (2012).***E.O. 12866 Review

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4 - 30 **Table 4 -**  **23. Summary of Avoided Health Incidences from PM**  **2.5 -**  **Related and Ozone**  

** Related Co** **- benefits for Final Emission Guidelines Rate**  **- based Scenario**  **in 2025*** **PM 2.5**  **- related Health**  **Effects** Avoided Premature Mortality Krewski et al. (2009) (adult) 740 Lepeule et al. (2012) (adult) 1,700 Woodruff et al. (1997) (infant) 2 Avoided Morbidity Emergency department visits for asthma (all ages) 370 Ac ute bronchitis (age 8

– 12) 1,100 Lo wer respiratory symptoms (age 7

– 14) 14,000 Upper respiratory symptoms (asthmatics age 9

– 11) 20,000 Minor restricted - activity days (age 18

– 65) 520,000 Lost work days (age 18

– 65) 89,000 Asthma exacerbation (age 6

– 18) 48,000 Hospital admissions

—

respiratory (all ages) 220 Hospital admissions

—cardiovascular (age > 18) 270 Non -Fatal Heart Attacks (age >18) Peters et al. (2001) 860 Pooled estimate of 4 studies 93 **Ozone -**  **r elated 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 PM 2.5 precursors are based on regional benefit -per - ton estimates for all precursors . Co - benefits for ozone are based on ozone season NOx emissi ons. Confidence intervals are unavailable for this analysis because of the benefit -per - ton methodology. In general, the 95 th percentile confide nce interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krew ski et al. (2009) and ±46 percent based on Lepeule et al. (2012).***E.O. 12866 Review

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4 - 31 **Table 4**  **- 24.**  **Summary of Avoided Health Incidences from PM 2.5**  **- Relat**  **ed and Ozone**  

** Related Co** **- B**  **enefits for Final Emission Guidelines Rate**  **- based Scenario**  **in 2030*** **PM 2.5**  **- 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 Ac ute bronchitis (age 8

– 12) 2,000 Lo wer respiratory symptoms (age 7

– 14) 26,000 Upper respiratory symptoms (asthmatics age 9

– 11) 37,000 Minor restricted - activity days (age 18

– 65) 970,000 Lost work days (age 18

– 65) 160,000 Asthma exacerbation (age 6

– 18) 90,000 Hospital admissions

—

respiratory (all ages) 440 Hospital admissions

—cardiovascular (age > 18) 540 Non -Fatal Heart Attacks (age >18) Peters et al. (2001) 1,700 Pooled estimate of 4 studies 180 **Ozone -**  **r elated 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 PM 2.5 precursors are based on regional benefit -per - ton estimates for all precursors . Co - benefits for ozone are based on ozone season NOx emissi ons. Confidence intervals are unavailable for this analysis because of the benefit -per - ton methodology. In general, the 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krew ski et al. (2009) and ±46 percent based on Lepeule et al. (2012).***E.O. 12866 Review

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4 - 32 **Table 4 -**  **25. Summary of Avoided Health Incidences from PM**  **2.5 -**  **Related and Ozone**  

** Related Co** **- benefits for the Final Emission Guidelines Mass**  **- based Scenario in**  **2020*** **PM 2**  **.5 -**  **related Health Effects**  Avoided Premature Mortality Krewski et al. (2009) (adult) Lepeule et al. (2012) (adult) Woodruff et al. (1997) (infant) Avoided Morbidity Emergency department visits for asthma (all ages) Ac ute bronchitis (age 8

– 12) Lo wer respiratory symptoms (age 7

– 14) Upper respiratory symptoms (asthmatics age 9

– 11) Minor restricted - activity days (age 18

– 65) Lost work days (age 18

– 65) Asthma exacerbation (age 6

– 18) Hospital admissions

—

respiratory (all ages) Hospital admissions

—cardiovascular (age > 18) Non -Fatal Heart Attacks (age >18) Peters et al. (2001) Pooled estimate of 4 studies **Ozone -**  **r elated Health**  **Effects** Avoided Premature Mortality Bell et al. (2004) (all ages) Levy et al. (2005) (all ages) Avoided Morbidity Hospital admissions

—

respiratory causes (ages > 65) Hospital admissions

—

respiratory causes (ages < 2) Emergency room visits for asthma (all ages) Minor restricted - activity days (ages 18 - 65) School absence days * All estimates are rounded to whole numbers with two significant figures. Co - benefits for PM 2.5 precursors are based on regional benefit -per - ton estimates for all precursors . Co - benefits for ozone are based on ozone season NOx emissi ons. Confidence intervals are unavailable for this analysis because of the benefit -per - ton methodology. In general, the 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krew ski et al. (2009) and ±46 percent based on Lepeule et al. (2012).***E.O. 12866 Review

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4 - 33 **Table 4 -**  **26. Summary of Avoided Health Incidences from PM**  **2.5 -**  **Related and O zone**  ****

**Related Co -**  **benefits for Final Emission Guidelines Mass -**  **based Scenario in 2025** * **PM 2.5**  **- related Health**  **Effects** Avoided Premature Mortality Krewski et al. (2009) (adult) Lepeule et al. (2012) (adult) Woodruff et al. (1997) (infant) Avoided Morbidity Emergency department visits for asthma (all ages) Ac ute bronchitis (age 8

– 12) Lo wer respiratory symptoms (age 7

– 14) Upper respiratory symptoms (asthmatics age 9

– 11) Minor restricted - activity days (age 18

– 65) Lost work days (age 18

– 65) Asthma exacerbation (age 6

– 18) Hospital admissions

—

respiratory (all ages) Hospital admissions

—cardiovascular (age > 18) Non -Fatal Heart Attacks (age >18) Peters et al. (2001) Pooled estimate of 4 studies **Ozone -**  **r elated Health**  **Effects** Avoided Premature Mortality Bell et al. (2004) (all ages) Levy et al. (2005) (all ages) Avoided Morbidity Hospital admissions

—

respiratory causes (ages > 65) Hospital admissions

—

respiratory causes (ages < 2) Emergency room visits for asthma (all ages) Minor restricted - activity days (ages 18 - 65) School absence days * All estimates are rounded to whole numbers with two significant figures. Co - benefits for PM 2.5 precursors are based on regional benefit -per - ton estimates for all precursors . Co - benefits for ozone are based on ozone season NOx emissi ons. Confidence intervals are unavailable for this analysis because of the benefit -per - ton method ology. In general, the 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krew ski et al. (2009) and ±46 percent based on Lepeule et al. (2012).***E.O. 12866 Review

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4 - 34 **Table 4**  **- 27.**  **Summa ry of Avoided Health Incidences from PM**  **2.5 -**  **Related and Ozone**  

** Related Co** **- B**  **enefits for Final Emission Guidelines Mass**  **- based Scenario**  **in 2030** * **PM 2.5**  **- related Health**  **Effects** Avoided Premature Mortality Krewski et al. (2009) (adult) Lepeule et al. (2012) (adult) Woodruff et al. (1997) (infant) Avoided Morbidity Emergency department visits for asthma (all ages) Ac ute bronchitis (age 8

– 12) Lo wer respiratory symptoms (age 7

– 14) Upper respiratory symptoms (asthmatics age 9

– 11) Minor restricted - activity days (age 18

– 65) Lost work days (age 18

– 65) Asthma exacerbation (age 6

– 18) Hospital admissions

—

respiratory (all ages) Hospital admissions

—cardiovascular (age > 18) Non -Fatal Heart Attacks (age >18) Peters et al. (2001) Pooled estimate of 4 studies **Ozone -**  **r elated Health**  **Effects** Avoided Premature Mortality Bell et al. (2004) (all ages) Levy et al. (2005) (all ages) Avoided Morbidity Hospital admissions

—

respiratory causes (ages > 65) Hospital admissions

—

respiratory causes (ages < 2) Emergency room visits for asthma (all ages) Minor restricted - activity days (ages 18 - 65) School absence days * All estimates are rounded to whole numbers with two significant figures. Co - benefits for PM 2.5 precursors are based on regional benefit -per - ton estimates for all precursors . Co - benefits for ozone are based on ozone season NOx emissi ons. Confidence interv als are unavailable for this analysis because of the benefit -per - ton methodology. In general, the 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krew ski et al. (2009) and ±46 percent based on Lepeule et al. (2012).***E.O. 12866 Review

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4 - 35 **Figure 4**  **- 1**  **. Monetized Health Co**  **- benefits of Rate**  **- based and Mass**  **- based Scenarios for** 

**the Final Emission Guidelines in 2025 *** *T h e PM 2.5 graph s show the estimated PM 2.5 co - benefits at disc o unt 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. Th e ozone graph s show the estimated ozone co - benefits derived from six ozone mo rtality studies ( i.e., Bell et al. (2004), Schwartz (2005), Huang et al. (2005), Bell et al. ( 2005 ) , Levy et al. ( 2005 ), and Ito et al. ( 2005). Ozone co - benefits occur in the analysis year, so they are the same for all discount rates. These estimates do not include climate benefits. The mo netized co - benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. **PM 2.5**  **Ozone*****E.O. 12866 Review

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4 - 36 **Low Health Co -**  **benefits High Health Co**  **- bene**  **fits**

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

**Discount Rate for Rate**  **- based Scenario**  **for Final Emission**  **Guidelines in**  **2025*** * “Low Health Co - benefits” refers to the com bined health co - benefits estimated using the Bell et al. (2004) mo rtality study for ozone with the Krewski et al. (2009) mortality study for PM 2.5 . “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 PM 2.5 . 4.3.6 Characterization of Uncertainty in the Estimated Health Co - benefits In any complex anal ys is using estimated parameters and inputs from numerous models, there are likel y to be many sources of uncertainty. This analysis is no exception. This analysis includes many

data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing co - benefits, and assumptions regarding the future state of the world (i.e., regulations, technology , and human behavior). Each of these inputs may be uncertain and would affect the estimate of co - benefits . When the uncertainties from each stage of the analy sis are compounded, even small uncertainties can have large effects on the total quantified benefits. Inaddition, the use of the benefit - per - ton approach adds additional uncertainties beyond those for analy ses based directly on air quality modeling. Therefore, the estimates of co - benefits in each anal ys is year should be viewed as representative of the general magnitude of co - benefits of the illustrative plan sc enario , rather than the actual co - benefits anticipated from implementing the final emission guidelines .

SO2 86% Directly emitted PM2.5 (EC+OC) 0% Directly emitted PM2.5 (Crustal) 0% NOx (as PM2.5) 8% NOx (as Ozone) 6% 

### Rate **- based**  **Scenario**

SO2 81% Directly emitted PM2.5 (EC+OC) 0% Directly emitted PM2.5 (Crustal) 0% NOx (as PM2.5) 7% NOx (as Ozone) 12% 

### Rate **- based Scenario** ***E.O. 12866 Review

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4 - 37 This RIA does not include the ty pe of detailed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAAQS RIA (U.S. EPA, 2008 b) 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 anal ys es presented in the PM NAAQS RIA and Ozone NAAQS RIA can provide some i nformation regarding the uncertainty inherent in the estimated co - benefits results presented in this analy sis. For example, s ensitivity anal ys es conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions could change the estimated PM 2 .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 PM 2.5 - related mortality co - benefits by between 33 percent and −14 percent . Although we generall y do not calculate confidence intervals for benefit - per - ton estimates and they can provide an incomplete picture about the overall uncertainty in the benefits estimates , the PM NAAQS RIA can provide an indication of the random sampling error in the health impact and economic valuation functions using Monte Carlo methods. Ingeneral, the 95 th percentile confidence interval for monetized PM 2.5 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 95 th 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 RIA s for which the EPA conducts scenario - specific air quality modeling , we do not have information on the specific location of the air quality changes associated with the final emission guidelines . As such, it is not feasible to estimate the proportion of co - benefits occurring in different locations, such as designated nonattainment areas. Instead, we applied benefit - per ton 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 control ling 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 e stimates to emission reductions occurring in any specific location, as these are all based on a broad emission reduction***E.O. 12866 Review

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4 - 38 scenario and therefore represent average benefits - per - ton over the entire region. The benefit - per ton for emission reductions in specif ic locations may

be very different than the estimates presented here. To the extent that the geographic distribution of the emissions reductions achieved b y implementing the final emission guidelines is different than the emissions in the air quality model ing of the proposal, the co - benefits may be underestimated or overestimated. The appendix to this chapter provides additional uncertainty information regarding the benefit - per ton estimates applied in this RIA, including an evaluation of the similarities and differences in the spatial distribution of emissions in the Clean Power Plan proposal modeling and the final illustrative plan scenarios in Chapter 3 of this RIA. Our estimate of the total monetized co - benefits is based on the EPA’s interpretation of th e best available scientific literature and methods and supported by the SAB - HES and the National Academies of Science (NRC, 2002 ). Below are key assumptions underl yi ng the estimates for PM 2.5 - related premature mortality , which accounts for 98 percent of the monetized PM 2.5 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 PM 2.5 varies considerabl y in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM I SA concluded that “many constituents of PM 2.5 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 closel y related to specific outcomes” (U.S. EPA, 2009 b ). 2. We assume that the health impact function for fine particles is log - linear without a threshold. Thus, the es timates include health co - benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations . 3. We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specificall y, we assume that some of the incidences of premature mortality related to PM 2.5 exposures occur in a dis tributed fashion over the 20 ye ars following exposure based on the advice of the SAB - HES (U.S. EPA SAB, 2004c), which affects the valuation of mortality co - benefits at different discount rates.***E.O. 12866 Review

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4 - 39 In general, we are more confident in the magnitude of the risk s we estimate from simulate d PM 2.5 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 PM 2.5 concentrations that fall below the bulk of the observed data in these studies. Concentration benchmark analy ses (e.g., lowest measured level [LML ] , one standard deviation below the mean of the air quality data in the study, etc.) allow readers to deter mine the portion of population exposed to annual mean PM 2.5 levels at or above different concentrations, which provides some insight into the level of uncertainty in the estimated PM 2.5 mortality benefits. In this anal ys is, we appl y two concentration benchmark approaches (LML and one standard deviation below the mean) that have been incorporated into recent RIAs and the EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably

less, and the scientific evidence provides no clear dividing line. However, the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health co - benefits of air quality improvements. 82 Rather, the co benefits estimates reported in this RIA are the best estimates because they reflect the full range of air quality conc entrations associated with the emission reduction strategies. The PM I SA concluded that the scientific evidence collectively is sufficient to conclude that the relationship between long - term PM 2.5 exposures and mortality is causal and that overall the studies support the use of a no - threshold log - linear model to estimate PM - rel ated long - term mortality (U.S. EPA, 2009b). For this anal ys is, 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 PM 2.5 concentration in the baseline of the air quality modeling used to calculate the benefit - per - t on estimates for this final RIA using 12 km grid cells across the contiguous U.S. It is important to

82 F or a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 - mortality relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration -Response Function for PM 2.5 - related Mortality (U.S. EPA, 2010b).***E.O. 12866 Review

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4 - 40 note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population and change in air quality . In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction policy . The most important aspect, which we are unable to quantify without rule - specific air quality modeling, is the shift in exposure anticipated by implementing the final emission guidelines. Therefore, caution is warranted when interpreting the LMLassessment 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., LMLand 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 LMLfor each of the major cohort studies. **Table 4 -**  **28. Population Exposure in the Clean Power Plan Proposal Modeling (used to** 

**generate the benefit -**  **per -**  **ton estimates) Above and Below Various**  **Concentrations Benchmarks in the Underlying Epidemiology Studies *** Epidemi ology

Study Below

1 Standard Dev iation . Below A Q Mean At or Above 1 Standard

Dev iation Below

A Q 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 10 th and 25 th percentile. For Krew ski, the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m3 . For Lepeule et al. , the LML is 8 μg/m 3 and we do not have the data for one standard deviation below the mean. It is im portant 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 PM 2.5 d o not occur.***E.O. 12866 Review

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4 - 41 **Figure 4**  **- 3**  **. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure** 

**in the Clean Power Plan Proposal Modeling (used to generate the benefit**  **- per**  **- ton**  **estimates) Am**  **ong the populations exposed to PM 2.5**  **in the baseline:** 88 % are exposed to PM 2.5 levels at or above the LML of the Krew ski et al. (2009) study 46 % are exposed to PM 2.5 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**P e** **r c** **e n** **t a** **g e** **o f** **P o** **p u** **l a** **t i** **o n** **E x** **p o** **s e** **d**

**Baseline Annual Mean PM 2.5**  **Level (μg/m 3**  **) LML of Lepeule et al.**  **(2012) study LML of Krewski et**  **al. (2009) study*****E.O. 12866 Review

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4 - 42 **Figure 4**  **- 4.**  **Cumulative Distribution of Adult Population (age 30+) by Annual Mean** 

**PM 2.5**  **Exposure in the Cle an Power Plan Proposal Modeling**  **(used to generate the benefit**  **- per**  **- ton estimates)** 

**4.4 Combined Climate Benefits and Health Co**  **- benefits Estimates**  In this analy sis, we were able to monetize the estimated co - benefits associated with the decreased emissions of CO 2 and reduced exposure to PM 2.5 and ozone, but we were unable to monetize the co - benefits associated with reducing exposure to mercury , hydrogen chloride, carbon monoxide, SO 2 , and NO 2 , as well as ecos ystem effects and visibility impairment. Specificall y, we estimated the combined climate benefits at discount rates of 5 percent, 3 percent, 2.5 percent, and 3 percent (95 th percentile) (as recommended by the interagency

working group) , and health co - benefits at discount rates of 3 percent and 7 percent (as recommended b y **Am ong the populations exposed to PM**  **2.5 in the baseline:**  88 % are exposed to PM **2.5** levels at or above the LML of the Krewski et al. (2009) study 46 % are exposed to PM 2.5 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**C u** **m u** **l a** **t i** **v e** **p e** **r c** **e n** **t a** **g e** **o f** **p o** **p u** **l a** **t i** **o n** **e x** **p o** **s e** **d**

**Baseline Annual Mean PM 2.5**  **Level (μg/m 3**  **) LML of Krewski et**  **al. (2009) study LML of** 

**Lepeule et al. (2012)**  **study*****E.O. 12866 Review

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4 - 43 the EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2014 ) and OMB’s Circular A - 4 [ OMB, 2003 ] ). Different discount rates are applied to SC - C O 2 than to the health co - benefit estimates because CO 2 emissions are long - lived and subsequent damages occur over many years. Moreover, several rates are applied to SC - C O 2 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 - C O 2 interagency group centered its attention on the 3 percent discount rate but emphasized the importance of considering all four SC - C O 2 estimates. 83 The EPA has evaluated the range of potential impacts by c ombining all SCC values with health co - benefits values at the 3 percent and 7 percent discount rates. Combining the 3 percent SC - C O 2 values with the 3 percent health benefit values assumes that there is no difference in discount rates between intragenerati onal and intergenerational impacts. Table s 4 - 29 through 4 - 31 provide the combined climate and health benefits for the illustrative plan scenarios evaluated for each analysis year: 2020, 2025, and 2030 . Figure 4 - 5 shows the breakdown of the monetized benefi ts by pollutant for the illustrative plan scenario s evaluated in 2025 as an illustrative anal ys is year using a 3 percent discount rate for both climate and health benefits .

83 See the 2010 SCC TSD. Docket ID EPA - HQ - OAR- 2009 - 0472 - 114577 or http://www. whitehouse.gov/sites/default/files/omb/inforeg/for - agencies/Social - Cost - of - Carbon - for - RIA.pdf for details.***E.O. 12866 Review

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4 - 44 **Table 4 -**  **29. Combined Climate Benefits and Health Co**  **- Benefits for Final Emission** 

**Gui delines in 2020 (billions of 2011$)** * **SCC Discount Rate Climate**  **Benefits Only Climate and Health Benefits (Discount Rate Applied to**  **Health Co -**  **Benefits) 3%**  **7% Rate**  **- based Scenario**  **63 mi**  **llion metric tons CO 2**  5% $0.80 $1.5 to $2.6 $1.4 to $2.5 3% $2.8 $3.5 to $4.6 $3.4 to $4.5 2.5% $4.1 $4.8 to $5.9 $4.8 to $5.8 3% (95 th percentile) $8.2 $8.9 to $10 $8.8 to $9.9 **Mass -**  **based Scenario -**  **mi llion metric tons CO**  **2** 5% 3% 2.5% 3% (95 th percentile) *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 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 co benefits reflect the sum of the PM 2.5 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)). T he mo netized health co - benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP ; ecosystem effects ; or visibility impairment. **Table 4**  **- 30.**  **Combined Climate Benefi ts and Health Co**  **- Benefits for**  **Final Emission**

**Guidelines in 2025 (billions of 2011$)** * **SCC Discount Rate Climate**  **Benefits Only Climate and Health Benefits (Discount Rate Applied to**  **Health Co -**  **Benefits) 3%**  **7% Rate**  **- based Scenario**  **210 mi**  **llion metric tons CO 2**  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% (95 th percentile) $31 $38 to $49 $38 to $47 **Mass -**  **based Scenario -**  **mi llion metric tons CO**  **2** 5% 3% 2.5% 3% (95 th percentile) *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 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 co benefits reflect the sum of the PM 2.5 and o zone 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)). T he mo netized health co - benefits do not include reduced health effects fr om direct exposure to NO 2 , SO 2 , and HAP ; ecosystem effects ; or visibility impairment.***E.O. 12866 Review

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4 - 45 **Table 4**  **- 31.**  **Combined Climate Benefits and Health Co -**  **Benefits for Final Emission** 

**Guidelines in 2030 (billions of 2011**  **$)*** **SCC Discount Rate Climate**  **Benefits Only Climate and Health Benefits (Discount Rate Applied to**  **Health Co -**  **Benefits) 3%**  **7% Rate**  **- based Scenario**  **377 mi**  **llion metric tons CO 2**  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% (95 th percentile) $61 $75 to $95 $74 to $92 **Mass -**  **based Scenario -**  **mi llion metric tons CO**  **2** 5% 3% 2.5% 3% (95 th percentile) *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 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 co benefits reflect the sum of the PM 2.5 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)). T he mo netized health co - benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP ; ecosystem effects ; or visibility impairment. **Low Health Co -**  **benefits High Health Co**  **- benefits** 

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

**Emission Guidelines in 2025 for Rate**  **- based and Mass**  **- based Scenarios**  **and Pollutant**  **(3% discount rate)*** * “Low Health Co - benefits” refers to the com bined health co - benefits estimated using the Bell et al. (2004) mo rtality study for ozone with the Krewski et al. (2009 ) mortality study for PM 2.5 . “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 PM 2.5 .

CO2 58% SO2 36% Directly

emitted PM2.5 (EC+OC) 0% Directly emitted PM2.5 (Crustal) 0% NOx (as PM2.5) 3% NOx (as Ozone) 3% 

### Rate **- based**  **Scenario**

CO2 37% SO2

51% Directly emitted PM2.5 (EC+OC) 0% Directly emitted PM2.5 (Crustal) 0% NOx (as PM2.5) 5% NOx (as Ozone) 7% 

### Rate **- based Scenario** ***E.O. 12866 Review

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4 - 46 **4.5 Unquantified Co**  **- benefits**  The mon etized 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 co benefits from several important benefit categories, including co - benefits associated with exposure to several HAP (including mercury and hydrogen chloride) SO 2 and NO 2 , as well as ecos ys tem effects, and visibility impairment due to the absence of air quality modeling data for these pollutants in this analy sis. This does not imply that there are no co - benefits associated with these emission reductions. In this section, w e 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 NO 2 Asthma hospital admissions (all ages)

— — NO 2 ISA 1 Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA 1 Respiratory emergency department visits (all ages)

— — NO 2 ISA 1 Asthma exacerbation (asthmatics age 4

– 18)

— — NO 2 ISA 1 Acute respiratory symptoms (age 7

– 14)

— — NO 2 ISA 1 Premature mortality

— — NO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA2,3

Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65)

— — SO 2 ISA 1 Asthma emergency department visits (all ages)

— — SO 2 ISA 1 Asthma exacerbation (asthmatics age 4

– 12)

— — SO 2 ISA 1 Acute respiratory symptoms (age 7

– 14)

— — SO 2 ISA 1 Premature mortality

— — SO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 1,2 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, 2000 1 Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 2 Cardiovascular effects

— — IRIS; NRC, 2000 2,3***E.O. 12866 Review

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4 - 47 Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More Information Genotoxic, immunologic, and other toxic effects

— — IRIS; NRC, 2000 2,3 Reduced incidence of morbidity from exposure to HAP Effects associated with exposure to hydrogen chloride — — ATSDR, IRIS 1,2 Improved Environment Reduced visibility impairment Visibility in Class 1 areas

— — PM ISA 1 Visibility in residential areas

— — PM ISA 1 Reduced effects on materials Household soiling

— — PM ISA 1,2 Materials damage (e.g., corrosion, increased wear)

— — PM ISA2 Reduced effects from PM deposition (metals and organics) Effects on Individual organisms and ecosystems

— — PM ISA2

Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation

— — Ozone ISA 1 Reduced vegetation growth and reproduction

— — Ozone ISA 1 Yield and quality of commercial forest products and crops — — Ozone ISA 1 Damage to urban ornamental plants

— — Ozone ISA2 Carbon sequestration in terrestrial ecosystems

— — Ozone ISA 1 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

— — NO x SO x ISA 1 Tree mortality and decline

— — NO x SO x ISA2 Commercial fishing and forestry effects

— — NO x SO x ISA2 Recreational demand in terrestrial and aquatic ecosystems — — NO x SO x ISA2 Other non - use effects NO x SO x ISA2 Ecosystem functions (e.g., biogeochemical cycles)

— — NO x SO x ISA2

Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA2 Coastal eutrophication

— — NO x SO x ISA2 Recreational demand in terrestrial and estuarine ecosystems — — NO x SO x ISA2 Other non - use effects NO x SO x ISA2 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA2 Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA2 Injury to vegetation from NO x exposure — — NO x SO x ISA2 Reduced ecosystem effects from exposure to methylmercury Effects on fish, birds, and mammals (e.g., reproductive effects) — — Mercury Study RTC 2 Commercial, subsistence and recreational fishing

— — Mercury Study RTC 1 1 We assess these co - benefits qualitatively due to data and resource limitations for this RIA. 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.***E.O. 12866 Review

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4 - 48 4.5.1 HAP Co - benefits Due to methodology

and resource limitations, we were unable to estimate the co - benefits associated with reducing emissions of the hazardous air pollutants in this analy sis . The EPA’s SAB - HES concluded that “the challenges for assessing progress in health improvement as a result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure - response functions, uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of tracking health progress for diseases, such as cancer, that have long latency periods” (U.S. EPA - SAB, 2008 ). In 2009, the EPA convened a workshop to address the inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations from this workshop included identifying research priorities, focusing on susceptible and vulnerable populations, and improving dose - response relationships (Gwinn et al. , 2011 ). Chapter 4 of the MATS RIA (U.S. EPA, 2011b) describes the health effects associated with HAP emitted by

EGUs. Below we describe the health effects associated with the two HAP for which we were able to quantify emission reductions for the final emission guidelines: mercury and hydrogen chloride. Using the IP M modeling described in Chapter 3 of this RIA, w e estimate that the illustrative plan scenarios for the final emission guidelines would reduce mercury emissions by up to [X] tons and hydrogen chloride b y up to [X] tons by 2030. These HAP emission reductions are beyond those achieved by MATS. 4.5.1.1 Mercury Mercury in the environment is transformed into a more toxic form, methylmercury (MeHg). Because Hg is a persistent pollutant, MeHg accumulates in the food chain, especially the tissue of fish. When people consume these fish, they consume MeHg. In 2000, the NAS Study was issued which provides a thorough review of the effects of MeHg on human health (NRC, 2000) . 84 Many of the peer - reviewed articles cited in this section are publications

84 National Research Council (NRC). 2000 . Toxicological Effects of Methylmercury . Washington, DC: National Academies Press.***E.O. 12866 Review

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4 - 49 originall y cited in the Mercury Study . 85 In addition, the EPA has conducted literature searches to obtain other related and more recent publications to complement the material summarized by the NRC in 2000. In its review of the literature, the NAS found neurodevelopmental effects to be the most sensitive and best documented endpo ints and appropriate for establishing a reference dose ( RfD ) (NRC, 2000); in particular NAS supported the use of results from neurobehavioral or neurops ychological tests. The NAS report noted that studies o n animals reported sensory effects as well as effe cts on brain development and memory functions and support ed 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 conclude d the following: “The population at highest risk is the children of women who consumed large amounts of fish and seafood during pregnancy . The committee concludes that the risk to that population is likely

to be sufficient to result in an increase in the number of children who have to struggle to keep up in school.” The NAS summarized data on cardiovascular effects available up to 2000. Based on these and other studies, the NRC concluded that “Although the data base is not as extensive for cardiovascular ef

fects as it is for other end points (i.e. , neurologic effects) , the cardiovascular sy stem 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 s ys tem effe cts. The pharmacokinetics of some of the exposure measures (such as toenail Hg levels) are not well understood. The studies

85 U.S. Environme ntal Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress , EPA

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4 - 50 have not yet received the review and scrutiny of the more well - established neurotoxicity data base. The Mercury

Study noted that Me Hg is not a potent mutagen but is capable of causing chromosomal damage in a number of experimental sy stems. The NAS concluded that evidence that human exposure to MeHg caused genetic damage is inconclusive; they note that some earlier studies showing chro mosomal damage in l ymphocy tes 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 d amage in ly mphocy tes , as well as effects on chromosomes . 86 Lo ng - term MeHg exposures in this population were believed to occur through consumption of fish, suggesting that genotoxic effects (largel y chromosomal aberrations) may result from dietary and chroni c MeHg exposures similar to and above those seen in the Faroes and Seychelles populations. Although exposure to some forms of Hg can result in a decrease in immune activity or an autoimmune response (ATSDR, 1999), evidence for immunotoxic effects of MeHg i s limited (NRC, 2000) . 87 Based on limited human and animal data, MeHg is classified as a “possible” human carcinogen b y the International Agency for Research on Cancer (IARC, 1994) 88 and in IRI S (U.S. EPA, 2002 ). 89 The existing evidence supporting the possibility of carcinogenic effects in humans from low - dose chronic exposures is tenuous. Multiple human epidemiological studies

86 Amorim, M.I.M., D. Mergler, M.O. Bahia, H. Dubeau, D. Miranda, J. Lebel, R.R. Burbano, and M. Lucotte. 2000. Cytogenetic damage related to low levels of methyl mercury contamination in the Brazilian Amazon. An. Acad. Bras. Ciênc. 72(4): 497 - 507. 87 Agency f

or Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological Profile for Mercury. U.S. Department of Health and Human Services, Public Health Service, Atlanta, GA. 88 International Agency for Research on Cancer (IARC). 1994. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, and Exposures in the Glass Manufacturing Industry. Vol. 58. Jalili, H.A., and A .H. Abbasi. 1961. Poisoning by ethyl mercury toluene sulphonanilide. Br. J. Indust. Med. 18(Oct.):303 - 308 (as cited in NRC , 2000). 89 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on Methylmercury. National Cent er for Environmental Assessment. Office of Research and Development. Available at http://www.epa.gov/iris/subst/0073.htm .***E.O. 12866 Review

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4 - 51 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. How ever, 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 analy sis. 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 barel y tolerable for 1 hour. 90 The greatest impact is on the upper respiratory

tract; exposure to high concentrations can rapidl y 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 Airway s Dy sfunction Sy ndrome ( RADS ) , a chemically , or irritant - induced type of asthma. Children may

be more vulnerable to corrosive agents than adults because of the relativel y 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 e xposed. Hydrogen chloride has not been classified for carcinogenic effects. 91 4.5 . 2 Additional NO2 Health Co -Benefits In addition to being a precursor to PM 2.5 and ozone, NOx emissions are also linked to a variety of adverse health effects associated with direct exposure. W e were unable to estimate the

90 Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen Chloride. Atlanta, GA: U.S. Dep artment of Health and Human Services. Available at http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147#bookmark02 . 91 U.S. Environmental Protection Agency (U.S. EPA). 1995. “Integrated Risk Information System File of Hydrogen Chloride.” Washington, DC: Rese arch and Development, National Center for Environmental Assessment. This material is available at http://www.epa.gov/iris/subst/0396.htm .***E.O. 12866 Review

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4 - 52 health co - benefits associated with reduced NO 2 exposure in this analysis. Therefore, this anal ys is only quantified and monetized the PM 2.5 and ozone co - benefits associated with the reductions in NO 2 emissions. Following a comprehensive review of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Nitrogen

— Health Criteria (NOx I SA) (U.S. EPA, 2008c) concluded that there is a likel y causal relationship betw een respiratory health effects and short - term exposure to NO 2 . These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory

s ymptoms, airway

hy perresponsiveness, airway inflammation, and lung function. The NOx I SA also concluded that the relationship between short - term NO 2 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 NO 2 alone. Although the NOx I SA stated that studies consistently

reported a relationship between NO 2 exposure and mortality , the effect was generall y smaller than that for other pollutants such as PM. 4.5 . 3 Additional SO2 Health Co -Benef its In addition to being a precursor to PM 2.5 , SO 2 emissions are also linked to a variety of adverse health effects associated with direct exposure. W e were unable to estimate the health co benefits associated with reduced SO 2 in this analy sis because we do not have air quality modeling data available. Therefore, this anal ys is only quantifies and monetizes the PM 2.5 co benefits associated with the reductions in SO 2 emissions. Following an extensive evaluation of health evidenc e from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Sulfur

— Health Criteria (SO 2 IS A) concluded that there is a causal relationship between respiratory health effects and short - term exposure to SO 2 (U.S. EPA, 2008a) . The immediate effect of SO 2 on the respiratory s ys tem in humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease. A clear concentration response relati onship has been demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversel y affected. Based on our review of this information, we***E.O. 12866 Review

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4 - 53 identified three short - term morbidity endpoints that the SO 2 IS A identified as a “causal relationship”: asthma exacerbation, respiratory- related emergency department visits, and respiratory- related hospitalizations. The differing evidence and associate d strength of the evidence for these different effects is described in detail in the SO 2 IS A. The SO 2 IS A also concluded that the relationship between short - term SO 2 exposure and premature mortality was “suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to SO 2 alone. Although the SO 2 IS A stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality , there was a lack of robustness of the observed associations to adjustment for pollutants. We did not quantify these co - benefits due to data constraints. 4.5 . 4 Additional NO2 and SO2 Welfare Co -Benefits As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur

— Ecological Criteria (NOx/SOx IS A) (U.S. EPA, 2008d), SO 2 and NOx emissions also contribute to a variety of adverse welfare effects, including acidic deposition, visibility impairment, and nutrient enrichment. Deposition of nitrogen causes acidification, which can cause a loss of biodiver sity

of fishes, zooplankton, and macro invertebrates in aquatic ecosy stems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial ecos ys tems. In the northeastern U.S. , the surface waters affected b y 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 o f acidification in terrestrial ecos ys tems 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 th ese 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***E.O. 12866 Review

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4 - 54 estuaries can disrupt an important source of food production, particularl y fish and shel lfish production, and a variety of cultural ecosystem services, including water - based recreational and aesthetic services. Terrestrial nutrient enrichment is associated with changes in the ty pes and number of species and biodiversity in terrestrial sy stems . 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 grasse s fuel more frequent and more intense wildfires. (U.S. EPA, 2008d) 4.5 . 5 Ozone Welfare Co -Benefits Exposure to ozone has been associated with a wide array of vegetation and ecosy stem effects in the published literature (U.S. EPA, 2013b ). Sensitivity to ozo ne is highl y 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 ecos ys tem. Suc h effects are considered adverse to the public welfare and can include reduced growth and/or biomass production in sensitive plant species, including forest trees, reduced yi eld and quality of crop s, visible foliar injury , species composition shift, and changes in ecos ys tems and associated ecos ys tem 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 emitte d from these reactions, relative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zone, such as fuel ox ygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory

sy stem to the blood, which can cause h ypoxia (reduced ox ygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ sy stems that depend on concentration and duration of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2 010a) 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***E.O. 12866 Review

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4 - 55 includes those who have angina pectoris (cardiac chest pain), as well as those who have experienced a heart attack. Other subpopulations potentially at risk include individuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very

earl y or late life stages, such as older adults or the developing young. The evidence is suggestive of a causal relationship between short - term exp osure 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 s ys tem effects linked to short - and long - term exposure to CO. 4.5 . 7 Visibility Impairment Co -Benefits Reducing secondary

formation of PM 2.5 would improve levels visibility in the U.S. because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009 b ). Fin e particles with significant light - extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people’s enjo ym ent of daily activities and their overall sense of wellbe ing. 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 co ntributor to light extinction in California and the upper Midwestern U.S., particularl y during winter (U.S. EPA, 2009 b ). Previous analy ses (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 un able 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. **4.6 References**  40 CFR Chapter I [EPA

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–0171; FRL

– 9091

– 8] RI

N 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.***E.O. 12866 Review

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4 - 56 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 Short Term Mortality

in 95 U.S. Urban Communities, 1987 - 2000.” Journal of the American Medical Association. 292(19): 2372 - 8. Bell, M.L., F. Dominici , and J.M. Samet. 2005. “A Meta - Analy sis of Time - Series Studies of Ozone and Mortality

with Comparison to the National Morbidity , Mortality , and Air Pollution Study .” Epidemiology . 16(4): 436 - 45. Gwinn, M.R. , J. Craig , D.A. Axelrad , R. Cook , C. Dockins , N. Fann , R. Fegley , D.E. Guinnup , G. Helfand , B. Hubbell , S.L . Mazur , T. Palma , R.L. Smith , J. Vandenberg , and B. Sonawane. 2011. “Meeting report: Estimating the benefits of reducing hazardous air pollutants

— summary of 2009 workshop and future considerations.” E nvironmental Health Perspectives. 119(1): 125 - 30. 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. Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the Concentration -Response Relationship Between PM 2.5 Exposure and Mortality . Prepared for: Office of Air Quality

Planning and Standards, U.S. Environmental Protection Agency , Research Triangle Park, NC. September. Available at: <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_tsd_expert_interview_summaries.p df>. Accessed June 6, 2015. Industrial Economics, Incorporated (IEc). 2009. Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air Toxics Case Study : Health Benefits of Benzene Reductions in Houston, 1990

–2020. Final Report, July 14, 2009. Available at: <http://www.epa.gov/air/sect812/dec09/812CAAA_Benzene_Houston_Final_Report_July _20 09.pdf>. Accessed June 6, 2015. Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis Report Contributi on 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_s ynt hesis_report.htm>. Accessed June 6, 2015.***E.O. 12866 Review

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4 - 57 Intergovernmental Panel on Clim ate Change (IPCC). 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation . A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and N ew York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2013. Climate Change 2013: The Phy sical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2014a. Climate Change 2014: Impacts, Adaptation, and Vulnerability . Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2014b. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Ito, K., S.F. De Leon , and M. Lippmann. 2005. “Associations Between Ozone and Daily Mortality : Anal ys is and Meta - Anal ys is.” Epidemiology . 16(4): 446 - 57. Karl, T. , J. Melillo , and T. Peterson, Eds. 2009. Global Climate Change Impacts in the United States . Cambridge, United Kingdom: Cambridge University Press. 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. HEIResearch 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. Lev y, J.I. , S.M. Chemerynski , and J.A. Sarnat. 2005. “Oz one Exposure and Mortality : An Empiric Bayes Metaregression Analy sis.” Epidemiology. 16(4): 458 - 68. 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. Available at <http://nca2014.globalchange.gov>***E.O. 12866 Review

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4 - 58 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. National Research Council (NRC). 2008. Esti mating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC. National Research Council (NRC). 2010. Ocean Acidification: A National Strategy to Meet the Challenges of a Changing Ocean . National Academies Press. Washington, DC. National Research Council (NRC). 2011a. Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millennia. National Academies Press, Washington, DC. National Research Council (NRC) 2 011b. National Security Implications of Climate Change for U.S. Naval Forces . National Academies Press. Washington, DC. National Research Council (NRC, 2011c). Understanding Earth's Deep Past: Lessons for Our Climate Future . National Academies Press. Washi ngton, DC National Research Council (NRC). 2012a. Sea - Level Rise for the Coasts of California, Oregon, and W ashington: Past, Present, and Future . National Academies Press. Washington, DC. National Research Council (NRC). 2013a. Climate and Social Stress: Implications for Security Analysis . National Academies Press. Washington, DC. National Research Council (NRC). 2013b. Abrupt Impacts of Climate Change: Anticipating Surprises . National Academies Press. Washington, DC. Office of Management and Budget (OMB) . 2003. Circular A - 4: Regulatory Analysis. Washington, DC. Available at: < http://www.whitehouse.gov/omb/circulars/a004/a - 4.html>. Pizer, W., M. Adler, J. Aldy , D. Anthoff, M. Cropper, K. Gillingham, M. Greenstone, B. Murray , R. Newell, R. Richels, A. Rowe ll, S. Waldhoff, J. Wiener. 2014. “Using and improving the social cost of carbon.” Science, Vol. 346, No. 6214, 12/05/14, pp 1189 - 1190. Ren, C. , G.M. William , L. Morawska , K. Mengensen , and S. Tong. 2008a. “Ozone Modifies Associations between Temperature and Cardiovascular Mortality: Anal ys is of the NMMAPS Data.” Occupational and Environm ental Medicine. 65 : 255 - 260.***E.O. 12866 Review

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4 - 59 Ren, C. , G.M. Williams , K. Mengersen , L. Morawska , and S. Tong. 2008b. “Does Temperature Modify Short - Term Effects of Ozone on Total Mortality in 60 Large Eastern U.S. Communities? An Assessment Using the NMMAPS Data.” Environment International . 34: 451

–458. Ren, C. and S. Tong. 2006b. “Temperature Modifies the Health Effects of Particulate Matter in Brisbane, Australia.” International Journal of Biometeorology. 51:87

– 96. Ren. C. , G.M. Williams , and S. Tong. 2006a. “Does Particulate Matter Modify the Association between Temperature and Cardiorespiratory Diseases? Environmental Health Persp ectives, 114:1690

– 1696. Roberts, S. 2004. “Interactions between Particulate Air Pollution and Temperature in Air Pollution Mortality

Time Series Studies.” Environmental Research . 96:328

– 337. Roman, H. , 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 Science and Technology . 42(7):2268 - 2274. Schwartz, J. 2005. “How Sensitive is the Associ ation between Ozone and Daily Deaths to Control for Temperature?” American Journal of Respira tory and Critical Care Medicine. 171 (6): 627 - 31. Sisler, J.F. 1996. Spatial and Seasonal Patterns and L ong - Term Variability of the Composition of the Haze in the United States: An anal ys is of data from the IMPROVE network. CIRA Report, I SSN 0737 - 5352 - 32, Colorado State University . U.S. Environmental Protection Agency (U.S. EPA). 1995. Regulatory Impact Analysis for the Petroleum Refinery NESHAP. Revised Draft for Promulgation . Office of Air Quality Planning and Standards, Research Triangle Park, N.C. Available on the Internet at < http://yosemite1.epa.gov/ee/epa/ria.nsf/vwTD/9F39F2C26150BB21852564620063317F >. Accessed June 6, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2008 a . Integrated Science Assessment for Sulfur Oxides

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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.***E.O. 12866 Review

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4 - 60 U.S. Environmental Protection Agency (U.S. EPA). 2008b. Final Ozone NAAQS Regulatory Impact Analysis . EPA - 452/R - 08 - 003. Office of Air Quality Planning and Standards Health and Environmental Impacts Division, Air Benefit and Cost Group Research Triangle Park, NC. March. Available at: < http://www.epa.gov/ttnecas1/regdata/RIAs/6 - ozoneriachapter6.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment fo r 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 Pro tection Agency (U.S. EPA). 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur

– Ecological Criteria National (Final Report). National Center for Environmental Assessment

– RTP Division, Research Triangle Park, NC. EPA/600/R 08/139. Decemb er. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=201485>. 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). 2010a. Integrated Science Assessment for Carbon Monoxide. National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R - 09/019F. January . Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid=218686>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Technical Support Document: Summary of Expert Opinions on the Existence of a Threshold in the Concentration - Response Function for PM 2.5 - related Mortality . Research Triangle Park, NC. June. Available at: <http://www.epa.gov/ttn/ecas/regdata/Benefits/thresholds tsd.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2010c. Valuing Mortality Risk Reductions for Environmental Policy: A White Paper: SAB Review Draft . National Center for Enviro nmental Economics December. Available at: <http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE - 0563 - 1.pdf/$file/EE - 0563 - 1.pdf>. Accessed June 4, 2015.***E.O. 12866 Review

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4 - 61 U.S. Environmental Protection Agency (U.S. EPA). 2010d. Section 3: Re -analysis of the Benefits of Attaining A lternative Ozone Standards to Incorporate Current Methods . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/s3 - supplemental_anal ys is - updated_benefits11 - 5.09.pdf >. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act from 1990 to 2020 . Office of Air and Radiation, Washington, DC. March. Available at: <http://www.epa.gov/cleanairactbenefits/feb11/fullreport_rev_a.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. 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). 2011c. Regulatory Impact Anal ys is: National Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters. February . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/boilersriafinal110221_psg.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2011d. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards . EPA - 452/D - 11 - 003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. April. Available at: <http://www.epa.gov/ttn/naaqs/standards/pm/data/20110419pmpafinal.pdf>. 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. EPA 452/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). 2012b. Regulatory Impact Analysis: Petroleum Refineries New Source Performance Standards Ja. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. June. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/refineries_nsps_ja_final_ria.pdf>. Accessed June 4, 2015.***E.O. 12866 Review

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4 - 62 U.S. Environmental Protection Agency (U.S. EPA). 2013. Technical Support Document : Estimating the Benefit per Ton of Reducing PM 2.5 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/sourceapportionm entbpttsd.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. Environmental Protection Agency (U.S. EPA). 2014 . Guidelines for Preparing Economic Analyses. EPA 240 - R - 10 - 001 . National Center for Environmental Economics, Office of the Administrator . Washington, DC. Available at: <http://yosemite.epa.gov/EE \epa\eed.nsf/webpages/Guidelines.html >. Accessed June 6, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2014a. 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. Offi ce 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. U.S. Environmental Protection Agency (U.S. EPA). 2014b. Health Risk and Exposure Assessment for Ozone: Final Report . Office of Air Quality Planning and Standards, Research Triangle Park, NC. EPA - 452/R - 14 - 004a. Available at: <http://www.epa.gov/ttn/naaqs/standards/ozone/data/20140829healthrea.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 2014c. Welfare Risk and Exposure Assessment for Ozone: Final . EPA - 452/R - 14 - 005a. Office of Air Quality Planning and Standards, Research Triangle Park, NC. August. Available at: <http://www.epa.gov/ttn/naaqs/standards/ozone/data/20141021welfarerea.pdf>. Accessed June 4, 2015.***E.O. 12866 Review

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4 - 63 U.S. Environmental Protection Agency (U.S. EPA). 2014d. Regulatory Impact Anal ys is of the Proposed Revisions to the National Ambient Air Quality Standards for Ground - Level Ozone . EPA - 452/P - 14 - 006. Office of Air Quality Planning and Standards, Research Triangle Park, NC. November. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/20141125ria.pdf>. Accessed June 4, 2015 . U.S. Environmental Protection Agency

— Science Advisory

Board (U.S. EPA - SAB). 2002. Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants: Developing Best Estimates of Dose - Response Functions An SAB Workshop Report of an EPA/SAB Wo rkshop (Final Report). EPA - SAB - EC - WKSHP - 02 - 001. January . Available at: <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/34355712EC011A358525719A005B F6F6/$File/ecwkshp02001%2Bappa - g.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency

— Science Advisory

Board (U.S. EPA - SAB). 2008. Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPA COUNCIL - 08 - 002. July . Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B9 1173651AED9E85257487004EA6CB/$F ile/EPA - COUNCIL - 08 - 002 - unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency

— Science Advisory

Board (U.S. EPA - SAB). 200 0. An SAB Report on EPA’s White Paper Valuing the Benefits of Fatal Cancer Risk Reduc tion. EPA - SAB - EEAC - 00 - 013. July . Available at: <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334524148BCCD6852571A70051 6498/$File/eeacf013.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency

— Science Advisory

Board (U.S. EPA - SAB). 20 04 c . Adv isory 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>. Access ed 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/D4D7EC9DAED A8A548525748600728A83/$Fi le/EPA - COUNCIL - 08 - 001 - unsigned.pdf>. Accessed June 4, 2015.***E.O. 12866 Review

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4 - 64 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 Rev iew 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). 20 10a. Review of EPA’s DRAFT Health Benefits of the S econd 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>. Accesse d June 4, 2015. U.S. Environmental Protection Agency

— Science Advisory

Board (U.S. EPA - SAB). 2011. Review of Valuing Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA - SAB - 11 - 011 July. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/ EPA - SAB - 11 - 011 - unsigned.pdf>. Accessed June 4, 2015. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “ The Relationship b etween Selected of postneonatal infant mortality and particulate air pollution in the United States.” Environmental Health Perspectives. 105 (6): 608 - 612.***E.O. 12866 Review

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4A - 1 

### A **PPENDIX** 

### 4A: **G ENERATING**  

### R**EGIONAL** 

### B **ENEFIT** 

### - **PER**

### - **T ON**  

### E **STIMATES** 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 Analy sis (RIA). Specificall y, this appendix describes the methods for generating benefit - per - ton estimates by region for PM 2.5 and ozone precursors emitted b y 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 B**  **enefit -**  **per -**  **T on**  **E stimates**  As described in the Technical Support Document: Estimating the Benefit per Ton of Reducing PM

2.5 Precursors from 17 Sectors (U.S. EPA, 2013) , the general procedure for calculating average benefit - per - ton coefficients generall y 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 PM 2.5 , nitrate , sulfate , and ozone at a 12km 2 grid resolution across the contiguous U.S. that are attributable to the proposed Clean Power Plan. 2. For eac h grid cell, e stimate the health impacts, and the economic value of these impacts, associated with the attributable ambient concentrations using the environmental Ben efits M a pping and An al ys is P rogram – Community Edition (BenMAP - CE v 1.1 ) . 92 Aggregate those impacts and economic values to the three regions of East, West, and California. 3. D ivide 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 PM 2.5 benefits are divided b y directly emitted PM 2.5 emissions, sulfate benefits are divided b y SO 2 emissions, nitrate benefits are divided by NO x emissions , and ozone benefits are divided by ozone - season NOx emissions .

92 When estimating these impacts we apply effect coefficients that relate changes in total PM 2.5 mass to the risk of adverse health outcomes; we do not apply effect coefficients that are differentiated by PM 2.5 specie s .***E.O. 12866 Review

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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 PM 2.5 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 domain 93 using a full year of meteorological conditions for 2011. The modeling for 2011 was used as the anchor point for projecting ozone and annual PM 2.5 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 ye ar impacts on ozone and annual PM 2.5 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 decre es. The general methods for estimating the EGU emission s 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 NO x and SO 2 for each of the scenarios modeled are provi ded in Tables 4A - 1 and 4A - 2, respectivel y. The data indicate that, overall nationwide, EGU SO 2 and NO x emissions with proposed Option 1 (state) would be about 28% lower than the 2025 base case. **Table 4A**  **- 1.**  **State T otal**  **Annual EGU**  **E mission**  **s for NOx for the 2011 Base Year, 2025** 

**Base C ase, and 2025**  **Clean Power Plan Proposal ( Option 1**  **S tate)**  **(in thousands of tons)**  **State 2011 Base Year**  **2025 Base Case 2025**  **Clean Power Plan Proposal**  **( Option 1**  **S tate)**  Alabama 63 38 19 Arizona 35 17 4 Arkansas 38 43 9 California 6 33 28 Colorado 51 29 21 93 The modeling domain (i.e., region modeled) includes all of the lower 48 states plus adjacent portions of Canada and Mexico) at a spatial resolution of 1 2 km.***E.O. 12866 Review

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4A - 3 **State 2011 Base Year**  **2025 Base Case 2025**  **Clean Power Plan Proposal**  **( Option 1**  **S tate)**  Connecticut 1 1 1 Delaware 4 1 1 Florida 61 52 15 Georgia 54 33 18 Idaho - 1 0 Illinois 73 38 32 Indiana 121 97 90 Iowa 40 24 24 Kansas 44 28 27 Kentucky 92 59 74 Louisiana 47 18 14 Maine 2 4 2 Maryland 19 11 11 Massachusetts 5 2 1 Michigan 75 73 51 Minnesota 32 27 13 Mississippi 26 15 3 Missouri 66 61 58 Montana 20 16 15 Nebraska 37 38 35 Nevada 7 5 3 New Hampshire 4 1 0 New Jersey 6 7 2 New Mexico 23 7 6 New York 22 11 7 North Carolina 46 35 23 North Dakota 51 51 48 Ohio 104 63 60 Oklahoma 82 52 26 Oregon 5 3 3 Pennsylvania 149 106 71 Rhode Island 0 0 1 South Carolina 25 13 8 South Dakota 11 13 8 Tennessee 27 16 13 Texas 146 144 64 Tribal Data 65 33 33 Utah 51 49 33 Vermont 0 0 0 Virginia 38 21 12 Washington 7 3 2 West Virginia 58 49 46 Wisconsin 32 19 11 Wyoming 53 50 38 National Total 2,024 1,508 1,084***E.O. 12866 Review

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4A - 4***E.O. 12866 Review

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4A - 5 **Table 4A**  **- 2.**  **State T otal**  **Annual EGU E mission**  **s for SO 2**  **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 S**  **tate)** 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***E.O. 12866 Review

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4A - 6 **State 2011 Base Year**  **2025 Base Case 2025**  **Clean Power Plan Proposal**  **( Option 1**  **S tate)**  Utah 22 14 10 Vermont 0 0 0 Virginia 75 8 4 Washington 1 1 1 West Virginia 103 78 47 Wisconsin 92 17 11 Wyoming 55 23 17 National Total 4,665 1,504 1,077 As indicated above, the air quality modeling was used to project gridded ozone and annual PM 2.5 concentrations at the 12km2 resolution for the 2025 base case and the Clean Power Plan proposal scenario modeled for this anal ys is. The air quality modeling results were combined with monitored ozone and PM2.5 data to create projected spatial fields of annual PM 2.5 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 PM**  **2.5 Benefit** **- per**  **- T**  **on Estimates for EGUs Derived from Air Quality**  **Modeling of the Proposed Clean Power Plan** After estimating the 12km 2 resolution PM 2.5 benefits for each of the anal ys is years applied in this RIA (i.e., 2020, 2025, and 2030), we aggregated th e benefits results regionally (i.e., East, West, and California), as shown in Figure 4A - 1. 94 Due to the low emissions of SO 2 , NO X , and directly

emitted particles from EGUs in California and the high population density , we separated out California in order n ot 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. Lastl y, we adjusted the benefi t - per - ton estimates for a currency year of 2011$. 95 This method provides estimates of the regional average benefit - per - ton for a subset of the major PM 2.5 precursor s emitted from EGU sources. For precursor emissions of NOx, there is generall y a non - linear relationship between emissions and formation of PM 2.5 . This means that each ton of NOx reduced would have a different impact on ambient PM 2.5 depending on the 94 This aggregation is identified as the shapefile “Report Regions” in BenMAP’s grid definitions. 95 Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated the benefit - per - ton estimat es in 2010$. We then adjusted the resulting benefit - per - ton estimates to 2011$ using the Consumer Price Index.***E.O. 12866 Review

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4A - 7 initial level of emissions and potentially on the levels of emissions of other pollutants. In contrast, SO 2 is generall y linear in forming PM 2.5 . For precursors like NOx which form PM 2.5 non - linearl y, a marginal benefit - per - ton approach would better approximate the specific benefits associated with an emissions re duction 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 c alculate 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 estimate d in this RIA . B ecause most of the estimated co - benefits for the proposed guidelines ar e attributable to reductions in SO 2 emissions, the added uncertainty is likely to be small.

**Figure 4A**  **- 1.**  **Regional Breakdown** In this RIA, we estimate emission reductions from EGUs using IPM. 96 IP M outputs provide endogenousl y projected unit level emissions of SO 2 , NOx, CO 2 , Hg, HCl from EGUs,

96 See Chapter 3 of this RIA for additional information regarding the Integrated Planning Model ( IPM ) .***E.O. 12866 Review

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4A - 8 but CO, VOC, NH 3 and total directly emitted PM 2.5 and PM 10 emissions are post - calculated. 97 In addition, directly

emitted particle emissions calculated from IPM outputs do not include speciation, i.e. they

are only the total emissions. In order to conduct air quality modeling, directl y emitted PM 2.5 from EGUs is speciated into components during the emissions modeling process based on emission profiles for EGUs b y 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 configur ation . Model - predicted concentrations of nitrate and sulfate include both the directl y emitted nitrate and sulfate from speciated PM2.5 and secondaril y formed nitrate and sulfate from emissions of NOx and SO2, respectivel y. In order to estimate the benefi ts associated with reduced emissions of directly emitted particles without performing air quality modeling , we must determine the fraction of total PM 2.5 emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions. 98 Based on th e work by Fann, Baker, and Fulcher (2012), the national average EC+OC fraction of emitted PM 2.5 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 PM 2.5 from EGUs is 78% with a range of 26% to 83%. The national average is similar to the avera ges for the east and west regions at 78% and 81%, respectivel y. Onl y four states had crustal fractions less than 50%. In calculating the PM 2.5 co benefits in this RIA , we estimate the emission reductions of EC+OC and crustal emissions by applying the national average fractions (i.e., 78% crustal and 10% EC+OC) to the emission reductions of all directly

emitted particles from EGUs. Because the benefit - per - ton estimates for reducing emissions of EC+OC are larger than the benefit - per - ton estimate for crustal emissions, this assumption underestimates the monetized PM 2.5 co - benefits in certain states with higher EC+OC fractions, such as California and North Dakota.

97 Detailed documentation of this post -processing is available at http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Methodology.pdf 98 Crustal emissions are composed of compounds associated with minerals and metals from the earth’s surface, including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material represents particles not classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).***E.O. 12866 Review

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4A - 9 Although it is possible to calculate 95 th percentile confidence intervals using the appro ach described in this appendix (e.g., U.S. EPA, 2011b), we generall y do not calculate confidence intervals for benefit - per - ton estimates. Instead, we refer the reader to Chapter 5 of PM NAAQS RIA (U.S. EPA, 2012a) for an indication of the combined random s ampling error in the health impact and economic valuation functions using Monte Carlo methods. In general, the 95 th percentile confidence interval for the total monetized PM 2.5 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 95 th percentile confidence interval for the health impact function alone ranges from approximately ±30% for mortality incidence based on Krewski et al. (2009) an d ±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 locati ons. 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 dis count rates of 3% and 7% in 2020, 2025, and 2030 respectivel y. The benefit - per - ton values for 2020 and 2030 are based on appl yi ng the air quality modeling from 2025 to population and health information from 2020 and 2030. Estimated benefit - per - ton for the se years have additional uncertainty relative to 2025 because of potential differences in atmospheric responses to reductions in PM 2.5 precursors in those ye ars, however, these uncertainties are likel y to be relativel y small. Tables 4A - 6 through 4A - 8 prov ide the incidence per ton estimates for the EGU sector in 2020, 2025, and 2030 respectivel y.***E.O. 12866 Review

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4A - 10 **Table 4A**  **- 3.**  **Summary of Regional PM 2.5**  **B enefit** **- per**  **- Ton Estimates**  **Based on Air Quality Modeling f**  **rom Proposed Clean Power Plan**  **in 2020 (2011$)*** Pollutant Discount

Rate National Region East West California SO 2 3% $35,000 to $78,000 $37,000 to $83,000 $7,100 to $16,000 $110,000 to $240,000

7% $31,000 to $70,000 $33,000 to $75,000 $6,400 to $14,000 $97,000 to $220,000 Directly emitted PM 2.5 (EC+OC) 3% $150,000 to $340,000 $160,000 to $360,000 $30,000 to $68,000 $410,000 to $930,000 7% $130,000 to $290,000 $140,000 to $320,000 $27,000 to $61,000 $370,000 to $830,000 Directly emitted PM 2.5 (Crustal) 3% $24,000 to $55,000 $25,000 to $58,000 $12,000 to $28,000 $82,000 to $180,000 7% $22,000 to $49,000 $23,000 to $52,000 $11,000 to $25,000 $74,000 to $170,000 NOx (as PM 2.5 ) 3% $3,200 to $7,300 $3,300 to $7,500 $0,750 to $1,700 $24,000 to $54,000 7% $2,900 to $6,000 $3,000 to $6,800 $0,670 to $1,500 $22,000 to $49,000 * The range of estimates reflects the range of epidemiology studies for avoided premature mo rtality for PM 2 .5 . All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the benefit -per - ton estimates vary depending on the location and magnitude of their impact on PM 2.5 levels, which drive population expos ure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include reduced health effects from direct exposure to ozone, NO 2 , SO 2 , ecosystem effects, or visibility impairment. **Table 4A**  **- 4**  **. Summary of Regional PM**  **2.5 Benefit** **- per**  **- Ton Estimates**  **Based on Air Quality Modeling from Proposed Clean Power Plan**

**in 2025 (2011$)*** Pollutant Discount

Rate National Region East West California SO 2 3% $32,000 to $71,000 $33,000 to $75,000 $6,200 to $14,000 $95,000 to $210,000

7% $28,000 to $64,000 $30,000 to $68,000 $5,600 to $13,000 $85,000 to $190,000 Directly emitted PM 2.5 (EC+OC) 3% $140,000 to $310,000 $140,000 to $320,000 $27,000 to $60,000 $370,000 to $830,000 7% $120,000 to $270,000 $130,000 to $290,000 $24,000 to $54,000 $330,000 to $740,000 Directly emitted PM 2.5 (Crustal) 3% $22,000 to $49,000 $23,000 to $52,000 $11,000 to $25,000 $73,000 to $160,000 7% $20,000 to $44,000 $21,000 to $47,000 $9,900 to $22,000 $66,000 to $150,000 NOx (as PM 2.5 ) 3% $3,000 to $6,800 $3,100 to $7,000 $0,670 to $1,500 $22,000 to $49,000 7% $2,700 to $5,600 $2,800 to $6,300 $0,610 to $1,400 $19,000 to $44,000 * The range of estimates reflects the range of epidemiology studies for avoided premature mo rtality for PM 2 .5 . All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the benefit -per - ton estimates vary depending on the location and magnitude of their impact on PM 2.5 levels, wh ich drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include reduced health effects from direct exposure to ozone, NO 2 , SO 2 , ecosystem effects, or visibili ty im pairment.***E.O. 12866 Review

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4A - 11 **Table 4A**  **- 5**  **. Summary of Regional PM**  **2.5 Benefit** **- per**  **- Ton Estimates**  **Based on Air Quality Modeling from Proposed Clean Power Plan**

**in 2030 (2011$)*** Pollutant Discount

Rate National Region East West California SO 2 3% $37,000 to $85,000 $40,000 to $89,000 $7,800 to $18,000 $120,000 to $270,000

7% $34,000 to $76,000 $36,000 to $81,000 $7,100 to $16,000 $110,000 to $240,000 Directly emitted PM 2.5 (EC+OC) 3% $160,000 to $360,000 $170,000 to $380,000 $33,000 to $75,000 $450,000 to $1,000,000 7% $150,000 to $320,000 $150,000 to $340,000 $30,000 to $68,000 $410,000 to $920,000 Directly emitted PM 2.5 (Crustal) 3% $26,000 to $59,000 $28,000 to $62,000 $14,000 to $31,000 $90,000 to $200,000 7% $24,000 to $53,000 $25,000 to $56,000 $13,000 to $28,000 $81,000 to $180,000 NOx (as PM 2.5 ) 3% $3,400 to $7,800 $3,500 to $8,000 $0,820 to $1,900 $26,000 to $60,000 7% $3,100 to $6,400 $3,200 to $7,200 $0,740 to $1,700 $24,000 to $54,000 * The range of estimates reflects the range of epidemiology studies for avoided premature mo rtality for PM 2 .5 . All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the benefit -per - ton estimates vary depending on the location and ma gnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine p articles. The estimates do not include reduced health effects from direct exposure to oz one, NO 2 , SO 2 , ecosystem effects, or visibility impairment.***E.O. 12866 Review

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4A - 12 **Table 4A**  **- 6.**  **Summary of Regional PM 2.5**  **Incidence -**  **per -**  **Ton Estimates Based on Air Quality Modeling from Proposed Clean Power** 

**Plan in 2020** * Health Endpoint East West California SO 2 NOx EC+OC Crustal SO 2 NOx EC+OC Crustal SO 2 NOx EC+OC Crustal Premature Mortality Krewski et al. (2009)

– adult 0.003700 0.000340 0.016000 0.002500 0.000680 0.000073 0.002900 0.001200 0.010000 0.002400 0.040000 0.008000 Lepeule et al. (2012)

– adult 0.008300 0.000770 0.036000 0.005700 0.001500 0.000170 0.006600 0.002700 0.023000 0.005400 0.091000 0.018000 Woodruff et al. (1997)

– infants 0.000009 0.000001 0.000037 0.000006 0.000002 0.000000 0.000007 0.000003 0.000023 0.000007 0.000097 0.000019 Morbidity Emergency department visits for asthma 0.001900 0.000190 0.007800 0.001300 0.000290 0.000031 0.001200 0.000470 0.005300 0.001400 0.022000 0.004200 Acute bronchitis 0.005400 0.000510 0.023000 0.003700 0.001300 0.000200 0.005200 0.002100 0.019000 0.005000 0.077000 0.015000 Lower respiratory s ymptoms 0.069000 0.006500 0.300000 0.047000 0.016000 0.002500 0.067000 0.026000 0.240000 0.064000 0.970000 0.190000 Upper respiratory

s ymptoms 0.098000 0.009300 0.420000 0.068000 0.023000 0.003600 0.095000 0.038000 0.340000 0.092000 1.400000 0.270000 Minor restricted - activity days 2.700000 0.250000 11.000000 1.900000 0.580000 0.078000 2.400000 0.920000 9.400000 2.200000 35.000000 6.800000 Lost work days 0.450000 0.043000 1.900000 0.310000 0.098000 0.013000 0.410000 0.160000 1.600000 0.380000 6.000000 1.100000 Asthma exacerbation 0.240000 0.023000 1.000000 0.170000 0.056000 0.008800 0.230000 0.091000 0.840000 0.220000 3.400000 0.650000 Hospital Admissions, Respiratory 0.001100 0.000100 0.004500 0.000720 0.000150 0.000015 0.000640 0.000260 0.002500 0.000580 0.009400 0.001900 Hospital Admissions, Cardiovascular 0.001300 0.000120 0.005600 0.000910 0.000200 0.000019 0.000820 0.000330 0.003000 0.000680 0.011000 0.002200 Non - fatal Heart Attacks (Peters) 0.004100 0.000390 0.018000 0.002800 0.000650 0.000064 0.002800 0.001200 0.011000 0.002400 0.041000 0.007900 Non - fatal Heart Attacks (All others) 0.000450 0.000042 0.001900 0.000310 0.000070 0.000007 0.000300 0.000130 0.001100 0.000260 0.004400 0.000850 * All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence -per - ton estimates vary depending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The incidence benefit -per - ton estimates incorporate the conversion from precursor emissions to ambient fine particles.***E.O. 12866 Review

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4A - 13 **Table 4A**  **- 7**  **. Summary of Regional PM**  **2.5 Incidence**  **- per**  **- Ton Estimates**  **Based on Air Quality Mode ling from Proposed Clean Power** 

**Plan in 2025** * Health Endpoint East West California SO 2 NOx EC+OC Crustal SO 2 NOx EC+OC Crustal SO 2 NOx EC+OC Crustal Premature Mortality Krewski et al. (2009)

– adult 0.003900 0.000350 0.017000 0.002700 0.000750 0.000079 0.003200 0.001300 0.011000 0.002600 0.044000 0.008700 Lepeule et al. (2012)

– adult 0.008900 0.000800 0.038000 0.006200 0.001700 0.000180 0.007300 0.003000 0.026000 0.005800 0.099000 0.020000 Woodruff et al. (1997)

– infants 0.000008 0.000001 0.000035 0.000006 0.000002 0.000000 0.000007 0.000003 0.000022 0.000007 0.000093 0.000018 Morbidity Emergency department visits for asthma 0.002000 0.000200 0.006300 0.001300 0.000320 0.000033 0.001000 0.000510 0.005500 0.001500 0.018000 0.004400 Acute bronchitis 0.005700 0.000520 0.024000 0.003900 0.001300 0.000210 0.005600 0.002200 0.020000 0.005300 0.080000 0.015000 Lower respiratory s ymptoms 0.072000 0.006700 0.310000 0.050000 0.017000 0.002700 0.071000 0.028000 0.250000 0.067000 1.000000 0.200000 Upper respiratory

s ymptoms 0.100000 0.009600 0.440000 0.071000 0.024000 0.003800 0.100000 0.040000 0.360000 0.096000 1.500000 0.280000 Minor restricted - activity days 2.800000 0.250000 12.000000 1.900000 0.610000 0.083000 2.500000 0.970000 9.600000 2.300000 36.000000 6.900000 Lost work days 0.470000 0.043000 2.000000 0.320000 0.100000 0.014000 0.430000 0.160000 1.600000 0.390000 6.100000 1.200000 Asthma exacerbation 0.250000 0.023000 1.100000 0.170000 0.059000 0.009300 0.250000 0.097000 0.880000 0.230000 3.500000 0.680000 Hospital Admissions, Respiratory 0.001200 0.000110 0.005100 0.000810 0.000180 0.000017 0.000740 0.000300 0.002800 0.000650 0.011000 0.002100 Hospital Admissions, Cardiovascular 0.001400 0.000130 0.006200 0.001000 0.000220 0.000022 0.000930 0.000380 0.003300 0.000750 0.012000 0.002400 Non - fatal Heart Attacks (Peters) 0.004600 0.000430 0.020000 0.003100 0.000740 0.000071 0.003200 0.001300 0.012000 0.002700 0.046000 0.008900 Non - fatal Heart Attacks (All others) 0.000490 0.000046 0.002100 0.000340 0.000080 0.000008 0.000340 0.000140 0.001300 0.000290 0.004900 0.000950 * All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence -per - ton estimates vary depending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The incidence benefit -per - ton estimates incorporate the conversion from precursor emissions to ambient fine particles .***E.O. 12866 Review

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4A - 14 **Table 4A**  **- 8.**  **Summary of Regional PM 2.5**  **Incidence -**  **per -**  **Ton Estimates Based on Air Quality Modeling from Proposed Clean Power** 

**Plan in 2030** * Health Endpoint East West California SO 2 NOx EC+OC Crustal SO 2 NOx EC+OC Crustal SO 2 NOx EC+OC Crustal Premature Mortality Krewski et al. (2009)

– adult 0.004200 0.000380 0.018000 0.002900 0.000840 0.000087 0.003600 0.001500 0.013000 0.002800 0.048000 0.009600 Lepeule et al. (2012)

– adult 0.009600 0.000850 0.041000 0.006700 0.001900 0.000200 0.008100 0.003400 0.029000 0.006400 0.110000 0.022000 Woodruff et al. (1997)

– infants 0.000008 0.000001 0.000033 0.000005 0.000002 0.000000 0.000007 0.000003 0.000021 0.000006 0.000088 0.000017 Morbidity Emergency department visits for asthma 0.001600 0.000160 0.006600 0.001100 0.000260 0.000027 0.001100 0.000420 0.004500 0.001200 0.019000 0.003500 Acute bronchitis 0.005900 0.000540 0.025000 0.004100 0.001400 0.000220 0.005900 0.002300 0.021000 0.005400 0.083000 0.016000 Lower respiratory sy

mptoms 0.075000 0.006800 0.320000 0.052000 0.018000 0.002800 0.075000 0.030000 0.260000 0.069000 1.100000 0.200000 Upper respiratory

s ymptoms 0.110000 0.009800 0.460000 0.074000 0.026000 0.004000 0.110000 0.042000 0.370000 0.099000 1.500000 0.290000 Minor restricted - activity days 2.900000 0.260000 12.000000 2.000000 0.650000 0.088000 2.700000 1.000000 9.800000 2.300000 37.000000 7.100000 Lost work days 0.480000 0.043000 2.000000 0.330000 0.110000 0.015000 0.450000 0.170000 1.700000 0.400000 6.300000 1.200000 Asthma exacerbation 0.260000 0.024000 1.100000 0.180000 0.063000 0.009800 0.260000 0.100000 0.920000 0.240000 3.700000 0.710000 Hospital Admissions, Respiratory 0.001300 0.000120 0.005600 0.000900 0.000200 0.000019 0.000830 0.000340 0.003200 0.000740 0.012000 0.002400 Hospital Admissions, Cardiovascular 0.001600 0.000150 0.006800 0.001100 0.000250 0.000024 0.001000 0.000420 0.003800 0.000850 0.014000 0.002700 Non - fatal Heart Attacks (Peters) 0.005000 0.000460 0.021000 0.003500 0.000830 0.000079 0.003600 0.001500 0.014000 0.003100 0.052000 0.010000 Non - fatal Heart Attacks (All others) 0.000540 0.000049 0.002300 0.000370 0.000090 0.000009 0.000380 0.000160 0.001500 0.000330 0.005600 0.001100 * All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence -per - ton estimates vary depending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The incidence benefit -per - ton estimates incorporate the conversion from precursor emissions to ambient fine particles.***E.O. 12866 Review

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4A - 15 **4A .**  **4 Regional Ozone**  **Benefit -**  **per -**  **T on Estimates**  The process for generating the regional ozone benefit - per - ton estimates is consistent with the process for PM 2.5 . 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 N Ox 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 PM 2.5 , 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 estimate d for the proposed guidelines . B ecause most of the estimated co - benefits for the proposed guidelines are attributable to changes in ambient PM 2.5 , 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 NO X emissions derived from modeling the Clean Power Plan proposal during the ozone - season onl y (May to September). As shown i n Table 4A - 1, ozone - season NOx emissions from EGUs are slightly less than half of all - year NO X emissions. Because we estimate ozone health impacts from May to September only , this approach underestimates ozone co benefits in areas with longer ozone seasons such as southern California and Texas. When the underestimated benefit - per - ton estimate is multiplied by ozone - season onl y 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 all ye ar NOx emissions and ozone - season onl y NOx for 2020, 2025, and 2030, respectivel y. Tables 4A - 12 through 4A - 14 provide the ozone season incidence - per - ton estimates for 2020, 2025, and 2030, respectivel y. Similar to PM2.5, the ozone benefit - per - ton values for 2020 and 2030 are based on appl yi ng 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***E.O. 12866 Review

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4A - 16 in those years. Uncertainties may be somewhat larger in the case of ozone due to high degree of dependence of ozone responses to baseline meteorology and emissions levels. **Table 4A**  **- 9**  **. Summary of**  **R egional 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 NO x 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 (2**  **011$)*** 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 NO x 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 NO x precursor emissions to ambient ozone.***E.O. 12866 Review

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4A - 17 **Table 4**  **A -**  **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 Ac ute 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 Ac ute 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 Ac ute 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.***E.O. 12866 Review

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4A - 18 **4A.6 R** **eferences** 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, N C: 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. Domin ici . 2004. “Ozone and Short Term Mortality

in 95 U.S. Urban Communities, 1987 - 2000.” Journal of the American Medical Association. 292(19): 2372 - 8. Bell, M.L., F. Dominici , and J.M. Samet. 2005. “A Meta - Analy sis 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.co m> . Accessed June 6, 2015. Fann, N., C.M. Fulcher , and B.J. Hubbell. 2009. “The Influence of Location, Source, and Emission Ty

pe 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 PM 2.5 - Related Health Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across the U.S.” Environment Internationa l. 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. “Bayes ian 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 Dail y Mortality : Anal ys is and Meta - Anal ys is.” Epidemiology . 16(4): 446 - 57.***E.O. 12866 Review

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4A - 19 Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. 2009. Extended Follow - Up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEIResearch Report, 140, He alth 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. 12 0(7):965 - 70. Lev y, J.I. , S.M. Chemerynski , and J.A. Sarnat. 2005. “Ozone Exposure and Mortality : An Empiric Bayes Metaregression Analy sis.” 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 Respira tory 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, PM 2.5, 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.pd f> . Accessed June 4, 2015. U.S. Environm ental 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 E PA - 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. EPA 452/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.***E.O. 12866 Review

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4A - 20 U.S. Environmental Protection Agency (U.S. EPA). 2013. Technical Support Document: Estimating the Benefit per Ton of Reducing PM 2.5 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.***E.O. 12866 Review

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5 - 1 

### C **HAPTER** 

### 5: **E CONOMIC**  

### I **MPACTS** 

### – **M ARKETS**  

### O **UTSIDE THE** 

### U **TILITY** 

### P **OWER** 

### S **ECTOR 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 c onstitute 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, me asures for its state plan, by way of meeting its state - specific goal. Given the flexibilities afforded states in compl ying 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 b y the states, and the strategies adopted b y affected EGUs, will ultimately drive the magnitude and timing of secondary impacts from changes in the price of electricity, and the demand for inputs b y the electri city 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 imp acts 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 employ ment 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 allo cated 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***E.O. 12866 Review

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5 - 2 deploy ment of renewables, energy effici ency , or natural gas to promote manufacturing demand or employ

ment 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 anal yz ed. 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 equilib rium (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 Analy sis (EMPAX) CGE model to estimate the secondary market effects based on the cost impacts projected b y the Integrated Planning Model (IPM) for the directl y 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 Analy sis under Section 812 of the Clean Air Act Amendments the EPA incorporated a set of health benefits arising from air quali ty improvement into the EMPAX CGE model when studying the econom y- wide impacts of the Clean Air Act (U.S. EPA 2011). While the external Council on Clean Air Compliance Analy sis (Council) review of this study stated that inclusion of benefits in an economy- wide model “represent[ed] a significant step forward in benefit - cost anal ys is” (Hammitt 2010), the EPA recognizes that serious technical challenges***E.O. 12866 Review

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5 - 3 remain when attempting to evaluate the benefits and costs of potential regulatory actions using econom y- wide models . In light of these challenges, the EPA has established a Science Advisory Board (SAB) panel on econom y- wide modeling to consider the technical merits and challenges of using this anal ytical tool to evaluate costs, benefits, and economic impacts in regulatory development. The panel will also be asked to identify potential paths forward for improvements that could address the challenges posed when econom y- wide models are used to evaluate the effects of regulations. The final panel membership was anno unced in March 2015 and the first of multiple face - to - face meetings of the SAB panel has been scheduled for October 2015. The EPA will use the recommendations and advice of this panel as an input into its process for improving benefit - cost and economic imp act analy ses 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 accuratel y representing costs, benefits, energy efficiency savings in economy- wide modeling, this chapter considers the energy impacts associated with the illustrative plan approaches analyzed and a qualitative assessment of the factors that will, in part, determine the timing and magnitude of effects in other markets. **5. 3**  **Summary of Secondary Market Impacts of Energy Price Changes** Electricity , natural gas, and coal are important inputs to the production of other goods and services. Therefore, changes in the price of these commodities will shift the production costs for sectors that use electricity , natural gas, and coal in the production of other goods and services. Such changes in production costs may lead to changes in the quantities and/or prices of the goods or services produced and changes in imports and exports. The EPA used IPM to estimate e lectricity, natural gas, and coal price changes based on the illustrative plan approaches modeled for this rule. IPM is a multi - regional, dy namic, deterministic linear programming model of the U.S. ele ctric power sector that is described in more detail in Chapter 3. The Retail Price Model (RPM) uses forecast changes in wholesale***E.O. 12866 Review

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5 - 4 prices and the cost of demand - side energy efficiency

programs to forecast changes in average retail prices. The prices are ave rage prices over consumer classes and regions weighted b y 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 th e projected natural ga s prices. IPM (and its integrated gas resource and suppl y 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 re spond s accordingl y. Inaddition, IPM (and the natural gas module) are viewing a very

long time horizon (through 2050 ) , such that the impacts in certain ye ars may be responsive to other modeling assumptions or drivers. The modeling framework is simultaneous ly

solving for all of these key market and policy

parameters (both electric and natural gas), resulting in the impacts shown. **Table 5**  **- 1.**  **Estimated Percentage Changes in Average Energy Prices**  **by Energy Type for** 

**the Final Emission Guidelines, Rate -**  **based and Mass**  **- based Illustrative Plan**  **Approaches** Rate - based Approach 2020 2025 2030 Electricity Price Change [X]% [X]% [X]% Delivered Natural Gas Price Change 5.3 % - 7.7 % 2.5 % Delivered Coal Price Change - 0.8 % - 5.0 % - 3.8 % Mass - based Approach 2020 2025 2030 Electricity Price Change [X]% [X]% [X]% Delivered Natural Gas Price Change [X]% [X]% [X]% Delivered Coal Price Change [X]% [X]% [X]% For ye ars 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. Conversel y, for ye ars when prices of these inputs decreased, one would expect increases in production and decreases in market prices within these sectors. Smaller changes in input price changes are assumed to lead to smaller impacts within secondary markets. However, a number of factors in addition to the magnitude and sign of the energy price changes, influence the magnitude of the impact on produc tion and market prices for sectors using electricity, natural gas, or coal as inputs to production. These factors are discussed below.***E.O. 12866 Review

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5 - 5 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 directl y 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 o f total production costs. Therefore, more energy- intensive sectors would potentially experience greater cost increases when electricity , natural gas, or coal prices increase, but would also experience greater reduced costs when these input prices decrease. 99 5. 3 .2 Ability to Substitute between Inputs to the Production Process The ease with which producers are able to substitute other inputs for electricity , natural gas, or coal, or even amongst those commodities, influences the impact of price changes for th ese inputs. Those sectors with a greater ability to substitute across energy inputs or to other inputs will be able to, at least partiall y, 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 a bility of producers in sectors experiencing changes in their input pri ces 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. I f 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 th e same sector. Changes in the price of electricity, natural gas, and coal can influence the quantities of goods imported or exported from sectors using these inputs. When the cost of domestic 99 The net direct effect of this rulemaking on the production costs of a sector that is attributable to a change in the electricity price also depends on the expenditures the sector makes to reduce its demand for electricity under any energy efficiency program that was adopted to achieve a state goal. That said, those expenditures may lead to other reduced expenditures for the sector, such as reduced natural gas use from weatherization projects.***E.O. 12866 Review

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5 - 6 production increases due to more expensive inputs, imports may i ncrease as consumers substitute towards relativel y 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 incre ases that offset some portion of domestic decreases, an effect commonly

referred to as “leakage.” 100 Alternativel y, if those imports are less emissions - intensive than the sectors that have contracted, emissions may fall even further. The potential for change s in global pollutants such as carbon dioxide (CO 2 ) and other GHG emissions is noteworthy . Unlike most criteria pollutants and hazardous air pollutants, the impacts of CO 2 emissions are not affected b y 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 ultimatel y 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 b y s ectors that use these energy inputs in the production process. The direction and magnitude of these impacts are influenced b y a number of fact ors. 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 effic iency

services and products, will also affect secondary

100 EPA does not anticipate significant leakage in the utility power sector to occur from this r egulation due to changes in the location of where domestically consumed electricity is produced because the nature of electricity transmission does not lend itself to significant imports or exports of electricity.***E.O. 12866 Review

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5 - 7 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 CO 2 emissions elsewhere. **5. 6**  **References** Hammitt, J.K. 2010. Review of the Final Integrated Report for the Second Section 812 Prospective Study

of the Benefits and Costs of the Clean Air Act. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8e eea4c885257242006935a3/1E6218D 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 Anal ys is. Office of Air Quality Planning and S tandards, 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). 20 10 . Regulatory Impact Anal ys is 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 Cost s 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.***E.O. 12866 Review

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6 - 1 

### C **HAPTER** 

### 6: **E MPLOYMENT**  

### I **MPACT** 

### A**NALYSIS 6.1**  **Introduction** Executive Order 13563 directs federal agencies to consider regulatory impacts on job creation and employ

ment. According to the Executive Order, “our regulatory sy stem 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 anal ys es have not ty picall y included a separate anal ysis of regulation - induced employ ment impacts, 101 we ty

picall y conduct employ ment anal ys es for economicall y sign ificant rules . During the current economic recovery , employ ment impacts are of particular concern and questions may arise about their existence and magnitude. Th is chapter discusses and projects potential employ ment impacts for the utility power , coal and natural gas production, and demand - side energy efficiency sectors which may result from the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility

Generating Units (herein referred to as “final emission guidelines ” or the “Clean Power Plan Final Rule”). Section 6. 2 describes the theoretical framework used to anal yz e regulation - induced employ ment 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 employ ment. Section 6. 4 provides background regarding recent emplo ym ent trends in the electricity generation, coal and natural gas extraction, r enewable energy , and demand - side energy efficiency- related sectors. Section 6. 5 presents the EPA’s quantitative projections of potential employ ment impacts in these sectors. These projections are based in part on a detailed model of the electricity production sector used for this regulatory anal ys is. Additionally , this section discusses projected employ ment impacts due to demand - side energy efficiency

activities. Section 6. 6 offers several conclusions.

101 Labor expenses do, however, contribute t ow ard total costs in the EPA’s standard benefit - cost analyses.***E.O. 12866 Review

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6 - 2 **6. 2**  **Economic Theory and Employment** Regulatory

employ ment impacts are difficult to disentangle from other economic changes affecting employ

ment decisions over time and across regions and industries. L abor market responses to regulation are complex. They depend on labor demand and supply elasticities and possibl e labor market imperfections (e.g., wage stickiness, long - term unemploy ment, etc.). The unit of measurement (e.g., number of jobs, ty pes of job hours worked, and earnings) may affect observability

of that response. Net employ ment impacts are composed of a mix of potential declines and gains in different areas of the economy (e.g., the directly

regulated sector, upstream and downstream sectors, etc.) over time. In light of these difficulties, economic theory provides a constructive framework for anal ys is. Mi croeconomic theory

describes how firms adjust their use of inputs in response to changes in economic conditions. 102 Labor is one of many inputs to production, along with capital, energy , and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a function of market prices and technological constraints. 103 , 104 Berman and Bui (2001) adapt this model to analyze how environmental regulations affect labor demand. 105 They model environmental regulation as effectivel y requiring certain f actors of production, such as pollution abatement capital, at levels that firms would not otherwise choose. Berman and Bui (2001) model two components that drive changes in firm - level labor demand: output effects and substitution effects. 106 Regulation affec ts the profit - maximizing quantity of output by

changing the marginal cost of production. If a regulation causes marginal cost to increase, it will place upward pressure on output prices, leading to a decrease in demand, and resulting in a decrease in produ ction. The output effect describes how, holding labor intensity constant, a decrease in

102 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 103 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function f rom cost - minimization. 104 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs ) . 105 Morgenstern, Pizer, and Shih (2002) develop a similar model. 106 The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a cost effect; and 3) a factor - shift effect.***E.O. 12866 Review

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6 - 3 production causes a decrease in labor demand. As noted by Berman and Bui, although many assume that regulation s 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 compl y 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 potentiall y 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 pro duction. 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 b y 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 employ ment effects on regulated firm s 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 emplo ym ent, thus most studies must rely on confidential plant - level employ ment data from the U.S. Census Bureau, ty picall y combined with pollution abatement expenditure data , that are too dated t o be reliabl y 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 employ ment (i.e. the impact of that more stringent regulation has on employ ment relative to less stringent or no regulation), not the net effects.***E.O. 12866 Review

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6 - 4 The conceptual framework described thus far focused on regulatory effects on plant - level decisions within a regulated industry . Employ ment impacts at an individual plant do not nec essarily

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) o ther factors of production can be easily substituted for labor, (3) the suppl y of other factors is highl y elastic, or (4) labor costs are a large share of total production costs. 107 For example, if all firms in an industry are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may not change much, and output of individual firms may change slightly. 108 Inthis 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 fo r 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 An alysis ( RIA) . In addition to changes to labor demand in the regulated industry , net employ ment 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 emplo ym ent in the firms supporting this technology . At the same time, the regulated industry is purchasing the equipment , and these costs may impact labor demand at regulated firms. Therefore, it is important to con sider the net effect of compliance actions on employ ment across multiple sectors or industries.

107 See Ehrenberg & Smith, p. 108. 108 This discussion draws from Berman and Bui (2001), pp. 293.***E.O. 12866 Review

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6 - 5 If the U.S. econom y is at full employ ment, even a large - scale environmental regulation is unlikely to have a noticeable impact on aggregate net employ ment. 109 In s tead, labor in affected sectors would primaril y be reallocated from one productive use to another (e.g., from producing electricity or steel to producing high efficiency equipment), and net national employment effects from environmental regulation would be small and transitory (e.g., as workers move from one job to another). 110 Some workers may retrain or relocate in anticipation of new requirements or require time to search for new jobs, while shortages in some sectors or regions could bid up wages to attrac t 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 employ ment may adversely

impact individuals and communities just as localized increase s may have positive impacts. If , on the other hand, the econom y is operating at less than full employment, economic theory does not clearl y indicate the direction or magnitude of the net impact of environmental regulation on employ

ment; it could cause eith er a short - run net increase or short - run net decrease (Schmalansee and Stavins, 2011). An important research question is how to accommodate unemploy ment as a structural feature in economic models. This feature may be important in assessing large - scale regulatory impacts on employ

ment (Smith , 2012). Environmental regulation may also affect labor supply and productivity . In particular, pollution and other environmental risks may impact labor productivity or employees’ ability to work. 111 While the theoretical f ramework for anal yz ing labor suppl y effects is analogous to that for labor demand, it is more difficult to study empirically . There is a small emerging literature described in the next section that uses detailed labor and environmental data to assess these impacts. To summarize, economic theory provides a framework for anal yz ing the impacts of environmental regulation on employ ment. The net employ

ment effect incorporates expected 109 Full employment is a conceptual target for the economy where everyone who wants to work and is available to do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero. 110 Arrow et al. 1996; see discussion on bottom of p. 8. In practi ce, distributional impacts on individual workers can be important, as discussed in later paragraphs of this section. 111 E.g. Graff Zivin and Neidell (2012).***E.O. 12866 Review

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6 - 6 employ ment 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. Finall y, 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 anal yz ing various aspects of labor demand, relying on the theoretical framework discussed in the preceding section. 112 This work focuses primarily o n effects of employment policies such as labor taxes and minimum wages. 113 Incontrast, the peer - reviewed empirical literature specificall y estimating employ ment effects of environmental regulations is growing, but is more limited. Inthis section, we present an overview of the latter. As discussed in the preceding section on theory , determining the direction of employ ment effects in regulated industries is challenging because of the complexity of the output and substitution effects. Complying with a new or m ore 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 employ ment effects at a national level (Greenstone , 2002). Moreover, environmental regulations may affect sectors that support pollution reduction earlier than th e regulated industry . Rules are usually announced well in advance of their effective dates and then typically provide a period of time for firms to invest in technologies and process changes to meet the new requirements. When a regulation is promulgated, the initial response of firms is often to order pollution control

112 Again, see Hamermesh (1993) for a detailed treatment. 113 See Ehrenberg & Smith (2000), Chapter 4: “E mp loyment Effects: Empirical Estimates” for a concise overview.***E.O. 12866 Review

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6 - 7 equipment and services to enable compliance when the regulation becomes effective. Estimates of short - term increases in demand for specialized labor within the environmental protection sector have been prepared for several EPA regulations in the past, including the Mercury and Air Toxics Standards (MATS). 114 Overall, the peer- reviewed literature does not contain evidence that environmental regulation has a large impact on net employ ment (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 employ ment 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 relativel y small, and not always statistically signific ant, 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 ty pe of geographic relocation may be occurring. Overall, the peer - reviewed literature does not contain evidence that environmental regulation has a large impact on net employ

ment (either negative or positive) in the long run across the whole econom y. A small literature examines impacts of environmental regulations on manufacturing employ ment. Greenstone (2002) and Walker (2011, 2013) study the impact of air quality regulations on manufacturing emplo ym ent, 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 employ ment impacts by

focusing on neighboring counti es with different air quality regulations. They

find limited evidence that environmental regulations may cause employ

ment to be lower within “county- border - pairs.”

114 U.S. EPA (2011b).***E.O. 12866 Review

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6 - 8 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 emplo ym ent. Moreover this result is sensitive to model specification choices. The few studies in peer- reviewed journal s evaluating emplo ym ent impacts of policies that reduce CO 2 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 s ector, Anger and Oberndorfer (2008) fin d that the initial allocation of emission permits did not significantl y affect employ ment 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 employ ment impacts of controlling CO 2 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 effectivel y put a price on emissions either through taxes or tradable permits. A performance standard may not generate similar employ

ment 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 employ ment impacts of environmental regulation. Given the difficulty with estimating national impacts of regulations, EPA has not generally

estimated econom y- wide employ ment impacts of its regulations in its benefit - cost anal ys es. However, in its continuing effort to advance the evaluation of costs, benefits, and economic impacts associated with environmental regulation, EPA has formed a panel of experts as part of EPA’s Science Advisory Board (SAB) to advise EPA on the technical merits and challenges of using economy- wide economic models to evaluate the impacts of its***E.O. 12866 Review

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6 - 9 regulations, including the impact on net national employ ment. Once EPA receives guidance from this panel it will carefully consider this input and then decide if and how to proceed on econom ywide modeling of employment impacts of its regulations. EPA received several comments regarding the potential net national e mploy ment impact of the proposed emission guidelines . Many of these comments referred to analy ses developed using non - transp arent, non - peer reviewed models. Additionally , some of the anal ys is pre - dated the Clean Power Plan proposal . However, one comment wa s based on an “economy- wide assessment of the employ ment impacts associated with the U.S. Environmental Protection Agency ’s (EPA’s) proposed Clean Power Plan” using the Long - term Inter - industry Forecasting Tool (LIFT) model. The LIFT model , which is from the Interindustry Forecasting Project (Inforum) at the University of Maryland, h as been u sed in the peer - reviewed academic literature 115 and has also been used to examine the economic impacts of other national polic ies [ Meade (2009) ; Werling (2011)] . 116 The commenter note d that “While EPA’s anal ys is provides a reasonable first approximation of the proposed rule’s emplo ym ent effects, its focus on direct employ ment impacts does not capture various indirect employ ment impacts that may

be of interest to pol icy

makers and the public.” [...] “These include the employ ment impact associated with changes in electricity and other energy prices (both positive and negative, depending on the ye ar), the productivity

impacts associated with heat rate improvements at power plants, households and businesses re - directing expenditures to other uses because of increased demand side energy

efficiency , expenditures crowded out by energy efficiency

expenditures, and changes in investments for air pollution control devices.”

115 See, for example, Almon (1991) and Mccarthy (1991). 116 The commenter provides the following description of the LIFT model: “LIFT is a 97 - sector dynamic representation of the U.S. national economy. The model combines an interindustry input / output (I - O) formulation with extensive use of regression analysis to employ a ’bottom - up’ approach to macroeconomic modeling. That is, the model works like the actual economy, build ing 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.”***E.O. 12866 Review

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6 - 10 As mentioned previously, EPA is currentl y engaged in an SAB process on economy- wide modeling. 117 EPA will not make any determinations on whether modeling the economy- wide impacts of its regulations

– including emplo ym ent 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 ch arge questions to the SAB: “Are there other economy- wide modeling approaches that EPA could consider in conjunction with CGE models to evaluate the short run implications of an air regulation (e.g., macro - economic, disequilibrium, input/output models)? What are the advantages or disadvantages of these approaches?” 6.3.3 Labor Supply Impacts The empirical literature on environmental regulatory employ ment impacts focuses primarily on labor demand. However, there is a nascent literature focusing on regulation - induced effects on labor suppl y. 118 Although this literature is limited by empirical challenges, researchers have found that air quality improvements lead to reductions in lost work days (e.g., Ostro, 1987). Limited evidence suggests worker productivity may also improve when pollution is reduced. Graff Zivin and Neidell (2012) used detailed worker- level productivity data from 2009 and 2010, paired with local ozone air quality monitoring data for one large California farm growing multiple crops, with a piece - rate pay ment 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 p arts per billion (ppb) decreases in ozone concentrations increases worker productivity b y 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 119

117 For further information see: http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument 118 For a recent review

see Graff

- Zivin and Neidell (2013). 119 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light of this recent research, however, the EPA is considering how best to incorporate possible productivity effects in the future.***E.O. 12866 Review

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6 - 11 This section has outlined the challenges associated with estimating regulatory effects on both labor demand and supply for specific sectors. These challenges make it difficult to estimate net national employ

ment estimates that would appropriatel y capture the way in which costs, compliance spending, and environmental benefits propagate through the macro - econom y. Q uantitative estimates are further complicated b y the fact that macroeconomic models often have little sectoral detail and usually assume that the economy is at full employment. **6 .**  **4 Recent Employment**  **T rends**  The U.S. electricity

s ys tem 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 employ ment trends in the electricity s ys tem. 6.4.1 Electric Power Generation In 2014 , the electric power generation, transmission and distribution sector (NAICS 2211) employed about 390 ,000 workers in the U.S. 120 Installation, maintenance, and repair occupations accounted for the largest share of workers (25 percent). 121 These categories include inspection, testing, repairing and maintaining of electrical equipment and/or installation and repair of cables used in electrical power and distribution systems. Other major occupation categories include offic e and administrative support (18 perc ent), production occupations (16 percent), architecture and engineering (10 percent), business and financial operatio ns (7 percent) and management (7 percent). As shown in Figure 6.1, employ ment in the Electric Power Ind ustry 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 t o about 390,000 workers in 2014 .

120 U.S. Bureau of Labor Statistics. “Current Employment Survey S easonally Adjusted Employment for Electric Power Generation, Transmission, and Distribution (national employment).” Series ID: CES4422110001. Available at <http://www.bls.gov/data/>. Accessed June 9, 2015 . 121 U.S. Bureau of Labor Statistics, Occupational Employment Statistics, M ay 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>.***E.O. 12866 Review

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6 - 12 **Figure 6.1. Electric Power Industry Employment**  6.4.2 Fossil Fuel Extraction 6. 4 . 2.1 Coal Mining The coal mining sector (NAI CS 2121) is primarily engaged in coal mining and coal mine site development, excluding metal ore mining and nonmetallic mineral mining and quarrying. In 2014 , BL S reported about 7 4 ,000 coal mining employees (Figure 6.2) . During the 2000 to 2015, period, coal mining employ ment peaked in 2011 at about 87,000 employees. 0 50 100 150 200 250 300 350 400 450 500 

### Po wer Generation and Supply Employment

(NAICS = 2211, Annual Average, 1000s of Employees)***E.O. 12866 Review

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6 - 13 **Figure 6**  **.2 .**  **Coal Production Employment** 6. 4 . 2.2 Oil and Gas Extraction In 2014 , there were close to 200 ,000 employees in the oil and gas extraction sector (NAICS 211). 122 This sector includes production of crude petroleum, oil from oil shale and oil sands, production of natural gas, sulfur recovery from natural gas, and recovery of h ydrocarbon liquids. Activities include the development o f gas and oil fields, exploration activities for crude petroleum and natural gas, drilling, completing, and equipping wells, and other production activities. 123 Incontrast with coal, and looking at Figure 6.3, there has been a sharp increase in employ ment in this sector over the past decade.

122 BLS, Current Employment Statistics. Seasonally adjusted employment for oil and gas extraction (national em ployment), NAICS 211. Series ID: CES1021100001. Available at <http://www.bls.gov/data/>. Accessed June 9, 2015. 123 U .S. Bureau of Labor Statistics. 20014. Available at: < http://www.bls.gov/iag/tgs/iag211.htm Accessed Feb. 1 9 >.***E.O. 12866 Review

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6 - 14 **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 s ys tem 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 electri c power generation. Similarly , manufacturers of energy efficient appliances are not reported separatel y from conventional appliance manufacturers and green building design is not separate from the construction sector. Instead, clean energy technology and s ervices are supported by industries throughout the econom y. 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

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6 - 15 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 BL S Green Goods and Services survey s have subdivide d 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 gre en if it met a threshold of green revenue as a proportion of total revenue. DOC broadl y defined green jobs in 2010 as those “created and supported in businesses that produc e green products and services.” 124 They further classified green jobs into a broad and a narrow category . The narrow category includes only

products deemed to be green without disagreement, while the broad category is more inclusive definition of green goods and services to over 22,000 product codes in the 2007 Economic Census to estimate their contribution to the U.S. economy . The report found that the number of green jobs in 2007 ranged from 1.8 million to 2.4 million jobs, accounting for between 1.5 and 2 percent of total private sector employ ment. 125 BL S used an expansive definition of cle an or green jobs in 2010 and 2011. It goes beyond direct clean energy- related investments and includes “those in businesses that produce goods and provide services that benefit the environment or conserve natural resources. These goods and services, which are sold to customers, include research and development, installation, and maintenance services for renewable energy and energy

efficiency

and education and training related to green technologies and practices” but also include recycling and natural resource conservation, such as forestry management. 126 Based on survey s across the 325 industries it identified as potential producers of green goods and services, BLS counts approximately 2.3

124 U.S. Department of Commerce Economics and Statistics Administration. 2010. “Measuring the Green Economy ,” April. Available at: <http://www.es a.doc.gov/sites/default/files/reports/documents/greeneconomyreport_0.pdf >. 125 U.S. Department of Commerce Economics and Statistics Administration. 2010. “Measuring the Green Economy, April. Available at: <http://www.esa.doc.gov/sites/default/files/reports/documents/greeneconomyreport_0.pdf > . 126 BLS has identified 325 detailed industries (6 - digit NAICS) as potential producers of green goods and services. Available at: < http://www.bls.gov/ggs/ggsoverview. htm>. (A ccessed on 1 - 14 - 14, l ast modified date: March 19 , 2013.***E.O. 12866 Review

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6 - 16 million jobs in the green economy in 2010, rising 7.4 percent to 2.5 mi llion in 2011, 127 compared to increases of about one percent across all occupations in the entire economy over the same period. 128 The table below, Table 6 - 1, presents BLS green job estimates nationally and for the utility sector. **Table 6**  **- 1.**  **U .**  **S .**  **Green Goods and Services (GGS) Employment (annual average)** 

Total GGS Em ployment Utility GGS

Em ployment Total GGS Grow th 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 emp loy ment. For historical trends in this sector, we therefore, rel y on a Brookings Institution study , Muro et al. (2011). This study built a national database of “clean econom y” jobs from the bottom up, verifying each company individually. 129 They include a li st of categories similar but not identical to that of BLS, including agricultural and natural resources conservation, education and compliance, energy and resource efficiency , greenhouse gas reduction, environmental management and recycling, and renewable energy. This study found about 138,000 jobs in the renewable energy sector in 2010, with an overall average annual growth rate of 3.1 percent from 2003 - 2010. Table 6 - 2 details the national results by energy source.

127 U.S. Department of Labor, U.S. Bureau of Labor Statistics. (n.d.). 2011. “ Green Goods and Services 2010 - 2011 .” (Retrieved on January 14, 2014). Available at :< http://www.bls.gov/ggs/ggsoverview.htm>. 128 U.S. Department of Labor, U.S. Bureau of Lab or 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/oe s_nat.htm. 129 <http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf > p. 15.***E.O. 12866 Review

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6 - 17 **Table 6 -**  **2. Renewable Electricity Genera**  **tion -**  **Related Employment** Sector Jobs, 2010 2003 - 2010 Average Annual Grow th 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 employ ment numbers and growth in the solar sector. 130 6.4.3.3 Employment Trends in Demand - Side Energy Efficiency Activities U.S. government data used for calculating the historical trends in the demand - side energy efficiency sector come from the BL S green goods and services survey s. 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 econom y” jobs from the bottom up, verifying each company individually . 131 This study found about 428,000 jobs in the Energy and Resource Efficiency sector in 2010, with an overall average annual growth rate of 2.6 percent from 2003 - 2010. Table 6 - 3 details the results b y energy sector.

130 The Solar Foundation, National Solar Jobs Census 2014. < http://www.thesolarfoundation.org/national - solar jobs - census - 2014/> 131 <http://www.brookings.edu/~/media/Series/resources/0713_clean_ economy.pdf > p. 15***E.O. 12866 Review

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6 - 18 **Table 6**  **- 3.**  **Energy and Resources Efficiency -**  **Related E**  **mployment** Sector Jobs, 2010 2003 - 2010 Average Annual Grow th 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 employ ment databases. While definitions and timeframes vary , all show positiv e employ ment 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 CO 2 emission performance rat es by placing new orders for efficiency - related or renewable energy equipment and services to reduce GHG emissions. Installing and operating new equipment or improving heat rate efficiency could increase labor demand in the electricity generating sector itself, as well as a ssociated equipment and services sectors. Specificall y, the direct employment effects of supply - side initiatives include changes in labor demand for manufacturing, installing, and operating higher efficiency or renewable energy

electricity

generating asset s supported by the initiative while reducing the demand for labor that would have been used b y less efficient or higher emitting generating assets. Once implemented, increases in operating efficiency would impact the utility power sector’s demand for fuel and plans for EGU retirement and new construction. In addition, EPA expects state compliance plans to also include demand - side energy efficiency policies and programs that ty pically change energy

consumption patterns of business and residential consumers b y reducing the quantity of energy required for a given level of production or service. Demand - side initiatives generall y aim to increase the use of cost - effective energy efficiency

technologies (e.g., including more efficient appliances and air conditioning sy stems, 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

co nsumption, such as through***E.O. 12866 Review

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6 - 19 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 emplo ym ent b y encouraging firms and co nsumers to shift to more efficient products and processes than would otherwise be the case. Employ ment in the sectors that provide these more efficient devices and services would be expected to increase, while emplo yment in the sectors that produce less ef

ficient devices would be expected to contract. This analy sis uses the cost projections from the engineering - based Integrated Planning Model (IPM) to project labo r 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 us e, and reductions in electricity

generation due to demand - side energy efficiency

activities. To project labor requirements for demand - side energy efficiency

activities, the anal ys is uses a different approach that combines data on historic changes in employ ment and expenditures in the energy efficiency sector with projected changes in expenditures in the sector arising from stat e 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

play s an important role in compliance strategies 6.5.1 Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction The anal ytical approach used in this analy sis is a bottom - up engineering method combining EPA’ s cost analy sis of the emission guidelines with data on labor productivity , engineering estimates of the amount and ty pes of labor needed to manufacture, constru ct, and operate different ty

pes of generating units, and prevailing wage rates for skilled and general labor categories. This approach is different from the ty pes of economic analy ses discussed in s ection 6. 2 . Lacking robust peer- reviewed methods to estimate econom y- wide impacts , the engineering - based anal ys is focuses on the direct impact on labor demand in industries closely involved with electricity generation. The engineering approach projects labor changes measured***E.O. 12866 Review

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6 - 20 as the change in each analy sis year in job - ye ars 132 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 ty pes of labor required to implement improvements in generating efficiency . It then uses the EPA’s estimated effect of efficiency improvements on fuel demand to project reductions in the amount of labor required to produce coal and gas. This analy sis relies on projections and the co st analy sis from IPM, which uses industryspecific data and assumptions to estimate costs an d energy impacts of the final guidelines (see Chapter 3). The EPA uses IPM to predict coal generating capacity that is likely to undertake improvements in heat rate efficiency (HRI). 133 IP M 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 analy tical methods used in many

previous EPA regulato ry analy ses. The most relevant prior analy sis 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 mai ntaining generating units, as well as labor needed to mine coal and natural gas) are the same as we used in MATS (with updated data where available), the methods used to estimate the labor associated with installing new capacity and implementing heat rate improvements were developed for th e purpose of the Clean Power Plan RIA. The bottom - up engineering - based labor anal ys is in the MATS RIA primarily was concerned with the labor needs of retrofitting pollution control equipment. A central feature of the labo r anal ys is for this RIA , however, involves the quantity and timing of the labor needs of building new renewable and NG CC units and retiring coal units . The estimated response of the utility power sector involves changes in the amount and timing of retireme nts of existing coal and oil/gas units, as well as changes in the amount and timing of building new NGCC units and 132 Job - years are not individual jobs, but rather the amount of work performed by the equivalent of one full - time individual for one year. For example, 20 job - years in 2020 may r epresent 20 full - time jobs or 40 half - time jobs in that year. 133 Heat rate improvements (HRI) could include a range of activities in the power plant to lower the heat rate required to generate a net electrical output. Assuming all other things being equal, a lower heat rate is more efficient because more electricity is generated from each ton of coal.***E.O. 12866 Review

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6 - 21 renewable generating capacity . In addition to the changes in retirements and building new units, there are also estimated changes in the util ization of existing generating units. For example, as presented in Chapter 3, the IP M analy sis of the rate - based control scenario finds that in 202 5 (part way through the 2022 - 2029 interim plan performance period) less total generating capacity is needed than in the base case. The estimated reduction in capacity by 2025 with the rate - based scenario is 49.4 GW less than the estimated base case capacity (a 4.8 percent net capacity reduction). This 49.4 GW net reduction includes more retirements of coal units (an additional 22.9 GW of coal - fired capacity retired) and oil/gas steam units (an additional 9.3 GW of oil/gas retirement) compared to the base case, as well as a reduction in the amount of new natural gas units needed to be built by 2025 (a decrease of 10.9 GW in new capacity from the amount forecast in the base case) and non - hydro renewables (1.7 GW less renewable capacity built). An important aspect of the labor analy sis 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 actuall y paid for, in a concentrated amount of time before th e new capacity begins to generate electricity . IPM assumes 134 that new NG CC units take 3 years to build, and both natural gas combustion turbines and wind - powered renewables take 2 ye ars. 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 CO 2 and precursor pollutants from fossil fuel - fired generation. The avoided new capacity , however, also has significant labor impacts. A portion of the labor that would have been used to build the new capacity in the base case will not be employed in the power generation sector with the implementation of the GHG guidelines , though it likely will be employed in construction elsewhere . Similarly , less labor involved with operating and providing fuel for new units will be needed with the GHG guidelines than in the base case.

134 Ta ble 4. 13 , IPM 5. 13 Documentation.***E.O. 12866 Review

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6 - 22 A critical compo nent 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 s ection 6. 5 .2. All of the labor impacts of the demand side 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 demand side programs. The IP M labor expenditure projections are di stributed across different labor categories (e.g., general construction labor, boilermakers and engineering) using data from engineering anal ys es of labor’s overall share of total expenditures, and apportionment of total labor cost to various labor categories. Hourl y labor expenditures (including wages, fringe benefits, and emplo ye r - paid costs including taxes, insurance and administrative costs) for each category are used to estimate the labor quantity (measured in full - time job - ye ars) consistent with the c ompliance scenario projections. Projected labor impacts arising from changes in fuel demand are primaril y derived from labor productivity data for coal mining (tons mined per employee hour) and natural gas extraction (MMBtu produced/job - ye ar). Tables 6.4 and 6.5 present projected changes relative to the baseline of four labor categories: 1. manufacturing, engineering and construction for building, designing and implementing heat rate improvements; 2. manufacturing and construction for new generating capacity ; 3. o perating and maintenance for existing generating capacity ; and 4. extraction of coal and natural gas fuel. All of the employ

ment estimates presented in Tables 6 - 4 and 6 - 5 are estimates occurring in a single ye ar. 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 ye ar of three different intervals . The three intervals are from 201 8 through 2020 (a three ye ar interval) , during which there are modest labo r impacts from the early changes in the power utility sectors operations , from 2021 through 2025 ( five ye ars), and 2026 through 20 29 (5 years). The construction - related labor anal ys is are based on the IPM estimates of the net change in capital investment that occurs during each multi - ye ar interval to fund building new units completed during that interval. The new build labor anal ys is uses the net change in capital investment to estimate the amount and ty pe of labor needed during the interval to build the new capacity . The anal ys is assumes that the new built labor within each interval is evenl y distributed throughout the***E.O. 12866 Review

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6 - 23 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 impact s are estimated based on the assumed capital cost of $100/kw (see s ection 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. IP M 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 ye ar estimates for 2020, 2025 and 2030. These single ye ar O&M labor estimates are not merel y the average annual averag es labor needs throughout each multi ye ar interval. There are O&M labor changes occurring in the all years throughout the entire period 2017 - 2030, but the labor impact s in each labor category change each year. The fuel related labor estimates are also sing le - year estimates, and not multi - ye ar averages. The labor anal ys is 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 inherentl y estimates of the fuel usage in a single year. As with the O&M labor impacts, the fuels - related labor impacts occur in every

year throughout 2017 - 2030, and the labor impact changes every year. It should be noted that the supply- side labor impact estimates in Tables 6 - 4 and 6 - 5 reflect the supply- side changes that will potentially occur with each illustrative plan scenario. These labor impacts include not only the impacts of Building Blocks 1 through 3, but also the changes in total generation needed that result from the demand - side energy efficiency

activities expected to be an important component of state compliance strategies . The additional labor impact estimates from demand - side energy efficiency activities are presented below in s ection 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.***E.O. 12866 Review

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6 - 24 **Table 6 -**  **4 .**  **Engineering -**  **Based a**  **Changes in Labor Utilization, Rate**  **- based Scenario** 

**(Number of Job -** **Years b**  **of Employment in a Single Year) Construction**  **- related (One**  **- time) Changes** * **2018 -**  **2020 2021**  **- 2025**  **2026 -**  **2030 Heat Rate Improvem**  **ent: Total 0**  **15,400 2,200**  Boilermakers and General Construction 0 11,000 1,600 Engineering and Management 0 2,800 400 Equipment - related 0 1,200 200 Material - related 0 400 0 **New Capacity Construction: Total 500**  **- 15,600**  **400** Renewables 700 - 5,000 23,300 Natural Gas - 200 - 10,600 - 22,900 **Recurring Changes  ** 2020** **2025 2030**  **Operation and Maintenance: Total -**  **9,100 -**  **17,000 -**  **19,600** Changes in Renewables 600 - 100 1,100 Changes in Gas 300 - 1,100 - 3,700 Retired Coal - 8,000 - 13,300 - 14,700 Retired Oil and Gas - 2,000 - 2,500 - 2,300 **Fuel Extraction: Total 100**  **- 7,800**  **- 13,900**  Coal - 1,300 - 7,300 - 13,300 Natural Gas 1,400 - 500 - 600 **Supply -**  **Side Employment Impacts -**  **Quantified -**  **8,500 -**  **25,000 -**  **30,900** a Job - year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates (tons coals or MMBtu gas). b All job - year estimates on this are full - time equivalent (FTE) jobs. Job estimates in the Demand - Side energy efficiency section (below) include both full - time and part - time jobs. *Construction - related job - year changes are one - time impacts, occurring during each year of the 2 to 4 year period during which construction and HRI installation activities occur. Construction - related f igures in table are average job - years during each of the years in each range. Negative construction job - year estimates occur when additional generating capacity must be built in the base case, but is avoided in the final rule due to HRI or Demand - side energy efficiency programs. **Recurring Changes are job - years associated with annual recurring jobs including operating and maint enance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job - years, while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job - years. In addition, the re are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 2020; the recurring jobs prior to 2020 are not estimated.***E.O. 12866 Review

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6 - 25 **Table 6 -**  **5 .**  **Engineering -**  **Based a**  **Changes in Labor Utilization, Mass**  **- Based Illustrative Plan** 

**Approa ch**  

## (**Number of Job -** **Years of Employment in Year) Construction**  **- related (One**  **- time) Changes** * **20XX -**  **20XX 20XX**  **- 20XX**  **20XX -**  **2 0XX**  **Heat Rate Improvem ent: Total**  **[X] [X]**  **[X]** Boilermakers and General Construction [X] [X] [X] Engineering and Management [X] [X] [X] Equipment - related [X] [X] [X] Material - related [X] [X] [X] **New Capacity Construction: Total [X]**  **[X] [X]**  Renewables [X] [X] [X] Natural Gas [X] [X] [X] **Recurring Changes  ** 2020** **2025 2030**  **Operation and Maintenance: Total [X]**  **[X] [X]**  Changes in Renewables [X] [X] [X] Changes in Gas [X] [X] [X] Retired Coal [X] [X] [X] Retired Oil and Gas [X] [X] [X] **Fuel Extraction: Total [X]**  **[X] [X]**  Coal [X] [X] [X] Natural Gas [X] [X] [X] **Supply -**  **Side Employment Impacts -**  **Quantified [X]**  **[X] [X]**  a Job - year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates (tons coals or MMBtu gas). b All job - year estimates on this are full - time equivalent (FTE) jobs. Job estimates in the Demand - Side energy efficiency section (below) include both full - time and part - time jobs. *Construction - related job - year changes are one - time impacts, occurring during each year of the 2 to 4 year period during which construction and HRI installation activities occur. Construction - related f igures in table are average job - years during each of the years in each range. Negative construction job - year estimates occur when additional generating capacity must be built in the base case, but is avoided in the final rule due to H RI or Demand - side energy efficiency programs. **Recurring Changes are job - years associated with annual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of pos itive job - years, while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job - years. In addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 2020; the recurring jobs prior to 2020 are not estimated. 6. 5. 2 Projected Changes in Employment in Demand - Side Energy Efficiency Activities While not a component of the best s ys tem 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***E.O. 12866 Review

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6 - 26 efficiency , specificall y, to support a significant number of jobs in related industries. For more information on E PA’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. Inthis section, we project employ ment 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 anal yz ed (see Chapter 3), and U.S. go vernment data on employ ment and expenditures in energy efficiency . 135 This approach is limited by the fact that we do not know which options states will choose for demand - side energy efficiency

activities and by

uncertainties associated with methods. These i llustrative employ ment projections are gross; thus they do not include impacts of any shift in resources from other sectors. Nor does this analysis attempt to quantify employ ment impacts arising from changes in consumer expenditures away from energy

toward s 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 emplo ye es (full - time and part time) 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 sy stems (e.g., weatherization of older homes), or encouraging changes in behavior (e.g., reducing air conditioning during periods of high electricity demand). Employ ment impacts of demand - side energy efficiency

programs have not been extensively

studied in the peer - reviewed, published economics literat ure. Instead, most research has focused on consumer response to and amount of energy savings achieved by

these programs (e.g., Allcott (2011a, 2011b), Arimura et al. (2012)). Results suggest that demand - side energy efficiency programs reduce energy

use and generate small increases in consumer welfare. These

135 Investments in demand - side energy efficiency reduce energy required for a given activity by encouraging more efficient technologies (e.g., ENERGY STAR appliances), implementing energy improvements for existing systems (e.g., weatherization of older homes), or encouraging changes in behavior (e.g., reducing air conditioning during periods of high electricity demand).***E.O. 12866 Review

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6 - 27 policy impacts are due to low investment in energy efficiency

as described in “energy

paradox” literature (Gillingham, Newell, and Palmer (2009), Gillingham and Palmer (2014)). 136 Two recent articles discu ss employ ment effects of demand - side energy efficiency programs. Aldy (2013) describes clean energy investments funded b y the American Recovery and Reinvestment Act of 2009, which “included more than $90 billion for strategic clean energy investments inten ded to promote job creation and the deploy ment 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 employ

ment 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 - ye ars 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 employ

ment anal ys is i n this rulemaking as there are important differences in expected employ ment impacts from a historically large fiscal stimulus specifically targeting job creation during a period of exceptionally high unemploy

ment versus environmental regulations taking eff

ect several ye ars from now. Yi (2013) anal yz es clean energy policies and employ

ment 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 associate d with 1 percent more clean energy employ ment within that MSA. These estimates are not transferable to this rulemaking since states are likel y to change intensity as well as number of clean energy programs. Lacking a peer- reviewed methodology , we use the f ollowing 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 employ

ment by

136 For mo re information on this efficiency paradox see Chapter 3.***E.O. 12866 Review

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6 - 28 expenditure s on energy

efficiency

activities t o 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 employ ment in energy

efficiency

sectors comes from the “energy efficiency” industry category of the BL S Green Goods and Services survey . 137 Using BL S Green Goods and Services information on 132 energy efficiency

industries, as identified by BL S, 138 we adjusted the list to remove ten industries expe cted to not be directly affected b y the rule, e.g. transportation. 139 Next we used this detailed list of 122 industries to extract 2011 BL S data on green emplo ym ent. 140 Employ ment data at the most - detailed industry level (6 - digit NAI CS) is available onl y for a portion of these 122 industries. Therefore we use both the most detailed industry

level (6 - digit NAICS) and also a more aggregate level (4 - digit NAICS) to estimate a range of energy efficiency employ ment with the 2011 BL S Green Goods and Services data. 141 BL S does not collect data on energy efficiency

expenditures directl y , however. Instead, BL S collects data on the share of revenues associated with green goods and services, at the establishment level . 142 We multiply data on total revenues by NAICS b y the share of green revenues reported b y BLS to obtain a measure of green revenues by industry . The onl y U.S.

137 For more details on this survey, see section 6.5.3.1 . 138 See detailed listing available here: http://www.bls.gov/ggs/naics_2012.xlsx . Category 2 is “Energy Efficiency”. More information is availab le here: http://www.bls.gov/ggs/ggsfaq.htm#3 . 139 The ten industries we removed from the list were: NAICS 483114 Coastal and Great Lakes passenger transport , 483212 Inland water passenger transportation , 485111 Mixed mode transit systems , 485112 Commuter ra il systems , 485113 Bus and other motor vehicle transit systems , 485119 Other urban transit systems , 485210 Interurban and rural bus transportation , 485410 School and employee bus transportation , 485999 All other ground passenger transportation , and 926120 Transportation program administration . 140 BLS Green Goods and Services data is available here: http://download.bls.gov/pub/time.series/gg/gg.data.1.AllData . A BLS technical note indicates that the scope of the GGS survey changed between 2010 and 2011 ( http ://www.bls.gov/news.release/ggqcew.tn.htm). Some industries and establishments that were not previously included in the 2010 survey were included in 2011. Rather than using the change from 2010 to 2011, here we use only the 2011 data. 141 At the 6 - digit NAI CS level, 27 of the 122 energy efficiency industries listed have employment data available. At the 4 - digit NAICS level, 46 of the 122 energy efficiency industries listed have employment data available. 142 More information is available here: http://www.bls. gov/ggs/ggsfaq.htm#5 .***E.O. 12866 Review

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6 - 29 Government data source containing this revenue information for all NAI CS sectors is the U.S. Economic Census. This Census is conducted at 5 - ye ar intervals (the latest available ye ar is 2012 ), however, making it unsuitable for directl y pairing with 2011 data from BL S . Instead, we use U.S. Census Bureau data on total value of shipments by industry , for 2011, from the Annual Survey of Manufacturers. 143 The disadvantage of this data source is that the manufacturing sector makes up only

50 percent of the 132 NAICS codes belonging to the energy efficiency sector as defined b y the BLS Green Goods and Services survey s, with the remainder in the construction or service sectors. Thus, this analy sis 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 NAI CS. We pair our BLS GGS data by industry , either by

4 - digit or 6 - digit NAI CS, with data from the Annual Survey

of Manufacturers. At the more detailed, 6 - digit level, 17 industries have data available for both employ ment and total value of shipments. 144 At the less detailed, 4 - digit level, 15 industries have data available for both employ ment and total value of shipments. 145 Using this approach we obtain estimates of 2.07 demand - side energy efficiency

jobs per million 2011 dol lars of expenditure , using the less - detailed industry level (4 - digit NAI CS), and 3.29 demand - side energy

efficiency

jobs per million 2011 dollars of expenditure , using the more detailed 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 effici ency 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

employ ment impacts, relative to the one percent growth assumed in this anal ys is. The first year cost of saved energy

143 Census data on total value of shipments, by industry, for 2011 is available here: http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_2011_31GS101&prodType =table . 144 The 17 industries are: NAICS 321219, 321991, 321992, 327993, 327999, 332913, 332996, 333414, 333415, 334513, 334514, 334515, 335110, 335222, 335311, 335312, and 335999. 145 The 15 industries are: NAICS 314100, 321100, 324100, 326100, 327100, 327300, 327400, 332100, 333300, 334200, 334300, 334400, 335200, 336300, and 337900.***E.O. 12866 Review

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6 - 30 accounts for both the costs to the utilities that are funding the demand - side energy efficiency programs (known as the program costs), and the additional cost to the end - user purchasing a more energy efficient technology (known as the participant costs). 146 Total costs were divided evenly, 50 percent each, between program costs and participant costs. First - ye ar costs are not annualized; they are the projected expenditures on demand - side energy eff iciency activities in that year. As shown in the Greenhouse Gas Abatement Measures TSD, Table 5 - 29, first - ye ar costs for a 1 percent growth target in energy efficiency activities are projected to be $18.1 billion (2011 $) in 2020. Multiplying this dollar e xpenditure by the jobs per dollar estimates results in projected employ ment 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. Employ ment imp acts 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. The se estimates are shown i n Table 6 - 6 below.

**Table 6**  **- 6.**  **Estimated Demand -**  **Side Energy Efficiency Employment Impacts: Targe t 1** 

**percent Growth in Energy Efficiency** Source Factor Employ ment impact (jobs)* 2020 2025 2030 EPA low estimate, using BLS and Census data, and power sector modeling projections 2.07 37,570 52,590 52,440 EPA high estimate, using BLS and Census data, and power sector modeling projections 3.29 59,700 83,590 83,360 *Since these figures represent number of employees (full - or part - time) they should not be added to the full - time equivalent job - years reported in Table 6 - 5. Energy efficiency costs are from 1 percent growth target projections for the continental U.S. (excludes Alaska, Hawaii, and U.S. Territories): $1 8.1 billion (2011 $) in 2020 . First - year energy efficiency costs are the same for rate - based and mass - based sc enarios. See chapter 3 of this RIA and Greenhouse Gas Abatement Measures TSD, Table 5 - 29, for more information . Although this approach has the advantage of using a range of estimates, derived from U.S. government data, on energy efficiency

employ ment per m illion dollars in industry shipments , this approach is limited by its focus on manufacturing sectors and direction of bias (overestimation or underestimation) cannot be determined at this time. Our estimates of 2.07 to 3.29 demand - side energy efficiency jo bs per million dollars of 2011 expenditure fit with other estimates in the literature, focused on government data sources,

146 See Section 3.6.2, “Demand - Side Energy Efficiency Total Costs”, in this RIA, for more information.***E.O. 12866 Review

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6 - 31 and are on the smaller end of the range. 147 Figure 6.6 shows the range of estimates, including EPA low (2.07) and EPA high (3.29). The 2010 Department of Commerce report estimates overall emplo ym ent per million dollar values, ranging from 4.65 to 4.85 (Department of Commerce 2010a). 148 The report also contains some case studies, and for those focused on energy efficiency , the Department of Commerce estimates 6.21 jobs per million dollars in green buildings activities and 7.53 jobs per million dollars for energy efficiency

appliances. 149 Lawrence Berkeley

National Lab (Goldman et al. 2010) reports a wide range of estimates: from 2.5 jobs per million dollars for energy service companies (ESCOs), to 8.9 jobs per million dollars for low income weatherization activities. The Pacific Northwest National Labs report (Anderson et al. 2014), i n surveying the literature, estimates 8 jobs per million dollars of consumer electricity bill savings and 11 jobs per million dollars of initial energy efficiency investments.

147 See Section 6.5.3 for more information on sources in the literature. 148 Calculated values using data on employment and shipments reported in Department of Commerce (2010a), Table 2, p. 12. 149 Calculated values using data on employment and shipments reported in Department of Commerce (2010a), Appendix 2, Table 2B , p. 4. http://www.esa.doc.gov/sites/default/files/appendix2_0.pdf***E.O. 12866 Review

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6 - 32 **Figure 6.4. Demand -**  **Side Energy Efficiency Employment: Jobs per One Million D ollars** 

**(2011$)** There is more uncertainty involved in this approach than the standard bottom - up engineering anal ys is used to estimate electricity generation and fuel production employ ment 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. Demand side energy

efficiency

improvements, in contrast, encompass a wide array of activities (subsidies for efficient appliances, “smart meter s,” 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

BL S. 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 th ese labor requirements. The EPA has identified several other limitations of this approach, outlined below. Job Reclassification . Job numbers in this chapter represent gross changes in the affected sector. As such they

may over - estimate impacts to the extent that jobs created displace 11 8.9 8 7.53 6.5 6.21 6.2 4.85 4.65 3.29 2.5 2.07

0 5 10

Jobs per Million $ PNNL initial investments LBNL low income weatheriz at ion

PNNL bill savings DOC EE appliances LBNL state and local gov't EE R

DOC green buildings LBNL ratepayer funded EE

DOC ov erall high DOC ov erall low EPA high LBNL ESCOs EPA low

Sources: EPA calculation, LBNL (2013), PNNL (2014), DOC (2010)



### Demand-Side EE Employment***E.O. 12866 Review

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6 - 33 workers employed elsewhere in the econom y. For demand - side efficiency activities this potential over- statement is may be higher than in other sectors. If states encourage consumers to purchase ENERGY STAR appliances, for exampl e, 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 employ ment projection is calculated base d 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. employ ment impact of those expenditures. Fix ed 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 t echnological 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 exh austing opportunities for low cost efficiency gains). Non - additional Activities. Here we assume that all activities financed b y 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 employ ment impacts of the proposed guidelines. **6 .**  **6 Conclusion**  This chapter presents qualitative and quantitative discussions of potential employ ment impacts of the final guidelines for electricity generation, fuel production, and demand - side energy effic iency

sectors. The qualitative discussion identifies challenges associated with estimating net emplo ym ent effects and discusses anticipated impacts related to the rule. It includes an in - depth discussion of economic theory underlying anal ys is of employ ment impacts. The employ

ment impacts for regulated firms can be decomposed into output and substitution effects, both of which may be positive or negative. Consequently , economic theory alone cannot predict the direction or magnitude of a regulation’s employ ment 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. Finall y, economic theory suggests that environmental regulatio ns may have positive impacts on labor supply and productivity as well.***E.O. 12866 Review

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6 - 34 We examine the peer- reviewed economics literature anal yz ing various aspects of labor demand, rel yi ng on the above theoretical framework. Determining the direction of employ ment 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 b y regulated firms (and firms in other relevant industries) in their production processes. The available literature illustrates some of the difficulties for empirical estimation: f or example, there is a paucity

of publicl y data on plant - level employment, thus most studies must rely on confidential plant - level employ ment data from the U.S. Census Bureau, ty pically combined with pollution abatement expenditure data, that are too dated to be reliabl y informative, or other measures of the stringenc y of regulation. In addition, the most commonly used empirical methods can only

estimate the gross effects of regulation on employ ment (i.e. the impact of that more stringent regulation has on employ ment relative to less stringent or no regulation), not th e net effects. Empirical analy sis 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 b y faci lity Econometric studies of environmental rules converge on the finding that emplo ym ent effects, whether positive or negative, have been small in regulated sectors. The illustrative quantitative anal ys is in this chapter projects a subset of potential emplo ym ent 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 suc h, given the wide range of approaches that may be used, quantifying the associated employ ment impacts is difficult. EPA’s employment anal ys is includes projected employ ment impacts associated with these guidelines for the electric power industry , coal and n atural gas production, and demand side energy

efficiency

activities. These projections are derived, in part, from a detailed model of the electricity production sector used for this regulatory anal ys is, and U.S. government data on employ ment and labor prod

uctivity . In the electricity , coal, and natural gas sectors, the EPA estimates that these guidelines could have an employ ment impact of roughly

[X] job - ye ars in 20XX for the rate - based scenario and [X] job - years for the mass - based scenario of that same ye ar (see Tables 6 - 4 and 6 - 5).***E.O. 12866 Review

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6 - 35 Employ ment 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 appro ximately range from 37,570 to 59,700 jobs in 2020, 52,590 to 83,590 jobs in 2025, and from 52,440 to 83,360 jobs in 2030 for the rate - based approach and a 1 percent growth target for energy efficiency expenditures (see Table 6 - 6). The IPM - generated job - ye ar numbers for the electricity , coal and natural gas sectors should not be added to the demand - side efficiency job impacts , since the former are reported in full - time equivalent jobs, whereas the latter do not distinguish between full - and part - time employ ment. Finally , note again that this is an illustrative anal ys is, 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 compl ying with the emission guidelines, the impacts reported in this chapter are illustrative of actions states may take. **6. 7**  **References** Aldy , J.E. 2013. “A Preliminary Assessment of the American Recovery and Reinvestment Act’s Clean Energy

Package.” Review of Environmental Economics and Policy . 7 ( 1 ): 136 - 155. Allcott, H. 2011a. “Social Norms and Energy Conservation.” Journal of Public Economics . 95 ( 9

– 10 ) : 1082

– 1095. Allcott, H. 2011b. “ Rethinking Real - Time Electricity Pricing , ” Resource and Energy Economics . 33 : 820

– 842. Almon, C. 1991. “The Inforum Approach to Interindustry Modeling,” Economic Sy stems Research 3(1):1 - 8. Anderson, D., O. Livingston, D. Belzer, D., and M. Scott. 2014. “Assessing National Emplo ym ent Impacts of Investment in Residential and Commercial Sector Energy Efficiency : Review and Example Analy sis” [Technical Report]. Pacific Northwest National Laboratory . Available at < http://www.pnnl.gov/main/publications/external/technical_reports/PNN L 23402.pdf > . Accessed June 4, 2015. Anger, N. and U. Oberndorfer. 2008. “Firm Performance and Emplo ym ent in the EU Emissions Trading Scheme: An Empirical Assessment for Germany.” Energy Policy . (36):12

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6 - 36 Arimura , T. H.; S. Li ; R. G. Newell, and K. Palmer . 2012 “Cost - Effectiveness of Electricity Energy Efficiency

Programs . ” Energy Journal . 33 ( 2 ): 63 - 99. Arrow, K . J. ; M . L. Cropper ; G . C. Eads ; R . W. Hahn ; L . B. Lave ; R . G. Noll ; Paul R. Portney ; M . Russell ; R . Schmalensee ; V. K . Smith and R . N. Stavins . 1996 . “Benefit - Cost Analy sis in Environmental, Health, and Safety Regulation : A Statement of Principles.” American Enterprise Institute, the Annapolis Center, and Resources for the Future; AEI Press. Available at <http://www.hks.harvard.edu/fs/rst avins/Papers/Benefit Cost Analy sis in Environmental.AEI.1996.pdf >. A ccessed June 5, 201 5 . Berman, E. and L. T. M. Bui . 2001. “Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin.” Journal of Public Economics . 79 (2): 265 - 295. Bezdek, R. H. ; R. M. Wendling , and P. Diperna . 2008. “Environmental Protection, the Econom y, and Jobs: National and Regional Anal ys es.” Journal of Environmental Management . 86 (1): 63 - 79. Brown, R.S. and L. R. Christensen. 1981. “Estimating the E lasticities of S ubstitution in a M odel of P artial S tatic E quilibrium: An Application to U.S. Agriculture 1947 to 1974 , ” in : Berndt, E.R. ; B. C. Field. (Eds.), Modeling and Measuring Natural Resource Substitution. Chan, H . S ., S . Li , and F . Zhang . (2013) “F irm C ompetitiveness and the European Union Emissions Trading Scheme . ” Energy Policy . (63):1056 - 1064. Commins, N ., S . Lyons , M . Schiffbauer , and R . S.J. Tol . 2011 . “Climate Policy and Corporate Behaviour . ” The Energy Journal . (32):51 - 68. Council of Economic Advisers . 2011. “The E conomic Impact of the American Recovery and Reinvestment Act of 2009 . ” Sixth quarterly report.

. Ehrenberg, R . G. and R . S. Smith . 2000. Modern Labor Economics: Theory and Public Policy. Addison Wesley

L ongman, Inc., Chapter 4. Executive Order 13563 (January 21, 2011). “Improving Regulation and Regulatory Review. Section 1. General Principles of Regulation.” Federal Register 76(14): 3821 - 3823. Gillingham, K .; R . Newell and Karen Palmer . 2009 . “Energy Efficiency

Economics and Pol icy . ” Annual Review of Resource Economics . 1 (1): 597 - 619. Gillingham, K . and K . Palmer . 2014 . “Bridging the Energy Efficiency

Gap: Policy

Insights from Economic Theory

and Empirical Evidence . ” Review of Environmental Economics and Policy . 8 ( 1 ): 18 - 38.***E.O. 12866 Review

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6 - 37 Glob al Insight. 2008. U.S. Metro Economies: Current and Potential Green Jobs in the U.S. Economy. U.S. Conference of Mayors . Available at: <http://www.usmayors.org/pressreleases/uploads/greenjobsreport.pdf >. Goldman, C.; M. C. Fuller; E. Stuart; J. S. Peters; M. McRae and N. Albers, et al. 2010. Energy Efficiency Services Sector: Workforce Size and Expectations for Growth. Lawrence Berkeley National Laboratory . Graff Zivin J . and M. Neidell . 2012. “The Impact of Pollution on Worker Productivity .” American Economic Review . 102(7) : 3652 - 73. Graff Zivin, J . and M . Neidell . 2013. “Environment, Health, and Human Capital.” Journal of Economic Literature . 51 :( 3 ): 689 - 730. Greenstone, M. 2002. “The Impacts of Environment al Regulations on Industrial Activity : Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures.” Journal of Political Economy . 110(6): 1175 - 1219. Hamermesh, D. S. 1993. Labor Demand. Princeton, NJ: Princeton University Pres s . Chapter 2. Kahn, M . E. and E . T. Mansur . 2013 . “Do L ocal E nergy P rices and Regulation A ffect the G eographic C oncentration of Employ ment? ” Journal of Public Economics . 101 : 105

– 114. Layard, P.R.G. and A. A. Walters. 1978. Microeconomic Theory . McGraw - Hill, Inc. Chapter 9. List, J. A. ; D. L . Millimet ; P. G. Fredriksson and W. W. McHone . 2003. “Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator.” The Review of Economics and Statistics . 85 (4): 944 - 952. Marshall, A. 1920. Principles of Economics . Library of Economics and Liberty . Available at: <http://www.econlib.org/library /Marshall/marP.html > . Mccarth y, M.B. 1991. “Lift: Inforum's Model of the U.S. Econom y,” Economic Systems Research 3(1):15 - 36. Meade, D.S. 2009. “An Analy sis of the Economic Impacts of the 2007 Energy Independence and Security Act”, in Energy Policy and International Competitiveness , M . Grassini and R. Bardazzi, eds. Firenze University Press, pgs. 3 - 28. Muro, M. ; J. Rothwell ; D. Saha & Partnership, w. B. 2011. Sizing the Clean Economy: A National and Regional Green Jobs Assessment. Brookings Institution, Metropolitan Policy Program . Available at <http://www.brookings.edu/~/media/series/resources/0713_clean_econ omy .pdf >. Accessed June 4, 2015.***E.O. 12866 Review

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6 - 38 Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of Environmental Economics Management . 14:87 - 98. Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analy sis for the Transport Rule.” White Paper. Boston, MA . Exelon Corp. Smith, K . 2012. “ Reflections : InSearch of Crosswalks between Macroeconomics and Environmental Economics.” Review of Environmental Economics and Policy . 6(2) : 298 - 317. Southwest Energy

Efficiency

Project. 2002. “The New Mother Lode: The Potential for More Efficient Electricity

Use in the Southwest.” Available at: <http://www.swenergy .org/ publications/nml/>. Accessed June 4, 2015. The Pew Charitable Trust. 2009. “ The Clean Energy Econom y: Repowering J obs, Businesses and Investments Across America . ” Available at: < http://communitywealth.org/content/clean - energy- econom y- repowering - jobs - businesses - and - investments across - america>. Accessed June 4, 2015. The Solar Foundation in partnership with BW Research and GW Institute. 2014. “ The National Solar Jobs Census .” Available at: <http://www.thesolarfoundation.org/national - solar - jobs census - 2014/>. Sommers, D. 2013. “BLS Geen Jobs Overview,” Monthly Labor Review. U.S. Bureau of Labor Statistics, January , p. 3 - 16. Available at <http://www.bls.gov/opub/mlr/2013/01/art1full.pdf >. Accessed June 4, 2015. U.S. Department of Commerce, Economics and Statistics Administration. 2010a. “Measuring the Green Econom y.” Available at: <http://www.esa.doc.gov/Reports/measurin g - green econom y> . Accessed June 4, 2015 . U.S. Department of Commerce, International Trade Administration . 2010 b. Environmental Industries Fact Sheet, using 2008 data from Environmental Business International, Inc. , April. Available at: <http://web.ita.doc. gov/ete/eteinfo.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac 6d85256883006c452c? OpenDocument>. Accessed June 4, 2015. U.S. Department of Labor, Bureau of Labor Statistics. 2012a. Databases, Tables & Calculators by Subject. Available at <http://data.b ls.gov/timeseries/LNS14000000 >. Acccessed on April 16, 2012 . U.S. Department of Labor, Bureau of Labor Statistics (2012b, April 6). “The Employ ment Situation

—March 2012.” News Release. Available at <http://www.bls.gov/news.release/pdf/empsit.pdf > . Accessed April 16, 2012 .***E.O. 12866 Review

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6 - 39 U.S. Department of Labor, U.S. Bureau of Labor Statistics, (n.d.). “ Employ ment Projections Program .” and available at: <http://www.bls.gov/emp/ep_table_102.htm>. U.S. Department of Labor, U.S. Bureau of Labor Statistics. (n.d.). “ Green Goo ds and Services, 2010 - 2011 . ” Available at: <http://www.bls.gov/ggs/ggsoverview.htm>. Retrieved January 14, 2014, U.S. Energy

Information Agency . 2013. “ Annual Energy Outlook 2014: Early

Release. ” Available at: http://www.eia.gov/forecast/aeo/er/early _elecgen.cfm . U.S. Environmental Protection Agency (U.S. EPA). 2011a . 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. U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Anal ys is for the Mercury and Air Toxics Standards (MATS). Office of Air Quality Planning an d Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf > . Acessed June 4, 2015. U.S. International Trade Commission. “ Environmental and Related Services. ” Publication No . 4389, Investigation No. 332 - 533 , March, 2013 . Available at <http://www.usitc.gov/publications/332/pub4389.pdf >. Accessed June 4, 2015. Walker, R. 2011.“Environmental Regulation and Labor Reallocation." American Economic Review . 101(2) : 442 - 47 . Wei, M., S. Patadia, and D. M. Kammen. 2 010. “Putting Renewables And Energy Efficiency

to Work: How Many

Jobs Can the Clean Energy Industry Generate in the US?” Energy Policy , 38 (2):919 - 931. Werling, J.F. 2011. “Preliminary Economic Anal ysis for OSHA’s Proposed Crystalline Silica Rule: Industry

and Macroeconomic Impacts”, Final Report for the Occupational Safety and Health Administration, Available at <https://www.osha.gov/silica/Employ ment_Anal ysis.pdf>. Accesse d June 9, 2015. Yi, H. 2013. “Clean Energy Policies and Green Jobs: An Evaluation of Green Jobs in U.S. Metropolitan Areas.” Energy Policy . 56: 644 - 65.***E.O. 12866 Review

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6A - 1 

### A **PPENDIX** 

### 6A: **E STIMATING**  

### S **UPPLY** 

### S **IDE** 

### E **MPLOYMENT IMPACTS** This appendix presents the methods used to estimate the supply- side employ ment 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 employ ment analy sis is limited to the direct changes in the amount of labor needed in the power, fuels and generating equipment sectors directl y influenced by the illustrative plan approaches anal yz ed 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 employ ments are based on methods previously

developed for the Mercury and Air Toxics Standards (MATS) Regulatory Impact Analy sis (RIA). The methods used in this analy sis to estimate the recurring labor impacts (e.g., labor asso ciated 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 anal ys is in the MATS RIA was primarily concerned with the labor n eeds 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 ex isting coal fired EGUs. These construction - related compliance activities in the Clean Power Plan Final Rule required developing additional appropriate anal ytical methods that were not needed for the MATS anal ys is. The newl y developed anal ytical 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 anal ytical approach used in this analy sis is a bottom - up engineering method combining the EPA ’s cost analy sis of the final emission guidelines with data on labor productivity , engineering estimates of the amount and ty pes of labor needed to manufacture,***E.O. 12866 Review

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6A - 2 construct, and operate different ty pes 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 5.1 5 , using the Integrated Planning Model (IPM®) 150 , along with data from secondary sources, to estimate the first order emplo ym ent impacts for 2020, 2025, and 2030. Throughout the suppl y- side labor analy sis the engineering appro ach projects labor changes measured as the change in each anal ys is year in job - ye ars 151 employed in the power generation and directl y related sectors (e.g., equipment manufacturing, fuel supply and generating efficiency services). Job - years are not individual jobs, nor are they necessaril y permanent nor full time jobs. Job - years the amount of work performed b y one full time equivalent (FTE) employee in one ye ar. For example, 20 job - ye ars 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 employ ment impacts (both positive and negative) are divided into five categories:  additional employ

ment to make HRI 152 at existing coal fired EGUs;  additional construction - related employ ment to manufacture and install additional new generating capacity

(renewables, and natural gas combined cycle or combustion turbine units) when needed as part of earl y compli ance actions;

150 Results for this analysis were developed using various outputs from EPA’s Base Case v.5.13 using ICF’s Integrated Planning Model (IPM®). This case includes a ll of the underlying modeling that was developed by EPA with technical support from ICF International, Inc. See http://www.epa.gov/powersectormodeling/BaseCasev513.html for more information. 151 Job - years are not individual jobs, but rather the amount of work performed by the equivalent of one full - time individual for one year. For example, 20 job - years in 2020 may represent 20 full - time jobs or 40 half - time jobs in that year. 152 Heat rate improvements could include a range of activities in the power plant to lower the heat rate required to generate a net electrical output. Assuming all other things being equal, a lower heat rate is more efficient because less fuel is needed per unit of electric output.***E.O. 12866 Review

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6A - 3  lost construction - related employ ment opportunities due to reductions in the total amount of new generating capacity needed to be built in the later ye ars because of reduced overall demand for electricity because of demand - side energy efficien cy

activities;  lost operating and maintenance employ ment opportunities due to increased retirements of coal and small oil/gas units;  changes (both positive and negative) in coal mining and natural gas extraction employ ment 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 instal ling HRI. This ty pe of employ

ment effects involves project - specific labor that is used for 2 to 4 years to complete a specific construction and installation ty

pe of project. There are other labor effects, however, which continue year after ye ar. For exampl e, 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 employ ment to prov ide 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 employ

ment changes due to HRIwere estimated based on the incremental MW capacity estimated to implement such improvements by 2020 as indicated by

the anal ys is conducted b y EPA. The heat rate improvement job impacts were assumed to have all occurred b y 2020 and thus this study

assumes there will be no HRIrelated jobs after 2020 (i.e. , no permanent O&M relate d jobs due to HRIfor 2025 or 2030). EPA modeled the heat rate improvements exogenousl y 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 w ill occur over a four - ye ar period culminating in 2020. Hence, the per - ye ar cost of heat rate was calculated to be $25/kW, and this cost was used in the next step.***E.O. 12866 Review

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6A - 4 This cost was then allocated to four categories based on the estimates provided by Andover T echnology

Partners (ATP), which were adapted from proxy projects involving installation of combustion control retrofits, such as those installed under the Best Available Retrofit Technology

(BART) submissions from coal - fired power plants located in Wyoming and Arizona. For more details, refer to the Staudt (2014) report. 153 These proxies were chosen to ensure that the types of activities involved and their associated costs would be representative of those investments EPA expects power plants to undertake for efficiency upgrades. In formation 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 primaril y 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 employ ment gains for heat rate improvement were estimated 154 for 2020 using the assumptions summarized in Table 6A - 1 below. Output per workers in future ye ars were adjusted to acco unt for growth in labor productivity , based on historical evidence of productivity

growth rates for the relevant sectors. **Table 6A -**  **1. Labor Productivity Growth Rate due to Heat Rate Improvement**  **Share of the Total Capital Cost**  **Output/Worker (2020)**  **Labor Productivity**  **Growth Rate** Boilermaker and Gen. Const. 40% $78,500 0% Management/Engineering 20% $141,000 1.3% Equipment 30% $458,000 3.2%

153 Staudt, James, Andover Technology Partners, Inc. Estimating Labor Effects of Heat Rate Improvements. Report prepared for the Clean Power Plan P roposed Rule , March 6, 2014. 154 Total value of shipments or receipts in 2007 and total employees were taken from 2007 Economic Census, Statistics by Industry for Mining and Manufacturing sectors. The average annual growth rate of labor productivity wa s 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. Fo r 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.***E.O. 12866 Review

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6A - 5 Materials 10% $424,000 - 1.2% For these output per worker figures, a power sector construction industry (NAI CS 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 (NAI CS 33121) was used for materials. Use of machinery

manufacturing for equipment and steel for materials was based on an anal ys is of the ty pes of materials and equipment needed for these projects, and what EPA determined to be the most appropriate industry s ectors for those. For more details, refer to the Staudt (2014) report. 6A.2.1 Employment Changes Due to Building (or Avoiding) New Generation Capacity Employ ment 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 capacitie s were aggregated by generation ty

pe into the following categories: • Combined Cycle,

• Combustion Turbine, and

• Renewables (which includes biomass, geothermal, landfill gas, onshore wind, and solar). For each category , the analy sis 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 pro vide annualized capital costs ($MM), EPA first converted the annualized capital costs to changes in the total capital investment using the corresponding capital charge rates. 155 These total capital investments were then converted to annual capital investment s using assumptions about the estimated duration of the construction phase, in order to estimate the

155 Capital charge rates obtained from EPA’s resource, EPA #450R13002: Documentation for EPA Base Case v.5.13 u sing the Integrated Programming Model (IPM) .***E.O. 12866 Review

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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. 156 Specific ass umptions 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 b y Staudt (2014), based primarily on published b udgets for new unit assembled in a study for the National Energy

Technology

L aboratory

(NETL). For more details, refer to the Staudt (2014) report. Annual capital costs for each generation type provided b y 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% Com bined Cycle 65% 10% 18% 7% Com bustion 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 ye ar 2007 were forecasted to the ye ars under anal ys is u sing the relevant labor productivity

growth rate. Table 6A - 4 shows the figures for each of the five productivities: general power plant construction; engineering and management; material use; equipment use;

156 Ibid.***E.O. 12866 Review

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6A - 7 and plant operators. The resulting values were mu ltiplied 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 ye ar. Because IPM reports cumulative changes for new generating capacity for any given run ye ar, 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 IP M run year (e.g., 2025) represents the average labor impact that occurs in all years between that IPM run year and the previ ous run ye ar (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 emp loy ment estimates used a simpler methodology as the one described above. The operating employ ment estimates use the IPM estimated change in FOM costs for the IPM run ye ar. Because the FOM costs are inherently estimates for a single year, the operating empl oy

ment estimates are for a single year onl y. While there are obviously operating employ ment effects occurring in every year throughout the entire IPM estimation period (2017 - 2030), the labor anal ys is only estimates the single year labor impacts in the IPM run ye ars: 2020,***E.O. 12866 Review

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6A - 8 2025 and 2030. The total direct workers for $1 million and labor productivity growth rate provided for plant operators in Table 6A - 4 were used to estimate the employ ment impact. 6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements Employ ment changes due to plant retirements were calculated using the IPM projected changes in retirement capacities for coal and oil/gas units for the relevant year and the estimated changes in total FOM costs due to those retiring units. Thus, the basic assumption in this analysis was that increased retirements (over the base case) will lead to reduced FOM expenditures at those plants which were assumed to lead to direct job losses for plant workers. In order to estimate the total FOM changes due to retirements, EPA first estimated the average FOM costs ($/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 prox y fo r the lost economic output due to fossil retirements. Thus, changes in the FOM costs for these retiring units were derived b y taking the product of the incremental change in capacity and the average FOM costs. These values were converted to lost employ ment using data from the Economic Census and BLS on the output/worker estimates for the utility sector. 157 **Table 6A -**  **5. Average FOM Cost Assumptions**  **2020 2025**  **2030** Coal 65 68 69 Oil and Gas 21 22 22 Note that the retirement related emplo ym ent 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 separatel y and discussed in the next section). 6 A.2.3 Employment Changes due to Coal and Oil/Gas Retirements Two ty pes of employ

ment impacts due to projected fuel use changes were estimated in this section. First, emplo ym ent losses due to either reductions or shifts in coal demand were 157 The same specific sources as cited before, howe ver, used workers and total payroll.***E.O. 12866 Review

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6A - 9 estimated using an approach similar to EPA’s coal employ ment anal ys es under Title IV of the Clean Air Act Amendments. Using this approach, changes in coal demand (in short tons) for various coal suppl yi ng regions were taken from EPA’s base and policy case runs for the prop osed EGU GHG NSPS. These changes were converted to job - ye ars using U.S. Energy information Administration ( EIA) data on regional coal mining productivity (in short tons per employee hour), using 2008 labor productivity estimates. 158 , 159 Specificall y, the incr emental 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 imp acts 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 - ye ars for the specific ye ar (converting the TCF of gas used projected by IPM into MM Btu using the

158 From EIA Annual Energy Review, Coal Mining Productivity Data. Used 2008. 159 Unlike the labor productivity estimates for various equipment resources which were forecasted to 2020 using BLS average growth rates, this study uses the most recent historical productivity estimates for fuel sectors. In general, labor productivity for the fuel sectors (both coal and natural gas) showed a significantly higher degree of variability in recent years than the manufacturing sectors, which wo uld have introduced a high degree of uncertainty in forecasting productivity growth rates for future years.***E.O. 12866 Review

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6A - 10 appropriate conversion factors). In addition, the pipeline construction costs were estimated using endogenousl y determined gas market model parameters in IPM used by EPA for the MATS rule (using assumptions for EPA’s Base Case v4.10). This an aly sis assumed that the need for additional pipeline would be proportionate to those projected for the MATS rule and were hence extrapolated from those estimates. 160 The job - ye ars associated with the pipeline construction were included in the natural gas emp loy ment estimates.

160 See “Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015”. Technical Support Document, March 2011.***E.O. 12866 Review

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7 - 1 

### C **HAPTER** 

### 7 **: S**  **TATUTORY AND** 

### E **XECUTIVE** 

### O **RDER** 

### A**NALYSIS 7.1**  **Executive Order 12866 :**  **Regulatory Planning and Re view, and Executive Order**  **13563: Im**  **proving 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 re sponse to OMB recommendations have been documented in the docket. The EPA prepared an analy sis 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 (CO 2 ) 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 CO 2 emission performance rates. Actions taken to comply

with the guidelines will also reduce the emi ssions of directly- emitted PM 2.5 , SO 2 and NO X . The benefits associated with these PM 2.5 , SO 2 and NO X reductions are referred to as co benefits, as these reductions are not the primary objective of this rule. The EPA has used the social cost of carbon estim ates presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (May 2013, Revised June 2015) (“current TSD”) to anal yz e CO 2 climate impacts of this rulemaking. We r efer to these estimates, which were developed b y the U.S. government, as “SC - CO 2 estimates.” The SC - CO 2 is an estimate of the monetary value of impacts associated with a marginal change in CO 2 emissions in a given ye ar. The four SC - CO 2 estimates are associ ated with different discount rates (model average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th percentile at 3 percent), and each increases over time. In this summary , the EPA provides the estimate of climate benefits associated with the SC - CO 2 value deemed to be central in the current TSD: the model average at 3 percent discount rate. The EPA estimates that, in 2020, the final guidelines will yield monetized climate benefits (in 2011$) of approximately $[X] billion (3 percent model average ). The air pollution health co - benefits in 2020 are estimated to be $[X] billion to $[X] billion (2011$) for a 3 percent***E.O. 12866 Review

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7 - 2 discount rate and $[X] billion to $[X] billion (2011$) for a 7 percent discount rate. The annual, illustrative plan costs estimated by IP M and inclusive of demand - side energy efficiency

program and participant costs and MRR costs, are approximately $[X] billion (2011$) in 2020. The quantified net benefits (the difference between monetized benefits and costs) in 2020 are estimated to range from $[X] billion to $[X] billion (2011$) using a 3 percent discount rate (model average). The EPA estimates that, in 2025, the final guidelines will yield monetized climate benefits (in 2011$) of approximately $[X] billion (3 percent model average). The air pollution health co - benefits in 2025 are estimated to be $[X] billion to $[X] billion (2011$) for a 3 percent discount rate and $[X] billion to $[X] billion (2011$) for a 7 percent discount rate. The annual, illustrative state plan costs estimated by IPM and inclusive of demand side energy efficiency program and participant costs and MRR costs, are approximately $[X] billion (2011$) in 2025. The quantified net benefits (the difference between monetized benefits and costs) in 2025 are estimated to range from $ [X] billion to $ [X] billion (2011$) using a 3 percent discount rate (model average). The EPA estimates that, in 2030, the final guidelines will yield monetized climate benefits (in 2011$) of approximately $[X] billion (3 percent model average). The air pollution health co - benefits in 2030 are estimated to be $[X] billion to $[X] billion (2011$) for a 3 percent discount rate and $[X] billion to $[X] billion (2011$) for a 7 percent discount rate. The annual, illustrative state plan costs estimated by IPM and inclusive of demand side energy efficiency program and participant costs and MRR costs, are approximately $[X] billion (2011$) in 2030. The quantified net benefits (the difference between monetized benefits and costs) in 2030 are estimated to range from $ [X] billion to $ [X] billion (2011$) using a 3 percent discount rate (model average). There are additional important benefits that the EPA could not monetize. Due to current data and modeling limitations, our estimates of the bene fits from reducing CO 2 emissions do not include important impacts like ocean acidification or potential tipping points in natural or managed ecos ys tems. Unquantified benefits also include climate benefits from reducing***E.O. 12866 Review

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7 - 3 emissions of non - CO 2 greenhouse gases (e.g., nitrous oxide and methane) 161 and co - benefits from reducing direct exposure to SO 2 , NOx and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as from reducing ecosy stem effects and visibility impairment. Based upon the foregoing discussion, it remains clear that the benefits of this proposal are substantial, and far exceed the costs. Additional benefit, cost and net benefit estimates are provided in Chapter 8 of this report. **7. 2**  **Paperwork Reduction Act (PRA)**  The information colle ction requirements in this rule have been submitted for approval to OMB under the PRA. The Information Collection Request (ICR) document prepared b y the EPA has been assigned the EPA I CR number 2503.02. You can find a copy of the I CR 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 CO 2 emission performance goals that could be addressed through eith er 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 b y the EPA, to establi sh 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 sub mitted 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 h as yet sought TAS eligibility for this purpose, this action is not

161 Although CO 2 is the predominant greenhouse gas released by the power sector, electricity generating units also emit small amounts of nitrous oxide and methane. For more detail about power sector emissions, see RIA Chapter 2 and the U.S. Greenhouse Gas Reporti ng Program’s power sector summary, http://www.epa.gov/ghgreporting/ghgdata/reported/powerplants.html.***E.O. 12866 Review

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7 - 4 anticipated to impose any information collection burden on tribal governments over the 3 - ye ar period covered b y this I CR. This rule does impose specific requirements on state and U.S. terri tory 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 CO 2 emissions from existing sources in the utility powe r sector. These recordkeeping and reporting requirements are specifically authorized by

CAA section 114 (42 U.S.C. 7414). All information submitted to the EPA pursuant to the recordkeeping and reporting requirements for which a claim of confidentiality is made is safeguarded according to agency policies set forth in 40 CFR part 2, subpart B. The annual burden for this collection of information for the states (averaged over the first 3 years following promulgation) is estimated to be a range of 529,000 to 85 9,000 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 overl y- conse rvative 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 follo wing promulgation of this action) is estimated to be 23,700 to 38,200 hours at a total annual labor cost of $1.68 to $2.71 million. The total annual burden for the federal government associated with the state collection of information (averaged over the first 3 years following promulgation) is estimated to be 54,901 hours at a total annual labor cost of $3.04 million. The total annual burden for the federal government associated with the territorial collection of information (averaged over the first 3 ye ars 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***E.O. 12866 Review

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7 - 5 technical amen dment 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 ex isting sources, and it is those requirements that could potentially impact small entities. Our anal ys is here is consistent with the analy sis of the analogous situation arising when the EPA establishes NAAQS, which do not impose any requirements on regulate d 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 detaile d 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 c arbon 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 associati ons, 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 .***E.O. 12866 Review

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7 - 6 **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 uniquel y affect small governments. The emiss ion guidelines do not impose any direct compliance requirements on EGU s located in states , U.S. territories or areas of Indian country . As explained in section XII. B above, the rule also doe s 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. Specificall y, states and U.S. t erritories are required to develop plans to implement the guidelines under CAA section 111(d) for affected EGUs . The burden for states and U.S. t erritories to develop CAA section 111(d) plans in the 3 - ye ar period following promulgation of the rule was estimated and is listed in section XII. B abo ve, but this burden is estimated to be below $100 million in any one year. Thus, this rule is not subject to the requirements of section 202 or section 205 of the UMRA. This rule is also not subject to the requirements of section 203 of UMRA because it contains no regulatory

requirements that might significantly or uniquel y 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. t erritory and tribal governmental entities while formulating the provisions of this rule. S ection s III.A and XI.F of the p reamble to the proposed carbon pollution emission guidelines for existing EGUs (79 FR 34845 - 34847 ; June 18, 2014) and section s II.D and VI.F of the preamble to the proposed c arbon 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 th e i nput provided over the course of the stakeholder outreach as we ll 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 impose s substantial direct compliance***E.O. 12866 Review

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7 - 7 costs on state or local governments, and the federal government will not provide the funds n ecessary 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 o f 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 newl y constructed EGUs. This outreach addressed planned actions for new, reconstructed, modified and existing sources. The EPA invited the following 10 national organizations representing state and local elected officials to a meeting on April 12, 2011, in Washington DC: (1) National Governors Association; (2) National Conference of State Legislatures, (3) Council of State Governments, (4) National League of Cities, (5) U.S. Conference of Mayors, (6) National Association of Counties, (7) International City /County Management Association, (8) National Association of Towns and Townships, (9) County Executives of America, and (10) Environmental Co uncil 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 standard s for modified and reconstructed EGUs. In addition, as described in s ection 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 b y the EPA allowed state leaders, including governors, state attorney s 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 lo cal officials. The EPA received comments from over 400 entities representing state and local governments.***E.O. 12866 Review

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7 - 8 Several themes emerged from state and local government comments. Commenters raised concerns with the building blocks that comprise the best sy stem of emission reduction (BSER), including the stringency

of the building blocks, and the timing of achieving interim CO 2 levels. They also identified the potential for electric s ys tem reliability issues and stranded assets due to the proposed timeframe for plan submittals and CO 2 emission reductions. In addition, states commented on state plan development and implementation topics, including state plan approaches, earl y actions, trading programs, interstate crediting for RE, and EPA guidance and outreach. Commenters identified overarching concerns regarding the stringency of the CO 2 goals and the timeframe for achieving reductions that encompassed the building blocks, the BSER, and associated timing for achievement of interim CO 2 levels. State commenters, in particular, identified changes to the stringency of the building blocks , concerns with the timeframe over which reductions must be achieved, and concerns with the approaches and measures used for the BSER. For the final rule, in response to stakeholder comment s, 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 re finements. To many commenters, the proposal’s 2020 compliance date, together with the stringency of the interim CO 2 goal, bore significant reliability implications. In this final rule, the agency is addressing those concerns via adjustments to the compliance timeframe (an 8 - ye ar interim period that begins in 2022) and to the approach for meeting interim CO 2 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 sy stem reliability. The EPA is also requiring states to consult with relevant I SOs/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 c hallenges arise.***E.O. 12866 Review

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7 - 9 Commenters provided compelling information indicating that it will take longer than the agency initially anticipated to adjust investments and achieve interim CO 2 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 ye ars 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 b y the EPA. For the state plan approache s, commenters expressed concerns with the proposed “portfolio approach” for state plans, including concerns with enforceability of requirements, and identified a “state commitment approach” with backstop measures as an option for state plans. In this final rule, in response to stakeholder comments on the portfolio approach and alternative approaches, the EPA is finalizing a “state measures” approach that includes a requirement for the inclusion of backstop measures. State commenters also expressed interest in the EPA ensuring that early actions to reduce CO 2 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 CO 2 goals. M easures that a state takes prior to 2022, or programs already in place b y 2022, that result in CO 2 emission reductions during the 2022 - 2029 period will contribute toward meeting both the interim and final CO 2 emission performance rates. Thus, states with currentl y existing programs and policies, and states that put in place new programs and policies earl y, 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 CO 2 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 s tates and stakeholders about the value of EPA support in developing and/or administering tracking s ys tems to support state administration of rate - based and mass based 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***E.O. 12866 Review

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7 - 10 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 lo wer cost, the final rule provides additional options to allow individual EGUs to use creditable out - of - state reductions to achieve required CO 2 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 emissio n measurement and verification (EM&V), and to maintain regular contact/forums with states throughout the implementation process. To provide state and local government s and other stakeholders with an understanding of the rule requirements, and to provide ef ficiencies where possible and reduce the cost and administrati ve burden, the EPA will continue outreach throughout the plan de velopment 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 webi nars 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 state s , tribes, and t erritories 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 f

or 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 d ocument on this final rulemaking , which has also been placed in the docket for this rulemaking .***E.O. 12866 Review

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7 - 1 1 As required b y s ection 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 timel y manner when it sent the draft of this final action to OMB for review pursuant to Executive Order 12866. A cop y of the certification is included in the public version of the official record for this final action . **7 .**  **6 Executive Order 13175:**  **Consultation and C oordination with Indian Tribal**  **Governments** This action ha s 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 s ection 111(d) for affected EGUs in their areas of Indian country . To the extent that a tribal government seeks and attains TAS status for that purpose, these emission guidelines would require that planning requirements be met and emission management implem entation plans be executed b y the tribes. The EPA notes that this rule does not directl y impose specific requirements on affected EGU s , including those located in areas of Indian country , but provides guidance to any tribe approved by the EPA to address CO 2 emissions from EGU s subject to section 111(d) of the CAA. The EPA also notes that none of the affected EGUs are owned or operated b y tribal governments. As described in s ection s 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 section s 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 trib al 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, way s of life and hunting and treaty rights. The tribes were also interested in the scope of the guidelines being considered by the agency***E.O. 12866 Review

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7 - 12 (e.g., over what time period, relationship to state and multi - state plans) and how tribes will participate in these planning activities. The EPA consult ed 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 th e supplemental proposal. One issue raise d by tribal officials was the potential impacts of the June 18, 2014 proposal and th e supplemental proposal on tribes with budgets that are dependent on revenue from coal mines and power plants, as well as employ ment at the mines and power plants. The trib es noted the high unemploy ment 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 CO 2 emission standard based on the highest historical CO 2 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 e lectricity 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 Navaj o 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***E.O. 12866 Review

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7 - 13 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 Co mmunity 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 area s of Indian country . The Fort Mojave Tribe and the Navajo Nation expressed concern w ith using data from 2012 as the basis for the goal for their areas of Indian country ; in their view, that year was not representative for the affected EGU. On April 28, 2015, the EPA held an additional consultation with the Navajo Nation. The issues raised b y the Navajo Nation during the consultation included whether the EPA has the authority t o set less stringent stan dards on a case - by- case basis, and a suggested “parity glide path” that would account and adjust for the very

low electricity usage b y the Navajo Nation and promote Navajo Nation economic growth and demand . As required b y s ection 7(a), the EPA’s Tribal Con sultation 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 Env ironmental Health Risks**  **and Safety Risks**  This action is subject to Executive Order 13045 (62 FR 19885, April 23, 1997) because it is an economicall y significant regulatory action as defined b y Executive Order 12866, and the EPA believes that the environmental health or safety risk addressed by

this action has a disproportionate effect on children. Accordingl y, the agency has evaluated the environmental health and welfare effects of climate change on children. CO 2 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 CO 2 emission***E.O. 12866 Review

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7 - 14 reductions resulting from implementation of these final guidelines, as well as substantial ozone and PM 2.5 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 elderl y, and the poor, are most vulnerable to c limate - related health effects. The assessment literature since 2009 strengthens these conclusions b y providing more detailed findings regarding these groups’ vulnerabilities and the projected impacts they may experience. These assessments describe how chil dren’s unique phy siological and developmental factors contribute to making them particularl y vulnerable to climate change. Impacts to children are expected from heat waves, air pollution, infectious and waterborne illnesses, and mental health effects resul ting from extreme weather events. In addition, children are among those especiall y 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 household s, especiall y those with children, if climate change reduces food availability and increases prices, leading to fo od insecurity within households . **7 .**  **8 Executive Order 13211:**  **Actions Concerning Regula tions That Significantly Affect**  **Energy Supply, Distribution, or Use**  This action, which is a significant regulatory action under EO 12866, is likely to have a significant effect on the supply , distribution, or use of energy . The EPA has prepared a Statement of Energy Effects for this action as follows. We estimate a [X] to [X] percent change in retail electricity prices on average across the contiguous U.S. in [20XX], and a [X] to [X] percent reduction in coal - fired electricity generation as a result of this rule. The EPA projects that utility power sect or delivered natural gas prices will increase/decrease by about [X] to [X] percent in [20XX]. For more information on the estimated energy effects, please refer to the economic impact anal ys is for this proposal. The analy sis 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.***E.O. 12866 Review

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7 - 15 **7 .**  **10 Executive Order 12898: Federal Actions to Ad**  **dress Environmental Justice in Minority Populations and Low**  **- Income Population**  **s** 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 low income 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 enjoy s 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 Findin g. See, section VIII.A of the preamble where the EPA summarizes the public health and welfare impacts from GHG emissions that were detailed in the 2009 Endangerment Finding under CAA section 202(a)(1). 162 As part of the Endangerment Finding, the Administrator considered climate change risks to minority populations and low - income populations, finding that certain parts of the population may be especiall y vulnerable based on their characteristics or circumstances. Populations that were found to be particularly

vulnerable to climate change risks include the poor, the elderl y, the very yo un g, 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 Pro tection of Children . The Administrator placed weight on the fact that certain groups, including children, the elderl y, and the poor, are most vulnerable to climate - related health effects.

162 “Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“En dangerment Finding”).***E.O. 12866 Review

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7 - 16 The record for the 2009 Endangerment Finding summarizes the s trong s cientific 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 cl imate 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 res ources such as local water and food supplies. Inaddition, Native American tribal communities possess unique vulnerabilities to climate change, particularl y those impacted by degradation of natural and cultural resources within established reservation boun daries and threats to traditional subsistence lifestyl es. Tribal communities whose health, economic well - being, and cultural traditions that depend upon the natural environment will likely

be affected by the degradation of ecos ystem goods and services asso ciated with climate change. The 2009 Endangerment Finding record also specifically noted that Southwest native cultures are especially vulnerable to water quality and availability

impacts. Native Alaskan communities are already experiencing disruptive impacts, including coastal erosion and shifts in the range or abundance of wild species crucial to their livelihoods and well - being. The most recent assessments continue to strengthen scientific understanding of climate change risks to minority populations and low - income populations in the United States. 163 The new assessment literature provides more detailed findings regarding these populations’ vulnerabilities and projected impacts they may experience. In addition, the most recent assessment rep orts provide new information on how some communities of color may be uniquel y

163 Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe, Eds., 2014: Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program, 841 pp. IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, 1132 pp. IPCC, 2014: Climate Change 2014: Impacts, Adaptation, a nd 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.***E.O. 12866 Review

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7 - 17 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 e xtreme 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 h ealth effects of climate change. In addition, assessment reports also find that climate change poses particular threats to health, well - being, and way s 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 especiall y 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 b y limiting GHG emissions through the establishment of CO 2 emission guidelines for existing affected fossil fuel - fired EGUs. In addition to reducing CO 2 emissions, the guidelines fin aliz ed in this rulemaking would reduce other emissions from affected EGUs that reduce generation due to higher adoption of energy efficiency

and renewable energy . These emission reductions will include SO 2 and NOx, which form ambient PM 2.5 and ozone in the atm osphere, and hazardous air pollutants (HAP) , such as mercury

and h ydrochloric acid. In the final rule revising the annual PM 2.5 NAAQS, 164 the EPA identified low - income populations as being a vulnerable population for experiencing adverse health effects relat ed to PM exposures. Low - income populations have been generall y found to have a higher prevalence of pre - existing diseases, limited access to medical treatment, and increased nutritional deficiencies, which can increase this population’s risk to PM - related and ozone - related effects. 165 Therefore, in areas where this rulemaking reduces exposure to PM 2.5 , ozone, and methylmercury , low - income populations will also benefit from such emissions reductions . The RIA for this rulemaking, included in the docket for this rulemaking, provides

164 “National Ambient Air Quality Standards for Particulate Matter, Final Rule,” 78 FR 3086 (Jan. 15, 2013). 165 U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report) . EPA - 600 - R - 08 - 139F. National Center for Environmental Assessment

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7 - 18 additional information regarding the health and ecosy stem effects associated with these emission reductions. Additionally , as outlined in the community considerations s ection IX of the preamble, the EPA has put a number of conditions in place to help ensure that this action will not have potential disproportionately high and adverse human health or environmental effects on all communities (low income, communities of color, indigenous populations). As described in the community

considerations section of the preamble, the EPA also conducted a proximity analy sis, which is available in the docket of this rulemaking. **7 .1**  **1 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).***E.O. 12866 Review

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8 - 1 

### C **HAPTER** 

### 8 **: C**  **OMPARISON OF** 

### B **ENEFITS AND** 

### C **OSTS 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 Impac t Anal ys is (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 (CO 2 ) emission performance rates for two source catego ries 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 impact s reported in this RIA are not definitive estimates, but are instead illustrative of plan approaches states may take. The EPA has us ed the social cost of carbon estimates presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (May 2013, Revised June 2015) (“current SC - CO 2 TSD”) to anal yz e CO 2 climate impacts of this rulemaking . 166 We refer to these estimates, which were developed b y the U.S. government, as “SC - C O 2 estimates.” The SC - C O 2 is an estimate of the monetary value of impacts associated with a marginal change in CO 2 emissions in a given ye ar. The four SC - C O 2 estimates are associated with different discount rates (model average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th 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 - C O 2 value deemed to be central in the current SC - CO 2 TS D (the model average at 3% discount rate). In addition to reducing CO 2 emissions , implementing these final emission guidelines is expected to reduce emissions of SO 2 and NO X , which are precursors to formation of ambient PM 2.5 , as well as directly emitted f ine particles. Therefore, reducing these emissions would also reduce human exposure to ambient PM 2.5 and ozone precursors, and

166 Docket ID EPA - HQ - OAR - 2013 - 0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Econo mic 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 o f Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised June 201 5 ). Available at:***E.O. 12866 Review

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8 - 2 the associated PM 2.5 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 CO 2 emissions do not include important impacts like ocean acidification or potential tipping points in natural or managed ec os ys tems. Unquantified impacts also include those associated with changes in emissions of other pollutants that affect the climate, such as methane. In addition, the analy sis does not quantify co - benefits from reducing exposure to SO 2 , NO x , and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecos ystem e ffects and visibility impairment . Based upon the foregoing discussion, it remains clear that th is final rule’s combined climate benefits and human health co - benefits associated with the reduction in other air pollutants substantial ly

outweigh the illustrative plan scenario costs .***E.O. 12866 Review

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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 $0.8 0 $3.1 $6.4 3% discount rate $2.8 $10 $20 2.5% discount rate $4.1 $15 $29 95th percentile at 3% discount rate $8 .2 $31 $61 Air Quality Co - benefits Discount Rate 3% 7% 3% 7% 3% 7% **Air Quality Health Co**  **- benefits**  **c** $0.7 to $1.8 $0.6 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31 **Compliance Costs d**  $ 2.4 $1.1 6 $ 8 .4 **Net Benefits e**  $1.1 to $2.2 $1.0 to $2.1 $17 to $27 $1 6 to $2 5 $2 6 to $4 6 $25 to $4 3 **Non -**  **Monetized Benefits**  Non - monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in emissions of NO X , SO 2 , 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 CO 2 emission changes and does not account for changes in non - CO 2 GHG emissions. Also, different discount rates are applied to SC - CO 2 than to the other estimates because CO 2 emissions are long - lived and subsequent damages occur over many years. The benefit estimates in this table are based on the average SC - CO 2 estimated for a 3% discount rate, however we emphasize the im portance and value of considering the full range of SC - CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three SC - CO 2 estimates (model average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC - CO 2 estimates are year - specific and increa se over time. c The air quality health co - benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . 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 mo netized co - benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in c ausing premature mortality because the scientific evidence is not yet sufficient to allow

differentiation of effect estimates by particle type. d Total costs are approximated by the illustrative plan scenario costs estimated using the Integrated Planning Model for the proposed guidelines and a discount rate of approximately 5 percent. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs. e The estimates of net benefits in thi s summary table are calculated using the global SC - CO 2 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 PM 2.5 concentrati ons 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 o ver the full compliance period. **Table 8**  **- 2**  **. Monetized Benefits, Costs, and Net**  **Benefits Under the Mass -**  **based Illustrative Plan Approach Scenario**  **(billions of 2011$) a** ***E.O. 12866 Review

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8 - 4 **Mass -**  **Based Scenario 2020**  **2025 2030**  **Climate Benefits b**  5% discount rate $[X] $[X] $[X] 3% discount rate $ [X] $ [X] $ [X] 2.5% discount rate $ [X] $ [X] $ [X] 95th percentile at 3% discount

rate $ [X] $ [X] $ [X] Air Quality Co - benefits Discount Rate 3% 7% 3% 7% 3% 7% **Air Quality Health Co**   ** benefits** **c** $[X] to $[X] $[X] to

$[X] $[X] to

$[X] $[X] to

$[X] $[X] to

$[X] $[X] to

$[X] **Costs d**  $[X] $[X] $[X] **Net Benefits e**  $[X] to $[X] $[X] to

$[X] $[X] to

$[X] $[X] to

$[X] $[X] to

$[X] $[X] to

$[X]

**Non -**  **Monetized Benefits** Non - monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in emissions of NO X , SO 2 , 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 CO 2 emission changes and does not account for changes in non - CO 2 GHG emissions. Also, different discount rates are applied to SC - CO 2 than to the other estimates because CO 2 emissions are long - lived and subsequent damages occur over many years. The benefit estimates in this table are based on the average SC - CO 2 estimated for a 3% discount rate, however we emphasize the im portance and value of considering the full range of SC - CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three SC - CO 2 estimates (model average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC - CO 2 estimates are year - specific and increase over time . c The air quality health co - benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration - response functions from different epidemiology stud ies. The reduction in premature fatalities each year accounts for over 98 percent of total mo netized co - benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow

differentiation of effect estimates by particle type. d Total costs are approximated by the illustrative plan scenario costs estimated using the Integrated Planning Model for the proposed guidelines and a discount rate of approximately 5 percent. This estimate includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs. e The estimates of net benefits in this summary t able are calculated using the global SC - CO 2 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 P M 2.5 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 benef its of the rule over the full compliance period.***E.O. 12866 Review

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8 - 5 **8 .**  **2 Uncertainty Analysis**  The Office of Management and Budget’s c ircular Regulatory Analysis (Circular A - 4) provides guidance on the preparation of regulatory analyses required under E.O. 12866, and requires an uncertainty

analy sis for rules with annual benefits or costs of $1 billion or more. 167 This final rulemaking surpasses that threshold for both benefits and costs. Throughout the RIA , we considered a number of sources of uncertainty , both quantitatively and qualitatively , on benefits and costs . We summarize three key elements of our anal ys is 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 PM 2.5 and ozone and the use of a benefits - per - ton approach in estimating these co - benefits.  Characterizing uncertainty in monetizing climate - related benefits. Som e of these elements are evaluated using probabilistic techniques, whereas for others the underl yi ng likelihoods of certain outcomes are unknown and we use scenario anal ys is 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 estimate s of the costs of these methods of intensity reduction are reported within the cost anal ys is of this rule and are included in the cost modeling in the RIA. A significant source of uncertainty under this regulation is the ultimate approach states will take to comply

with the guidelines, which will affect both the costs and benefits of this rule. For this reason we modeled two potential illustrative plan scenarios for each regulatory option: the rate - based illustrative plan scenario and the mass - based illustrative plan scenario. The cost ... 167 Office of Management and Budget (OMB), 2003, Circular A - 4 , http://www. whitehouse.gov/omb/circulars_a004_a - 4 and OMB, 2011. Regulatory Impact Analysis: A Primer. http://www. whitehouse.gov/sites/default/files/omb/ inforeg /regpol/circular - a - 4_regulatory - im pact - analysis - a primer.pdf***E.O. 12866 Review

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8 - 6 The reductions of CO 2 and other pollutants from fossil fuel combustion .... However, from this anal ysis we see that the net ben efits of the two scenarios, given consistent as sumptions, 8.2.2 Uncertainty Associated with Estimating the Social Cost of Carbon The 2010 SC - CO 2 TSD noted a number of limitations to the SC - C O 2 analysis, including the incomplete way

in which the integrated assessment models (IAM) capture catastrophic and non - catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. 168 Currently integrated assessment models do not assign value to all of the important phy sical, 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 understandabl y lags behind the most recent research. These individual limitations do not all work in the same direction in terms of their influence on the SC C O 2 estimates, though taken together they suggest that the SC - C O 2 estimates are likel y conservative. In particular, the IPCC Fourth Assessment Report (2007) concluded that “It is very likely that [ SC - C O 2 estimates] underestimate the damage costs because they cannot include many non - quantifiable impacts” and the IP CC Fifth Assessment report observed that SC - CO 2 estimates continue to omit various impacts that would likely increase damages. The 95 th percentile estimate was included in the recommended range for regulatory impact anal ys is, in part, to address these concerns. The modeling underl yi ng the development of the SC - C O 2 estimates addressed uncertainty in several ways. An ensemble of three IAMs were used to generate the SC - C O 2 estimates to capture differences in model structures that, in part, reflect u ncertainty in the scientific literature about these relationships. Parametric uncertainty was explicitly

addressed in

168 Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Exec utive 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. whiteho use.gov/sites/default/files/omb/inforeg/for - agencies/Social - Cost - of - Carbon - for - RIA.pdf>.***E.O. 12866 Review

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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 analy sis considered five different socioeconomic and emissions forecasts to capture th e sensitivity of the SC - CO 2 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 anal ys is produced 45 different distributions of the SC - CO 2 estimates for each emissions year. To produce a range of plausible estimates that are man ageable in regulatory anal ys is 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 - C O 2 estimates. Chapter 4 of this RIA provides a comprehensive discussion about the methodology and application of the SC - CO 2 ; s ee both the 2010 TSD and current SC - CO 2 TSD for a full description. In addition, OMB’s Office of Information and Regulatory Affairs received comments regarding uncertainty and the SC - CO 2 estimates in response to a separate request for public comment on the approach used to develop the estimates. Commenters discussed the anal ys es and presentation of uncertainty in the TSD as well as the implications of uncertainty for use of the SC - CO 2 estimates in regulatory impact anal ys is. In their response, the interagency working group ( IW G ) acknowledge d uncertainty in the SC - CO 2 estimates but disagreed with commenters that suggested the uncertainty undermine s use of the SC - CO 2 estimates in regulatory impact anal ys is. The IW G went on to note that the uncertainty in the SC - CO 2 estimates is fully acknowledged and comprehensivel y discussed in the TSDs and supporting academic literature, and that while all regulatory impact anal ys i s involves uncertainty , these analy s e s can provide useful information to decision makers and the public. See the IWG Response to Comments for the complete response. 8.2. 3 Uncertainty Associated with PM 2.5 and Ozone Health Co -Benefits Assessment Our estim ate 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 underl yi ng the estimates for***E.O. 12866 Review

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8 - 8 PM 2.5 - related premature mortality , which accounts for 98 percent of the monetized PM 2.5 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 PM 2.5 varies considerabl y in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates b y particle ty pe. The PM I SA concluded that “many constituents of PM 2.5 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 analy sis. Thus, the estimates include health co - benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the fi ne 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. Specificall y, w e assume that some of the incidences of premature mortality related to PM 2.5 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 co benefits at different discount rates. EPA quantitatively assessed uncertainty in the air quality health co - benefits, including p robabilistic approaches. In addition , EPA provides the 95 th percentile confidence interval for avoided PM - related premature de aths and the associated economic valuation using two key epidemiology

studies . EPA provides the PM - related results using alternate concentration - response relationship provided b y an expert elicitation and alternate ozone - related results using concentration - response relationships provided b y alternate epidemiology studies. Inaddition, 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 th ose uncertainties influencing the benefit assessment, see Chapter 4 of this RIA .***E.O. 12866 Review

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8 - 9 As noted and described in Chapter 4 of this RIA, we use a benefit - per - ton approach to quantify health co - benefits. A ll benefit - per - ton have inherent limitations, including th at 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 , e xposure, 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 PM 2.5 precursor emitted from EGUs, which assumes a linear atmospheric response to emission reductions. The regional benefit - per - ton estimates, although less subject to these ty pes 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 PM 2.5 levels, which drive population exposure. **8 .**  **3 References**  Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Cha nge 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_s ynt hesis_report.htm>. Accessed June 6, 2 015. 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. EPA COUNCIL - LTR - 05 - 001. December. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$File/co uncil_ltr_05_001.pdf

>. Acce ssed June 4, 2015. U.S. Environmental Pro tection Agency (U.S. EPA). 2009 . Integrated Science Assessment for Particulate Matter (Final Report) . EPA - 600 - R - 08 - 139F. National Center for Environmental Assessment

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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